Particle Swarm Optimization Support Vector Machine Research Articles

Simultaneous measurement of NH3 and NO by mid-infrared tunable diode laser absorption spectroscopy based on machine-learning algorithms

Accurate measurements of NH3 and NO are essential for controlling NOx 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 NH3 and NO concentrations based on machine learning algorithms to address the challenges posed by overlapping interference of CO2 and H2O 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 NH3 and NO. Upon testing, the PSO-SVM demonstrates average relative errors of 4.0 % and 0.8 % for NH3 and NO concentrations, respectively. The corresponding measurement precision is 0.05 ppm for NH3 and 0.42 ppm for NO, better than the conventional integral absorbance method. The minimum detection limit (MDL) for NH3 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.

Establishment of critical non-depositing velocity prediction model for sediment in drip irrigation laterals based on PSO-SVM

Accurately determining the critical non-depositing velocity for sediment (CNDVS) in drip irrigation laterals is essential to address issues of sediment deposition and clogging in drip irrigation pipes caused by the use of muddy water. This paper focuses on three main factors: the percentage of intermediate-sized sediment particles (P), pipe diameter (D), and sediment concentration (S), all of which significantly influence the CNDVS in drip irrigation laterals. To predict the CNDVS, a comprehensive gradient test was developed and the Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was applied to establish a response surface for predicting the CNDVS. Additionally, the PSO algorithm optimized the penalty parameter (C), loss parameter (ε), and kernel parameter (δ) in the SVM, resulting in a prediction model for the CNDVS. The proposed model was trained and tested using experimental data, and its prediction performance and accuracy were compared with the regression fitting model. The results indicate that within a specific range, the CNDVS increases with higher values of the three factors. The PSO-SVM model, with optimal parameters combination values of C = 486.37, ε = 0.04, and δ = 68.49, provided the most prediction accurate. Compared with the regression fitting model, the PSO-SVM model has the smaller values of Mean Absolute Error (MAE) = 0.0049, Root Mean Square Error (RMSE) = 0.0057, and Percent Bias (PBIAS) = 0.0004, and the larger value of Nash–Sutcliffe Efficiency (NSE) = 0.9874, Willmott index (WI) = 0.9401, and Coefficient of Determination (R2) = 0.9875. These results demonstrate that the PSO-SVM model established in this study exhibits high prediction performance and accuracy. The findings of this research can serve as a foundation for developing optimal operating and flushing flow velocity within the laterals in muddy water drip irrigation systems.

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

AbstractTo solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

A form-grinding wheel optimization method for helical gears based on a PSO-SVM model

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.

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

Coal structure identification based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms

Coal structure is closely related to microscopic and macroscopic properties of coal, and its accurate identification is of great significance to coalbed methane (CBM) reservoir evaluation, hydraulic fracturing performance and production efficiency prediction. The identification of coal structure based on geophysical logging data has become a popular topic as well as a challenge. The emerging Machine Learning (ML) provides convenience for this issue. Under this background, in the case of the Shanxi Formation No. 3 coal seam and the Taiyuan Formation No. 15 coal seam in Mabidong Block, Qinshui Basin, China, this paper proposed a new coal structure identification method based on geophysical logging data using Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms. Our results showed that the vertical resolution of logging data can be effectively improved when sym5 wavelet basis and third level decomposition were selected in wavelet decomposition, and 4.5 was selected as the weighting coefficient in wavelet reconstruction. The coal structure prediction model based on the logging data processed by WT was established by PSO-SVM algorithm, where PSO was used for parameter optimization (optimal penalty factor C and width parameter σ) and SVM with Radial Basis Function (RBF) kernel was used for model establishment. The Hold-out Cross Validation (HO CV) method was used to test the generalization ability of the prediction model, and the accuracy (ACC) of coal structure prediction in the training set and testing set was 94.26% and 88.46%, respectively. The prediction model was applied to identify the coal structure of two coring wells and the predicted coal structure class was consistent with the true coal structure class, confirming the validity of the model. The coal structure prediction results in the whole study area showed that the tectonic conditions control the coal structure. This work provides new insights for coal structure identification.

Forecast earthquake precursor in the Flores Sea

<span>Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. <br /> The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.</span>

An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology.

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.

