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The Stochastic Reduced-Order Models (SROMs) are a non-embedded uncertainty analysis method that has the advantages of high computational efficiency, easy implementation, and no dimensional disasters. Recently, it has been widely used in the field of EMC simulation. In the process of optimizing electromagnetic protection design, the worst-case estimation value is an extremely important uncertainty quantification simulation result. However, the SROMs have a large error in providing this result, which limits its application in the field of EMC simulation prediction. An improved SROM based on the Radial Basis Function (RBF) neural network algorithm is proposed in this paper, which improves the fitness function in the genetic algorithm center clustering process and constructs an RBF neural network model to obtain accurate worst-case estimation results. The accuracy improvement effect of the algorithm proposed in this paper in worst-case estimation is quantitatively verified by using a parallel cable crosstalk prediction example from published literature. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate identification of individual Amur tigers is vital for their conservation, as it helps us understand their population and distribution. Existing identification methods often fall short in accuracy, and our study focuses on creating a more accurate method for identifying individual Amur tigers using advanced deep learning techniques. We improved an existing neural network model called InceptionResNetV2 by adding features like dropout layers and dual-attention mechanisms to better capture the unique stripe patterns of each tiger and reduce errors during training. We tested our model on a large dataset of tiger images and found it to be highly effective, achieving an average recognition accuracy of over 95% for different body parts, with left stripes reaching the highest 99.37%. This method significantly outperforms previous models and provides a reliable tool for wildlife researchers and conservationists to monitor and protect Amur tigers. By improving the ability to track individual tigers, our research offers practical benefits for preserving this endangered species and enhancing wildlife management practices globally. Accurate and intelligent identification of rare and endangered individuals of flagship wildlife species, such as Amur tiger (Panthera tigris altaica), is crucial for understanding population structure and distribution, thereby facilitating targeted conservation measures. However, many mathematical modeling methods, including deep learning models, often yield unsatisfactory results. This paper proposes an individual recognition method for Amur tigers based on an improved InceptionResNetV2 model. Initially, the YOLOv5 model is employed to automatically detect and segment facial, left stripe, and right stripe areas from images of 107 individual Amur tigers, achieving a high average classification accuracy of 97.3%. By introducing a dropout layer and a dual-attention mechanism, we enhance the InceptionResNetV2 model to better capture the stripe features of individual tigers at various granularities and reduce overfitting during training. Experimental results demonstrate that our model outperforms other classic models, offering optimal recognition accuracy and ideal loss changes. The average recognition accuracy for different body part features is 95.36%, with left stripes achieving a peak accuracy of 99.37%. These results highlight the model's excellent recognition capabilities. Our research provides a valuable and practical approach to the individual identification of rare and endangered animals, offering significant potential for improving conservation efforts. [ABSTRACT FROM AUTHOR]

Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship's flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training. [ABSTRACT FROM AUTHOR]

This paper presents an indoor positioning strategy based on Visible Light Communication that relies on LED luminaires as transmitters, whose luminous flux is modulated at different frequencies, and a light sensor as a receiver. Then, a previously trained Artificial Neural Network (ANN) uses the illuminance signal gathered by the receiver as input to estimate the sensor's position. The main contribution of the technique is that the ANN is trained by using an illuminance estimator based on the lighting distribution of the luminaires, which is obtained through the IES file provided by the luminaire's manufacturer, without the need to collect data from the environment. In this work, the designed illuminance estimator is validated by comparing it to the well-known commercial software DIALux and Relux. The algorithm's setting and the performance evaluation of the ANN are explained. Then, the impact of the lighting uniformity and the environment's number of divisions on the accuracy of the results is analyzed. Error analyses are also made by adding uncertainty to the illuminance measurements obtained by the sensor. Finally, the work is compared to several papers in the field. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques. [ABSTRACT FROM AUTHOR]

