Optimized Support Vector Machine Research Articles

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.

Interpretable machine learning assessment

With the surge of machine learning in AI and data science, there remains an urgent need to not only compare the performance of different methods across diverse datasets but also to analyze machine learning behaviors with sensitivity using an explainable approach. In this study, we introduce a uniquely designed diagnostic index: d-index to tackle this challenge. This tool integrates classification effectiveness from multiple dimensions, delivering a transparent and comprehensive assessment that transcends the limitations of traditional evaluation methods in classification. We propose two innovative concepts: breakeven states and imbalanced points in this study. Integrated with the d-index, these concepts afford a more profound understanding of the learning behaviors across different machine learning models compared to the existing classification metrics. Significantly, the d-index excels as a powerful tool, identifying learning singularity problems (LSPs) that remain elusive to most current machine learning models and imbalanced learning techniques. Furthermore, leveraging the d-index, we unravel the mechanisms behind imbalanced point generation in binary and multiclass classification. We also put forth a novel technique: identifying a priori informative kernels to optimize support vector machine learning, ensuring outstanding d-index values with the fewest necessary support vectors. Moreover, we address a seldom-discussed state of overfitting in deep learning, where overfitting occurs despite the training and testing loss curves exhibiting favorable trends throughout the epochs. To the best of our knowledge, this work represents a pioneering stride in the realm of explainable machine learning assessments and will inspire further studies in this area.

Retracted: Application of Optimized Support Vector Machine Model in Tax Forecasting System

Retracted: Application of Optimized Support Vector Machine Model in Tax Forecasting System

Application of graphene gas sensor technological convergence PSO-SVM in distribution transformer insulation condition monitoring and fault diagnosis

This study aims to improve the real-time monitoring and fault diagnosis of distribution transformers by utilizing a combination of five thin film gas detectors, these detectors include metal-modified graphene composite films and SnO2/RGO humidity sensors, which were prepared using the hydrothermal method. The experiment focused on investigating humidity and main fault characteristic gases that can reflect the insulation status of transformers. Additionally, a gas sensor array was constructed using a deep confidence neural network model. Based on the analysis of dissolved gas in transformer oil, the study extensively discusses the insulation fault diagnosis model and constructs the transformer fault diagnosis model using various methods including TRM, Particle swarm optimization support vector machine. The results demonstrated that the SnO2/RGO thin film humidity sensor exhibited high humidity sensitivity, and the other thin film gas sensors also exhibited good sensitivity. The average accuracy of the three classification methods mentioned is 80%, 92%, and 96%, respectively. These findings highlighted that the vector machine model not only improved the fault diagnosis accuracy but also possessed the characteristics of fewer parameters and a fast rate of convergence. Consequently, it effectively addressed the issue of early diagnosis of potential transformer faults. This study was of significant practical importance for ensuring the secure operation of the power grid.

CRDI Engine Emission Prediction Models with Injection Parameters Based on ANN and SVM to Improve the SOOT-NOx Trade-Off

Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been widely used to solve non-linear problems. In the current study, based on 112 groups of experimental data, ANN and SVM models were established and compared to improve the trade-off relationship between SOOT and NOx emissions of a Common Rail Diesel Injection (CRDI) engine fueled with Fischer-Tropsch (F-T) diesel under different operating conditions and injection parameters. The model parameters for the different predictive targets were selected by evaluating the mean square error (MSE) and determination coefficient. Compared to the number of network iterations, the number of implied nodes had a greater effect on the MSE of the ANN model. Compared to the penalty parameter, the width coefficient had a weaker impact on the SVM performance. A comparative analysis showed that the SVM had better predictive accuracy and generalization ability than the ANN, with a maximum error not exceeding five percent and a determination coefficient of over 0.9. Subsequently, the optimal SVM model was combined with the NSGA-II algorithm to determine the optimal injection parameters for the CRDI engine, resulting in solutions to simultaneously decrease the SOOT and NOx emissions. The optimized injection parameters resulted in a 3.7–7.1% reduction in SOOT emission and a 1.2–2.6% reduction in NOx emissions compared to the original engine operating conditions. Based on limited experimental samples, SVM is inferred to be a useful tool for predicting the exhaust emissions of engines fueled with F-T diesel and can provide support for optimizing injection parameters.

