49 results on '"Rapid prediction"'
Search Results
2. Early prediction of ochratoxigenic Aspergillus westerdijkiae on traditional Italian caciocavallo during ripening process by MS-based electronic nose
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Cervellieri, Salvatore, Longobardi, Francesco, Susca, Antonia, Anelli, Pamela, Ferrara, Massimo, Netti, Thomas, Haidukowski, Miriam, Moretti, Antonio, and Lippolis, Vincenzo
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- 2025
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3. Preliminary investigation into the prediction of indicators of beef spoilage using Raman and Fourier transform infrared spectroscopy
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Liu, Qingsen, Dong, Pengcheng, Fengou, Lemonia-Christina, Nychas, George-John, Fowler, Stephanie Marie, Mao, Yanwei, Luo, Xin, and Zhang, Yimin
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- 2023
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4. Integration of Fluorescence Spectroscopy Along with Mathematical Modeling for Rapid Prediction of Adulteration in Cooked Minced Beef Meat.
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Saleem, Asima, Imtiaz, Aysha, Yaqoob, Sanabil, Awais, Muhammad, Awan, Kanza Aziz, Naveed, Hiba, Khalifa, Ibrahim, Al‐Asmari, Fahad, and Qian, Jian‐Ya
- Subjects
CHICKEN as food ,FLUORESCENCE spectroscopy ,FLUORIMETRY ,PRINCIPAL components analysis ,FOOD quality ,MEAT analysis - Abstract
This study explores the potential of fluorescence spectroscopy (FS), coupled with principal component analysis (PCA) and partial least square regression (PLSR), to detect meat adulteration rapidly and non‐destructively in cooked minced beef. We aimed at evaluating FS as a simple and efficient tool for identifying cheaper meat species, that is chicken, used as adulterants in beef. Fluorescence spectra were collected at one fixed emission wavelength (410 nm) and three excitation wavelengths (290, 322, and 340 nm) from both pure and adulterated cooked meat samples. Adulteration levels ranging from 10% to 90% were assessed by mixing chicken meat with beef, followed by fluorescence analysis. The results indicated that the PCA model explained 100% of the variance, with 96% accounted for by the first principal component, showing clear discrimination between pure and adulterated samples. PLSR models demonstrated excellent predictive accuracy, with cross‐validated coefficients of determination of 0.95, highlighting FS's capability in distinguishing between pure and adulterated meats even after cooking. The cross‐validated grouping success rate was ~97%, reinforcing the reliability of the technique. This study represents the first investigation using FS to predict adulteration in cooked meat, providing a benchmark for future research. The findings suggest that FS, in combination with mathematical modeling, holds great promise as a rapid, cost‐effective, and nondestructive method for detecting meat adulteration, with significant potential for practical application in food industry quality control. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Scenario Superposition Method for Real‐Time Tsunami Prediction Using a Bayesian Approach.
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Fujita, Saneiki, Nomura, Reika, Moriguchi, Shuji, Otake, Yu, LeVeque, Randall J., and Terada, Kenjiro
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DISTRIBUTION (Probability theory) ,SINGULAR value decomposition ,SUBDUCTION zones ,TSUNAMIS ,DATABASES - Abstract
In this study, we propose a scenario superposition method for real‐time tsunami wave prediction. In the offline phase, prior to actual tsunami occurrence, hypothetical tsunami scenarios are created, and their wave data are decomposed into spatial modes and scenario‐specific coefficients by the singular value decomposition. Then, once an actual tsunami event is observed, the proposed method executes an online phase, which is a novel contribution of this study. Specifically, the predicted waveform is represented by a linear combination of training scenarios consisting of precomputed tsunami simulation results. To make such a prediction, a set of weight parameters that allow for appropriate scenario superposition is identified by the Bayesian update process. At the same time, the probability distribution of the weight parameters is obtained as reference information regarding the reliability of the prediction. Then, the waveforms are predicted by superposition with the estimated weight parameters multiplied by the waveforms of the corresponding scenarios. To validate the performance and benefits of the proposed method, a series of synthetic experiments are performed for the Shikoku coastal region of Japan with the subduction zone of the Nankai Trough. All tsunami data are derived from numerical simulations and divided into a training data set used as scenario superposition components and a test data set for an unknown real event. The predicted waveforms at the synthetic gauges closest to the Shikoku Islands are compared to those obtained using our previous prediction method incorporating sequential Bayesian updating. Plain Language Summary: In this study, we propose a new tsunami waveform prediction method that represents actual tsunami waveforms by superposing scenario waveforms resulting from a series of precomputed tsunami simulations. The prediction scheme uses a Bayesian approach to sequentially estimate the weight parameters assigned to the scenarios using actual real‐time wave height data. At the same time, it identifies the probability distribution of the weight parameters and provides information to understand the forecast error. The actual wave heights of the target event can be predicted by multiplying of the wave heights in the scenario database by the estimated weight parameters. To verify the advantages of the proposed method, a demonstration test is conducted for the Shikoku region of Japan, which is threatened by a large tsunami risk due to the Nankai Trough subduction zone. We show that the proposed method can predict wave heights even when the target event is not present in the scenario database, and we show its superiority over the previously proposed method. Key Points: The proposed scenario superposition method achieves real‐time tsunami waveform prediction via a linear combination of precomputed scenariosA set of parameters weighting each scenario is probabilistically estimated using a Bayesian approachThree‐hour tsunami waveforms can be predicted using wave heights measured at existing gauge locations in less than 10 min [ABSTRACT FROM AUTHOR]
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- 2024
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6. Rapid Raman spectroscopy analysis assisted with machine learning: a case study on Radix Bupleuri.
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Guo, Fangjie, Yang, Xudong, Zhang, Zhengyong, Liu, Shuren, Zhang, Yinsheng, and Wang, Haiyan
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MACHINE learning , *FISHER discriminant analysis , *SUPPORT vector machines , *RAMAN spectroscopy , *LIQUID chromatography - Abstract
BACKGROUND RESULTS CONCLUSION Radix Bupleuri has been widely used for its plentiful pharmacological effects. But it is hard to evaluate their safety and efficacy because the concentrations of components are tightly affected by the surrounding environment. Thus, Radix Bupleuri samples from different regions and varieties were collected. Based on the experimental and computational Raman spectrum, machine learning is emphasized for certain obscured characteristics; for example, linear discriminant analysis (LDA), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM).After dimension reduction by LDA, models of SVM, XGBoost and LightGBM were trained for classification and regression prediction of Bupleurum production regions. Support vector classifiers achieved the best accuracy of 98% and an F1 score above 0.96 on the test set. Support vector regression has a good fitting performance with an R2 score above 0.90 and a relatively low mean square error. However, complex models were prone to overfitting, resulting in poor generalization ability.Among these machine learning models, the typical LDA‐SVM models, consistent with the high‐performance liquid chromatography results, demonstrate great performance and stability. We envision that this rapid classification and regression technique can be extended to predictions for other herbs. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]
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- 2024
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7. A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes
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Jiarui Yang, Kai Liu, Ming Wang, Gang Zhao, Wei Wu, and Qingrui Yue
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Convolutional neural networks ,Physical continuity ,Rapid prediction ,Urban pluvial flooding processes ,Weighted cellular automata ,Disasters and engineering ,TA495 - Abstract
Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.