The paper presents a dynamic model of the TMM-3M heavy mechanized bridge during the span lowering stage. The model is constructed as a multi-body mechanical system, taking into account the elastic deformation of the cable, rear outriggers, front tires, and front suspension system. It is a mechanical model driven by a cable mechanism. Lagrangian equations of the second kind have been applied to establish a system of differential equations describing the oscillations of the mechanical system and serve as the basis for investigating the dynamics of the span-lowering process. The system of differential equations is solved using numerical methods based on MATLAB simulation software. The study has revealed laws of the displacement, velocity, and acceleration of components within the mechanical system, especially those related to the bridge span depending on the choice of the drive speed of the engine during lowering by operator. The research results show that the lowering time increases from 52 seconds to 104 seconds when the engine speed decreases from 1800 rpm to 900 rpm. The tension force on the cable is surveyed to confirm the safety conditions during the span-lowering process. The study also provides recommendations for selecting appropriate engine speeds to minimize span-lowering time while ensuring the safety conditions of the TMM-3M bridge during the span-lowering process. This research is an important part of a comprehensive study on the working process of the heavy mechanized bridge TMM-3M to make practical improvements, aiming to reduce deployment time, decrease the number of deployment crew members, and increase the automation capability of the equipment. [ABSTRACT FROM AUTHOR]

Plastic deformation forming with metal pipe blanks by contact blast loading inside pipes is an interesting moldless forming technique, also a complex and error-prone process. Some advantages are very characteristic of this forming technique such as no cost of mold, tooling and low energy consumption, no complicated control equipment compared to other forming techniques such as casting, rolling, tube hydrostatic forming, bending - welding. Up to now, the calculation and design of this forming technique mainly use some existing reference empirical formulas, so the experimental results are only suitable in the range of small pipe diameters, and still there are significant deviations for larger pipe diameters. In order to increase the predictability and accuracy of forming process by contact blast loading inside large pipes, this paper presents a study on the influence of the mass of highly explosive material - TNT to the forming ability of large steel pipes from API-5LX-42 mild steel materials by modern 3D numerical simulation using Abaqus/Cae software. Four output criteria with maximum values are used to evaluate the efficiency of this forming process, includ- ing maximum diameter of the blast zone (Dmax ≤2*Do), Von Mises stress (Smax ≤UTS), Hoop plastic strain component (PE22 max ≤1), and Pipe wall thinning rate (€7-max ≤60%). The results of this research on the plastic deformation forming process using numerical simulation can be used for the next experimental step to evaluate the difference between simulation and experiment, as well as use this data in the calculation and design of pipe products with circular or square cross-sections to save both time and money of trial and error before application in actual manufacturing. [ABSTRACT FROM AUTHOR]

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model's focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model's accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy ( m A P 0.5 ) and ( m A P 0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Processes titled "Process Monitoring and Fault Diagnosis." The issue explores the application of data analytic techniques to enhance stable operation and safety in chemical processes and related industries. The collection of research papers covers various topics, including process fault detection, bearing fault diagnosis, remaining useful life prediction, and more. The papers introduce cutting-edge methodologies and demonstrate their reliability through validation. The issue aims to foster communication and the development of advanced process monitoring techniques. [Extracted from the article]

This document is a compilation of various scientific papers and preprints covering a wide range of topics, including protein folding, cell migration, machine learning, deep neural networks, neural scaling laws, representations and generalization in artificial and brain neural networks, and the neuron as a direct data-driven controller. The papers discuss different aspects of these subjects, providing a comprehensive overview of the current research in these fields. This document can be a valuable resource for library patrons conducting research on these topics, particularly in the fields of neuroscience and artificial intelligence. [Extracted from the article]

The Journal of Chemical Physics has released a special issue focused on software for atomistic machine learning. This issue aims to address the lack of journals dedicated to publishing scientific software papers. The collection of papers in this issue provides insight into the tools and goals of software implementations in the field of atomistic machine learning. The articles cover a range of topics, including machine-learning interatomic potentials, sampling, dataset repositories, workflows, and auxiliary tooling and analysis. The article concludes by emphasizing the importance of software implementations in the field and encourages further submissions on relevant topics. [Extracted from the article]

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy neural network predictive control scheme for studying the coagulation process of wastewater treatment in a paper mill. An adaptive fuzzy neural network is employed to model the nonlinear relationships between the removal rate of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability. The system includes a fuzzy neural network emulator of the reaction process, a fuzzy neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method is used to realize the optimization procedure. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. [Copyright &y& Elsevier]