Multi-objective diesel engine emission management and control technology based on SVM and NSGA-II

To improve the emission control effect of diesel engines, a pre-emission model for diesel engines using biofuels is constructed using support vector machine. Considering that traditional support vector machine training relies on initial parameters and is prone to local convergence, a genetic algorithm model is adopted to construct an improved diesel engine pre-emission model. At the same time, with the optimization goal of diesel engine emissions of particles and gases, the improved diesel engine pre-emission model is used to obtain emission data as training parameters. The second generation non-dominated genetic algorithm is introduced to construct a multi-objective diesel engine emission control model, achieving optimal control of diesel engine emissions. In the experimental analysis of diesel engine pre emission models, the proposed pre emission model has a better prediction effect in nitrogen oxides, with an average prediction accuracy of 0.986, which is better than the Particle Swarm Optimization-Support Vector Machine model's 0.906, and is more accurate in detecting emissions. Simultaneously predicting particulate matter, the average prediction accuracy of the proposed model is 0.978, while the prediction accuracy of the Particle Swarm Optimization-Support Vector Machine model is 0.902, indicating that the overall prediction effect of the proposed method is better. In the experimental analysis of multi-objective optimization models, the proposed multi-objective optimization model performs best in diesel emission optimization. The proposed model can achieve convergence in the shortest time, and the minimum nitrogen oxide emissions are 210.7 ppm. Compared with Non-dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization models, the particle emission optimization effect is improved by 23.5 % and 18.6 %. From this, it can be seen that the proposed method has excellent performance in diesel engine emission monitoring and pollutant optimization, surpassing related technologies in the same period. The research technology provides important technical references for the monitoring and emission control of harmful substances in diesel engines.

Predicting pKa of the carboxylic acid group in water solutions of amino acids based on molecular structures using machine learning QSPR methods

Amino acids as biomolecules are essential building blocks of proteins, which are fundamental to life and living organisms. The pKa values of amino acid residues in proteins can impact their structure and function. Furthermore, the pKa can be used to optimize experimental conditions for protein purification. In this research, a data set containing the pKa of the carboxylic acid group for 52 amino acids from various families in a water solution was used. The quantity structure property relationship (QSPR) approach was applied in our work for predicting the pKa using different machine learning methods. Initially, the amine molecules were drawn, and optimized and 3224 molecular descriptors were calculated for each molecule. Selecting among molecular descriptors using genetic algorithm-multi linear regression (GA-MLR) shows that the model with 8 descriptors is the best economic one. The comparison of results shows that the feed forward neural network (FFNN) is better than the particle swarm optimization-support vector machine (PSO-SVM) model. The coefficients of determination at different models follow the order of R2 FFNN= 0.9987 > R2 PSO-SVM= 0.9971 > R2 GA-MLR= 0.9951. The R2test for FFNN and PSO-SVM are near the R2train and this evidence proves the validation of models. Consequently, by using FFNN, the pKa of the carboxylic acid group in water solutions of amino acids can be predicted without any experimental costs with a low average absolute relative error (AARE%) of 2.82.

Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion

The multiple loads of the Regional Integrated Energy System (RIES) possess characteristics of randomness and relatively higher complexity. The current forecasting methods struggle to effectively handle the non-stationary sequence of these multiple loads, leading to less accurate load forecasting. To address this problem, this paper proposes a multi-model fusion prediction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Genetic Algorithm-Long Short Term Memory (GA-LSTM), Radial Basis Fusion-Autoencoder (RBF-AE), and Particle Swarm Optimization-Support Vector Machine (PSO-SVM). First, the load sequence is decomposed into different frequency Intrinsic Mode Functions (IMFs) components using CEEMD. The IMFs components are then grouped based on their zero-crossing rate and Sample Entropy (SE), resulting in three distinct groups: high-, medium-, and low-frequency components. Next, the high-frequency load component, which exhibit strong randomness, are predicted using GA-LSTM. The medium-frequency load component, which have weaker randomness, are predicted using RBF-AE. The smooth and periodic low-frequency load component are predicted using PSO-SVM. The prediction results from these three models are reconstructed to obtain the final predictive value. Finally, experimental results confirm that the forecasting model can effectively handle non-stationary load sequences and demonstrate the highest level of forecasting accuracy.

Feature recognition of complex systems using cumulative residual Tsallis signal entropy and grey wolf optimized support vector machine

In this paper, the cumulative residual Tsallis singular entropy (CRTSE) is introduced to measure the complex characteristics of nonlinear signals. Firstly, we do singular value decomposition on time series, which can reduce the interference of noise on information extraction, and the singular values represent the information characteristics of the signal. Then the statistical distribution of the signal is described by the cumulative residual function of singular values, and the Tsallis entropy is calculated to quantify the complexity. We verify the effectiveness and robustness of CRTSE through simulation experiments. Finally, we propose a grey wolf optimized support vector machine based on CRTSE called CRTSE-GWOSVM to intelligently diagnose complex systems. The results show that CRTSE can effectively measure the complex characteristics of time series, GWOSVM is superior to SVM and particle swarm optimized support vector machine (PSOSVM) in data identification, and CRTSE-GWOSVM model can identify complex systems more effectively and accurately.

Landslide Susceptibility Evaluation of Bayesian Optimized CNN Gengma Seismic Zone Considering InSAR Deformation

Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments.

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy.