A retrieval model of surface geochemistry composition based on remotely sensed data

Abstract The geochemical sampling work in the difficult and dangerous areas is very hard; hence, it can be greatly improved by combining with the remotely sensed data. Thus, a retrieval model is proposed by Kernel Principal Component Analysis and Artificial Bee Colony (ABC) optimized Support Vector Machine (SVM) models based on Landsat 8 remotely sensed data and the geochemical data in the study area. The analysis results show that the geochemical data delineate the areas with relatively enriched elements, but indicate the low-abnormal ore (chemical) points, and the anomalies delineated by the inversion data are better for this purpose, for better indication. At the same time, the distribution and intensity of the corresponding abnormal areas found that the abnormal areas delineated by the inversion data basically contain the abnormal areas delineated by the original data, and the anomalies located at the ore spots are obviously enhanced; it shows that the SVM model of ABC Optimization can establish the relation between geochemistry data and remote sensing data, can supply the original data effectively, and can also provide the direction for the next prospecting work.

Research on Defect Detection in Kubo Peach Based on Hyperspectral Imaging Technology Combined with CARS-MIV-GA-SVM Method.

Due to the dark red surface of ripe fresh peaches, their internal injury defects cannot be detected using the naked eye and conventional images. The rapid and accurate detection of fresh peach defects can improve the efficiency of fresh peach classification. The goal of this paper was to develop a nondestructive approach to simultaneously detecting internal injury defects and external injuries in fresh peaches. First, we collected spectral data from 347 Kubo peach samples using hyperspectral imaging technology (900-1700 nm) and carried out pretreatment. Four methods (the competitive adaptive reweighting algorithm (CARS), the combination of CARS and the average influence value algorithm (CARS-MIV), the combination of CARS and the successive projections algorithm (CARS-SPA), and the combination of CARS and uninformative variable elimination (CARS-UVE)) were used to extract the characteristic wavelength. Based on the characteristic wavelength extracted using the above methods, a genetic algorithm optimization support vector machine (GA-SVM) model and a least-squares support vector machine (LS-SVM) model were used to establish classification models. The results show that the combination of CARS and other feature wavelength extraction methods can effectively improve the prediction accuracy of the model when the number of wavelengths is small. Among them, the discriminant accuracy of the CARS-MIV-GA-SVM model reaches 93.15%. In summary, hyperspectral imaging technology can accomplish the accurate detection of Kubo peaches defects, and provides feasible ideas for the automatic classification of Kubo peaches.

Functional regionalization of land resources considering eco-efficiency in Nanjing Metropolitan Area, China

The rapid urbanization in the region has caused a shift in land-use structure and, as a result, the degrading of ecosystems. Elucidating the spatial–temporal evolution of ecosystem services (ESs) and ecological function zoning, it was beneficial to develop land use optimization strategy structures for different regions to support regional sustainable development. Taking the Nanjing Metropolitan Area (NMA), China, as a research area, we investigated the spatial–temporal evolution of land use and ESs in 2000, 2005, 2010, 2015, and 2020 by using biophysical models, quantified the trade-offs/ synergies and constraint effects among ESs using Spearman coefficients and constraint lines, and subsequently realized ecological function zoning through the Self-Organizing Feature Map (SOM) and Support Vector Machine (SVM) models on this basis. The results indicated that: (1) constructed land was the greatest area expansion type (an increase of 2,864.46 km2), while cropland was the primary source of its increase (89.69% of the total transfer of constructed land); (2) Habitat Quality (HQ), Landscape Aesthetics (LA), Net Primary Production (NPP), Soil Retention (SR), Nitrogen Retention (NR), and Water Yield (WY) showed a general spatial pattern of “high in the southern mountains and low in the northern plains”, whereas Crop Production (CP) was more susceptible to create trade-offs with other ESs and has four types of constraint effects; (3) to ensure the maximal spatial variation in geography condition, we combined the SOM-SVM to divide the NMA area into 6 ecosystem service zones and advised land development and utilization by zoning characteristics. In addition, we compared the outcomes of zoning before and after SVM optimization to demonstrate the suitability of combining the SOM-SVM approach in zoning. Our research can provide a reference for the sustainable management of regional land resources and landscape planning.

