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2nd International Conference on Intelligent Computation Technology and Automation

作者： Zhou Xian-cheng Hunan Univ Commerce Sch Comp & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9780769538044

In order to solve the problems of the fuzzy C-means (FCM) clustering algorithm when it is applied to the image segmentation such as making itself easily traps into local optimum and huge calculation, an image segmentation algorithm based on the modified particle swarm optimization(MPSO) and FCM clustering algorithm is proposed. The simulation results and the comparison between the proposed algorithm and FCM algorithm indicate that the proposed algorithm can obtain better segmentation effects and excel the existing FCM algorithm in several performance, such as the average dispersion, the maximum intra-distance between pixel and their cluster center, and the minimum inter-distance between any pair of clusters.

关键词： particle swarm algorithm fuzzy C-means clustering image segmentation

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Fault Diagnosis Based on particle swarm Fuzzy Clustering algorithm

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International Conference on Mechanical Engineering, Industry and Manufacturing Engineering (MEIME 2011)

作者： Wang Zhixiao Niu Qiang China Univ Min & Technol Coll Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9783037851371

Fuzzy c-means clustering algorithm (FCM) is sensitive to noise and less effective when handling high dimensional data set. Given that particle swarm optimization algorithm (PSO) has strong global search capability and efficient performance, a new PSO based fuzzy clustering algorithm is proposed. particles in the new algorithm are encoded by membership in FCM. The new algorithm adopts a new strategy to meet the constraints of FCM, so as to optimize the clustering effect of FCM. Finally, this algorithm is applied to motor fault diagnosis. Experiment shows that the new algorithm made up for the shortcomings of FCM, improved the efficiency and accuracy of clustering and bettered fault diagnosis results.

关键词： fault diagnosis clustering particle swarm algorithm fuzzy c means

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Thermal Layout Optimization of Stacked Chips Based on Hybrid algorithm of Simulated Annealing and particle swarm

Thermal Layout Optimization of Stacked Chips Based on Hybrid...

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Zang, Mingxiang Wang, Meng Lai, Xinquan He, Huisen Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781467329644;9781467329637

As the density of chip package increases, the thermal layout optimization of stacked chips has become more and more important. The thermal layout optimization of stacked chips is studied in this paper. Firstly, a hybrid algorithm of simulated annealing and particle swarm (SA-PSO) is proposed for thermal layout optimization. Secondly, the fitness function is obtained based on thermal superposition model and heat conduction equation. Then the temperature of chips is chosen as the evaluation index and the corresponding optimization scheme is achieved. Finally, finite element analysis (FEA) software is used to evaluate the thermal field distribution of stacked chips and the result shows the temperatures of stacked chips are lowered after optimization. Thus, we can prove that the chip layout optimization algorithm proposed in this paper is effective and robust in thermal layout optimization of stacked chips.

关键词： stacked chips simulated annealing algorithm particle swarm algorithm thermal layout optimization finite element analysis

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Research on fault diagnosis of amorphous alloy transformers by using vibration signals and a PSO-optimized full-process WPT-SVM model

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2025年

作者： Liu, Daosheng Yang, Wentao Liu, Longsheng Zhao, Zhe Jiangxi Univ Sci & Technol Sch Elect Engn & Automat 1958 Hakka Ave Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Jiangxi Prov Key Lab Multidimens Intelligent Perce Ganzhou Peoples R China

The prominent vibration characteristics of amorphous alloy transformer (AMT) make it possible to apply the vibration method for real-time fault monitoring of AMT. Therefore, in order to solve the AMT vibration monitoring problem and enhance the diagnostic efficiency, this study proposes an AMT fault diagnosis model based on particle swarm optimization (PSO) to optimize the parameters of wavelet packet transform (WPT) and support vector machine (SVM).The optimal vibration signal acquisition point is determined by finite element analysis to ensure high signal quality. The PSO algorithm is used to optimize the number of WPT decomposition layers, wavelet basis functions, SVM kernel parameters g and penalty parameters c to enhance the accuracy of feature extraction and classification. Additionally, principal component analysis (PCA) reduces the dimensionality of the redundant frequency band energy after WPT feature extraction, minimizing data redundancy. Overall, the full-process optimization significantly improves AMT fault diagnosis efficiency compared with single-aspect optimization.

关键词： Amorphous alloy transformer vibration signal fault diagnosis particle swarm algorithm support vector machine principal component analysis

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Using the combination of particle swarm algorithms and fuzzy approach to provide a clustering method for network nodes with coverage maintenance in wireless sensor networks 7

Using the combination of particle swarm algorithms and fuzzy...

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7th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Heydariyan, Seyyed Amir Reza Taghdisi Mohajerzadeh, Amir Hussein Int Univ Imam Reza Software Engn Mashhad Iran Ferdowsi Univ Mashhad Comp Engn Mashhad Iran

ISBN: (纸本)9781538608043

Wireless sensor network (WSN) is an inexpensive newfound technology with many applications in various fields (such as biology Environment, war and natural disasters). A network consisting of a large number of sensor nodes and collecting information from the environment in a distributed environment. The main limitations include limited energy, low communication capacity, low storage volume, and low bandwidth. This study provides a clustering method for network nodes by optimized particle swarm and fuzzy approach This method determines the cluster head according to node's distances from each other and the left amount of energy and the distance from the sink and the density of each node, and determines the rest of the nodes in the network as sub clusters. Selecting the number and the place of cluster heads in order to have the most energy efficiency in the network is a Np-Hard problem, and on the other hand PSO algorithms is very flexible in solving dynamic problems. In this method we maintain the coverage and reduce the energy consumption by combining the particle swarm algorithms and fuzzy approach. This method is faster than the other methods.