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- 2024
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8. Digital twin based stress field prediction method for offshore floating power generation platform connectors
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Yu CAO, Lin GAN, and Tao ZHANG
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offshore floating power generation platform ,connector ,rapid prediction ,digital twin ,reduced order model ,Naval architecture. Shipbuilding. Marine engineering ,VM1-989 - Abstract
ObjectivesWhen assessing the safety of the connectors of a multi-module offshore floating power generation platform, in order to compensate for the inability to carry out the real-time monitoring of the structural stress field across the whole domain due to a limited numbers of sensor, a digital twin method based on a simulation database is proposed that can rapidly predict the platform's stress field. MethodsBy downgrading the three-dimensional physical model of the connectors to a one-dimensional digital model, the stress field data is interpolated and deduced in digital space, thereby achieving the rapid prediction of the structural stress field across the whole domain and its visual display.ResultsThe results show that the simulation model is in good agreement with the test results, with a maximum absolute error of 8.61%; for the interpolation of data under different loading angles, when the interpolation step of the loading angle is 10°, the aver-age absolute error of stress is 1.98%; and for the interpolation of data under different loads, when the interpolation step of the load is 10 t, the average absolute error of stress is 1.28%, achieving the rapid prediction and visualization of the connectors' stress field distribution. Conclusions The digital twin-based model of connectors can provide useful references for the rapid dynamic perception and scientific prediction of the structural strength of offshore floating power generation platforms.
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- 2024
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9. Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation.
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Habib, Maan and Okayli, Maan
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MACHINE learning , *STANDARD deviations , *CONCRETE industry , *PRINCIPAL components analysis , *COMPRESSIVE strength - Abstract
This study rigorously examines the impact of various data preprocessing techniques on the accuracy of machine learning models in predicting concrete's compressive strength. It develops ten regression models under nine distinct preprocessing scenarios, including normalization, standardization, principal component analysis (PCA), and polynomial features, utilizing a comprehensive dataset featuring normal and high-strength performances. The results reveal that using polynomial features and kernel PCA significantly enhanced model performance, with R values soaring to 93.27 and 94.65% during training and 88.51 and 88.77% during testing, respectively. This indicates their strong ability to capture the hidden nonlinear relationships within data. Conversely, discretization exhibited the least effectiveness, with the highest normalized root mean square error values of 14.2 (training) and 16.8 (testing) and normalized mean absolute error values of 11.6 (training) and 13.6 (testing), suggesting a potential loss of essential data granularity. Additionally, the study found that machine learning techniques generally surpassed traditional regression models, with higher R values being a consistent trend. These findings offer a nuanced understanding of the importance of preprocessing choice in concrete strength prediction and provide valuable insights for the concrete industry and data scientists, emphasizing the critical role of data preprocessing in achieving optimal model accuracy in materials science. [ABSTRACT FROM AUTHOR]
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- 2024
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10. A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes.
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Yang, Jiarui, Liu, Kai, Wang, Ming, Zhao, Gang, Wu, Wei, and Yue, Qingrui
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CONVOLUTIONAL neural networks ,CELLULAR automata ,DEEP learning ,FLOODS ,PHYSICAL distribution of goods - Abstract
Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Rapid prediction of flow and concentration fields in solid-liquid suspensions of slurry electrolysis tanks.
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Lu, Tingting, Li, Kang, Zhao, Hongliang, Wang, Wei, Zhou, Zhenhao, Cai, Xiaoyi, and Liu, Fengqin
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Slurry electrolysis (SE), as a hydrometallurgical process, has the characteristic of a multitank series connection, which leads to various stirring conditions and a complex solid suspension state. The computational fluid dynamics (CFD), which requires high computing resources, and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE tank. Through scientific selection of calculation samples via orthogonal experiments, a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each factor. Then, a prediction model of the SE tank was constructed using the K-nearest neighbor algorithm. The results show that with the increase in levels of orthogonal experiments, the prediction accuracy of the model improved remarkably. The model established with four factors and nine levels can accurately predict the flow and concentration fields, and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937, respectively. Compared with traditional CFD, the response time of field information prediction in this model was reduced from 75 h to 20 s, which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Rapid Prediction of Multi-physics Coupling in Heat Pipe ReactorBased on Neural Network
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ZHANG Junda, LIU Xiaojing, XIONG Jinbiao, CHAI Xiang, ZHANG Tengfei
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heat pipe reactor ,multi-physics coupling ,neural network ,rapid prediction ,Nuclear engineering. Atomic power ,TK9001-9401 ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
In the present study, a data-driven neural network methodology was proposed for the rapid prediction of multi-physics interactions in heat pipe reactors, and grounded in a comprehensive framework of multi-physics coupling. By leveraging neural networks, certain numerical computation modules within the framework were effectively replaced, which facilitated an efficient resolution of the iterative process inherent to the nuclear-thermal-mechanical multi-physics coupling. In the process of multi-physics coupling, there exists a sophisticated iterative mechanism, wherein the computational analyses of distinct physical domains largely function autonomously. When contemplating the integration of neural network paradigms as surrogates within the multi-physics coupling framework, it is pivotal to target the numerical computations intrinsic to individual physical domains. By deploying one or multiple neural network models as substitutes, this methodology not only sustains the scalability inherent to rapid predictive schemes but also attenuates the complexity associated with the neural network’s surrogate functionalities. Concurrently, this approach necessitates a reduced dataset for training, and through the amalgamation of diverse neural network models, markedly augments the adaptability of the overarching rapid predictive framework. Furthermore, multiple neural networks, operating autonomously, can be dynamically calibrated with respect to their training data volumes based on their predictive performance. This not only optimizes their predictive accuracy but also facilitates the cost-effective expansion of training datasets in accordance with specific model requirements. Taking “Megapower” as the subject of analysis, pertinent neural network models were constructed and employed for rapid predictive parameter estimation, with the results juxtaposed against traditional numerical computations. The discrepancies between the key parameters yielded by rapid predictions and those from numerical simulations were minimal. Specifically, the maximum stress differential does not exceed 2 MPa, and the average fuel temperature difference is less than 3 K. Notably, while numerical computations demand six hours of processing time, the rapid prediction on the same platform requires a mere 4 minutes, marking a reduction in computational time by over 95%. Given these outcomes, it can be posited that neural network-based rapid prediction schemes exhibit high precision and accelerated processing speeds. Coupled with their inherent flexibility and modest training data requirements, and the capacity for targeted model optimization, it is advocated that neural network-based rapid prediction serves as a preferred methodology for addressing challenges such as core optimization in scenarios that demand large-scale computations. Concurrently, the neural network models utilized in rapid predictions have less stringent computational resource requirements and offer greater adaptability to varied environments. Capitalizing on these attributes, in conjunction with contemporary compact mobile reactors, one can facilitate on-site deployments with relative ease. Such integrations pave the way for prompt alerts in accident scenarios, thereby enhancing the safety of the reactor core.
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- 2024
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13. Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism
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Yu Shao, Jiarui Chen, Tuqiao Zhang, Tingchao Yu, and Shipeng Chu
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cru-net ,deep learning ,rapid prediction ,spatiotemporal rainfall ,urban flooding ,Information technology ,T58.5-58.64 ,Environmental technology. Sanitary engineering ,TD1-1066 - Abstract
Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning investigations. In this study, we introduce a novel deep learning model known as CRU-Net equipped with an attention mechanism to predict inundation depths in urban terrains based on spatiotemporal rainfall patterns. This method utilizes eight topographic parameters related to the height of urban waterlogging, combined with spatial rainfall data as inputs to the model. Comparative evaluations between the developed CRU-Net and two other deep learning models, U-Net and ResU-Net, reveal that CRU-Net adeptly interprets the spatiotemporal traits of rainfall and accurately estimates flood depths, emphasizing deep inundation and flood-vulnerable regions. The model demonstrates exceptional accuracy, evidenced by a root mean square error of 0.054 m and a Nash–Sutcliffe efficiency of 0.975. CRU-Net also accurately predicts over 80% of inundation locations with depths exceeding 0.3 m. Remarkably, CRU-Net delivers predictions for 3 million grids in 2.9 s, showcasing its efficiency. HIGHLIGHTS The incorporation of spatial rainfall distribution into urban flood forecasting models is performed in this study.; Integration of attention mechanisms enhanced the identification of high-risk flood areas.; The developed model can predict rapid floods within seconds.;
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- 2024
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14. 基于神经网络的热管反应堆多物理场 耦合快速预测.