The authors of the paper titled "Towards Neuro-Fuzzy Compensated PID Control of Lower Extremity Exoskeleton System for Passive Gait Rehabilitation" designed a neuro-fuzzy compensated PID control for passive gait rehabilitation using a lower extremity exoskeleton system. In the paper "A Comparative Survey of Convex Combination of Adaptive Filters", the authors evaluate the convex combination of adaptive filters, which are mostly based on the least mean square (LMS) algorithm, the affine projection (AP) algorithm, and the recursive least mean square (RLS) algorithm. The paper "A Novel Weighted Superposition Attraction Algorithm-based Optimization Approach for State of Charge and Power Management of an Islanded System with Battery and Super Capacitor-based Hybrid Energy Storage System" proposes a Hybrid Energy Storage System (HESS) employing a Battery and a Super Capacitor (SC). The authors of the paper titled "Computer-Aided Detection and Diagnosis of Thyroid Nodules Using Machine and Deep Learning Classification Algorithms" propose a computer-assisted method for detecting and segmenting tumour regions in ultrasound thyroid images using machine and deep learning classification algorithms. [Extracted from the article]

Diclofenac sodium is an emerging contaminant that can be harmful for ecology and human health. This substance can be degraded by a heterogeneous Photo-Fenton process, CoFe2O4 as catalyst, H2O2 as oxidant and UV radiation. The aims of the work are the comparison of different artificial neural networks to characterize the relationship between diclofenac degradation and H2O2 consumption, with the Total Organic Carbon achieved in the mineralization of the drug and the testing of the selected model capacity to predict the Total Organic Carbon concentration, by employing the reused catalyst. The best performing backpropagation neural network was constituted with a ten neurons hidden layer with sigmoid transfer function and one linear neuron, as output. It was determined that the model can approximate the trend between the input data (Absorbance and H2O2 concentration) and output ones (Total Organic Carbon concentration) when it was validated with data from reactions employing CoFe2O4 for second and third time. The development of these models is of interest due to the consequent reduction of time and costs in experimental work. It represents a study of the evolution of chemical indicators in the treatment of emerging contaminants. [ABSTRACT FROM AUTHOR]

In this paper, we propose a binary feedback scheme for congestion control in computer networks using a gradient-based mechanism which employs neural network modelling of the system dynamics. The optimal direction for rate adjustment at the source is based on a single bit feedback signal which depends upon the sign of the sensitivity of the system performance index with respect to queue input rate. The paper presents the scheme and the analysis that went into the choice of the various decision mechanisms. Simulation results are presented to show the performance of the gradient based technique as compared to the conventional queue-based approach. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

HIDRALERTA is a forecast and early warning system for coastal and port regions capable of predicting emergency situations, as well as carrying out risk assessment. The system concentrates on evaluating wave overtopping and flooding scenarios. It uses offshore sea-wave and wind forecast data, as well as tide data, as input to determine wave overtopping at specific locations. The calculation of the mean overtopping discharge over a structure is made through artificial neural network tools and/or empirical formulae, which are able to define the spatial distribution of the flow behind the structure. The HIDRALERTA system is composed of four modules developed in Open Source Web Technology, mainly using the Python Language. The Warning System can be configured and adapted for specific scenarios. The system has been successfully tested for the Port and Bay of Praia da Vitória, Terceira Island, Azores, Portugal, and is now under development and being implemented at the ports of Madalena do Pico and S. Roque do Pico. This paper describes the work carried out to date on the system and its application to these two ports in the Pico Island, Azores. [ABSTRACT FROM AUTHOR]

Presents information on a study which described the application of neural network techniques to the papermaking industry. Information on the database provided by Tillis Russell for the study; Preprocessing and coding of data; Neural-network training; Results of the study; Conclusion.

This paper presents a vibration-based damage identification method that utilises a 'damage fingerprint' of a structure in combination with Principal Component Analysis (PCA) and neural network techniques to identify defects. The Damage Index (DI) method is used to extract unique damage patterns from a damaged beam structure with the undamaged structure as baseline. PCA is applied to reduce the effect of measurement noise and optimise neural network training. PCA-compressed DI values are, then, used as inputs for a hierarchy of neural network ensembles to estimate locations and severities of various damage cases. The developed method is verified by a laboratory structure and numerical simulations in which measurement noise is taken into account with different levels of white Gaussian noise added. The damage identification results obtained from the neural network ensembles show that the presented method is capable of overcoming problems inherent in the conventional DI method. Issues associated with field testing conditions are successfully dealt with for numerical and the experimental simulations. Moreover, it is shown that the neural network ensemble produces results that are more accurate than any of the outcomes of the individual neural networks. [ABSTRACT FROM AUTHOR]

The issue contains five papers, all relevant to the core intersectional topic of AI and complex systems. This Special Issue, titled "Artificial Intelligence and Complex Systems", was created with the goal of collecting the latest research, both foundational and application-orientated, at the intersection of AI and complex systems, ranging across real-world domains as varied as education and fraud detection. The discussion in the paper also suggests that context is fundamentally necessary to understand complex systems using AI methodologies. [Extracted from the article]

Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the process nominal states and therefore are not enough for classification problems intended to determine abnormal process conditions. This work proposes the use of techniques to augment the original real data standards, dismissing the need for experiments that could jeopardize process safety. It uses the Monte Carlo technique to artificially increase the number of model inputs coupled to the nearest neighbor search (NNS) by geometric distances to consistently classify the generated patterns in normal or faulty statuses. Finally, a radial basis function neural network is trained with the augmented data. The methodology was validated by a study case in which 3381 pulp and paper industrial data points were expanded to monitor the formation of particles in a recovery boiler. Only 5.8% of the original process data were examples of faulty conditions, but the new expanded and balanced data collection leveraged the classification performance of the neural network, allowing its future use for monitoring purpose. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms. [ABSTRACT FROM AUTHOR]

Facial emotion detection systems have witnessed significant advancements, particularly with the utilization of convolutional neural networks (CNNs). This paper provides a thorough survey of such systems, beginning with an introduction to artificial intelligence and the evolutionary trajectory of neural networks, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and CNNs. The paper elaborates on CNNs' architecture and functionality, elucidating key components such as convolutional layers, pooling layers, and fully connected layers, while also spotlighting prominent CNN architectures like AlexNet and ResNet. It delineates the broad scope and diverse applications of facial emotion detection systems across various domains, including marketing research, crowd testing, AI robots, banking, and entertainment. In the literature review section, recent research papers on CNN models for facial expression recognition are synthesized, highlighting variances in datasets, methodologies, and accuracy levels. The paper concludes that CNNs represent the current pinnacle of facial emotion classification techniques, surpassing previous methodologies such as eigenfaces. It underscores the efficacy of deep CNN architectures trained on extensive facial image datasets in proficiently identifying emotions from facial expressions. Moreover, the paper emphasizes the necessity for ongoing endeavors to enhance accuracy, particularly concerning complex emotions like disgust. In essence, CNNs exhibit substantial promise for the development of real-world facial emotion detection systems, heralding a new era of sophisticated emotion recognition technology. [ABSTRACT FROM AUTHOR]

This is the Replicated Computational Results (RCR) Report for our TOSEM paper "Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks", where we propose a novel client-server architecture allowing to leverage the high accuracy of huge neural networks running on remote servers while reducing the economical and latency costs typically coming from using such models. As part of this RCR, we provide a replication package, which allows the full replication of all our results and is specifically designed to facilitate reuse. [ABSTRACT FROM AUTHOR]

The present study examines global research in the domain of "Quantum Neural Networks" (QNN) on metrics such as publication output, document type, country, collaboration patterns, institutional and author affiliation, subjects, journal name, and citation patterns. The data for the study was sourced from the Scopus database for the period 1990-2019. During the last 30-year period, the global research output in the subject was 546 publications. The study describes the QNN research in terms of top most productive countries, research organizations, research authors, the most popular subject areas, and source journals. The study also characterizes the most highly cited papers in the subject. [ABSTRACT FROM AUTHOR]

Most international academic papers are written in English, and the use of tenses in English academic papers often follows some conventional rules. Automatically extracting and analyzing English tenses in scientific papers have begun to attract researchers' attention for the global environment. In the analysis of the English tense of scientific papers, consider that the neural network model that combines attention mechanism and sequential input network such as Long Short-Term Memory (LSTM) network has a long training time, low extraction accuracy, and cannot parallelize text input. We propose an environmental affection-driven English tense analysis model, which includes an attention mechanism and LSTM model and conducts a temporal analysis of English texts based on an affective computing model. In this paper, our proposed method is verified based on the self-built healthcare exercise-based corpus over public English environment. By comparison, the experimental results show that the method proposed in this paper has better performance than ordinary Convolutional Neural Network (CNN), Support Vector Machine (SVM), and LSTM based on attention mechanism. [ABSTRACT FROM AUTHOR]

The second paper, titled "Design and Analysis of Zeta Converter for Power Factor Correction using Cascade PSO-GSA Tuned PI and Reduced-Order SMC" describes a single-phase power factor correction method employing a DCDC Zeta converter in continuous conduction mode.To increase converter performance, it presents a non-linear cascade control of particle swarm optimization-gravitational search algorithm (PSO-GSA) tuned proportional-integral and sliding mode controller in outer and inner loops. In particular, an optimisation framework is developed that accounts for several processes, including data gathering from various sources, KPI description, and the optimisation procedure itself. Wind farm plan optimization or WFLOP, is a difficult optimisation problem with many constraints. Heuristic optimisation techniques outperformed standard methods in the study. [Extracted from the article]

Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer's experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model's generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability. [ABSTRACT FROM AUTHOR]

This document discusses the importance of quality and security in critical infrastructure systems, particularly in relation to cyber-attacks and software errors. It highlights the significant financial losses and potential human casualties that can result from these threats. The document also introduces several research papers that address different aspects of software and computer system quality, security, and reliability. These papers propose various models and methods, such as intelligent classification algorithms for assessing anthropogenic disasters, leveraging artificial intelligence for detecting fraudulent banking transactions, enhancing the decision-making process for higher education institution admissions, and developing algorithms for autonomous mobile robots and obstacle detection. The document concludes with acknowledgments and a statement of no conflicts of interest. [Extracted from the article]

Abstract: Using advanced machine learning techniques as an alternative to conventional double-entry volume equations, a regression model of the inside-bark volume (dependent variable) for standing Eucalyptus globulus trunks (or main stems) has been built as a function of the following three independent variables: age, height and outside-bark diameter at breast height (DBH). The experimental observed data (age, height, outside-bark DBH and inside-bark volume) for 142 trees (E. globulus) were measured and a nonlinear model was built using a data-mining methodology based on support vector machines (SVM) and multilayer perceptron networks (MLP) for regression problems. Coefficients of determination and Furnival’s indices indicate the superiority of the SVM with a radial kernel over the allometric regression models and the MLP. [Copyright &y& Elsevier]

This paper describes the design and development of an operational diagram that was developed based on the solids flux theory and connects the process operational parameters, namely, mixed-liquor suspended solids, flow, volume, area, and recycle ratio in an unified graphical form to facilitate the design and operation of secondary clarifiers for an Indian pulp and paper industry. A control strategy was developed based on steady-state control methods by utilizing the batch settling data generated in stationary phase complimented with artificial neural network models. These diagrams evaluate the operating strategies in response to changes in flow rate or settleability of solids. These operational diagrams developed based on steady-state control analysis, can be used for controlling the performance of the activated sludge process subjected to long term variations in the influent flowrate and substrate concentration. The plant operator can make a rational decision on his own to control the process using the operation diagrams. [ABSTRACT FROM AUTHOR]

The Brazilian energy spot price is obtained through a chain of dynamic stochastic optimization models that works with the uncertainties related to its continental hydrogeneration-based power system. In that sense, this paper presents an information theoretic learning neural forecasting model for daily streamflow prediction of Brazilian hydroelectric power plants. More precisely, the maximum correntropy criterion was used as the error function of a multilayer perceptron. After the prediction stage, the generated outputs were used as one of the inputs of the model chain that is used to compute the hourly energy spot price in Brazil. To the best of the authors' knowledge, it is the first paper that aims to analyze the impact of the streamflow prediction on the Brazilian hourly energy spot price formation. In terms of streamflow forecast, results indicated that the predictions originated from the proposed forecasting model were equivalent to the ones from the official models, especially in the first predicted day. In the spot price graph analysis, the main result pointed that the curves provided from the modeled structure were closer to the values obtained using the actual flows than the official prices, which shows that the proposed work could produce prices more aligned to the real hydrological system conditions. From that, the study's relevance is due to the conclusion that the official process of streamflow forecasting can be improved to generated outputs more consistent with the actual system conditions, to avoid further expenses in the system operation due to potential unscheduled hydrothermal dispatches. [ABSTRACT FROM AUTHOR]

The use of externally bonded composite systems is recognized as an effective solution for strengthening existing reinforced concrete (RC) structures. Steel-reinforced grout (SRG) is an attractive option, because of its compatibility with the concrete substrate and mechanical properties. However, a critical aspect is the delamination that might affect the steel textile–mortar and the mortar–concrete substrate interfaces. An experimental and theoretical investigation of the SRG–concrete bond is reported in this paper. In particular, the bond performances of SRG-to-concrete joints, which varies the width of the SRG fabric, the displacement rate, and the applied load eccentricity, are analyzed for the stress that is associated with the bond capacity, slip, and failure modes based on the results that are obtained by direct single-lap shear tests. To assess a data set for model calibration, the findings of this paper and those in the technical literature are collected. Therefore, a machine learning (ML) approach that is based on an artificial neural networks (ANN) algorithm is implemented, and a new analytical formulation for the prediction of the SRG-to-concrete bond capacity is proposed. [ABSTRACT FROM AUTHOR]

