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In order to solve the diamond roller dressing wear situation is difficult to accurately online judgment problem. A set of built-in acoustic emission on-line monitoring diamond roller wear device was designed and developed. Based on Shannon information entropy theory and wavelet packet model, a method of acoustic emission signal processing for diamond wheel dressing was proposed. The method enabled the information entropy of wavelet packet coefficients to be calculated for each layer, and the optimal number of wavelet packet decomposition layers could be determined according to the variation rule of information entropy. Then, the wavelet packet decomposition feature parameter was downsized using principal component analysis, and the feature parameter that best characterizes the wear of diamond rollers was extracted. Finally, PSO-SVM (particle swarm optimization algorithm support vector machine) based on this feature parameter was established. The wear state of diamond rollers was monitored experimentally. The results show that the PSO-SVM model has the highest classification accuracy, with an average correct rate of more than 95.24%, and the effectiveness of PSO-SVM is verified by a large number of experiments. [ABSTRACT FROM AUTHOR]

Organic matter is the material basis of life, which is widely present in people's products and lives. The rapid and accurate detection of organic matter can be applied to the identification of fake and inferior products, the traceability of the origin of agricultural products, and the early warning of explosives in antiterrorism. By combining laser‐induced breakdown spectroscopy (LIBS) with argon, the spectral characteristics of five common organic compounds (nitroglycerin, metronidazole, polyformaldehyde, polypropylene, and phenolic resin), the plasma lifetime, the parameters of plasma thermodynamic state, and the distribution of three existing forms of the element C in two gas environments were compared, the influence of argon on organic LIBS results and the reasons for its enhancement were analyzed. The results indicate that argon makes the atomization of carbon chain structures in organic matter more complete, and reduce the interference of the element N in air on the organic LIBS results to a certain extent. The intensity differences of different organic substances are more obvious. support vector machine optimized by principal component analysis and particle swam optimization algorithm was used to classify five kinds of organic matter. The prediction accuracy of classification in air is 94.4%, and it improves to 100% in argon. This result provides a powerful method for high‐precision, real‐time in‐situ, and rapid identification of organic substances, which has important scientific significance for the study of organic substances LIBS. [ABSTRACT FROM AUTHOR]

A gear form-grinding optimization method is proposed to obtain an optimal profile of a form-grinding wheel by solving a transcendental equation of the contact line between a form-grinding wheel and a helical gear workpiece. Since the equation of the contact line is transcendental, the relationship between the installation angle of the form-grinding wheel and the shape of the contact line cannot be represented by explicit functions, which makes it difficult to obtain the optimal profile of the form-grinding wheel. An optimization method of the contact line between the form-grinding wheel and the gear is proposed using three evaluation parameters that are the overrun, the shift, and the offset. Some gear form-grinding performances, such as machining stroke, tooth deviation, and grinding chatter, can be quantitatively described using those evaluation parameters. Further, a method for optimizing evaluation parameters is proposed, which uses a particle swarm optimization-support vector machine (PSO-SVM) model with the advantage of small-sample robustness to solve evaluation parameters. The PSO-SVM model is trained under the condition of different installation angles of the form-grinding wheel as the input and the evaluation function as the output. The R-squared value of the overrun is 0.986 that vilidate the high accuracy of the PSO-SVM model. Gear form-grinding test results show the proposed method can effectively improve grinding accuracy. • Effects of the contact line shape on gear form-grinding are studied. • A particle swarm optimization-support vector machine (PSO-SVM) model is developed. • A form-grinding wheel optimization method using the PSO-SVM model is developed. • The optimization method can improve accuracy of gear form-grinding. • Effectiveness of the optimization method is verified by form-grinding tests. [ABSTRACT FROM AUTHOR]

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

With the rapid development of industry, coal-fired power generation accounts for a large proportion of the total power generation, emitting a large amount of harmful substances, such as PM2.5, seriously affecting human health. To investigate the PM2.5 collection efficiency of wire-plate electrostatic precipitator (ESP) at different temperatures, numerical simulations based on the multi-field coupling model of ESP were conducted. Support vector machine (SVM) model combined with particle swarm optimization (PSO) algorithm gives the PSO-SVM prediction model, and the simulated data are used as training data, PSO-SVM and back propagation neural network (BPNN) models are used to predict the temperature effect under different operating conditions. The results show that PM2.5 collection efficiency in the wire-plate ESP gradually decreases with increasing temperature, and the decreased rate becomes small constantly. Both PSO-SVM and BPNN models accurately describe the relationship between collection efficiency and temperature, the average relative errors of the two models for predicting the collection efficiency of 1.0 μm particles at different temperatures are 0.247% and 0.363%, respectively. Compared with BPNN, the overall error of PSO-SVM is 0.928% lower, suggesting that PSO-SVM model yields smaller relative error and higher prediction accuracy. The related findings can provide references for studying the collection performance and rapidly determining the operating parameters of ESP. [ABSTRACT FROM AUTHOR]