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张俊达, 刘晓晶, 熊进标, 柴 翔, and 张滕飞
- Abstract
Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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15. Impacts of DEM type and resolution on deep learning-based flood inundation mapping.
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Fereshtehpour, Mohammad, Esmaeilzadeh, Mostafa, Alipour, Reza Saleh, and Burian, Steven J.
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DEEP learning , *CONVOLUTIONAL neural networks , *MACHINE learning , *DIGITAL elevation models , *FLOOD risk , *WATER depth , *FLOODS - Abstract
The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Surrogate Models Based on Back-Propagation Neural Network for Parameters Prediction of the PWR Core
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Bei, Xinyan, Cheng, Maosong, Zuo, Xiandi, Yu, Kaicheng, Dai, Yuqing, and Liu, Chengmin, editor
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- 2023
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17. Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method.
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Wang, Zhendong, Ye, Qianhao, Li, Mingjie, Cheng, Xiangqiang, Wei, Jinjia, Yang, Fu, and Duan, Zhonghui
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FINITE volume method ,COAL ,PYROLYSIS ,PETROLEUM sales & prices ,POROUS materials - Abstract
In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27. [ABSTRACT FROM AUTHOR]
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- 2023
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18. Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform–Near-Infrared Spectroscopy and Machine Learning.
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Ye, Sitan, Weng, Haiyong, Xiang, Lirong, Jia, Liangquan, and Xu, Jinchai
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EPIGALLOCATECHIN gallate , *PARTIAL least squares regression , *MACHINE learning , *FOURIER transform infrared spectroscopy , *RANDOM forest algorithms , *KINETIC control , *TEA - Abstract
Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform–near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky–Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with Rp = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with Rp = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with Rp = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with Rp = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves. [ABSTRACT FROM AUTHOR]
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- 2023
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19. BIM and ANN-based rapid prediction approach for natural daylighting inside library spaces.
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Ni, Ting, Wang, Bo, Jiang, Jiaxin, Wang, Meng, Lei, Qing, Deng, Xinman, and Feng, Cuiying
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DAYLIGHTING , *STANDARD deviations , *BUILDING information modeling , *ARTIFICIAL neural networks - Abstract
The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis. [ABSTRACT FROM AUTHOR]
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- 2023
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20. Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning.
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Wang, Junyan and Wang, Naiyu
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EXTREME weather , *EMERGENCY management , *WEATHER , *DEEP learning , *PREDICTION models - Abstract
• Forecasting of road network functionality states during extreme rainfall events to facilitate proactive emergency responses. • Deep learning adaptation to develop end-to-end surrogate model for multidisciplinary physics-based simulations. • Coupling LSTM networks and ST-GCNs to predict road network functionality under highly unstable weather conditions. • Testing model predictive accuracy, efficiency and robustness with four flood-prone communities in Zhejiang Province, China. Rapid prediction of Road Network Functionality (RNF) during extreme rainfall-induced flooding is crucial for supporting proactive and real-time emergency planning, such as rescue, evacuation planning, and emergency supply distribution. Unlike normal operational conditions, extreme rainfall events introduce complex non-stationary, non-Euclidean characteristics to RNF due to intricate meteorological and hydrological processes, as well as the role of a community's road network in emergency response planning. Conventional physics-based flood simulations and flow-based road network analyses typically lack the computational efficiency required for real-time RNF predictions, hindering timely risk mitigation decisions. This study leverages the accuracy of physics-based simulations and the efficacy of deep-learning technologies to develop a deep learning-based surrogate model for Rain-to-RNF (R2R) predictions. This model couples Long Short-Term Memory (LSTM) networks with Spatial-Temporal Graph Convolutional Networks (ST-GCNs) to uniquely capture the spatiotemporal dynamics of RNF under extreme rainfall events. The predictive accuracy, stability, and versatility of the R2R surrogate model are demonstrated in four flood-prone communities in Zhejiang Province. Its implementation during Typhoon Fitow (2013) over a 30-hour intense rainfall showcases its promising predictive capacity and unparalleled computational efficiency. This research advances disaster management, enhancing the resilience and responsiveness of community infrastructure during extreme weather events. [ABSTRACT FROM AUTHOR]
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- 2024
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21. Early prediction of ochratoxigenic Aspergillus westerdijkiae on traditional Italian caciocavallo during ripening process by MS-based electronic nose.
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Cervellieri S, Longobardi F, Susca A, Anelli P, Ferrara M, Netti T, Haidukowski M, Moretti A, and Lippolis V
- Abstract
A rapid and non-invasive mass spectrometry-based electronic nose (MS-eNose) method, combined with chemometric analysis, was developed for the early detection of Aspergillus westerdijkiae on caciocavallo cheeses during ripening process. MS-eNose analyses were carried out on caciocavallo inoculated with ochratoxin A (OTA) non-producing species and artificially contaminated with A. westerdijkiae, an OTA producing species. Two classification models, i.e. PLS-DA and PC-LDA, were used to discriminate cheese samples in two classes, based on their contamination with toxigenic or non-toxigenic fungal species. Accuracy values were between 87 and 100 % and 86-100 %, in calibration and validation, respectively, with best results obtained at 15-ripening days with 98 % (PLS-DA) and 100 % (PC-LDA) of accuracy in validation. Moreover, eighteen potential volatile markers of the presence of A. westerdijkiae were identified by GC-MS analysis. Results show that MS-eNose represents a useful tool for a rapid screening in preventing A. westerdijkiae and related OTA contamination in caciocavallo cheese during ripening process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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- 2024
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22. A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches
- Author
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Xingyu Yan, Kui Xu, Wenqiang Feng, and Jing Chen
- Subjects
Flood inundation ,Neural networks ,Numerical simulations ,Rapid prediction ,Spatiotemporal prediction ,China ,Disasters and engineering ,TA495 - Abstract
Abstract Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.
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- 2021
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23. Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform–Near-Infrared Spectroscopy and Machine Learning
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Sitan Ye, Haiyong Weng, Lirong Xiang, Liangquan Jia, and Jinchai Xu
- Subjects
tea polyphenol ,EGCG ,Fourier Transform–near-infrared spectroscopy ,machine learning ,rapid prediction ,Organic chemistry ,QD241-441 - Abstract
Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform–near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky–Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with Rp = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with Rp = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with Rp = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with Rp = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves.
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- 2023
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24. Research on the Preliminary Prediction of Nuclear Core Design Based on Machine Learning.
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Lei, Jichong, Chen, Zhenping, Zhou, Jiandong, Yang, Chao, Ren, Changan, Li, Wei, Xie, Chao, Ni, Zining, Huang, Gan, Li, Leiming, Xie, Jinsen, and Yu, Tao
- Abstract
The reactor core design involves the search for and detailed calculation of a large number of schemes. Four different machine learning algorithms were used in this technical note: the C4.5 algorithm (an algorithm of decision trees), Support Vector Machine, Random Forest, and Multi-layer Perceptron, respectively. Uranium enrichment, the number of fuel rods containing burnable poison, and the concentration of burnable poison were taken as independent variables in the calculation. The k-eff unevenness coefficient, the radial power nonuniformity coefficient, the radial flux nonuniformity coefficient, and the core life were taken as the number of core parameters fulfilled (CPF). Machine learning models were constructed through learning the training data set, which consisted of a large number of assembly and core schemes whose nuclear design parameters were already known. Using the models, the CPF values for the unknown core data set (the test data set) were quickly predicted. The results show that the cross-validation accuracy of each algorithm was above 94% and that the C4.5 algorithm had the highest accuracy for the overall prediction of the CPF values. For the CPF value prediction of the test data set, the time for the training data set was within 10s, while the Random Forest algorithm has the highest prediction accuracy for CPF = 4 or CPF ≠ 4. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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25. Rapid warning of wind turbine blade icing based on MIV-tSNE-RNN.