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal

Abstract Anti-jamming cyclic frequency feature extraction is an important link in identifying communication interference signals, which is of great significance for eliminating electronic communication interference factors and improving the security of electronic communication environment. However, when the traditional feature extraction technology faces a large number of data samples, the processing capacity is low, and it cannot solve the multi-classification problems. For this type of problem, a method of electronic communication signal anti-jamming cyclic frequency feature extraction based on particle swarm optimization-support vector machines (PSO-SVM) algorithm is proposed. First, the SVM signal feature extraction model is proposed, and then the particle swarm optimization (PSO) algorithm is used. Optimize the kernel function parameter settings of SVM to raise the classifying quality of the SVM model. Finally, the function of the PSO-SVM signal feature extraction model is tested. The results verify that the PSO-SVM model begins to converge after 60 iterations, and the loss value remains at about 0.2, which is 0.2 lower than that of the SVM technique. The exactitude of signal feature extraction is 90.4%, and the recognition effect of binary phase shift keying signal is the best. The complete rate of signal feature extraction is 85%. This shows that the PSO-SVM model enhances the sensitivity of the anti-jamming cyclic frequency feature, improves the accuracy of the anti-jamming cyclic frequency feature recognition, reduces the running process, reduces the time cost, and greatly increases the performance of the SVM method. The good model performance also improves the application value of the method in the field of electronic communication.

Health state diagnosis of air duct for photovoltaic inverter based on PSO-SVM classifier

Air duct blockage is a common and severe problem for photovoltaic inverter operating outdoors. It affects their efficiency significantly. This study conducts many experiments to analyze how duct blockage impacts the operating state of inverter. The health state of cooling duct is classified into four levels based on the analysis results. The relevant features of air duct blockage are extracted from the experimental data, and an air duct state diagnosis model based on particle swarm optimization SVM algorithm is proposed. To validate the proposed method, this paper performs model validation using test data in an open environment and compares the classification performance of various classifiers. The results demonstrate the effectiveness of the proposed model in assessing the health status of the cooling air duct in photovoltaic inverter.

Multi-objective optimization of phase change cooling battery module based on optimal support vector machineoptimal support Vector Machine

An optimization model based on Particle Swarm Optimization-Support Vector Machine(PSO-SVM) and improved NSGA-II algorithm is established in this paper to balance the heat dissipation performance and lightweight of battery. First, the data set is obtained by orthogonal experiments, and then the PSO-SVM model for predicting the battery heat dissipation performance (maximum temperature and maximum temperature difference) is acquired by optimizing the parameters of the support vector machine through particle swarm optimization. With the goal of improving the battery heat dissipation performance and reducing the system mass, the improved NSGA-II algorithm is used for multi-objective optimization to obtain the Pareto solution set. Results show that the R2 (goodness of fit) of the maximum temperature and maximum temperature difference prediction models are 0.99478 and 0.9787, respectively, which verifies the accuracy of the PSO-SVM model. The maximum temperature, the maximum temperature difference, and the mass of the phase change material are in conflict. The temperature rise and temperature difference of the battery can be effectively controlled by sacrificing the mass of the phase change material. Four optimal solutions were selected by using LINMAP and TOPSIS. Compared with the initial model, the mass of phase change materials was reduced by 34.5 %, 29.5 %, 55.3 %, and 2.5 %. On the premise of meeting the requirements of thermal management performance, the entire system becomes lighter. Based on the four optimal solutions, the decision maker can choose from the Pareto optimal solution set according to different requirements for heat dissipation performance and lightweight.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

Application of Improved Particle Swarm Optimization SVM in Water Quality Evaluation of Ming Cui Lake

Water quality directly determines our living environment. In order to establish a more scientific and reasonable water quality evaluation model, it needs a lot of data support, but it will lead to a large increase in the calculation time of the evaluation model. This paper proposes an improved particle swarm optimization SVM model (CPOS-SVM) to solve this problem. In this paper, the Pareto optimal solution concept is used to sparsely process the training set, which can ensure that the number of training sets is reduced without loss of data characteristics, thus reducing the training time. In order to solve the problem of the kernel parameter g and penalty factor c on the SVM algorithm, which affects the accuracy of the SVM model but it is difficult to select why, a particle swarm optimization algorithm is used in this paper to optimize the kernel parameter and penalty factor and improve the accuracy of the model. In this paper, 480 sets of data from Ming Cui Lake from 2014 to 2022 are taken as the research object, and examples are analyzed in MATLAB 2020a. The results show that the training time of the CPOS-SVM model can be completed within 2 s and does not increase with the increase of data volume. Meanwhile, by comparing the SVM model, POS-SVM model, and POS-BP model, training time increases dramatically with the amount of data. The accuracy of the POS-SVM model is the highest, and the accuracy of the CPOS-SVM model is basically consistent with that of the POS-SVM, reaching 94%, while the accuracy of the SVM model and the POS-BP model are slightly worse. This indicates that the CPOS-SVM model has good application value in water quality evaluation.