As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations. [ABSTRACT FROM AUTHOR]

The effective identification of systemically important financial institutions (SIFIs) is key to preventing and resolving systemic financial risks; thus, it is of great research significance for emerging countries to supervise SIFIs and manage systemic financial risks. Since traditional research on identifying SIFIs does not consider emerging machine learning models, it is difficult to properly fit the characteristics of actual financial institutions' asset distribution. This paper proposes a new method for measuring SIFIs, integrating the PSO-SVM model into the Copula-CoVaR model. This new PSO-SVM-Copula-CoVaR model is meant to evaluate China's SIFIs based on the publicly traded price data of Chinese listed financial institutions. The empirical results show that, compared with the traditional parameter method (GARCH model) and the nonparametric method (kernel density estimation), the marginal distribution estimation method using the PSO-SVM method can better fit the distribution of an institution's financial asset return sequence. That is, the model proposed in this paper helps regulatory authorities improve the list of SIFIs more reasonably and implement effective regulatory measures. [ABSTRACT FROM AUTHOR]

A prevalent and complicated gynaecological condition that affects women’s reproductive health is PCOS. However, delayed diagnosis and treatment are frequently caused by a lack of understanding of its signs and symptoms. To help users and specialized physicians identify and anticipate ovarian cysts early, a PCOS prediction system integrating PSO-SVM was created to solve this issue. This study explores the application of data mining techniques, using PSO-SVM, to predict PCOS in the field of gynaecology. The dataset was taken from the Kaggle benchmark dataset, owned by Karnika Kapoor. There are 42 selected features and attributes of the PCOS dataset. The system used Python-based data preprocessing, data splitting, and PSO-SVM optimization for predicting PCOS disease. The evaluation showed that PSO-SVM with 20 particles and 100 iterations achieved the best accuracy for feature selection with an accuracy of 90.18%. The system exhibited promising predictive abilities. To enhance accuracy and user experience, future work should focus on longitudinal data integration, expert decision support, and collaboration with medical experts. The developed PSO-SVM-based PCOS prediction system significantly improves risk assessment and early identification, aiding patients, and medical practitioners. It serves as a valuable decision support tool for doctors, enabling quick and accurate diagnosis for early intervention and specialized treatment plans.

As a water-saving method, moistube irrigation has been widely used. To ensure the effectiveness of moistube irrigation the development of an infiltration prediction model under moistube irrigation based on the interaction of multiple factors is required. In this paper, soil water infiltration tests with different bulk densities (1.2 g/cm³, 1.3 g/cm³, and 1.4 g/cm³) and textures (loamy sand, sandy loam, and clay loam) under different pressure heads (1m, 1.5m, and 2m) were designed, and the test data were analyzed by gray correlation theory. The pressure head, bulk density, clay content, silt content, sand content, and initial water content were determined as input variables, and the model structure was composed with two parameters of Kostiakov's model as output variables. Then, the genetic algorithm was used to optimize the back propagation neural network and the particle swarm algorithm to optimize the support vector machine. The soil moisture prediction model under moistube irrigation was established, finally the model was compared and analyzed. The results showed that the consistency effect of the two models was good. However, compared with the BP neural network prediction model optimized by genetic algorithm, the particle swarm algorithm optimized the support vector machine based moistube irrigation prediction model had higher accuracy. The results of this experiment can provide theoretical support for the exploration and modelling prediction of soil water infiltration under moistube irrigation.

Abstract Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi‐sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one‐dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast‐ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO‐SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR.

To solve the problems of untimely and low accuracy of tunnel project collapse risk prediction, this study proposes a method of multi-source information fusion. The method uses the PSO-SVM model to predict the surrounding rock displacement. With the prediction index as the benchmark, the Cloud Model (CM) is used to calculate the basic probability assignment value. At the same time, the improved D-S theory is used to fuse the monitoring data, the advanced geological forecast, and the tripartite information indicators of site inspection patrol. This method is applied to the risk assessment of Jinzhupa Tunnel, and the decision-makers adjust the risk factors in time according to the prediction level. In the end, the tunnel did not collapse on a large scale.