- Author
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Zhang, Zhiqiang, Fan, Bin, Liu, Yong, Zhang, Peng, Wang, Jianguo, and Du, Wenliang
- Subjects
- *
WIND turbine blades , *ALGORITHMS , *MAGNITUDE (Mathematics) - Abstract
A fast early warning algorithm for wind turbine blade icing based on a RNN model is proposed. Through wind turbine blade history data and labels as model input, the evaluation of raw m-dimension data through mean impact value (MIV) indices eliminates data with an MIV index of less than one; the remaining n-dimension data is reduced to x-dimension by the tSNE method; dimensional data is inputted into the RNN, and the model output is the icing state of the wind turbine blade in a certain future period. Based on the SCADA data from a wind field, the model was verified by an example. Using a certain example case, if the model training data is 104 orders of magnitude, using the MIV-tSNE-RNN algorithm, the prediction accuracy can reach approximately 72 %; compared with the RNN model, the prediction accuracy is improved by approximately 150 % while reducing the algorithm running time by approximately 45 %. If the amount of data exceeds 104 orders of magnitude, using the MIV-tSNE-RNN algorithm, the prediction accuracy is improved by approximately 100 %. This algorithm can provide accurate and rapid prediction results for wind turbine blade icing according to actual needs. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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26. A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches.
- Author
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Yan, Xingyu, Xu, Kui, Feng, Wenqiang, and Chen, Jing
- Subjects
FLOOD warning systems ,MACHINE learning ,PREDICTION models ,ARTIFICIAL neural networks ,EMERGENCY management ,COMPUTER simulation ,FLOOD risk - Abstract
Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
27. Rapid prediction of pollutants behaviours under complicated gate control for the middle route of South-to-North water transfer project.
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Long, Yan, Yang, Yilin, Li, Youming, and Zhang, Yunxin
- Subjects
WATER transfer ,POLLUTANTS ,DECISION support systems ,HYDRAULIC structures ,WATER pollution - Abstract
Many crossing hydraulic structures have been constructed in the Middle Route of the South-to-North Water Transfer Project (MR-SNWTP), which increases the likelihood of sudden water pollution accidents. After an accident, managers need to assess the extent of pollution under conditions of gate control, and it's necessary to make suitable emergency control decision under this assessment. Therefore, we researched the rapid prediction of pollutants behaviours under conditions of complicated gate control in this paper, by presenting three characteristic parameters of pollutant migration and diffusion. According to the simulation results, the influencing reasons and rapid prediction formulas for the characteristic parameters (peak transport distance, pollutant longitudinal length and peak concentration) after a sudden water-soluble pollution accident are proposed. Also, the approval results show that the formulas can accurately predict the location and range of the pollutant after the emergency accident. Finally, the rapid prediction formulas for the characteristic parameters played a fundamental role in the decisions involved in the Emergency Environmental Decision Support System is proved by two application examples. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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28. Rapid evaluation of chicken meat freshness using gas sensor array and signal analysis considering total volatile basic nitrogen
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Xuxiang Tang and Zhi Yu
- Subjects
chicken meat ,total volatile basic nitrogen ,rapid prediction ,gas sensor array ,stochastic resonance ,Nutrition. Foods and food supply ,TX341-641 ,Food processing and manufacture ,TP368-456 - Abstract
Total volatile basic nitrogen (TVB-N) level rapid evaluation on chicken meat based on gas sensor array (GSA) technique was studied in this paper. GSA responses to chicken meat stored at 4°C were examined for 5 days. TVB-N content was synchronously measured by chemical examination. Principal component analysis (PCA) and non-linear double-layered cascaded serial stochastic resonance (DCSSR) were utilized for measurement data analysis. TVB-N examination results suggested that chicken meat stored for more than 3 days was not fresh. PCA showed poor discrimination abilities, while DCSSR signal-to-noise ratio (SNR) quantitatively characterized the freshness of all samples. Chicken meat TVB-N forecasting model was developed by non-linear fitting between SNR eigenvalues and TVB-N values. The predicting model was constructed. Validation experiment results demonstrated that the forecasting accuracy of the developed model reached 93.3%.
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- 2020
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29. Identification of Porosity and Permeability While Drilling Based on Machine Learning.
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Sun, Jian, Zhang, Rongjun, Chen, Mingqiang, Chen, Bo, Wang, Xiao, Li, Qi, and Ren, Long
- Subjects
- *
MACHINE learning , *PERMEABILITY , *POROSITY , *SUPPORT vector machines , *DATA logging , *ARTIFICIAL membranes - Abstract
The predictions of porosity and permeability from well logging data are important in oil and gas field development. Currently, many scholars use machine learning algorithms to predict reservoir properties. However, few scholars have researched the prediction of reservoir porosity and permeability while drilling. This approach requires not only a high prediction accuracy but also short model processing and calculation times as new logging data are incorporated while drilling. In this paper, four machine learning algorithms were evaluated: the one-versus-rest support vector machine (OVR SVM), one-versus-one support vector machine (OVO SVM), random forest (RF) and gradient boosting decision tree (GBDT) algorithms. First, samples of wireline logging data from the Yan969 wellblock of the Yan'an gas field were chosen for model training. To improve the accuracy and reduce the input parameter dimensions and model training time as much as possible, data correlation analysis was performed. Second, we used the grid search method to approximate ranges of reasonable parameter values and then used k-fold cross-validation to optimize the final parameters and avoid overfitting. Third, we used the four classification models to predict porosity and permeability while drilling with data from logging while drilling (LWD) logs. Finally, we indicate the best porosity and permeability prediction models to use while drilling. To ensure that the prediction accuracy is as high as possible and that the model training time is as short as possible, the OVO SVM algorithm was suggested for porosity and permeability prediction. Therefore, appropriate machine learning algorithms can be used to predict porosity and permeability while drilling. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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30. An improved rapid prediction method of the milling tool point frequency response function.
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Li, Xiaoru, Zhu, Jianmin, Tian, Fengqing, and Huang, Zhiwen
- Subjects
- *
FORECASTING , *SPINDLES (Machine tools) , *PARAMETER identification , *FINITE difference method , *IMPACT testing , *CUTTING tools , *PROCESS optimization - Abstract
During the prediction of the tool point frequency response function (FRF) based on receptance coupling substructure analysis (RCSA), in allusion to the problems that the existing tool model cannot simulate the contour of the tool precisely and the large amount of calculation for joint parameter identification during the calculation of the rotational FRF of the spindle, the joint model and the optimization algorithm affect the identification accuracy when acquiring the rotational FRF of the spindle. This study proposes a rapid prediction method for the tool point FRF on the basis of RCSA. The method divides the spindle-holder-tool system into two parts: the spindle-holder-partial shank and the remaining tool. On this basis, this study proposes a method of accurately establishing the model of the remaining tool with a 3D scanner and improves the calculation method of the rotational FRF of the spindle-holder-partial shank assembly through the reverse RCSA and finite difference method directly through the modal impact testing on the cutting tool. This study provides the experiments on a vertical machining center and compares the predicted FRF and the measured FRF on the tool point. The result indicates that the predicted FRF curve is consistent with the measured FRF curve with the relative error of natural frequency within 4.0%. This study confirms that the method is feasible, effective, and convenient. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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31. Rapid prediction of indoor airflow field using operator neural network with small dataset.
