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Simulation-Enhanced MQAM Modulation Identification in Communication Systems: A Subtractive Clustering-Based PSO-FCM algorithm Study

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INFORMATION 2024年第1期15卷 42-42页

作者： Quan, Zhi Zhang, Hailong Luo, Jiyu Sun, Haijun Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FCM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FCM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the SC-based PSO-FCM algorithm outperforms the conventional FCM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.

关键词： modulation recognition fuzzy c-means algorithm constellation diagram particle swarm optimization algorithm MQAM signal

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An improved retrieval method for liquid water content of the Antarctic ice sheet using SMOS data

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GEOCARTO INTERNATIONAL 2025年第1期40卷

作者： Zhou, Yi Feng, Yongjiu Cao, Yuze Chen, Shurui Wang, Rong Wang, Xiaofeng Xiao, Changjiang Ye, Zhen Tong, Xiaohua Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China Tongji Univ Shanghai Key Lab Planetary Mapping & Remote Sensin Shanghai Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai Peoples R China

With global climate warming, Antarctic ice sheet melting has garnered increasing attention, as changes in liquid water content (LWC) significantly affect sea level rise and regional climate. This study integrates SMOS L-band passive microwave data with the LS-MEMLS microwave emission model and employs the particle swarm optimization algorithm to retrieve the surface LWC of the Antarctic ice sheet and its spatiotemporal variations. We analyzed LWC, surface density, and melt days across different Antarctic regions, focusing on the trends in LWC and its relationship with multi-source remote sensing products. The results indicate a rising trend in LWC and melting of the Antarctic Peninsula and coastal ice shelves from 2018 to 2020, with a notable peak in 2020, potentially related to the anomalous climatic events. This research provides new methodological and theoretical insights into Antarctic ice sheet dynamics melt and their implications for the global climate system.

关键词： Antarctic ice sheet liquid water content SMOS melt detection particle swarm optimization algorithm

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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Cao, Kerang Liu, Chang Yang, Siqi Zhang, Yuxin Li, Lili Jung, Hoekyung Zhang, Shuo Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Shenyang 110142 Peoples R China Shenyang Matern & Child Hlth Hosp Shenyang 110014 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea

Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an XGBoost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-XGBoost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-XGBoost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to XGBoost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.

关键词： Cardiovascular disease Machine learning XGBoost algorithm Multi feature selection particle swarm optimization algorithm Model prediction

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Research on Speed Control of PMSM Based on a New Sliding Mode Reaching Law of Fast Integral Terminal Sliding Mode Control With Iterative-Based High-Gain Disturbance Observer

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第3期61卷 4352-4363页

作者： Shi, Song Wang, Yi Mai, Songping Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

A novel hybrid control algorithm is proposed in this paper to optimize the performance of the speed control system of permanent magnet synchronous motors (PMSM), allowing it to maintain ideal speed even under external disturbances and torque pulsations. Firstly, a new sliding mode reaching law (NSMRL) is presented and combined with a fast integral terminal sliding mode surface to reduce the system's convergence time without increasing the system's chattering. Secondly, a high-gain disturbance observer based on iterative learning control (ILC-HGO) is combined with the new sliding mode reaching law, enhancing the system's anti-disturbance capability. The stability of the proposed algorithm is theoretically analyzed using the Lyapunov method. Thirdly, MATLAB simulations and hardware-in-the-loop experiments were conducted to demonstrate the proposed algorithm's advantages over traditional approaches, showing faster convergence speed, smaller overshoot, and better robustness. Finally, the particle swarm optimization (PSO) algorithm is introduced to optimize the internal parameters of the control algorithm, further improving the control performance of the algorithm. The main contributions of this paper are as follows: 1) A new sliding mode reaching law (NSMRL) is proposed, significantly reducing the system's convergence time without increasing chattering;2) A high-gain disturbance observer based on iterative learning control (ILC-HGO) is combined with the new sliding mode reaching law, enhancing the system's anti-disturbance capability;3) The proposed algorithm is theoretically and experimentally verified, demonstrating significant improvements in convergence speed, overshoot, and robustness compared to traditional methods;4) The particle swarm optimization (PSO) algorithm is introduced to optimize the control algorithm's internal parameters, further enhancing system performance.