A machine-learning method to accurately recognize the leakage pressure-drop signals in trunk natural gas pipelines

A block valve installed along a trunk natural gas pipeline can automatically shut down when it detects a pressure-drop rate that is over a threshold value for a specified duration, indicating that leakage accidents have occurred. However, current methods have difficulties on identifying the differences among the pressure-drop rate signals caused by small-hole leakage, compressor startup, and block valve emergency shut-down conditions. This usually leads to improperly shutting down the block valve. Based on an optimized support vector machine, a machine-learning method is proposed to recognize the pressure-drop rate signals through the eigenvalue of the pressure-drop curve instead of only using the pressure-drop signal and its duration. Firstly, the singular decomposition method and the support vector machine method are applied to initially classify the pressure-drop rate signals of three operating conditions: the gas leakage, compressor startup, and block valve emergency shut-down conditions. Secondly, a teaching-learning-based optimization method is applied to optimize the penalty factor and kernel function parameter in the support vector machine model. Particularly, the tent chaotic maps and adaptive inertia weight methods are applied to improve the teaching-learning-based optimization method to balance its local and global search performance. Thirdly, an improved teaching-learning-based support vector machine method is established. Finally, 960 sets of simulated pressure-drop signals taken from three trunk natural gas pipelines and pressure-drop rate signals collected from an actual pipeline are applied to verify the accuracy of the proposed model. The results show that the improved teaching-learning-based support vector machine model achieved high classification and recognition accuracy. Specifically, it achieved accuracy rates of 99.4%, 100%, 98.3%, and 100% for the pressure-drop rate signals generated by pipelines A, B, C, and Cangzhou branch in the presence of pipeline leakage (the leak hole diameter is from 25 to 125 mm). Additionally, it demonstrated an average recognition accuracy of 97.42% for the pressure-drop rate signals generated by pipelines under other operating conditions. Through cooperating with Supervisory Control And Data Acquisition system, this method provides a more relevant approach to determine the shutdown conditions of a block valve while preventing mistaken actions.

Establishing a Berry Sensory Evaluation Model Based on Machine Learning.

In recent years, people's quality of life has increased, and the requirements for fruits have also become higher; blueberries are particularly popular because of their rich nutrients. In the blueberry industry chain, sensory evaluation is an important link in determining the quality of blueberries. Therefore, to make a more objective scientific evaluation of blueberry quality and reduce the influence of human factors, on the basis of traditional sensory evaluation methods, machine learning is introduced to establish a support vector regression prediction model optimized by the particle swarm algorithm. Ten physical and chemical flavor indices of blueberries (such as catalase, flavonoids, and soluble solids) were used as input data, and sensory evaluation scores were used as output data. Three different predictive models were applied and compared: a particle swarm optimization support vector machine, a convolutional neural network, and a long short-term memory network model. To ensure reliability, the experiments with each of the three models were repeated 20 times, and the mean of each index was calculated. The experimental results showed that the root mean square error and mean absolute error of the particle swarm optimization support vector machine were 0.45 and 0.40, respectively; these values were lower than those of the convolutional neural network (0.96 and 0.78, respectively) and the long short-term memory network (1.22 and 0.97, respectively). Hence, these results highlighted the superiority of the proposed model when sample data are limited.

Swarm-intelligent elephant herding optimized support vector machine for predicting heart disease

Considering the significant worldwide mortality rate of cardiac disease, early and precise prognosis is essential to enhancing patient outcomes. The accuracy, effectiveness, and dependability of the heart disease prediction techniques currently in use, however, continue to encounter difficulties. Therefore, it is essential to create novel strategies that can get beyond these constraints and offer a better way to forecast cardiac disease. A novel advanced elephant herding optimised support vector machine (AEHO-SVM) technique utilized for the study in suggest method for predicting cardiovascular disease. A heart disease dataset is used to assess the efficacy of the suggested AEHO-SVM algorithm. To deal with missing values, normalise the data, and minimise dimensionality, the dataset is pre-processed. The AEHO-SVM approach aims to maximise classification accuracy while lowering the possibility of misdiagnosis by optimising the hyperplane separation and changing the support vectors iteratively. A number of metrics, like sensitivity, accuracy, f-measure, and specificity are used for assess model's performance. The experiment show that the AEHO-SVM technique works better than conventional methods and achieves good predictive accuracy for the diagnosis of heart disease. A suggested AEHO-SVM technique makes use of the collective thinking of elephant herding and its built-in optimisation skills to provide a more effective and trustworthy prediction method.