In the context of shared wealth, the tourism industry, as an essential economic driver, the quality improvement of its human resources is crucial for promoting the development of the industry and realizing the balance of social wealth. In this study, a set of evaluation systems for tourism professionals is constructed based on the competency iceberg model, and the index weights are calculated using the hierarchical single sorting method. Further, the B.P. neural network evaluation method was improved by particle swarm optimization algorithm, and PSO-SVM neural network model was created to evaluate tourism professionals. Taking City A as a case study, the development of tourism industry and its deficiencies were deeply analyzed. The results show that about 36.40% of the residents believe that developing the tourism industry helps improve the living standard and promote shared prosperity. In comparison, 48.36% of the residents reflect the lack of local tourism professionals. In the comprehensive evaluation of tourism talents by individuals, residents and tourists, the mean scores of the indicators of responsibility, work motivation and complete psychological ability were high, respectively 4.49, 4.53 and 4.70.The study not only highlights the crucial role of tourism professionals in promoting the development of tourism industry, but also provides empirical evidence and solid suggestions for future talent cultivation and development strategies in tourism industry.

Featured Application: This method is developed to solve the dynamic scheduling problem of semiconductor manufacturing processes and can be extended to most mechanical processing scenarios and resource scheduling scenarios. There are currently many studies on data-driven optimization scheduling, but only a few studies have combined "closed-loop optimization" with "performance-driven". Therefore, this research proposed a PSO-SVM-based (particle swarm optimization optimized support vector machine) scheduling method that reconciles the composite dispatching rules (CDR), performance-driving ideology, and feedback mechanism ideology. Firstly, the composite dispatching rules coalesce flexible equipment maintenance, multiple process constraints, and dynamic dispatching. Secondly, the performance-driving ideology is carried out through two learning models based on the PSO-SVM algorithm, based on targeted optimizing performances. Thirdly, the feedback mechanism ideology makes the scheduling method realize closed-loop optimizations adaptively. Finally, the superiority of the proposed scheduling method is validated in a semiconductor manufacturing system in China. Compared with CDR, the proposed scheduling method combines MOV, PR, and EU, respectively EU_ O, EU_ P, PCSR and ODR increased by 7.85%, 5.11%, 8.76%, 8.14%, 6.60%, and 7.33%, indicating the superiority of this method. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification. [ABSTRACT FROM AUTHOR]

Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi‐sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one‐dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast‐ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO‐SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR. [ABSTRACT FROM AUTHOR]

Scientifically delineating ecological function zones is essential for national territory spatial planning and comprehensive management. In this study, we evaluated five ecosystem services, habitat quality, water yield, carbon sequestration, soil conservation, and food production, in Fuping County, China, and introduced the application of the topographic position index in exploring the topographic gradient effect of each service. We next applied the K-means clustering algorithm to identify the ecosystem services bundles and analyze the dominant type of ecosystem service in these bundles. A particle swarm optimization-support vector machine model was also constructed to identify the boundaries of ecological function zones and complete the ecological function zoning. The results are as follows: (1) In Fuping County, the high-value areas of habitat quality are distributed in the west, north, and southeast; those of soil conservation are in the northwest, northeast, and southwest; those of water yield are in the east and south; those of carbon sequestration are in the west, and those of food production is in the east. (2) The habitat quality first decreases and then increases with an increasing topographic gradient; food production and water yield decline with increasing topographic gradient; carbon sequestration and soil conservation increase with increasing topographic gradient. (3) Four types of ecosystem services bundles were identified. The dominant ecosystem functions of Type I, II, and III bundles are food production and water yield, carbon sequestration, and soil conservation, respectively. Type IV bundles generally have low levels of ecosystem services in the study area. (4) Four ecological function zones were delineated: food production zone, ecological conservation zone, potential restoration zone, and critical restoration zone. The research findings can provide a theoretical and practical basis for formulating and implementing ecological spatial management policies in the Taihang Mountains of China. [ABSTRACT FROM AUTHOR]

In view of the serious harm to human health caused by atmospheric fine particulate matter (PM2.5), accurate prediction of high concentrations of PM2.5 can help to provide timely warnings. On the other hand, due to the complexity of the formation and transmission process, it is difficult to accurately predict PM2.5. The aim of this paper is to develop a hybrid interval-valued time series prediction model, namely, BEMDCR-SE-PSO-SVM, by considering daily changes in pollutant concentrations and thereby realize interval-valued PM2.5 concentration prediction with high accuracy. The theoretical contributions in this paper include (1) the problem of edge effects corresponding to BEMD associated with interval-valued time-series is addressed by using the mirror extension method, and (2) the transformation between interval-valued time series and complex-valued signals is renewed from the perspective of centre/radius so that lower data fluctuations can be obtained. Technologically, sample entropy is introduced to provide an objective way to integrate decomposed similar IMFs so that subsequent prediction processes can be simplified. Finally, a numerical example is shown to illustrate the feasibility and validity of the developed hybrid interval-valued time series prediction model. [ABSTRACT FROM AUTHOR]

As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations.