关键词： Convergence Mathematical models Sliding mode control Torque Disturbance observers Robustness Prediction algorithms Permanent magnets particle swarm optimization Velocity control Permanent magnet synchronous motor high-gain disturbance observer iterative learning control a new reaching law particle swarm optimization algorithm

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Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

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Journal of Southeast University(English Edition) 2022年第3期38卷 235-241页

作者： Lei Chunli Zhao Mingqi Liu Kai Song Ruizhe Zhang Huqiang School of Mechanical and Electrical Engineering Lanzhou University of TechnologyLanzhou 730050China

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is ***,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is ***,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN *** experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and *** presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

关键词： temperature prediction high-speed motorized spindle particle swarm optimization algorithm back-propagation neural network robustness

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Design and key technology of the energy consumption management system for the liquid cooling data center

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ENERGY SCIENCE & ENGINEERING 2023年第3期11卷 1284-1293页

作者： Lai, Yibo Fang, Xiang Han, Rongjie Xuan, Yi Huang, Jiabin Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310009 Peoples R China

In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

关键词： energy consumption management system energy consumption sampling circuit liquid cooling data center particle swarm optimization algorithm resource configurator

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Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

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JOURNAL OF SUPERCOMPUTING 2023年第12期79卷 13889-13906页

作者： Zhou, Jian Wang, Weixin Lu, Jian Liu, Lingzhe Xian Polytech Univ Sch Elect & Informat Xian 710048 Shaanxi Peoples R China

The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particle swarm optimization least squares support vector machine (PSO-LSSVM) with a hybrid kernel function. The proposed method is based on a least square support vector machine and uses linear weighting of the polynomial kernel function (POLY) and Gaussian kernel function (RBF) to form a hybrid kernel function, and uses a particle swarm optimization algorithm to search for the optimal parameters. Finally, a mathematical model of the longitudinal and lateral passages of a SUH is established. According to the flight test data, the longitudinal and lateral channel models are trained and verified in the hover and low-speed forward flight states of a SUH. The experimental and comparison results demonstrate that the model established via this method has higher prediction accuracy and more accurate prediction results than a model established using a least squares support vector machine with a single kernel function. The identification accuracy of the SUH model is improved effectively.

关键词： Hybrid kernel function Least square support vector machines Mathematical modeling particle swarm optimization algorithm Small unmanned helicopter

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Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2024年第1期30卷 133-152页

作者： Kumaresan, Velmurugan Saravanasankar, S. Di Bona, Gianpaolo Kalasalingam Acad Res & Educ Krishnankoil India Univ Cassino & Lazio Meridionale Dept Civil & Mech Engn Cassino Italy

PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing ***/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service *** major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

关键词： Small and medium-sized enterprise Markov decision model particle swarm optimization algorithm Optimal decision-making process

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The High-Efficiency optimization Design Method for Two-Stage Miller Compensated Operational Amplifier

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第4期71卷 2029-2033页

作者： Yang, Yunqi Yin, Xiaoyu Chen, Dongdong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

In this brief, a high-efficiency optimization design method is proposed for a two-stage Miller-compensated operational amplifier (TSMCOA). In the proposed method, the parameters and performance metrics of TSMCOA are simulated by Cadence software. Then, the neural network (NN) models are utilized to describe the relationship between its parameters and performance metrics, which can greatly improve simulation efficiency. Based on the performance metrics of TSMCOA, a multi-objective function is established. Then, according to the NN models and multi-objective function, the parameters of TSMCOA are optimized by particle swarm optimization algorithm with linearly decreasing inertia weight (PSO-LDIW). The optimized area is 0.1371 mu m2 72.09 mu 20803

关键词： Multi-objective optimization neural network particle swarm optimization algorithm two-stage Miller-compensated operational amplifier

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APPLICATION OF NONLINEAR BIG DATA ANALYSIS TECHNIQUES IN COMPUTER SOFTWARE RELIABILITY PREDICTION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第1期25卷 55-64页

作者： Gao, Li Wang, Hai Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Nanning Univ Civil & Architectural Engn Coll Nanning 530000 Peoples R China

This research addresses the difficulties of underfitting, overfitting, and convergence to local minima in artificial neural networks for software dependability prediction. The work specifically focuses on enhancing the performance of the con-ventional PSO-SVM model for software reliability prediction. The analysis of the conventional PSO-SVM model and the special features of software reliability prediction serve as the foundation. An improved PSO-SVM software reliability prediction model is developed and the PSO-SVM model and a Backpropagation (BP) prediction model are compared experimentally. The critical metrics assessed include training error, and efficiency. The experimental results reveal that the training error of the enhanced PSO-LSSVM prediction model diminishes rapidly, levelling off after approximately 200 training generations. The BP prediction model requires 1,733 generations to meet training requirements. Furthermore, the improved PSO-LSSVM prediction model demon-strates significantly higher training efficiency than the BP prediction model. The optimized prediction model exhibits superior adaptability to small sample sizes, swift training, and high prediction accuracy, making it a more suitable choice for software reliability prediction applications.

关键词： particle swarm optimization algorithm Support vector machine Software reliability Prediction Training error Efficiency

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