In recent years, the integration of digital technologies has grown rapidly in the field of thermal comfort and energy efficiency for buildings. The concept of a digital twin, incorporating multiple digital technologies, has gained increasing attention. The literature lacks a review of the digital twin concept in thermal comfort and energy consumption for existing buildings. This paper conducts a review of the current state-of-the-art in digital twin (DT) technology for thermal comfort/ energy consumption in buildings. The review employs a scientometric approach and examines various technologies used in creating DTs and a systematic analysis of the methods, technologies, algorithms, and approaches used in digital twin experiments. The results show a growing number of studies in this area, with a focus on thermal comfort monitoring, visualization, tracking, energy management, prediction, and optimization for existing buildings. Furthermore, the prediction of energy consumption using algorithms such as Artificial Neural Networks (ANN), Artificial Intelligence (AI), deep neural networks, and YOLOv4 have been used in buildings. However, the wider adoption of a DT that can facilitate occupants, and thermal sensations, enhance human-centered solutions, and improve energy prediction levels are necessary. There is a need for further international collaboration to expand the studies on digital twins for thermal comfort and energy efficiency. The review highlights the limitations and areas of improvement, such as the limited adoption of sensors for environmental measures, the need for more focus on the subjective perception of occupants, and the need for more comparative studies of algorithms for predicting energy consumption. Further studies can be conducted in areas such as understanding occupant psychological responses/behaviors to comfort in the digital world. This will enhance a more consolidated and robust validation for building performance. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue of the journal Optical Review, featuring review articles by the winners of the Optics Awards for Excellent Papers given by the Optical Society of Japan (OSJ). The awards have been given since 1960, with 117 young researchers receiving them over 62 years. The two winning papers featured in this issue are "Three-dimensional deeply generated holography" by Dr. Ryoichi Horisaki and "Full-color-sorting metalenses for high-sensitivity image sensors" by Masashi Miyata and others. Dr. Horisaki's paper discusses a high-speed generation method of computer-generated holograms using deep neural networks, while Dr. Miyata's paper focuses on a color image sensor integrated with metasurface lenses. The preface concludes by expressing hope that readers will enjoy the reviews and find inspiration for their own research. [Extracted from the article]

Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions of interest and other regions usingK-means clustering and the Grabcut algorithm. We compared the deep learning performance results of original data, data using only K-means clustering, and data using both K-means clustering and the Grabcut algorithm. All data used in this paper were collected at Soonchunhyang University Cheonan Hospital in Korea and the experimental test proceeded with IRB approval. The training was conducted using Resnet 50, VGG, and Inception resnet V2 models, and Resnet 50 had the best accuracy in validation and testing. Through the preprocessing process proposed in this paper, the accuracy of deep learning models was significantly improved by at least 10% and up to 40%. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance. [ABSTRACT FROM AUTHOR]

In this paper, we provide a method for robust estimation of pedestrian pose that is especially useful for autonomous vehicles traveling toward pedestrians far away. Pedestrians in the far distance appear relatively small when seen by a camera, making it difficult to estimate the pedestrian's pose. We use fused deep neural networks (DNNs) to resolve the problems presented by pedestrians in the far distance. First, DNNs are used to detect pedestrians and enlarge the observed image. Next, the DNN method of pose estimation is applied. The proposed method uses a single camera to estimate the posture of a pedestrian in the far distance. Far-off pedestrians observed by cameras in moving cars appear as low-resolution images of non-rigid bodies. Detection and orientation estimation are difficult with conventional image processing methods. We used a series of DNNs to detect pedestrians, improve data availability, and estimate challenging postures to address these limitations. In this paper, we propose a method based on the multi-stage fusion of DNNs to solve a difficult problem for a single DNN. The experimental results established the superiority of the proposed method when applied to data challenging for conventional pose estimation methods. Applications of the proposed method include observing small objects and objects in the far distance. The method may be especially useful in surveillance systems, sports broadcasting, and other applications requiring human posture estimation. [ABSTRACT FROM AUTHOR]