With the gradual deterioration of the natural environment, a green economy has become a competing goal for all countries. As a trend of green innovation development, the digital economy has become a research hotspot for scientists. In this article, we study the supply chain management of enterprises in green innovation and digital economy development and complete the identification and demand prediction of warehouse goods through the Internet of Things (IoT) and artificial intelligence (AI). As the stuff meets the goods detection and storage, we employ an intelligent method to detect and classify the goods. The demand prediction analysis is carried out based on historical data on goods demand in the enterprise. The absolute error between the prediction result and the actual demand within 1 week is less than 30 goods by the particle swarm optimization-support vector machine (PSO-SVM) method used in this article. First, the goods identification task is completed based on video surveillance data using YOLOv4, and the recognition rate is as high as 98.3%. This article realises enterprises’ intelligent supply chain management through the intelligent identification of goods and the demand forecasting analysis of goods in the warehouse, which provides new ideas for green innovation and digital economy development.

In order to achieve a more efficient and accurate correction of near‐field error in the semiphysical radio frequency simulation system, the precise control parameters of the three antenna elements need to be obtained. This article is based on the method of moments electromagnetic simulation, and propose corresponding improvement ideas for the problems of limited optimization accuracy and low calculation efficiency in the near‐field error correction process. From the aspects of high‐precision intelligent inversion algorithm and high‐efficiency electromagnetic forward modeling, systematic optimization design and verification were carried out. The results prove that the control parameter filtering scheme based on PSO‐GA hybrid method has better optimization efficiency and accuracy than single genetic algorithm or differential evolution algorithm, which can provide more ideal initial amplitude and phase parameters for the subsequent selection of electromagnetic simulation and forward verification. In order to solve the problem of time‐consuming in the electromagnetic simulation, the multivariate vector forward model based on GA‐BP network and PSO‐SVM network are established, which can achieve high‐precision positioning of synthetic vector target points. The neural network method has been proved to be feasible on the basis of the current sample size. The paper selects hybrid algorithms to improve the shortcomings of single algorithm and uses algorithms to optimize neural networks, thereby obtaining better optimization results and reducing the time‐consuming of electromagnetic simulations, which can realize efficient correction of near‐field error. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

There are currently many studies on data-driven optimization scheduling, but only a few studies have combined “closed-loop optimization” with “performance-driven”. Therefore, this research proposed a PSO-SVM-based (particle swarm optimization optimized support vector machine) scheduling method that reconciles the composite dispatching rules (CDR), performance-driving ideology, and feedback mechanism ideology. Firstly, the composite dispatching rules coalesce flexible equipment maintenance, multiple process constraints, and dynamic dispatching. Secondly, the performance-driving ideology is carried out through two learning models based on the PSO-SVM algorithm, based on targeted optimizing performances. Thirdly, the feedback mechanism ideology makes the scheduling method realize closed-loop optimizations adaptively. Finally, the superiority of the proposed scheduling method is validated in a semiconductor manufacturing system in China. Compared with CDR, the proposed scheduling method combines MOV, PR, and EU, respectively EU_ O, EU_ P, PCSR and ODR increased by 7.85%, 5.11%, 8.76%, 8.14%, 6.60%, and 7.33%, indicating the superiority of this method.

A crucial necessity in integrated water resource management is flood forecasting. Climate forecasts, specifically flood prediction, comprise multifaceted tasks as they are dependant on several parameters for predicting the dependant variable, which varies from time to time. Calculation of these parameters also changes with geographical location. From the time when Artificial Intelligence was first introduced to the field of hydrological modelling and prediction, it has produced enormous attention in research aspects for additional developments to hydrology. This study investigates the usability of support vector machine (SVM), back propagation neural network (BPNN), and integration of SVM with particle swarm optimization (PSO-SVM) models for flood forecasting. Performance of SVM solely depends on correct assortment of parameters. So, PSO method is employed in selecting SVM parameters. Monthly river flow discharge for a period of 1969 - 2018 of BP ghat and Fulertal gauging sites from Barak River flowing through Barak valley in Assam, India were used. For obtaining optimum results, different input combinations of Precipitation (Pt), temperature (Tt), solar radiation (Sr), humidity (Ht), evapotranspiration loss (El) were assessed. The model results were compared utilizing coefficient of determination (R2) root mean squared error (RMSE), and Nash–Sutcliffe coefficient (NSE). The most important results are highlighted below. • First, the inclusion of five meteorological parameters improved the forecasting accuracy of the hybrid model. • Second, model comparison specifies that hybrid PSO-SVM model executed superior performance with RMSE- 0.04962 and NSE- 0.99334 compared to BPNN and SVM models for monthly flood discharge forecasting. • Third, applied optimization algorithm has easy implementation, simple theory, and high computational efficacy.Results revealed that PSO-SVM could be utilised as an improved alternate method for flood forecasting as it provided a higher degree of reliability and accurateness.