- Author
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Gao, Hu, Qian, Weixin, Dong, Jiankai, and Liu, Jing
- Subjects
COMPUTATIONAL fluid dynamics ,ISOTHERMAL flows ,DEEP learning ,MACHINE learning - Abstract
Indoor airflow is one of the most critical factors affecting room comfort. The accurate prediction of indoor airflow fields is essential for efficient environmental control. However, representative methods for calculating indoor airflow organization, such as computational fluid dynamics (CFD), are complex and time consuming. Therefore, this study proposes a machine learning approach that can rapidly reconstruct indoor airflow fields. This method employs an improved deep operator network (DeepONet) and a Fourier neural operator (FNO) neural network to learn a set of solutions to a problem resembling a flow field and then realizes an accuracy-preserving prediction of an unseen domain. First, we decompose the computational domain into subdomains and then reconstruct the flow field for the subdomains. Subsequently, the subdomains are stitched into the original computational domain. Using data enhancement techniques along with a small dataset, this method achieves satisfactory training results. The results indicate that for a two-dimensional isothermal flow field problem, both the improved DeepONet and FNO models accurately reconstruct the flow field. The mean squared errors are 0.28% and 1.89% for the improved DeepONet and FNO models, respectively, with both requiring less than 0.01s. This method does not require repeated training under different operating conditions, which significantly reduces the computation time. Compared with conventional CFD technology, the proposed method improves the speed by up to three to six orders of magnitude. • Rapid indoor flow field reconstruction with machine learning. • Improving computational speed by 3–6 orders of magnitude compared to traditional CFDs. • Enhancing training efficiency with data enhancement techniques for small datasets. • Reconstruction of flow fields using subdomain splicing techniques. • Learning flow-like problem solutions with Deep Operator Networks and Fourier Neural Networks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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32. Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy.
- Author
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Diallo, Abdoulaye Aguibou, Zengling Yang, Guanghui Shen, Jinyi Ge, Zichao Li, and Lujia Han
- Subjects
- *
WHEAT straw , *NEAR infrared spectroscopy , *RICE straw , *RICE , *NEAR infrared reflectance spectroscopy - Abstract
Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy. Rapid prediction of the lignocellulose (cellulose, hemicellulose, and lignin) and organic elements (carbon, hydrogen, nitrogen, and sulfur) of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages. In this study, 364 rice straw samples featuring different rice subspecies (japonica and indica), growing seasons (early-, middle-, and late-season), and growing environments (irrigated and rainfed) were collected, the differences among which were examined by multivariate analysis of variance. Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level (p <0.01), and the contents of cellulose and nitrogen had significant differences between different growing environments (p<0.01). Near infrared reflectance spectroscopy (NIRS) models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS). Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models, possibly because the CARS-PLS models selected optimal combinations of wavenumbers, which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency. As a major contributor to the applications of rice straw, the nitrogen content was predicted precisely by the CARS-PLS model. Generally, the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw. The acceptable accuracy of the models allowed their practical applications. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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33. Rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy.
- Author
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Chen, Shiyi, Ma, Mengli, Peng, Juan, He, Xiaogang, Wang, Qian, and Chu, Ganghui
- Subjects
- *
LIPASES , *NEAR infrared spectroscopy , *IMMOBILIZED enzymes , *ULTRAVIOLET-visible spectroscopy , *CANDIDA , *STATISTICAL correlation , *BIOCATALYSIS - Abstract
[Display omitted] • The ZIF-8 immobilized Candida rugosa lipase enzyme activity could be rapidly predicted by near-infrared spectroscopy. • 2nd derivative preprocessing with Competitive Adaptive Reweighted Sampling variable screening method optimized the model. • NIR quantitative method is simple, time-saving, and low-cost in predicting the immobilized enzyme activity. • The theoretical and practical basis for the prediction of other immobilized enzyme activities by NIRs. • Academic study on the successful interdisciplinary research work in enzymology and spectroscopy. Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV–Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample's enzyme activity data were linked with each sample's original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong relationship between NIRs and CRL/ZIF-8 enzyme activity. As a result, the established model could be implemented to quantify the enzyme activity of CRL/ZIF-8 quickly by including more variations of natural samples. The prediction method is simple, rapid, and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in enzymology and spectroscopy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Turning Zr(IV) into a phosphate ester mimetic enzyme via de novo synthesis for hydrolyzing organophosphorus warfare agents and rapid activity prediction by near-infrared spectroscopy.
- Author
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Chen, Shiyi, Tian, Feiyang, Peng, Juan, Meng, Yan, Wang, Yu, He, Xiaogang, Wang, Qian, and Chu, Ganghui
- Subjects
- *
NEAR infrared spectroscopy , *PHOSPHATE esters , *CHEMICAL warfare agents , *SODIUM dichromate , *ZIRCONIUM compounds , *ENZYMES , *ORGANOPHOSPHORUS pesticides - Abstract
[Display omitted] • The mimetic enzyme of UiO-66 has a lower cost to hydrolyze chemical warfare agents compared to alkaline phosphatase. • The mimetic enzyme activity of UiO-66 could be rapidly predicted by NIRs. • 2nd derivative preprocessing method with CARS variable screening methods optimized the model. • NIR quantitative method is simple, time-saving, and low-cost in predicting the mimetic enzyme activity. • It is a successful interdisciplinary academic research work in biomimetic enzymology and spectroscopy. Chemical warfare agents, especially organophosphorus warfare agents are a variety of various chemical substances employed in warfare that can massively cause damage to the organism even death. The partial catalytic function of Universitetet i Oslo-66 (UiO-66, a zirconium-based metal–organic framework) produced via de novo synthesis is similar to calf intestinal alkaline phosphatase (CIAP, EC 3.1.3.1) and has a lower usage cost, is expected to be a mimetic enzyme for hydrolyzing organophosphorus warfare agents due to the fact that it has lattice defects. 0.1–5.6 mL of acetic acid was added as a regulator to form different lattice defects. Typically, mimetic enzyme activity tests often require expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed in this research for predicting UiO-66 mimetic enzyme activity. p -Nitrophenyl phosphate disodium salt (p NPP) was selected as a lower toxic organophosphorus warfare agent to conduct the subsequent simulant research. The absorbance of the mimetic enzyme catalytic system was evaluated using Ultraviolet–Visible (UV–Vis) spectroscopy to investigate the amount of its activity and compare it with CIAP activity. The synthesized samples' enzyme activities ranged from 1.91 to 6.67 U/g and their catalytic costs ranged from 0.65 to 2.26 CNY. The powdered samples' near-infrared spectra were obtained. The sample's enzyme activity data were linked with each sample's original NIR spectra to establish the NIR model. A partial least squares (PLS) model of mimetic enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's RMSECV was 0.41 U/g, the correlation coefficient of calibration set (R_cv) value was 0.916, the root-mean-square error of prediction (RMSEP) set was 0.52 U/g, R was 0.948, and RPD was 2.51. As a result, the established model could be implemented to quantify the activity of UiO-66 quickly by including more variations of de novo -synthesized samples. The prediction method is simple, rapid, low cost and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in biomimetic enzymology and spectroscopy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Efficiency of preprocessing methods for discrimination of anatomically similar pine species by NIR spectroscopy
- Author
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F. Digdem Tuncer, Dilek Dogu, Esra Akdeniz, and TUNCER F. D., DOĞU A. D., AKDENİZ E.
- Subjects
NEAR-INFRARED SPECTROSCOPY ,RAPID PREDICTION ,MACHINE VISION ,Mühendislik, Bilişim ve Teknoloji (ENG) ,MICROFIBRIL ANGLE ,preprocessing methods ,PLS-DA ,MATERIALS SCIENCE ,COMPARATIVE WOOD ANATOMY ,PRINCIPAL COMPONENT ,Fizik Bilimleri ,classification ,ARTIFICIAL NEURAL-NETWORKS ,NONDESTRUCTIVE ESTIMATION ,Physical Sciences ,LEAST-SQUARES REGRESSION ,Genel Malzeme Bilimi ,Engineering and Technology ,wood identification ,SWIETENIA-MACROPHYLLA ,General Materials Science ,Mühendislik ve Teknoloji ,Engineering, Computing & Technology (ENG) ,Malzeme Bilimi ,discrimination - Abstract
Identification of wood species with fast, reliable and non-destructive methods is highly important for forestry and wood-related industries. Near-infrared spectra of anatomically similar pine species (Pinus sylvestris L. and Pinus nigra J.F. Arnold) were taken and analysed by partial least squared discriminant analysis (PLS-DA) for comparing the efficiency of preprocessing methods. Raw data were subjected to multiple scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay for derivatives (1st and 2nd Dr) and smoothing (Sm) and combination of these preprocessing methods (1st Dr, 1st Dr + SNV, 1st Dr + MSC, Sm + 1st Dr and Sm + 2nd Dr). The success of the models was determined by the accuracies of test sets that did not participate in the calibration phase. In this study, it was determined that not all the preprocessing methods improve the model performance. Smoothing with 1st derivatives (Sm + 1st Dr) enhanced 14.3% improvement and have the best performance (95%) for classification of pine species. For understanding modelled relationship, mean spectra and selectivity ratio were used. It was found that discrimination was held by the differences at their absorption, and the most important variables for wood classification were noted around 4000-7000 cm(-1).
