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Non-line-of-sight identification from WiFi CSI using particle swarm with multi-dimensional feature fusion

MEASUREMENT 2025年 242卷

作者： Li, Junhuai Guo, Yufan Fei, Rong Shi, Weiwei Wang, Kan Wang, Huaijun Qiu, Yuan Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shannxi Peoples R China Xian Univ Technol Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Shannxi Peoples R China Xian Univ Technol Shaanxi Univ Engn Res Ctr Human Machine Integrat Intelligent Robot Xian 710048 Shaanxi Peoples R China

Identifying line-of-sight (LOS) and non-line-of-sight (NLOS) conditions can reduce multipath propagation's negative impacts, ease communication link burdens, and improve wireless application performance. This paper proposes a lightweight, reliable and efficient NLOS recognition scheme focusing on channel state information (CSI), named PNLOS-MFC (NLOS identification based on particle swarm optimization algorithm with multi-dimensional feature fusion). We explore various optimization algorithms (PSO, GSA, GWO, ABC) to optimize SVM parameters, ultimately selecting the PSO-SVM-RBF structure with the best NLOS identification performance, accelerating channel identification. Next, to enhance the recognition rate of NLOS signals, we select the best CSI feature clusters by comparing and analyzing the recognition rate of NLOS signals in nine sub- clusters. Finally, the proposed model is compared to five different machine learning classifiers and traditional NLOS recognition algorithms, demonstrating superior performance with detection rates of 98.75% and 96.87% for NLOS in office and corridor scenarios.

关键词： WiFi environment sensing Channel state information Non-line-of-sight identification Neural network particle swarm algorithm

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A computational method to find the optimal driving-path for stable state transformation of multistable tensegrity

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COMPUTERS & STRUCTURES 2025年 310卷

作者： Xu, Zhiyin Lu, Jinyu Wu, Jiarong Liu, Jilei Gu, Xun Li, Na Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China Nanjing Forestry Univ Coll Civil Engn Nanjing 210037 Peoples R China

This paper introduces a computational approach for identifying the optimal driving-path for stable state transformations (SST) of multistable tensegrity structures. In order to capture the morphological changes of the tensegrity structure when the driving-nodes are actuated along arbitrary driving-path, a tracking method is firstly introduced. Then, the concept of segmenting-nodes is introduced to parameterize the driving-path. With the coordinates of segmenting-nodes on driving-path as optimization variables and the minimum energy threshold in the process of SST as objective function, a nonlinear optimization model is established and solved by particle swarm optimization algorithm. The feasibility of the proposed method is verified by three multistable tensegrity examples with different scales. In addition, the proposed method is compared with two existing methods, and the results further prove that the proposed method has the advantages of simple driving scheme and low energy threshold. The research provides instructive significance for the practical application of multistable tensegrity.

关键词： Multistable tensegrity Energy threshold Driving-path Optimization model Levenberg-Marquardt method particle swarm algorithm

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Parametric finite element modeling and topology optimization for stress reduction of spatial pipeline based on beam/solid element coupling

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第8期239卷 2864-2881页

作者： Ji, Wenhao Sun, Wei Ma, Hongwei Zhang, Yu Wang, Dong Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China Northeastern Univ Dept Educ China Key Lab Vibrat & Control Aeroprop Syst Shenyang Peoples R China

To efficiently perform the topology optimization for stress reduction of the pipeline system, this paper innovatively proposes a parametric finite element modeling (PFEM) method for pipeline systems based on beam/solid coupling. Furthermore, the multiple straight-line segment lengths are selected as the design variables, and a topology optimization method for stress reduction is proposed. For the PFEM, the incompatible solid (Solid-NC) element and three-dimensional (3D) Timoshenko beam element are implied to simulate the mechanical characteristics of the constrained and unconstrained regions of the pipeline, and then the beam/solid element coupling is realized by the penalty function method. The parametric geometric relationship of the pipeline system and the node coordinates of the finite element model are solved by the direction vector and vector decomposition methods, and the PFEM of the pipeline system is achieved and verified by ANSYS. Finally, the stress reduction optimization model of the pipeline system is established, and the stress reduction optimization of an actual pipeline is implemented using the particle swarm algorithm. The optimization results show that the maximum stress response of the optimal pipeline shape is 5.5 times lower than that before optimization, which shows the rationality of the optimization method.

关键词： Beam/solid coupling parametric finite element modeling pipeline system topology optimization stress reduction particle swarm algorithm

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A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

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Journal of Central South University 2016年第2期23卷 421-429页

作者：武善玉张平李方古锋潘毅 School of Computer Science and Engineering South China University of Technology Department of Computer Science College of Staten Island Department of Computer Science Georgia State University

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

关键词： service-oriented architecture (SOA) cyber physical systems (CPS) multi-task scheduling service allocation multi-objective optimization particle swarm algorithm

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A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第3期24卷 1901-1915页

作者： Liu, Xiao-huan Zhang, Degan Zhang, Jie Zhang, Ting Zhu, Haoli Tianjin Univ Technol Key Lab Comp Vis & Syst Minist Educ Tianjin 300384 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligent Comp & Novel Software Tianjin 300384 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these problems. We also designed training rules for the improved particle swarm optimization to train fuzzy neural network, and the hybrid algorithm is applied to solve the path planning problem of intelligent driving vehicles. The efficiency and practicability of the algorithm are proved by experiments.

关键词： particle swarm algorithm Fuzzy neural network Intelligent driving Path planning

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Machine Learning-Based Lithium Battery State of Health Prediction Research

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APPLIED SCIENCES-BASEL 2025年第2期15卷 516-516页

作者： Li, Kun Chen, Xinling Tiangong Univ Sch Econ & Management Tianjin 300387 Peoples R China

To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. Key features were extracted by analyzing the temperature, voltage, and current curves of the battery, and health factors with high correlation to SOH were selected as model inputs using the Pearson correlation coefficient. The PSO algorithm was employed to optimize model parameters, resulting in the construction of three predictive models: PSO-LSTM, PSO-CNN, and PSO-SVR. The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R2 exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. Among the three models, PSO-LSTM exhibited the best predictive performance, achieving a minimum MAE of 0.67%, RMSE of 0.94%, MAPE of 45.82%, and R2 as high as 0.9298 across the three datasets. These findings suggest that the PSO-LSTM model provides a robust reference for accurate SOH prediction of lithium-ion batteries and shows promising potential for practical applications.

关键词： lithium-ion battery state of health neural network model particle swarm algorithm support vector regression

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LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model

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FORESTS 2025年第3期16卷 464-464页

作者： Guo, Xiaoyu Wang, Weisen Meng, Fangyu Li, Mingjing Xu, Zhanghua Zheng, Xiaoman Sanming Univ Fujian Prov Key Lab Resources & Environm Monitorin Key Lab Engn Mat & Struct Reinforcement Sanming 365004 Peoples R China Fuzhou Univ Acad Geog & Ecol Environm Coll Environm & Safety Engn Fuzhou 350108 Peoples R China Fuzhou Univ Acad Digital China Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350108 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

Moso bamboo forests (MBFs) are unique subtropical ecosystems characterized by distinct leaf phenology, bamboo shoots, rapid growth, and carbon sequestration capability. Leaf area index (LAI) is an essential metric for evaluating the productivity and ecological quality of MBFs. However, accurate and large-scale methods for remote-sensing-based LAI monitoring during the winter growth stage remain underdeveloped. This study introduces a novel method integrating hyperspectral indices from Zhuhai-1 Orbit Hyperspectral Satellites (OHS) imagery with the particle swarm optimization-support vector machine (PSO-SVM) coupling model to estimate LAI in winter MBFs. Five traditional vegetation indices (VIRs) and their red-edge variants (VIREs) were optimized to build empirical models. Machine learning algorithms, including SVM, Random Forest, extreme gradient boosting, and partial linear regression, were also applied. The PSO-SVM model, integrating three VIRs and three VIREs, achieved the highest accuracy (R-2 = 0.721, RMSE = 0.490), outperforming traditional approaches. LAI was strongly correlated with indices, such as NDVIR, RVIR, EVIRE, and SAVI(R) (R > 0.77). LAI values of MBFs primarily ranged from 2.1 to 5.5 during winter, with values exceeding 4.5 indicating high winter bamboo shoot harvesting. These findings demonstrate the potential of OHS data to improve LAI retrieval models for large-scale LAI mapping, offering new insights into MBFs monitoring and contributing to sustainable forest management practices.

关键词： hyperspectral remote sensing red edge vegetation index leaf area index multi-purpose bamboo plant machine learning particle swarm algorithm

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ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3980-3980页

作者： Chen, Yiming Zhang, Hui Zhang, Chen Liu, Yi Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Univ Reading Dept Comp Sci Reading RG6 6DH England China Aerosp Sci & Industry Corp Res Inst 8511 Nanjing 210007 Peoples R China

In response to the issue that post-training quantization leads to performance degradation in mobile deployment, as well as the problem that the balanced consideration of quantization deviation by Clipping optimization techniques limits the improvement of quantization accuracy, this article proposes a novel clipping optimization method named ClipQ, which pays different attention to the parameters, aiming to preferentially reduce the quantization deviation of important parameters. The attention of the weight is positively related to its absolute value. Channel information entropy and principal component analysis are used to characterize the channel attention and spatial attention of activations, respectively. In addition, the particle swarm algorithm is applied in weight clipping to adjust the search step size and direction adaptively. ClipQ achieves high-precision quantization with very few calibration samples (<=50) and low time cost. Meanwhile, it does not bring extra computation, which is friendly to hardware. The experimental evaluation on image classification, semantic segmentation, and object detection shows that ClipQ outperforms other state-of-the-art clipping techniques, such as KL, ACIQ, and MSE. In 8-bit quantization, the average precision loss is 0.31% for image classification and 0.22% for object detection. More notably, it achieves almost lossless accuracy in semantic segmentation tasks.

关键词： neural network post-training quantization attention particle swarm algorithm

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Online tunable sound absorbing acoustic metamaterial with optional aperture and adjustable cavity

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APPLIED ACOUSTICS 2025年 231卷

作者： Yang, Xiaocui Shen, Xinmin Peng, Wenqiang Zhu, Dawei Song, Haichao Hu, Daochun Shen, Cheng Xu, Ming Zhu, Nannan Shi, Qi Nanjing Vocat Univ Ind Technol Engn Training Ctr 1 North Yangshan Rd Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Multifunct Lightweight Mat & Struct M 29 Yudao St Nanjing 210016 Jiangsu Peoples R China Army Engn Univ PLA Field Engn Coll Dept Mech Engn 88 Houbiaoying St Nanjing 210014 Jiangsu Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn 108 Deya St Changsha 410073 Hunan Peoples R China Jiangsu Shenglifang Environm Protect Technol Co Lt 1 Huayangang Nanjing 210003 Jiangsu Peoples R China

An online tunable acoustic metamaterial with the optional aperture and adjustable cavity is proposed to satisfy noise reduction requirements for various frequency ranges. Its sound absorption properties can be preliminarily optimized by the joint of three-dimensional finite element simulation model and particle swarm optimization algorithm. For the various frequency ranges of [250 Hz, 2000 Hz], [400 Hz, 800 Hz], [400 Hz, 1200 Hz], [600 Hz, 1400 Hz], and [1000 Hz, 1400 Hz], the theoretical average sound absorption coefficients increase from 0.3583 to 0.4604, from 0.6378 to 0.7591, from 0.4689 to 0.5799, from 0.5017 to 0.6305, from 0.7243 to 0.8754 respectively, which demonstrate the necessity of the parameter optimization. Meanwhile, the actual average sound absorption coefficients are further improved by direct optimization with tunable sample, which raise from 0.4236 to 0.4475, from 0.7232 to 0.7429, from 0.5421 to 0.5628, from 0.5906 to 0.6135, and from 0.8441 to 0.8630 respectively. This online tunable acoustic metamaterial can be treated as a universal sample to gain maximum sound absorption performance in a certain frequency range and a teaching appliance in the physics experiments on acoustic metamaterials, which show the excellent application prospects.

关键词： Online tunable acoustic metamaterial Sound absorption performance Optional aperture Adjustable cavity Finite element simulation particle swarm algorithm

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Optimal design and numerical studies of negative stiffness device-TMD controlled systems using PSO algorithm

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2025年 189卷

作者： Chen, Peng Wang, Bin Ma, Kaiqiang Sichuan Univ Dept Civil Engn Chengdu Sichuan Peoples R China Sichuan Inst Bldg Res Chengdu Peoples R China

In recent years, research on negative stiffness devices (NSDs) for vibration control has increased. This study examines the combination of NSDs with tuned mass dampers (TMDs) and explores the NSD-TMD as a nonlinear energy sink (NES). The particle swarm optimization (PSO) algorithm was employed to determine the optimal parameters for the TMD, NSD-TMD, and NES systems. The optimization procedure proves that the PSO algorithm is suitable for solving the optimization problem of the complex systems with strong nonlinearity. The overall procedure is effective and demonstrates strong convergence. The force transmissibility curves of the optimized systems were compared, revealing that the NSD-TMD achieved the best vibration performance, while the NES system demonstrated the target energy transfer (TET) property. Another interesting characteristic of the NSDTMD system is that it does not necessarily require a large additional mass or damping factor to achieve optimal performance. An actual vertical vibration control scenario for a pedestrian bridge was analyzed using various control strategies. Human-induced force excitations were simulated using a code-defined method. The acceleration responses of an uncontrolled pedestrian bridge and the three optimized control systems were compared in both time and frequency domains. Results further verified the force transmissibility curves. The NSD-TMD system outperformed the other two systems, particularly in reducing the first-order response. Conversely, the NES system proved less effective for controlling human-induced vibration with harmonic force excitations.

关键词： Negative stiffness device Tuned mass damper particle swarm algorithm Optimal design

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