Since the classification methods mentioned in previous studies are currently unable to meet the accuracy requirements for fault diagnosis in large-scale chemical industries, these methods are gradually being eliminated and rarely used. This research offers a probabilistic neural network (PNN) based on feature selection and a bio-heuristic optimizer as a fault diagnostic approach for chemical industries using artificial intelligence. The sample characteristics are initially simplified using heuristic feature selection and support vector machine recursive feature elimination (SVM-RFE). Using PNN as the principal classifier of the fault diagnostic model and employing a modified salp swarm algorithm (MSSA) linked with the bio-heuristic optimizer to optimize the hidden smoothing factor (σ) of PNN further improves the classification performance of PNN. The MSSA introduces the Lévy flight method, greatly enhancing exploration capabilities and convergence speed compared to the standard SSA. To validate the engineering application of the suggested method, a PSO-SVM-REF-MSSA-PNN model is created, and TE process data are utilized in tests. The model's performance is evaluated by comparing its accuracy and F1-score to other regularly used classification models. The results indicate that the data samples selected by PSO-SVM-RFE features simplify and eliminate redundant features more effectively than other feature selection techniques. The MSSA algorithm's optimization capabilities surpass those of conventional optimization techniques. The PNN network is more suitable for fault detection and classification in the chemical industry. The three considerations listed above make it evident that the proposed approach might greatly help identify TE process problems. [ABSTRACT FROM AUTHOR]

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods., Competing Interests: We declared that we have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (© 2024 The Authors.)

• To eliminate the influence of CO 2 and H 2 O on NH 3 and NO concentration measurements, this paper proposes a machine-learning modeling and concentration measurement method using combined spectral signals from reference gases. • Before the concentration measurements, the direct absorption spectroscopy (DAS) signals of NH 3 and NO underwent noise reduction using the variational mode decomposition algorithm optimized by the artificial bee colony algorithm and wavelet threshold denoising (ABC-VMD-WTD) method. The signal-to-noise ratio (SNR) of NH 3 and NO increased from 17.2 and 46.9 before denoising to 167.8 and 532.4 after denoising. • After testing, the average relative errors of NH 3 and NO concentrations measured by the particle swarm optimization-support vector machine (PSO-SVM) model are 4.0 % and 0.8 %, respectively, with measurement precision of 0.05 ppm and 0.42 ppm, which is better than the conventional integral absorbance method. • By analyzing the Allan deviation, the minimum detection limit (MDL) for NH 3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. Accurate measurements of NH 3 and NO are essential for controlling NO x emissions from coal-fired power plants, reducing ammonia slip, and improving the efficiency of selective catalytic reduction (SCR) operation. We propose a method for measuring NH 3 and NO concentrations based on machine learning algorithms to address the challenges posed by overlapping interference of CO 2 and H 2 O spectral lines in flue gas. Our approach introduces a novel method for acquiring mixture spectra for the model. Initially, we measure the spectra of individual components under different concentrations and temperatures. Subsequently, mixed spectral samples are generated by combining the measured spectra of the individual components. This approach simplifies the spectral measurement process while preserving accuracy. The particle swarm optimization support vector machine (PSO-SVM) algorithm is leveraged, providing a reliable foundation for the continuous and synchronous measurement of NH 3 and NO. Upon testing, the PSO-SVM demonstrates average relative errors of 4.0 % and 0.8 % for NH 3 and NO concentrations, respectively. The corresponding measurement precision is 0.05 ppm for NH 3 and 0.42 ppm for NO, better than the conventional integral absorbance method. The minimum detection limit (MDL) for NH 3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. The methodology of this paper is expected to play an important role in reducing the influence of interfering components and improving the accuracy of field measurements. [ABSTRACT FROM AUTHOR]

The surface burnishing tool (SBT) is optimized for aluminum alloys' surface hardness and surface roughness. The SBT's rolling body wears during machining and reaches a certain threshold where the machining performance decreases. Sandpapers are used to abrade the rolling bodies to simulate surface damage. The preconditioned tools are utilized for machining, and the spindle motor current signals are recorded. The SBT's hardening capacity is weakened when the rolling body's Sa exceeds 0.581, and the finishing capacity is weakened when the Sa exceeds 0.684. The PSO-SVM model accurately identifies the failure point of the SBT's hardening capacity with an accuracy of 96.67%. Another PSO-SVM model accurately identified the failure point of SBT's finishing capacity with an accuracy of 85.83%. [ABSTRACT FROM AUTHOR]

Chatter is one of the biggest unfavorable factors during the high speed machining process of a machine tool. It severely affects the surface finish and geometric accuracy of the workpiece. To address this obstacle and improve the quality and efficiency of products, it is significantly essential to detect chatter during machining. Therefore, a multi-feature recognition system for chatter detection on the basis of the fusion technology of wavelet packet transform (WPT) and particle swarm optimization support vector machine (PSO-SVM) was proposed in this paper. Firstly, the original vibration signals collected from the acceleration sensor were processed through wavelet packet transform (WPT). The noise and the irrelevant information were remarkably decreased. In addition, the wavelet packets containing chatter-emerging information were chosen and reconstructed. The fourteen time–frequency domain characteristics of the reconstructed vibration signal were calculated and chosen as the multi-feature vectors of chatter detection. Finally, to obtain the optimal radial basis function parameter g and penalty parameter C of the SVM prediction model, the optimization algorithms of k-fold cross-validation (k-CV), genetic algorithm (GA), and particle swarm optimization (PSO) were employed in optimizing the model parameters of SVM. It was indicated that the PSO-SVM improved obviously the accuracy of chatter recognition than the others. In addition, we applied the optimized SVM prediction model by PSO for detecting chatter state in end milling machining. Chatter recognition results indicated that the model accurately predicted the slight chatter state in advance. [ABSTRACT FROM AUTHOR]

The rebound index Cr is an important design parameter in engineering construction, and its determination is cumbersome and susceptible to errors. Explaining the macroscopic rebound characteristic parameter Cr from the perspective of microscopic mechanism is an important research area that is addressed in this study. In this paper, the different soil parameters, including the Cr parameter and the physical parameters (void ratio er liquid limit water content WL, and plasticity index Ip), have been determined through experiments for the undisturbed clay of Chongming East Shoal (CES), Shanghai. Further, scanning electron microscopy (SEM) imaging was used to analyze the microstructural features. Through SEM, the grey correlation degree, the average abundance of structural units ACp and the average equivalent diameter of pores ADv were determined as the soil microstructure parameters with the most significant correlation with Cr. The predictive analysis model of Cr was then carried out combined with the PSO-SVM algorithm. In order to evaluate the influence of microscopic parameters of soil on the prediction accuracy, four sets of input parameter combinations were used. The results indicate the high prediction accuracy of the developed model. Sensitivity analysis was also carried out, which showed that the sensitivity of Cr to ACp and ADv was significantly lower than e; however, the difference from wL and Ip was small, indicating that it is imperative to consider microscopic parameters while predicting Cr. This study, thus, provides a basis and method for predicting the rebound index of soil from the microstructure perspective. [ABSTRACT FROM AUTHOR]

In order to improve the utilization rate of coal resources, it is necessary to classify coal and gangue, but the classification of coal is particularly important. Nevertheless, the current coal and gangue sort-ing technology mainly focus on the identification of coal and gangue, and no in-depth research has been carried out on the identification of coal species. Accordingly, in order to preliminary screen coal types, this paper proposed a method to predict the coal metamorphic degree while identifying coal and gangue based on Energy Dispersive X-Ray Diffraction (EDXRD) principle with 1/3 coking coal, gas coal, and gangue from Huainan mine, China as the research object. Differences in the phase composition of 1/3 coking coal, gas coal, and gangue were analyzed by combining the EDXRD patterns with the Angle Dispersive X-Ray Diffraction (ADXRD) patterns. The calculation method for characterizing the metamorphism degree of coal by EDXRD patterns was investigated, and then a PSO-SVM model for the classification of coal and gangue and the prediction of coal metamorphism degree was developed. Based on the results, it is shown that by embedding the calculation method of coal metamorphism degree into the coal and gangue identification model, the PSO-SVM model can identify coal and gangue and also output the metamorphism degree of coal, which in turn achieves the purpose of preliminary screening of coal types. As such, the method provides a new way of thinking and theoretical reference for coal and gangue identification. [ABSTRACT FROM AUTHOR]

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%. [ABSTRACT FROM AUTHOR]

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

Coal and gangue separation technology based on X-ray has broad development prospects due to its low energy consumption and pollution-free environment, which is also the key part in the green and intelligent mines. Aiming at the low recognition accuracy for coal and gangue with the particle size of 5–15 mm for the dual-energy X-ray coal and gangue separation technology, the coal and gangue recognition method based on the combination of X-ray transmission and diffraction principle was proposed. The dual-energy X-ray images based on the X-ray transmission principle were collected for coal and gangue with particle size larger than 15 mm, and Rc, Glc, Gl, Ra were extracted as the recognition features. EDXRD patterns based on the X-ray diffraction principle were collected for coal and gangue with particle size less than 15 mm, and the characteristic diffraction peaks were extracted as the recognition features. Then, the PSO-SVM model was established for coal and gangue recognition. The test results show that the proposed method can broaden the particle size range for dry coal preparation based on X-ray, and the recognition accuracy of coal and gangue with particle size less than 15 mm is 98%, which is 16.7% higher than that of the method based on the X-ray transmission principle alone. The comprehensive recognition accuracy of coal and gangue with particle size of 5–100 mm reached 97.5%. Consequently, this paper provides a new technical approach for coal and gangue identification. [ABSTRACT FROM AUTHOR]

The igneous reservoirs in China's Nanpu area are characterized by diverse lithology, complex pore structures, and alterations, which has made it very difficult to accurately evaluate the reservoir effectiveness using logging methods. The influence of alteration and the singularity of the interpretation method are the main problems that limit the interpretation accuracy. Therefore, a solution to the problem of logging evaluation of altered igneous reservoirs is proposed in this paper. Firstly, a least square method combined with particle swarm optimization-support vector machine (PSO-SVM) is used to identify the lithology of igneous reservoirs. Secondly, a volume model of altered clay was designed in order to calculate the porosity and other physical parameters of the study formations. Finally, based on such single reservoir effectiveness evaluation methods such as mercury injection, nuclear magnetic T2 spectrum, and Formation MicroScanner Image porosity spectrum, comprehensive evaluation factors were designed for the purpose of analyzing the reservoir effectiveness of the igneous formations in the study area. The combination of multiple evaluation pore structure methods adequately considers the influence of reservoir storage capacity, pore size distribution, and fractures, which effectively improves the evaluation accuracy. The final evaluation results were found to be consistent with the actual production test conclusions, which confirmed that the proposed method of logging evaluation is suitable for the altered igneous reservoirs in the Nanpu area of eastern China. We believe that this approach can be extended to other areas of altered igneous reservoirs. [ABSTRACT FROM AUTHOR]

Wheel loader shovel loading operation tests showed that when the tire slips, it not only causes waste of the engine power, but also increases the tire wear. In this paper, the regression prediction model is combined with the electro-hydraulic proportional control technology. For the first time, the method of adjusting the posture of the working device is proposed to realize the skid-proof shovel control strategy of the wheel loader. In this control strategy: (1) The electro-hydraulic proportional control technology applied to this wheel loader is introduced. (2) The load spectrum of the wheel loader is analyzed during the shovel loading operation. Moreover, combined with the drive shaft torque, pilot pressure and boom cylinder displacement, the particle swarm optimization-support vector machine (PSO-SVM) algorithm is used to complete the construction of the regression prediction model of the boom cylinder displacement. (3) A controller is designed based on the fractional-order PID control algorithm. The skid-proof control strategy is simulated and verified by constructing the joint simulation model. The corresponding program is prepared in the hydraulic system controller and the effectiveness of the control strategy and algorithm are verified by the wheel loader shovel loading operation.

Masses are mammographic nonpalpable signs of breast cancer. These masses could be detected using screening mammography. This paper proposed a system utilizing orthogonal moment invariants (OMIs) features for mammographic masses detection and diagnosis. In this work, three sets of OMIs features were extracted. These OMIs features are Gaussian-Hermite moments (GHMs), Gegenbauer moments (GeMs), and Legendre moments (LMs). The extracted features are fused and presented to the particle swarm optimization (PSO) algorithm for feature selection. The classification step is achieved using the support vector machine (SVM). The proposed system is evaluated using 400 regions, extracted from the DDSM dataset. The obtained results reveal the promising application of OMIs features for masses detection and identification. It shows that fusing the OMIs features produces an acceptable detection performance where the area under the receiver operating characteristics (ROC) curve is $Az=0.969\pm 0.01$ and the best performance of OMIs features is $Az = 0.856\pm 0.053$ for characterizing the malignancy of masses.

Improving passive design methods are important for achieving zero-carbon building (ZCB) targets. Data-driven design approaches are more accurate than conventional methods for multiple-objective optimization (MOO) targets such as carbon emissions (CE), economics, and thermal comfort. In this study, a comprehensive data-driven-based PSO-SVM-NSGA-III method that assists in optimizing passive design parameters are proposed. First, the simulation results for the objectives of CE, incremental cost (IC), and time not comfort (TNC) are performed for an office building in a cold region in China using EnergyPlus and jEPlus software. Then, key influencing factors are chosen via a sensitivity analysis. Second, nonlinear mapping relationships between the passive design parameters and objectives are established with the PSO-SVM model. Compared to the BPNN, SVM, and PSO-BPNN algorithms, the PSO-SVM model exhibits superior prediction performance, with R 2 values of 0.977, 0.925, and 0.903 for CE, IC, and TNC, respectively. Third, the nonlinear mapping relationships are used in the established objective functions of NSGA-II and NSGA-III. NSGA-III performed 100 iterations within 21 min and exhibited robust diversity within its population, with a hypervolume value of 0.738. Finally, the Pareto-optimal solution set is formed with the PSO-SVM-NSGA-III framework. This data-driven method offers an efficient approach for improving building performance. • Data-driven-based PSO-SVM-NSGA-III method for optimizing ZCBs is proposed. • Simulation results generated for office building in a cold region in China. • Key influencing factors are chosen via a sensitivity analysis. • PSO-SVM model exhibits superior prediction performance. • Data-driven method offers an efficient approach for improving building performance. [ABSTRACT FROM AUTHOR]

In order to fully and accurately identify the fault category of gear，a feature space model based on multi-domain characteristic parameters such as time domain，frequency domain and energy is established. On this basis, an intelligent fault diagnosis method based on multi-domain feature and improved D-S evidence theory is proposed. Relevant feature parameters are extracted from the measured data as the diagnostic samples，and multiple evidences are constructed with the preliminary diagnosis results of particle swarm optimization support vector machine（PSO-SVM）. The experimental results verify the effectiveness of the final diagnosis results obtained by the improved D-S evidence theory in this work.

For the problem that it is difficult to extract fault features of vibration signals of transmission shaft system and the actual situation that it is difficult to obtain a large number of fault samples in fault diagnosis，a fault diagnosis method for transmission shafting based on variational mode decomposition（VMD）sand particle swarm optimization support vector machine （PSO-SVM）is proposed. Firstly, the vibration signal of transmission shaft system is subjected to VMD decomposition, and intrinsic mode function IMF is obtained. Then, the energy value of IMF and the corresponding energy entropy are calculated. Finally, Particle swarm optimization（PSO） is used to optimize the parameters of support vector machine（SVM）, and the energy value and energy entropy of normalized IMF are input into the PSO-SVM to identify the working state and fault type of transmission shaft system. The experimental results show that the accuracy of the method is 94.44%, and it can diagnose the fault of transmission shaft system accurately and effectively.

When a series arc fault occurs in indoor power distribution system, current value of circuit is often less than the threshold of the circuit breaker, but the temperature of arc combustion can be as high as thousands of degrees, which can lead to electrical fire. The arc fault experimental platform is used to collect circuit current data of normal work and arc fault. Five types of loads which are commonly used in indoor distribution system, such as resistive and inductive in series load, resistive load, series motor load, switching power load and eddy current load, are chosen. This paper uses four features of current in time domain, i.e. current average, current pole difference, difference current average and current variance. Ten features of current in frequency domain are extracted by Fast Fourier Transform (FFT). The learning vector quantization neural network (LVQ-NN) is designed to judge the load type. The support vector machine optimized by particle swarm optimization (PSO-SVM) is designed to detect the arc fault. The simulation results show the effectiveness of the proposed method.

In recent years, conventional artificial method leads to low efficiency in the classification of cervical cell, which requires professional completion. Therefore, the classification process is increasingly dependent on artificial intelligence. The traditional image classification method needs to extract a large number of features. Redundant features cause the recognition speed to be slow, and influence the recognition effect. To address these problems and obtain the highest recognition accuracy with the least number of features, this paper proposes a machine learning method based on feature selection algorithm for cervical cell classification. Firstly, we introduced classification and regression trees (CART) for cell feature selection, which reduces the dimension of input feature attributes. Subsequently, particle swarm optimization (PSO) was used to optimize the hyperparameters of support vector machine (SVM) in this paper, making the SVM model better for classification. Finally, the Herlev dataset was introduced to verify the classification performance. The experimental results show that the proposed algorithm can extract accurate and effective features and obtain high classification accuracy, thus verifying the effectiveness of the proposed algorithm. Moreover, the network structure of the proposed algorithm is relatively simple with a low computation cost, which makes it feasible of further extension to the classification application of other cancer cells. [ABSTRACT FROM AUTHOR]

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved. [ABSTRACT FROM AUTHOR]