- Published
- 2021
- Full Text
- View/download PDF
36. 南水北调东线山东段干渠突发水质污染事故快速预测.
- Author
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赵然杭, 彭 弢, 王好芳, 张联州, and 齐 真
- Subjects
- *
WATER transfer , *WATER diversion , *WATER supply , *WATER resources development , *SUSTAINABLE development - Abstract
The East Route of the South-to-North Water Transfer Project is a trans basin, long distance, large and comprehensive water diversion project to alleviate the contradiction between the supply and demand of water resources in the eastern part of China and support the sustainable development of the national economy and society in this region. Since the construction of the South-to-North Water Transfer Project, water safety and water quality guarantee have been a hot issue. In the middle line project, there has been a large number of researches concentrated in the source and the Beijing section. While in the east line project, the Shandong section, has already carried out the risk assessment of water transportation safety and the simulation of hydrodynamic water quality. In Shandong section, canals are mostly open type, crossing several local traffic roads, and some of the rivers and lakes bear the task of shipping. Therefore, the water conveyance safety is threatened by many potential water pollution accidents. In order to take effective emergency control and disposal measures in case of an emergency, it is necessary to carry out rapid prediction of the sudden water pollution events in the South-to-North Water Transfer project. Literature retrieval shows that, at present, many scholars at home and abroad have established a large number of simulation models with the aid of model software, but all of them need a large amount of basic data, and the operation of the model needs a lot of time. The sudden water pollution accident often happened suddenly, with random and acute. Once the sudden water pollution accident occurs, it is urgent to make a decision, and there is no time to run the simulation model to predict. So the rapid prediction is still an important problem for the scholars at home and abroad to study the sudden water pollution events. At present, there is no model to predict the process of the changes of pollutants quickly and accurately. Considering about all the situations above, based on the risk identification of water pollution incident in the main water transfer canal of Shandong section of the South-to-North Water Transfer Project, this study simulated some typical water pollution accidents in different canals of Shandong section. Also based on the transport and transformation of empirical formula of pollution, this study established a rapid prediction model of sudden water pollution accidents, and determined the parameters of the fast prediction model by using the simulation results of the main channel of the Shandong section of the South-to-North Water Transfer project. Finally, we used the results of computer numerical simulation to test the prediction results of the rapid prediction model. The test results showed that the relative error of influencing time and influencing range predicted by rapid prediction model were 0.52%-4.83% and 0.23%-7.15%, none of them were more than 10%. So it conclude that the established rapid prediction model can accurately predict the sudden water pollution accidents in the trunk section of the Shandong Section of the South to North Water Transfer project. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
37. Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model
- Author
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Jie Yang, Chunyu Du, Ting Wang, Yunzhi Gao, Xinqun Cheng, Pengjian Zuo, Yulin Ma, Jiajun Wang, Geping Yin, Jingying Xie, and Bo Lei
- Subjects
lithium ion batteries ,open circuit voltage ,concentration polarization theory ,universal relaxation model ,rapid prediction ,Technology - Abstract
The open circuit voltage of lithium ion batteries in equilibrium state, as a vital thermodynamic characteristic parameter, is extensively studied for battery state estimation and management. However, the time-consuming relaxation process, usually for several hours or more, seriously hinders the widespread application of open circuit voltage. In this paper, a novel voltage relaxation model is proposed to predict the final open circuit voltage when the lithium ion batteries are in equilibrium state with a small amount of sample data in the first few minutes, based on the concentration polarization theory. The Nernst equation is introduced to describe the evolution of relaxation voltage. The accuracy and effectiveness of the model are verified using experimental data on lithium ion batteries with different kinds of electrodes (LiCoO2/mesocarbon-microbead and LiFePO4/graphite) under different working conditions. The validation results show that the presented model can fit the experimental results very well and the predicted values are quite accurate by taking only 5 min or less. The satisfying results suggest that the introduction of concentration polarization theory might provide researchers an alternative model form to establish voltage relaxation models.
- Published
- 2018
- Full Text
- View/download PDF
38. 南水北调中线突发水污染事件的快速预测.
- Author
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龙岩, 徐国宾, 马超, and 李有明
- Abstract
Taking the typical main canal of the middle route of South-to-North Water Transfer Project as an example, the numerical simulation of sudden soluble water pollution in complex channel is carried out. First, the rapid prediction formulas of characteristic parameters (i. e., peak transport distance, pollutant longitudinal length and peak concentration) are presented based on numerical simulation, mathematical induction, and statistical analysis method. Then the rapid prediction formulas are verified by demonstration project. The results indicate that:① The peak transport distance and the add value of pollutant longitudinal length is decreasing with the channel velocity decreasing, but the peak concentration is increasing with the channel velocity decreasing; ② The relative errors between physical model test, numerical simulation and measured results are less than 15%. The rapid prediction formulas can provide decision support for managers making emergency control measures. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
39. Rapid prediction of airborne gaseous pollutant transport in aircraft cabins based on proper orthogonal decomposition and the Markov chain method.
- Author
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Wei, Yun, Zhang, Tengfei (Tim), and Jin, Huibin
- Subjects
AIRCRAFT cabins ,AIR pollutants ,TRANSPORT planes ,MARKOV processes ,PROPER orthogonal decomposition ,MARKOV chain Monte Carlo ,AIR flow - Abstract
Rapid prediction of airborne gaseous pollutant transport is important for designing a safe indoor environment. Current models generally solve the airflow fields by CFD first and then predict the transport of a pollutant in fixed airflow patterns. Every time the air-supply parameters are adjusted, the airflow field must be re-solved by CFD, which is time-consuming. This study proposed a model to improve the prediction efficiency. The model first applies proper orthogonal decomposition to the sampled airflow fields, to construct a database related to all the airflow fields in the sample ranges, and then uses the Markov chain method to obtain the airflow field with the desired air-supply parameters for construction of a transport probability matrix. Finally, the airborne gaseous pollutant transport can be predicted quickly in the fixed airflow pattern. The proposed model was applied to an aircraft cabin model, first with a single gaseous pollutant source and then with two sources, for validation of the proposed model. The results show that the proposed model can predict both the airflow field and the transport of a gaseous pollutant with outcomes similar to those obtained by the conventional CFD method, but with a much shorter computing time. When the database has been prepared in advance, the use of the model reduces the computing time by more than 90%. Further improvement of the proposed model in terms of accuracy and extension of the model to prediction of pollutant transport within unsteady airflow fields will be the main objectives of future work. • A model was proposed for rapid prediction of the pollutant transport. • The model accomplished the task without iterations. • The number of pollutant sources was unrestricted in the model. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Analytical Rapid Prediction of Tsunami Run-up Heights: Application to 2010 Chilean Tsunami.
