PSO-SVM Algorithm Research Articles

Optimization of Micro-EFP charge structures based on PSO-SVM algorithm

Abstract To achieve a high-performance design of micro Explosive Formed Projectile (EFP) structures, a method based on the combination of the Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM) with numerical simulation technology is proposed for the optimization and design of EFP structures. The efficient optimization of the EFP structure is achieved by establishing the mapping relationship between the EFP structure and performance and using the PSO algorithm for parameter optimization. The results show that the optimal parameters of the structure are R out/D k=1.347, R in/D k=1.552, δ/D k=0.045, σ/D k=0.060, and N (L/D k) =1.082. Under this optimization option, the EFP velocity is predicted to be 2362.6 m/s, and the numerical simulation results are 2385 m/s, with an error of only 0.95%. An effective optimization approach for the design of micro explosively formed projectiles is provided.

Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.

Machine learning assisted electrochemical sensing technology for the detection of nitrate ions in PM2.5

This study investigates a graphene-modified glassy carbon electrode for detecting nitrate ions in particulate matter (PM2.5). The electrode configuration consists of a glassy carbon base electrode drop-coated with chemically-reduced graphene (CRGN), followed by an optimized nitrate ion-selective membrane (NO3-ISM) layer. Electrochemical impedance spectroscopy demonstrates graphene’s ability to enhance electron transfer by mitigating water layer formation. The nitrate sensor exhibits a linear potentiometric response from 0.1 mM to 0.1 M nitrate and theoretical Nernstian sensitivity. However, chloride interferences are observed. To improve selectivity, a support vector machine model utilizing particle swarm optimization (PSO-SVM) is developed. This machine learning approach shows significantly higher predictive accuracy than univariate calibration, even in mixed nitrate-chloride samples. The combination of the graphene-modified sensor and PSO-SVM algorithm provides an optimized nitrate detection strategy, advancing technologies for air quality monitoring.

PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems

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

Retracted: The Application of PSO-SVM Algorithm in the Evaluation System of Sports Competition Events

Retracted: The Application of PSO-SVM Algorithm in the Evaluation System of Sports Competition Events

Effective foundation damping prediction of monopile-supported offshore wind turbines based on integrated fitting equation and PSO–SVM algorithm

Foundation damping affects the fatigue life of offshore wind turbines. However, the effective damping ratio determined using a finite element analysis is not always consistent with the input. A series of simplified finite element models were established using realistic structural properties for a monopile-supported wind turbine to analyze the influence of wall thickness, pile embedment length, structure density, and input damping ratio on the calculated effective damping ratio. An empirical fitting equation was proposed using a power function based on the comprehensive laws between various factors and the effective value. An integrated prediction approach combining this equation with a hybrid support vector machine and particle swarm optimization algorithm was subsequently described and its results compared with those of other intelligent algorithms. The results showed that the effective damping ratio was much smaller than the input value, with the former reaching a maximum of only 15% of the latter. The integrated prediction approach was shown to strike an equilibrium between computing efficiency and forecast accuracy. Considering the soil–structure interaction and elastoplastic behavior of soil only increased the effective damping ratio to 35% of the input value, and also obfuscated the roles of the various factors in the effective damping.

Identification method for subgrade settlement of ballastless track based on vehicle vibration signals and machine learning

High-frequency train loads and complex environmental factors over a long period will inevitably cause subgrade settlement, an issue that adversely affects line smoothness. The existing monitoring methods are inefficient, manpower consuming and small in scope. This paper takes the subgrade settlement disease of the double-block ballastless track as the research object and proposes a subgrade settlement identification method based on vehicle vibration signals. In addition, it establishes a vehicle-track-subgrade vertical coupling model that combines feature extraction with algorithms such as support vector machines (SVMs) and convolutional neural networks (CNNs) to accurately identify subgrade settlement diseases on ballastless tracks. The results show: I) the subgrade settlement is far more impactful than fastener failure and debonding of the supporting layer on the vehicle vibration, with the main frequency band concentrated in 0 to 10 Hz, and the vertical vibration acceleration of the vehicle body and bogie can be used as a sensitive feature to identify ballastless track subgrade settlement, II) both the particle swarm optimization (PSO)-SVM and CNN-SVM algorithms can effectively determine the existence of subgrade settlement and the values of the settlement wavelength and amplitude. Both algorithms run a 100 % identification of subgrade settlement, with the lowest identification rates of settlement wavelength being 97.56 % and 100 %, respectively and the lowest identification rates of amplitude being 84.78 % and 97.56 %, respectively. In general, the overall accuracy of the CNN-SVM always outperforms PSO-SVM, and III) changes to the vehicle passing speed will create a more obvious interference effect on the identification of the settlement amplitude. For the PSO-SVM algorithm, the identification rate for the amplitude has a difference of up to 8.70 %, while this is 2.44 % for the CNN-SVM algorithm, indicating the CNN-SVM algorithm is more robust than the PSO-SVM algorithm.

Research on classification and recognition of the skin tumors by laser ultrasound using support vector machine based on particle swarm optimization

As a common disease endangering human health, skin tumor causes millions of deaths every year. In order to effectively detect skin tumors, a laser ultrasonic detection method based on the PSO-SVM algorithm is proposed to identify and detect human skin tumors. The physical model of laser ultrasound in human skin tumors is established by FEM (finite element method), and the ultrasonic wave generated by laser in different tumor locations (the skin epidemis, the dermis and between the two layers) are analyzed in detail. The minimum value, the sample entropy, the permutation entropy and the shannon entropy of laser ultrasound are extracted as the sensitive features, and the three types of tumors are classified and identified by the SVM (support vector machine). The penalty factor C and kernel function parameters γ in SVM are optimized by the PSO(particle swarm optimization) algorithm. The simulation results show that the proposed PSO-SVM algorithm has a good classification effect on skin tumor prediction, which provides a new detection method for laser ultrasound to detect skin tumors.

Design of Financial Crisis Early Warning Model Based on PSO-SVM Algorithm

To address the problem that the accuracy of the SVM algorithm is affected by random parameters at the input end, a financial crisis early warning model (FCEWM) based on PSO-SVM is constructed based on the nonequilibrium sample characteristics of different financial conditions of listed companies in China’s gem. The model uses the PSO algorithm to optimize the parameters of SVM and selects 24 financial risk evaluation indexes as the input to predict the financial crisis. The results show that the proposed model is superior to other models in prediction accuracy and robustness.

Optimization of energy consumption of a green building using PSO-SVM algorithm

In this paper, the new novel optimization algorithm on the energy consumption of the represented green building with novel material is represented. The introduced green building is 320 square meters and takes advantage of a novel lay-up of composite material with the common materials. For this purpose, the supporting vector machine is firstly proposed to classify the properties of the building based on the energy consumption with high accuracy. As a matter of fact, the hyperparameters are optimized using the Particle Swarm Optimization method (PSO). Energy consumption of the building with a common material is calculated based on a fixed price with 99.98% accuracy. In fact, the highest demand for consumption from the administrative and residential sectors is close to 40% will be optimized for the first time. Based on the represented novel model, the layers of the novel represented composite are optimized for decreasing electricity usage as well as biogas. The results show a significant reduction in the amount of green building energy consumption by 28% for electricity and 42.44% for biogas compared to the common building. Furthermore, the remarkable decreasing cost of the green building with composite material to a value of 50% with compare to the common building proves the need to invest obviously in the construction of green buildings and the use of renewable resources in the world.

Recognition model and algorithm of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain

In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system, this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor. Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen, we analyze the existing problems regarding the recognition of projectiles. In order to optimize the projectile recognition effect, we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm. We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system. We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection, and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine. Based on experiments, we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm; we adjust the parameters of the support vector machine model, train the test signal data of every sky-screen, and gain the projectile signal classification results. Afterwards, according to the signal classification results, we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor, which verifies the feasibility of the proposed algorithm.

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

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.

The Application of PSO-SVM Algorithm in the Evaluation System of Sports Competition Events

With the development of society and the improvement of residents’ living standards, sports competition events have attracted much attention as a large-scale event that is popular with the people. Based on the process of holding sports competitions, this paper obtains 14 evaluation indicators from four dimensions of policy, social environment, economy, and natural environment, uses the PSO-SVM algorithm for a comprehensive evaluation, and finally selects Henan Province as the empirical research object. Empirical research shows that the PSO-SVM algorithm has certain applicability in evaluating SCE and can provide a reference for the government and event organizers. The evaluation index system and evaluation model constructed in this paper have certain practical value, but the training data are insufficient and the model still needs to be further improved.

Prediction of Undisturbed Clay Rebound Index Based on Soil Microstructure Parameters and PSO-SVM Model

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

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

The liquefaction of tailings sand caused by seismic loads is a major problem in ensuring the safety of tailings ponds. Liquefaction may cause uncontrolled fluidized failure of the dam body, causing considerable damage to the lives, property and environment of people downstream. In this paper, a prototype tailings sand is used as the material to consider the main factors affecting liquefaction (i.e., dynamic load, soil quality, burial and static conditions). By embedding acceleration, pore pressure and earth pressure sensors in the rigid design of the self-designed rigid model box, different types of seismic waves of different ground motion amplitudes (PGA) were induced in a shaking table test of tailings sand liquefaction. The seismic intensity, waveform (class II, III and IV seismic waves) and active earth pressure of the PGA characterizing dynamic factors were obtained, and the static factors were characterized. The dynamic shear stress ratio, the peak acceleration of the earthquake, the pore pressure of the drainage factor and the buried depth (overlying effective pressure) characterize the soil conditions. SPSS software was used to analyze the factor dimension reduction, and the most suitable factors for factor analysis were obtained. Particle Swarm Optimization (PSO) was used to optimize the parameters, and the improved PSO-SVM algorithm was compared with the existing genetic algorithm (GA) and grid node search (GS). The algorithm used in this paper is fast and has a relatively high accuracy rate of 92.7%. The established threshold model method is of great significance to predict the liquefaction of tailings sand soil under the action of ground motions and to carry out safety managemenin advance, which can provide a certain reference for the project.

Hybrid PSO-SVM algorithm for Covid-19 screening and quantification.

Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization–Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19. This paper presents a model for training the system to segregate and classify the presence of pneumonia which will in turn save around 50% of the time frame for physicians. This will be especially useful in places of outbreaks where a team of people are working together with the aid of artificial intelligence and/or medical background. The AI incorporated system was distributed in all areas of across the globe. It has been observed that challenges such as data security, testing time effectiveness of model, data discrepancy etc. were positively handled using the deployed system. Moreover, since the AI integrated system identifies the infected patients immediately physicians can confirm the infection and segregate the patients at the right period. A total of 200 training cases have been observed of which 150 were identified to be infected. The proposed work shows specificity of 0.85, a sensitivity of 0.956 and an accuracy of 95.78%.

Analysis of the Relationship between Dance Action and Health Psychology in the Process of Dance Performance Teaching Environment.

One of the public fundamental disciplines that is typically put up among the professional teaching units in universities is dance. In order for this sports project with fitness, mental health, and aesthetic functions to be widely developed in universities, the use of reasonable, scientific, and targeted teaching methods can effectively improve the instructional effect. At the same time, it has further promoted the quality of education in universities and implemented the guiding ideology of “health first.” In order to avoid the classifier's performance-degrading effects brought on by the high dimension, this research suggests combining the classifier's psychological stress identification algorithm with a particle swarm optimization (PSO) approach. The experimental findings reveal that the PSO-SVM algorithm, PSO-BP algorithm, improved PSO-SVM algorithm, and improved PSO-BP algorithm, respectively, have recognition rates for psychological stress of 82.50%, 84.50%, 90.17%, and 94.83%. Additionally, the recognition rates of the improved PSO classifier are significantly higher than those of the basic PSO algorithm, demonstrating the improved PSO algorithm's strong generalization ability in optimization.

Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine

In order to solve the problem of low accuracy in oil-gas pipeline leak detection, a pipeline leak detection method based on Particle Swarm Optimization (PSO) algorithm optimized Support Vector Machine (SVM) is introduced. This method uses PSO to solve the penalty factor ‘c’ and kernel function parameter ‘g’, and constructs the pipeline leakage detection model of SVM. We set up an experimental platform to collect negative pressure wave signals under different working conditions. After wavelet domain denoising and data preprocessing, four eigenvalues of Mean, Standard Deviation, Kurtosis and Skewness are extracted from the signals to form the eigenvector samples, which are taken as input of SVM, and four working conditions of normal, leakage, rise and fall are taken as output. Through experimental verification, the comprehensive performance of PSO-SVM algorithm is better than that of traditional SVM, Genetic Algorithm optimized SVM and grid search algorithm optimized SVM. The POS-SVM algorithm can be applied to the leak detection of oil-gas pipeline.

Evaluation of Livable City Based on GIS and PSO-SVM: A Case Study of Hunan Province

Under the background of accelerating urbanization and increasing stress of ecological environment, the construction of livable city has attracted extensive attention and become a hot spot in the study of urban problems in the world. The evaluation of livable city is a reference for the comparison of urban development and also one of the evaluation criteria for the comparison of urban competitiveness. This paper focuses on three different evaluation factors of ecological environment, economic development and public service to construct an evaluation model of environmental quality of livable cities. Then particle swarm optimization (PSO) is introduced to optimize the parameters of support vector machine (SVM), and a SVM algorithm based on PSO (PSO-SVM) is proposed to solve the livable city evaluation model. Finally, the spatial analysis combined with ArcGIS software obtained the livable city evaluation and division results of Hunan Province. The results show that PSO-SVM algorithm is superior to SVM, BA-SVM, GA-SVM, and has the advantages of faster speed and higher classification accuracy.