关键词： wireless sensor networks reducing energy consumption coverage maintenance particle swarm algorithm fuzzy method

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Study on Train Operation Adjustment Based on Collaborative particle swarm algorithms 2nd

Study on Train Operation Adjustment Based on Collaborative P...

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International Conference on Electrical and Information Technologies for Rail Transportation - Electrical Traction

作者： An, Yazheng Qin, Yong Meng, Xuelei Wang, Ziyang Beijing Jiaotong Univ Beijing Res Ctr Urban Traff Informat Sensing & Se Beijing 100044 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

ISBN: (纸本)9783662493700;9783662493687

The paper establishes the operation adjustment model in accordance with constraints of the train operation based on the characteristic of high real-time and dynamic and the fitness function aiming at getting the least level of delay time and the number of delay times. In order to solve the established operation adjustment model, the method of cooperation is introduced to improve the typical particle swarm algorithm, which improves the efficiency of operation and the global optimum. Finally, taking the train operation adjustment of dedicated passenger line between Beijing and Shenyang as an example, the model and algorithm are verified. It can be seen that the method is efficient and feasible and it has a good guiding significance for train operation adjustment.

关键词： Operation adjustment Delay times Cooperation particle swarm algorithm

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Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation

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APPLIED SCIENCES-BASEL 2025年第9期15卷

作者： Yin, Guanglin Lin, Chaoning Sheng, Taozhen Xue, Wenbo Li, Tongchun Chen, Siyu Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China NanJing Hohai Nanzi Hydropower Automat Co Ltd Nanjing 210032 Peoples R China Nanjing Hydraul Res Inst Ctr Big Data & Smart Water Nanjing 210029 Peoples R China Nanjing Hydraul Res Inst Dam Safety Management Dept Nanjing 210029 Peoples R China Minist Water Resources Dam Safety Management Ctr Nanjing 210029 Peoples R China

Featured Application This study provides a novel approach for assessing the long-term safety of the concrete gravity dam on a soft rock foundation. The proposed dynamic inversion method, based on an improved particle swarm optimization algorithm, enables accurate identification of time-dependent parameter deterioration in dam foundations. The proposed method provides practical solutions for real-time dam health monitoring, stability assessment, and maintenance optimization, enabling more reliable safety evaluations and informed engineering *** This study presents a dynamic inversion method for the concrete gravity dam on a soft rock foundation, aiming to accurately characterize the time-dependent trend of the dam's mechanical properties. Conventional static inversion methods often overlook temporal variations in material behavior, particularly the long-term weakening of soft rock foundations under environmental influences. To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. The results reveal an exponential decay in the foundation's elastic modulus (from 4.67 GPa to approximately 3.83 GPa), while the dam body maintains a stable modulus of 20.74 GPa. Comparative analyses demonstrate that the dynamic inversion approach, which accounts for time-dependent parameter degradation, significantly improves the displacement prediction accuracy of the dam. The results highlight the critical importance of incorporating temporal mechanical property variations in inversion analyses to ensure reliable structural assessments and enhance long-term dam safety management.

关键词： dynamic inversion concrete gravity dam soft rock foundation particle swarm algorithm dam displacement

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Bi-dimensional Empirical Mode Decomposition algorithm Based on particle swarm-Fractal Interpolation

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第12期12卷 5955-5977页

作者： An, Feng-Ping He, Xin Huaiyin Normal Univ Sch Phys & Elect Elect Engn Huaian 223300 JS Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 BJ Peoples R China Henan Univ Inst Intelligent Network Syst Kaifeng 475004 Peoples R China

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

关键词： Fractal particle swarm algorithm Bi-dimensional Empirical Mode Decomposition Optimization Image Interpolation

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Investigation into the evolution of airport cement pavement distresses based on association rule mining

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CONSTRUCTION AND BUILDING MATERIALS 2025年 462卷

作者： He, Yinzhang Xiong, Kun Yang, Zeyu Zhang, Jiupeng Li, Yan Hu, Qinshi Li, Yuanfa Huang, Guojing Zhang, Mingliang Tan, Xiaoyong Changan Univ Sch Highway Xian 710000 Peoples R China CAAC Key Lab lntelligent Construct & Maintenance Xian 710064 Peoples R China Northwestern Polytech Univ Sch Civil Aviat Xian 710072 Peoples R China East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Peoples R China

This study endeavors to enhance the comprehension of damage mechanism in airport cement pavements, analyzing patterns to pinpoint strategies that extend the service life of these pavements. First, a distress dataset was established through surveys conducted at five airports in northern China to explore the intrinsic patterns between these distresses. Then, the development and evolution of these distresses were meticulously investigated using an improved Binary particle swarm Optimization-Ant Colony algorithm (BPSO-ACA). The results demonstrated that the improved algorithm outperforms traditional methods, reducing computation time by 38.97 % and 9.85 % compared to the two Apriori algorithms, respectively. The analysis revealed that the evolution of airport cement pavement distresses followed a consistent, interconnected three-stage progression. To prolong the lifespan of airport cement pavements, it is crucial to implement robust lifecycle management, which involves strategic maintenance measures at critical phases of distress evolution to either mitigate or arrest their advancement.

关键词： Airport pavement Distress evolution Association rule mining particle swarm algorithm Ant colony algorithm

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Composite Improved algorithm Based on Jellyfish, particle swarm and Genetics for UAV Path Planning in Complex Urban Terrain

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SENSORS 2024年第23期24卷 7679页

作者： Wang, Qi Yi, Wenjun Nanjing Univ Sci & Technol Natl Key Lab Transient Phys Nanjing 210094 Peoples R China

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

关键词： jellyfish search algorithm particle swarm algorithm genetic algorithm UAV path planning complex urban terrain modelling CEC2005 benchmark functions

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