Bio-inspired swarm-intelligent with machine learning framework for prediction and classification of lung cancer

Early detection of lung cancer nodules, which is often dependent on a tomography scan filmic examination, greatly increases the probability of survival. Earlier tumor detection reduces lung cancer mortality by increasing the likelihood of successful therapy. Traditionally, radiologists have analyzed medical pictures for signs of lung cancer using a laborious and inaccurate systematic approach. The confidentiality and integrity of medical data have become a serious challenge for healthcare applications as a result of the tremendous improvement in the transmission of medical data in the healthcare sector. This research makes use of computational intelligence methods. In this research, a brand-new Enhanced vortex search algorithm optimized Support Vector Machine (EVSAO-SVM) is developed for detection and classification. Preprocessing using the Gaussian Filter, segmentation with Otsu thresholding, feature extraction with local binary patterns (LPB), and classification and prediction with the EVSAO-SVM are the processes that are simulated. Regarding performance criteria including accuracy, sensitivity, specificity, and F1-measure, this study indicates the suggested framework's superiority over the traditional approaches. The results of the tests conducted demonstrate that the suggested model can reach up to 95.42 percent sensitivity, 96.24 percent accuracy, 98.92 percent specificity, and 94.26 percent F1 measure.

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

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.

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.

Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art.

A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction

Water quality prediction plays a crucial role in pollution treatment. However, inaccurate long-term prediction resulting from complex information patterns and insufficient feature extraction may lead to unnecessary environmental costs. In this study, a novel Interval Decomposition Correlation Particle Swarm Optimization-Extreme Learning Machine (IDCPSO-ELM) model is proposed to improve long-term water quality prediction ability. We employ Multivariate Variational Mode Decomposition (MVMD), Variational Mode Decomposition (VMD), Sliding Correlation and Permutation Entropy to reconstruct features to reduce complexity. Seasonal and Trend decomposition using Loess-Empirical Wavelet Transform decomposition (STL-EWT) is applied to target variables to improve feature extraction ability. Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) is used to predict high-frequency components based on the reconstructive features, and Back Propagation Neural Network-Extreme Learning Machine (BPNN-ELM) is employed to predict the trend and lowest-frequency components. Grey Wolf Optimization Algorithm (GWO) is used to combine these results. Six stations with high potential pollution threats in the Haihe River basin of Beijing from 2021 to 2022 are taken into model competition. The results show that: (1) In short-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.0934, 0.0410 and 0.8061, respectively, which are better than Genetic Algorithm-Elman Network (GA-ELMAN), Cuckoo Search Algorithm-Back Propagation Neural Network (CS-BPNN), PSO-ELM and Sparrow Search Algorithm-Kernel Extreme Learning Machine (SSA-KELM) short-term prediction models. (2) In long-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.1114, 0.0420 and 0.8268, respectively, which are better than the Radial Basis Function Neural Network (RBFNN), Grey Wolf Optimization-Support Vector Machine Regression (GWO-SVR), Whale Optimization Algorithm-Deep Extreme Learning Machine (WOA-DELM), Wavelet Transform decomposition-GWO-SVR-BPNN (WT-GWO-SVR-BPNN) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-PSO-ELM-Long Short-Term Memory (CEEMDAN-PSO-ELM-LSTM) long-term prediction models. IDCPSO-ELM model is considered competitive and promising for long-term water quality prediction, particularly in areas with high potential pollution threats.

Implementation of novel pattern recognition approach using data mining for medical applications

The capacity of pattern recognition algorithms to analyze complicated data and retrieve useful information has attracted a lot of interest in the world of medical solutions. Data mining methods have become effective tools for pattern detection in the medical field, allowing for the extraction of insightful knowledge from huge and complex datasets. In this study, a diversified convergent squirrel search optimization algorithm and support vector machine (DCSSO-SVM) are combined to provide a unique pattern recognition method for medical applications. The DCSSO algorithm draws its inspiration from squirrels' efficient search-space exploration and exploitation during foraging. The suggested method integrates this algorithm with SVM to enhance feature selection, speed up convergence, and increase classification performance. To assess the effectiveness of the suggested DCSSO-SVM strategy, we assembled a dataset of medical images for this study. Other cutting-edge methods are contrasted with the suggested methodology. The experimental findings show that, regarding the accuracy, precision, f1-score, and recall metrics, the suggested DCSSO-SVM technique performs better than the current methods. It also has great toughness and support, making it appropriate for a variety of medicinal applications.

Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR)

Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight ω, self-learning factor c1, and social factor c2; then, the penalty parameter C and kernel function parameter σ of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R2 was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries.

Spatiotemporal Characteristics Prediction and Driving Factors Analysis of NPP in Shanxi Province Covering the Period 2001–2020

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm−2a−1. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage.