Swarm Optimization Support Vector Machine Research Articles

Quantitative Identification of Internal and External Wire Rope Damage Based on VMD-AWT Noise Reduction and PSO-SVM.

As a common load-bearing component, mining wire rope produces different types of damage during a long period of operation, especially in the case of damage inside the wire rope, which cannot be identified by the naked eye, and it is difficult to accurately detect such damage using the present technology. In this study we designed a non-destructive testing device based on leakage magnetism, which can effectively detect the internal defects of wire rope damage, and carried out simulation analysis to lay a theoretical foundation for the subsequent experiments. To address the noise reduction problem in the design process, a variational mode decomposition–adaptive wavelet thresholding noise reduction method is proposed, which can improve the signal-to-noise ratio and also calculate the wavelet energy entropy in the reconstructed signal to construct multi-dimensional feature vectors. For the quantitative identification of system damage, a particle swarm optimization–support vector machine algorithm is proposed. Moreover, based on the signal following the noise reduction step, seven different feature vectors, namely, the waveform area, peak value, peak-valley value, wavelet energy entropy classification, and identification of internal and external damage defects, have been determined. The results show that the device can be used to effectively identify internal damage defects. In addition, the comparative analysis showed that the algorithm can reduce the system noise and effectively identify internal and external damage defects with a certain superiority.

Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm.

Due to the influence of signal-to-noise ratio in the early failure stage of rolling bearings in rotating machinery, it is difficult to effectively extract feature information. Variational Mode Decomposition (VMD) has been widely used to decompose vibration signals which can reflect more fault omens. In order to improve the efficiency and accuracy, a method to optimize VMD by using the Niche Genetic Algorithm (NGA) is proposed in this paper. In this method, the optimal Shannon entropy of modal components in a VMD algorithm is taken as the optimization objective, by using the NGA to constantly update and optimize the combination of influencing parameters composed of α and K so as to minimize the local minimum entropy. According to the obtained optimization results, the optimal input parameters of the VMD algorithm were set. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a rolling bearing. The decomposition process of the rolling-bearing fault signal was transferred to the variational frame by the NGA-VMD algorithm, and several eigenmode function components were obtained. The energy feature extracted from the modal component containing the main fault information was used as the input vector of a particle swarm optimized support vector machine (PSO-SVM) and used to identify the fault type of the rolling bearing. The analysis results of the simulation signal and measured signal show that: the NGA-VMD algorithm can decompose the vibration signal of a rolling bearing accurately and has a better robust performance and correct recognition rate than the VMD algorithm. It can highlight the local characteristics of the original sample data and reduce the interference of the parameters selected artificially in the VMD algorithm on the processing results, improving the fault-diagnosis efficiency of rolling bearings.

A Machine Learning-Based Approach to Discriminating Basaltic Tectonic Settings

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.

Investigation on the optimal formulation and mechanism of marine organic silt improved with magnesium-cement-based stabilizer

This study aims to search for an environment-friendly single or composite stabilized agent for marine organic silt. The effectiveness of modified magnesium oxysulfate cement (MMOSC) with three admixtures, viz., sodium silicate, silica fume, and cement clinker, to improve the mechanical property of organic silt is assessed in detail, in which the unconfined compressive strength (UCS) at 7 days is evaluated to assess the quality of stabilized soil. In addition, a novel hybrid algorithm, particle swarm optimization–support vector machine (PSO–SVM), is developed to locate the optimal mixing ratio of the three admixtures, where PSO is utilized to search for the global optimal mixing proportion, and SVM to substitute for the laboratory experiment of UCS to estimate the UCS, qu, of stabilized organic soil. X-ray diffraction and scanning electron microscopy are used to examine the components and microstructural properties of the stabilized organic silt. The test results reveal that the three additives of sodium silicate, silica fume, and cement clinker, combined with MMOSC, are effective in strengthening the organic soil. The microstructure of stabilized silt is significantly enhanced in comparison to the original organic silt. The optimal mixing ratio of sodium, silica fume, and cement clinker can be determined by the developed PSO–SVM algorithm.

A new technology for rapid determination of isomers of hydroxybenzoic acid by terahertz spectroscopy

This study investigated the feasibility of using terahertz (THz) technology for the rapid identification of isomers. The time-domain spectra of 2-hydroxybenzoic acid (2-HA), 3-hydroxybenzoic acid (3-HA), and 4-hydroxybenzoic acid (4-HA) were measured by a THz time-domain spectroscopy system (THz-TDS) in the range of 0.3–1.8 THz. Aiming at the isomer classification problem, a THz spectral data classification model based on a variational mode decomposition–particle swarm optimization–support vector machine (VMD-PSO-SVM) method was proposed. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) were used to extract the first eight intrinsic mode functions (IMFs) of the time-domain signal. Principal component analysis (PCA) was used to extract the first 80 principal components of each modal component as the classification feature vector. The particle swarm optimization (PSO) and support vector machine (SVM) algorithms were used to construct 2-, 3-, and 4-HA classification models. We found that the prediction accuracy of the VMD-PSO-SVM model was significantly higher than that of EMD-PSO-SVM model regardless of the modal components. For both EMD and VMD, with the increase in the IMF number, the corresponding classification recognition accuracy tended to decrease. The results showed that the rapid identification model of hydroxybenzoic acid isomers based on THz spectroscopy and SVM was effective and feasible, providing an accurate and rapid method for the chemical synthesis and quality monitoring of biomedicine.

Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine

Accurate prediction of the survival risk level of patients with esophageal cancer is significant for the selection of appropriate treatment methods. It contributes to improving the living quality and survival chance of patients. However, considering that the characteristics of blood index vary with individuals on the basis of their ages, personal habits and living environment etc., a unified artificial intelligence prediction model is not precisely adequate. In order to enhance the precision of the model on the prediction of esophageal cancer survival risk, this study proposes a different model based on the Kohonen network clustering algorithm and the kernel extreme learning machine (KELM), aiming to classifying the tested population into five catergories and provide better efficiency with the use of machine learning. Firstly, the Kohonen network clustering method was used to cluster the patient samples and five types of samples were obtained. Secondly, patients were divided into two risk levels based on 5-year net survival. Then, the Taylor formula was used to expand the theory to analyze the influence of different activation functions on the KELM modeling effect, and conduct experimental verification. RBF was selected as the activation function of the KELM. Finally, the adaptive mutation sparrow search algorithm (AMSSA) was used to optimize the model parameters. The experimental results were compared with the methods of the artificial bee colony optimized support vector machine (ABC-SVM), the three layers of random forest (TLRF), the gray relational analysis–particle swarm optimization support vector machine (GP-SVM) and the mixed-effects Cox model (Cox-LMM). The results showed that the prediction model proposed in this study had certain advantages in terms of prediction accuracy and running time, and could provide support for medical personnel to choose the treatment mode of esophageal cancer patients.

A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines

With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.

Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion

PurposeSurface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation.MethodsTwenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared.ResultsIPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved.ConclusionThe sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man–machine devices and improving the safety of Pilates rehabilitation.

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

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%.

Evaluation of Pilot Manipulation Comfort in Different Flight Scenarios under Different Ambient Temperatures

This study aimed to evaluate pilot manipulation comfort in different flight scenarios under different ambient temperatures. To achieve this goal, we designed a test plan to devise the physiological indexes used in the evaluation of pilot manipulation comfort, collected and analyzed pilot EMG signals and mean skin temperature, and on this basis revealed the impacts of different ambient temperatures and different flight missions on muscle activation and mean skin temperature. Based on this, we extracted muscle activation and mean skin temperature as the evaluation indices for the evaluation of manipulation comfort and established a model for this evaluation based on an improved particle swarm optimization and least-squares support vector machine (IPSO-LSSVM). Based on this model, the predicted manipulation comfort values of the participants were 4, 5 and 6, which belong to the categories of fair, somewhat comfortable and rather comfortable, respectively. By comparing the prediction results of different evaluation methods, the method proposed in this study was verified to be effective.

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

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.

Research on ultrasonic defect identification method of well control manifold pipeline based on IAFSA-SVM

In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed. Firstly, perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the characteristic vector of characterizes the defect type; Then establish the support vector machine defect classification model, and use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. Finally, a software and hardware experimental platform for the classification of pipeline corrosion defects of the well control manifold is built to carry out software simulation and experimental analysis. The experimental results show that the recognition rate of the defect classification model based on improved artificial fish swarm optimization support vector machine parameters is 94.67% for ultrasonic defect signals at different depths.

A comparative study of different machine learning methods for reservoir landslide displacement prediction

This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (PSO–LSSVM), and long short-term memory neural network (LSTM), in the prediction of reservoir landslide displacement. The Baishuihe, Shuping, and Baijiabao landslides in the Three Gorges reservoir area of China were used for case studies. Cumulative displacement was decomposed into trend displacement and periodic displacement by the Hodrick–Prescott filter. The double exponential smoothing method and the five machine learning methods were used to predict the trend and periodic displacement, respectively. The five machine learning methods are compared in three aspects: highest single prediction accuracy, mean prediction accuracy, and prediction stability. The results show that no method performed the best for all three aspects in the three landslide cases. LSTM and PSO–ELM achieved better single prediction accuracy, but worse mean prediction accuracy and stability. PSO–KELM, PSO–LSSVM, and PSO–SVM always yielded consistent predictions with slight variations. On the whole, PSO–KELM and PSO–LSSVM are recommended for their superior mean prediction accuracy and prediction stability.

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

The scientific evaluation of water quality change trends and pollution characteristics is of great significance to improving the current situation of water resources. The particle swarm optimization support vector machine based on principal component analysis (PCA-PSO-SVM) was used to conduct a comprehensive evaluation of groundwater quality in Xinzhou city, and the results were compared with those of a variety of traditional water quality evaluation methods. The evaluation results show that the water quality evaluation model based on PCA-PSO-SVM is more comprehensive and objective. The overall groundwater quality situation in Xinzhou city is generally good. The upper pore water quality is mainly class II, and the lower karst water is mainly class III. The water quality shows significant spatial differences. Compared with the traditional water quality evaluation method, the improved SVM algorithm compensates for the defects of the traditional method. The model structure is stable, and the accuracy and calculation efficiency are high, which makes the method worthy of promotion and application. The research results can provide a scientific basis and reference value for the water quality evaluation of groundwater-related projects.

Prediction of Carbon Dioxide Solubility in Polymers Based on Adaptive Particle Swarm Optimization and Least Squares Support Vector Machine

AbstractSolubility is a significant physical and chemical property. The solubility of carbon dioxide(CO2) in polymers is an important application of green chemistry. Aimed at the problem of insufficient precision of the existing prediction model, a solubility prediction model based on the adaptive particle swarm optimization algorithm and the least‐squares support vector machine(APSO‐LSSVM) is proposed. Different from the traditional particle swarm algorithm, APSO algorithm improves the problem of easily falling into local optimal solution. The regularization parameters and kernel function tuning parameters of the LSSVM were optimized by APSO and then use this model to predict the solubility of CO2 in eight polymers within a wide range of temperatures and pressures. APSO‐LSSVM model has great prediction ability, with a good correlation between prediction data and experimental data, high prediction accuracy, short calculation time and strong stability. Compared with back propagation artificial neural network(BP ANN), back propagation‐particle swarm optimization algorithm artificial neural network(BP‐PSO ANN) and LSSVM, APSO‐LSSVM has better comprehensive performance. The average absolute relative deviation(AARD), root mean square error(RMSE), determination coefficient(R2) were respectively 0.2130, 0.0120, 0.9853. In addition, the model has good expansibility and can be applied to other fields such as chemistry and medicine.

Transfer inhibitory potency prediction to binary classification: A model only needs a small training set

One of the most laborious for drug discovery is to select compounds from a library for experimental evaluation. Hence, we propose a machine learning model only needs to be trained on a small dataset to predict the inhibition constant (Ki) and half maximal inhibitory concentration (IC50) for a compound. We transfer the prediction task to a simpler binary classification task based on a naive but effective idea that we only need the related rank of a compound to determine whether to take it for further examination. To achieve this, we design a data augmentation strategy to effectively leverage the relationship between the compounds in the training set. After that, we formulate a new reward function for deep reinforcement learning to balance the feature selection and the accuracy. We employ a particle swarm optimized support vector machine for the binary classification task. Finally, a soft voting mechanism is introduced to solve the contradiction from the binary classification. Sufficient experiments show that our model achieves high and reliable accuracy, and is capable of ranking compounds based on a selected set of molecular descriptors. The current results show that our model provides a potential ligand-based in silico approach for prioritizing chemicals for experimental studies.

Ship power load forecasting based on PSO-SVM.

Compared with the land power grid, power capacity of ship power system is small, its power load has randomness. Ship power load forecasting is of great significance for the stability and safety of ship power system. Support vector machine (SVM) load forecasting algorithm is a common method of ship power load forecasting. In this paper, water flow velocity, wind speed and ship speed are used as the features of SVM to train the load forecasting algorithm, which strengthens the correlation between features and predicted values. At the same time, regularization parameter C and standardization parameter σ of SVM has a great influence on the prediction accuracy. Therefore, the improved particle swarm optimization algorithm is used to optimize these two parameters in real time to form a new improved particle swarm optimization support vector machine algorithm (IPSO-SVM), which reduces the load forecasting error, improves the prediction accuracy of ship power load, and improves the performance of ship energy management system.

Using support vector regression and non-dominated sorting genetic algorithm in multi-objective optimization of milling of S50C steel under MQL condition

The modern machining industry faces reducing manufacturing cost pressure and improve product quality expectations. Due to this competition a manufacturer must continually identify cost cutting opportunities in manufacturing process. The keys technology represents cost-saving opportunities associated with reducing cutting fluid consumption, cutting energy consumption, and improves the overall performance of machining operations at the same time. Hence, multiple response optimization techniques have recently become the focus of research to improve product quality by increasing surface quality and reduce costs by reducing cutting force, cutting fluids, and energy consumption of the cutting process recently. Among various optimization methods, few Taguchi-based likes as TOPSIS, COPRAS, MOORA, VIKOR... were chosen to solve complex multiple criteria problems. However, the limitation of these techniques is that it was helped to rank and select the best parameters set for the implemented experiments only. In this work, an attempt was made to streamline the milling and coolant condition parameters of S50C carbon steel under MQL condition. Experiments were performed based on Taguchi's L27 orthogonal array. Five input factors includes machining parameters including cutting speed (Vc), feed (fz), and depth of the cut (ap) combined with two coolant parameters such as coolant air pressure (P), flow rate of lubricant (Q) were considered the variants, while specific cutting energy (Ec), material removed rate (MRR) and surface roughness were response variables. Particle swarm optimization Support Vector Machines (SVM) was used to generate the regression model, then Non-dominated Sorting Genetic Algorithm (NSGA) was used to optimize surface roughness (Ra), specific cutting energy (Ec), and production rate (MRR).

Fault identification of double-circuit transmission lines on the same pole based on EEMD energy ratio

In order to improve the sensitivity and reliability of traveling wave protection, on the basis of analyzing the relationship of the anti-traveling wave current amplitude in the window after the internal/external failure of the double circuit line on the same tower, a fault identification method based on EEMD energy ratio is proposed. Use EEMD decomposition to decompose the anti-traveling wave current in a time window after the fault into 7 scales, and extracts the EEMD energy ratio at each scale at both ends to form a feature vector. Then it is sent to the particle swarm optimization support vector machine (PSO-SVM) for training and testing, and the internal and external faults are identified. Experiments show that the algorithm has good fault identification ability, the fault accuracy is 95% and the method sensitivity is high.