- Author
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Byung Ho Choi, Kyeong Ok Kim, Jin-Hee Yuk, Kaistrenko, Victor, and Pelinovsky, Efim
- Subjects
TSUNAMI forecasting ,CHILE Earthquake, Chile, 2010 (February 27) ,EARTHQUAKES ,THEORY of wave motion ,WATER depth - Abstract
An approach based on the combined use of a 2D shallow water model and analytical 1D long wave run-up theory is proposed which facilitates the forecasting of tsunami run-up heights in a more rapid way, compared with the statistical or empirical run-up ratio method or resorting to complicated coastal inundation models. Its application is advantageous for long-term tsunami predictions based on the modeling of many prognostic tsunami scenarios. The modeling of the Chilean tsunami on February 27, 2010 has been performed, and the estimations of run-up heights are found to be in good agreement with available observations. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
41. Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries.
- Author
-
Pei, Lei, Wang, Tiansi, Lu, Rengui, and Zhu, Chunbo
- Subjects
- *
ELECTRIC potential , *CHEMICAL relaxation , *OPEN-circuit voltage , *LITHIUM-ion batteries , *ELECTRODES , *REGRESSION analysis - Abstract
Abstract: The open-circuit voltage (OCV) of a battery, as a crucial characteristic parameter, is widely used in many aspects of battery technology, such as electrode material mechanism analysis, battery performance/state estimation and working process management. However, the applications of OCV are severely limited due to the need for a long rest time for full relaxation. In this paper, a rapid OCV prediction method is proposed to predict the final static OCV in a few minutes using linear regression techniques, based on a new mathematical model developed from an improvement on a second-order resistance–capacitance (RC) model. As the improvement, an important discovery is demonstrated by experimental investigation and data analysis: the relaxation time (i.e., time constant) of the diffusion circuit of the second-order RC model is not a fixed constant, unlike an intrinsic value for a given material, but an apparent linear function of the open-circuit time. This improvement enables the new model to track the actual relaxation process very well. The accuracy and the rapidity of the new model and proposed method are validated with working-condition experimental data on battery cells with different cathodes, and the results of OCV prediction are very accurate (errors below 1 mV in 20 min). [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF
42. Rapid compositional analysis of Atlantic salmon ( Salmo salar) using visible-near infrared reflectance spectroscopy.
- Author
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Brown, Malcolm R, Kube, Peter D, Taylor, Richard S, and Elliott, Nick G
- Subjects
- *
ATLANTIC salmon , *NEAR infrared reflectance spectroscopy , *FOOD of animal origin , *FOOD quality , *FISH larvae , *UNSATURATED fatty acids - Abstract
Rapid measurement of salmon flesh quality parameters (>400 samples day−1) was demonstrated in the laboratory and remotely at industrial sites. Visible-near infrared spectroscopy ( VNIRS) was applied to predict astaxanthin ( AX) and fat content in farmed Atlantic salmon. Fish were sampled from thirteen batches (1-6 kg whole weight, containing 2.3-16.3% fat and 1.2-12.5 μg g−1 AX), and models validated on small (average ± SD: 1.4 ± 0.4 kg) and large fish (4.2 ± 0.9 kg). Both constituents were well predicted in minced Norwegian Quality Cutlet ( NQC) samples (r2 ≥ 0.86; standard error of prediction ( SEP) ≤0.7% for fat and ≤0.7 μg g−1 for AX). Comparable metrics were observed for AX prediction in whole NQCs (r2 = 0.80-0.88; SEP 0.7 μg g−1). Fat was better predicted in small fish than large fish for whole NQCs (r2 = 0.82, SEP 1.0% cf r2 = 0.59, SEP = 0.59%) and non-destructive scanning through the skin of whole, gutted fish (r2 = 0.77, SEP = 1.2% cf r2 = 0.49, SEP = 1.5%). Models were also developed for screening polyunsaturated fatty acid ( PUFA) concentrations, e.g. in NQCs for docosahexaenoic acid (r2 = 0.73) and n-3:n-6 PUFA (r2 = 0.89). [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
43. Rapid prediction method of α-Glycosidase inhibitory activity of Coreopsis tinctoria extract from different habitats by near infrared spectroscopy.
- Author
-
He, Xiaogang, Han, Xiang, Yu, Jiaping, Feng, Yulong, and Chu, Ganghui
- Subjects
- *
GLYCOSIDASES , *ALPHA-glucosidases , *PARTIAL least squares regression , *STANDARD deviations , *NEAR infrared spectroscopy , *ULTRAVIOLET spectroscopy , *INFRARED spectra - Abstract
[Display omitted] • The inhibitory activity of Coreopsis tinctoria extract on a-glucosidase could be rapidly predicted by near infrared spectroscopy. • Continuous wavelet transform (CWT) preprocessing method and 4381–7470 cm−1 spectra optimized the model. • The precise model was built for predicting inhibitory activity of a-glucosidase and it is effective and robust with a good accuracy according to two RPDs. • The quantitative method is simple and time-saving in predicting inhibitory activity. α- Glucosidase is one of the main enzymes causing elevated blood glucose, and Coreopsis tinctoria extract can be used as a natural inhibitor of α -Glucosidase. Therefore, a new method was proposed for predicting the inhibitory activity on α- Glucosidase of Coreopsis tinctoria extract based on near infrared spectroscopy. The absorbance of the inhibitory system was measured by ultraviolet spectroscopy, which was used to study the inhibitory activity on a-glucosidase of Coreopsis tinctoria extract. The near infrared spectra of the solid samples were collected. By selecting spectral preprocessing and optimizing spectral bands, a rapid prediction model of the inhibitory activity was established by partial least squares regression. The root mean square error of cross-validation (RMSECV), correlation coefficient (R) value and the ratio of prediction to deviation (RPD) value were used as indicators of the evaluation model. The near infrared spectrum model was established by combining the best spectral preprocessing of the continuous wavelet transform (CWT) and the best spectral band. The root mean square error of cross-validation (RMSECV) of this model was 0.815%, the correlation coefficient (R) value was 0.942, and the ratio of prediction to deviation (RPD) was 3.0. The root mean square error of prediction (RMSEP) of the model by prediction set was 0.819%, the correlation coefficient (R) value was 0.950, and the RPD was 3.2. The model shows that the fitting relationship between the predicted inhibition value and the reference inhibition value of the near infrared spectral model is good. The results showed that there was a good correlation between near infrared spectroscopy and the inhibitory activity of Coreopsis tinctoria extract. Thus, the established model was robust and effective and could be used for rapid quantification of α -Glucosidase inhibitory activity. The prediction method is simple and rapid, and can be extended to study the inhibition of other medicinal plants. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Hybrid model combining multivariate regression and machine learning for the rapid prediction of interior temperatures affected by thermal diodes and solar cavities.
- Author
-
He, Yi, Kou, Fangcheng, Wang, Xin, Zhu, Ning, Song, Yehao, Chu, Yingnan, Shi, Shaohang, Liu, Mengjia, and Chen, Xinxing
- Subjects
HELIOSPHERE ,MACHINE learning ,DIODES ,HEAT transfer ,HEAT pipes ,SOLAR thermal energy ,SOLAR heating - Abstract
Sustainable design often requires highly efficient building performance evaluations. This study proposed a hybrid model combining multivariate regression modelling (MRM) and machine learning modelling (MLM) for the rapid prediction of interior temperatures affected by heat pipe thermal diodes and solar cavities based on experimental data. A heat pipe thermal diode can promote unidirectional heat transmission from the solar cavity on the south side of our newly built experimental house to the indoor environment to increase the interior temperature and reduce the heating load in cold climates. Experimental data were collected and then imported, cleaned, and split according to MRM and MLM requirements, respectively. In MRM, linear multivariate formulas were generated according to the thermal diode's two different working conditions. In MLM, a machine-learning model was created and trained using the experimental data. The results our hybrid model produced were comprehensively evaluated via R-square, statistical discrepancies, and complex MRM analyses. The similarity between the prediction and experimental results clearly demonstrates our model's accuracy and efficiency. This research was an original attempt to integrate emerging computational tools and provide a means to perform highly efficient quantitative analysis of indoor thermal environments for environmental studies and sustainable designs in the early stages. • A hybrid model for rapid evaluation of indoor thermal environment was proposed. • Multivariate regression and machine learning jointly formulated the hybrid model. • Interior temperature affected by heat pipe thermal diode and solar cavity could be predicted in a very short time. • Efficiency and accuracy of the model were demonstrated by benchmark investigations. • Rapid evaluation on the built environment could benefit the development of sustainable architectural design. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. Construction of linear temperature model using non-dimensional heat exchange ratio: Towards fast prediction of indoor temperature and heating, ventilation and air conditioning systems control.
- Author
-
Ren, Chen and Cao, Shi-Jie
- Subjects
- *
TEMPERATURE control , *AIR conditioning , *ENERGY consumption of buildings , *GREEN'S functions , *COMPUTATIONAL fluid dynamics , *ENERGY consumption , *MINE ventilation - Abstract
• To develop a reliable linear model for rapid prediction of temperature fields. • A dimensionless heat exchange ratio 〈 β 〉 proposed for decoupling of momentum and energy equations when increasing to infinity. • Linear temperature model (LTM) constructed and validated with lower ranges of 〈 β 〉 (compared to infinity). • To facilitate at fast and efficient predictions of temperature and HVAC control. The prediction of non-uniform thermal environment plays an important role in the control of heating, ventilation and air conditioning (HVAC) system, to meet the growing demand for occupant thermal comfort and building energy consumption, in response to varying heat sources. The popular approach to the prediction issue is computational fluid dynamics (CFD), with considerable time and computational costs. Thus, this work aims to develop a reliable linear model for rapid prediction of temperature fields, i.e., linear temperature model (LTM). A dimensionless heat exchange ratio 〈 β 〉 (when increasing to infinity) is proposed for the decoupling of momentum and energy equations, which is defined as the heat emission of the heat source to the heat gain of the flow. This will further be used for the construction of LTM combining Green's function and energy equation. By using the simulation method, a ventilation case occupied with various heat sources is considered for the validation, i.e., lower ranges of 〈 β 〉 (compared to infinity) as well as the related prediction error of LTM for engineering application. It is found that 〈 β 〉 can be valid with the value above 2.7 when source intensity is equal to 34 W/m3. Using the curve fitting, the conjunction between 〈 β 〉 , source intensity and prediction error is suggested for the verification of feasible LTM. The LTM can be applicative to a full-scale environment, to improve physical environment by 68% and energy efficiency by 50%. This work will facilitate at fast and efficient predictions of temperature distribution and HVAC systems online control. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
46. Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model
- Author
-
Chunyu Du, Wang Ting, Xinqun Cheng, Yulin Ma, Jie Yang, Pengjian Zuo, Jingying Xie, Jiajun Wang, Yunzhi Gao, Bo Lei, and Geping Yin
- Subjects
Battery (electricity) ,Control and Optimization ,Materials science ,Thermodynamic equilibrium ,020209 energy ,Energy Engineering and Power Technology ,chemistry.chemical_element ,02 engineering and technology ,lcsh:Technology ,universal relaxation model ,Ion ,symbols.namesake ,0202 electrical engineering, electronic engineering, information engineering ,Nernst equation ,rapid prediction ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,lcsh:T ,Renewable Energy, Sustainability and the Environment ,Open-circuit voltage ,Mechanics ,021001 nanoscience & nanotechnology ,open circuit voltage ,concentration polarization theory ,chemistry ,symbols ,Relaxation (physics) ,Lithium ,0210 nano-technology ,lithium ion batteries ,Energy (miscellaneous) ,Voltage - Abstract
The open circuit voltage of lithium ion batteries in equilibrium state, as a vital thermodynamic characteristic parameter, is extensively studied for battery state estimation and management. However, the time-consuming relaxation process, usually for several hours or more, seriously hinders the widespread application of open circuit voltage. In this paper, a novel voltage relaxation model is proposed to predict the final open circuit voltage when the lithium ion batteries are in equilibrium state with a small amount of sample data in the first few minutes, based on the concentration polarization theory. The Nernst equation is introduced to describe the evolution of relaxation voltage. The accuracy and effectiveness of the model are verified using experimental data on lithium ion batteries with different kinds of electrodes (LiCoO2/mesocarbon-microbead and LiFePO4/graphite) under different working conditions. The validation results show that the presented model can fit the experimental results very well and the predicted values are quite accurate by taking only 5 min or less. The satisfying results suggest that the introduction of concentration polarization theory might provide researchers an alternative model form to establish voltage relaxation models.
- Published
- 2018
- Full Text
- View/download PDF
47. Definition of Damage Volumes for the Rapid Prediction of Ship Vulnerability to AIREX Weapon Effects
- Author
-
Stark, Sean Aaron, Aerospace and Ocean Engineering, Brown, Alan J., Sajdak, John Anthony, and Wang, Kevin Guanyuan
- Subjects
vulnerability ,blast ,rapid prediction - Abstract
This thesis presents a damage model developed for the rapid prediction of the vulnerability of a ship concept design to AIREX weapon effects. The model uses simplified physics-based and empirical equations, threat charge size, geometry of the design, and the structure of the design as inputs. The damage volumes are customized to the design being assessed instead using of a single volume defined only by the threat charge size as in previous damage ellipsoid methods. This methodology is validated against a range of charge sizes and a library of notional threats is created. The model uses a randomized hit distribution that is generated using notional threat targeting and the geometry of the design. A Preliminary Arrangement and Vulnerability (PAandV) model is updated with this methodology and used to calculate an Overall Measure of Vulnerability (OMOV) by determining equipment failures and calculating the resulting loss of mission capabilities. A selection of baseline designs from a large design space search in a Concept and Requirements Exploration (CandRE) are assessed using this methodology. Master of Science
- Published
- 2016
48. Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model.
- Author
-
Yang, Jie, Du, Chunyu, Wang, Ting, Gao, Yunzhi, Cheng, Xinqun, Zuo, Pengjian, Ma, Yulin, Wang, Jiajun, Yin, Geping, Xie, Jingying, and Lei, Bo
- Subjects
- *
LITHIUM-ion batteries , *LITHIUM ions , *STORAGE batteries , *ENERGY storage , *CATHODES - Abstract
The open circuit voltage of lithium ion batteries in equilibrium state, as a vital thermodynamic characteristic parameter, is extensively studied for battery state estimation and management. However, the time-consuming relaxation process, usually for several hours or more, seriously hinders the widespread application of open circuit voltage. In this paper, a novel voltage relaxation model is proposed to predict the final open circuit voltage when the lithium ion batteries are in equilibrium state with a small amount of sample data in the first few minutes, based on the concentration polarization theory. The Nernst equation is introduced to describe the evolution of relaxation voltage. The accuracy and effectiveness of the model are verified using experimental data on lithium ion batteries with different kinds of electrodes (LiCoO2/mesocarbon-microbead and LiFePO4/graphite) under different working conditions. The validation results show that the presented model can fit the experimental results very well and the predicted values are quite accurate by taking only 5 min or less. The satisfying results suggest that the introduction of concentration polarization theory might provide researchers an alternative model form to establish voltage relaxation models. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
49. E-nose based rapid prediction of early mouldy grain using probabilistic neural networks.
- Author
-
Ying X, Liu W, Hui G, and Fu J
- Subjects
- Avena chemistry, Avena microbiology, Biosensing Techniques methods, Edible Grain microbiology, Food Analysis methods, Humans, Neural Networks, Computer, Oryza chemistry, Oryza microbiology, Phaseolus chemistry, Phaseolus microbiology, Principal Component Analysis, Sensitivity and Specificity, Biosensing Techniques instrumentation, Edible Grain chemistry, Electronic Nose, Food Analysis instrumentation, Fungi chemistry, Models, Statistical
- Abstract
In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction.
- Published
- 2015
- Full Text
- View/download PDF
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