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Parameter optimisation of Wavelet Denoising for Pulsed Eddy Current Signals Based on particle swarm optimisation algorithm

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第5期39卷 1210-1224页

作者： Shao, Qianqiu Fan, Songhai Liu, Fenglian State Grid Sichuan Elect Power Res Inst Power Transmiss Transformat & Distribut Technol Ct Chengdu Peoples R China

To ensure the reliable operation of high voltage cables, it is crucial to routinely inspect the lead sealing using pulsed eddy current. But nevertheless, the signals can be impacted by noise in actual engineering. While retaining the useful high-frequency information in the original signal, wavelet denoising can filter the clutter efficiently. However, previous research on wavelet denoising has only used it as a straightforward filtering tool, ignoring the impact of changing the parameters on the actual denoising effect. In this study, a more accurate evaluation index called P-NCC is constructed for pulsed eddy current testing signals with a focus on maintaining the peak information. This index is then adopted as a fitness function for the particle swarm optimisation algorithm to determine the befitting wavelet denoising parameters and denoised signals. The results show that the signal-to-noise ratio of the signals after denoising is approximately 25 dB, and the distortion at the peak position is stable at about 1%. It demonstrates that the comprehensive index P-NCC is a reliable metric for assessing the quality of pulsed eddy current signals. The optimal wavelet denoising parameters for pulsed eddy current signals are found to be the Sym 4 wavelet, 10-layer decomposition and Median threshold function.

关键词： Lead sealing pulsed eddy current wavelet denoising particle swarm optimisation algorithm

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A NEW HYBRID WHALE particle swarm optimisation algorithm FOR ROBOT TRAJECTORY PLANNING AND TRACKING CONTROL

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MECHATRONIC SYSTEMS AND CONTROL 2024年第1期52卷 48-57页

作者： Zhang, Huakai Yancheng Teachers Univ Logist Support Grp Yancheng 224000 Peoples R China

In recent years, the use of robotic arms in the automation industry has become increasingly prevalent due to advances in science and technology. This paper aims to enhance the control accuracy of manipulators by designing a hybrid optimisation algorithm. The whale algorithm has been optimised and combined with the particle swarm optimisation (PSO) algorithm to achieve this goal. The trajectory of the manipulator is planned through the utilisation of the hybrid algorithm. The membership function of the fuzzy controller is optimised via the hybrid whale PSO algorithm, and combined with the sliding-mode controller, resulting in the design of a fuzzy sliding-mode controller. The results show that in the convergence curve, the iterations for the hybrid algorithm to reach the global optimal solution is 52, 75, and 183. The rate of convergence is faster. In the joint angle change curve, the total time of optimised B-spline interpolation is 19.95 s. The working efficiency of the manipulator is improved. In the calculation of tracking error, the optimised controller has a tracking error between-0.25 and 0.25, resulting in higher trajectory tracking accuracy. Therefore, the hybrid algorithm has good convergence, and the optimised controller has high accuracy, providing good technical support for trajectory planning and tracking control of robotic arms.

关键词： Robotic arm whale algorithm particle swarm optimisation algorithm fuzzy sliding-mode controller trajectory planning tracking control

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Determine the most effective process control parameters on activated sludge based on particle swarm optimisation algorithm (Case Study: South wastewater treatment plant of Tehran)

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY 2022年第19期102卷 7595-7616页

作者： Rafati, Mina Pazouki, Mohammad Ghadamian, Hossein Hossein Nia, Azarmidokht Jalilzadeh, Ali Mat & Energy Res Ctr Res Dept Energy Karaj Iran Arman Tadbir Palayesh Consulting Engineer Co Tehran Iran

Wastewater treatment plants (WWTPs) are major energy consumers and cause environmental impact on receiving waters. Many WWTPs are operated in a less-than-optimal manner with respect to both treatment and energy efficiency. A better solution is to optimize the operation and processes of the existing WWTPs. Therefore, the most effective process parameter for providing control strategies was *** South WWTP of Tehran was designed and simulated based on the activated sludge model using MATLAB/ Simulink software for the first time in the country to obtain the factors affecting WWTP. To calibrate this model some kinetic and stoichiometric coefficients were determined. Then the simulator validation was performed by three error calculation methods. Finally, the values of three main controllers, including the optimum rate or return activated sludge (RAS), internal recycle (IR) and oxygen transfer coefficient (KLa) are determined using the particle swarm Optimization algorithm. According to the results, coefficients such as Y, kd, K and KS for Oxidation-Ditch process were in the range of 0.303-0.331mgVSS /mg sCOD, 0.030-0.033 1/day, 1.65-1.93 1/day and 37.6-44.92 mg sCOD/l, respectively and the Mean of COD, BOD5, TSS and TN removal was obtained 94.8 +/- 0.4, 97.3 +/- 0.65, 94.7 +/- 1.5 and 56 +/- 7.46, respectively. Also, the percentage of Root Mean Square for TN, TSS, COD, BOD5 was 3.14%, 2.95%, 3.13% and 5.2%, respectively, Pearson correlation coefficient was 0.88, 0.93, 0.89, 0.99 and absolute mean error of 2.62%, 2.47%, 2.6% and 3.58%, respectively, which shows that the simulation outputs are compatible with the effluent of plant. To achieve the best process performance conditions, RAS and IR must be considered for 1.7 and 0.8 percent of influent wastewater and KLa is suggested to be 154 d(-1). Therefore, by using the optimization performed, the effect of other controllers on the process can be investigated and selected.

关键词： Simulation activated Sludge Model No. 1 internal Recycle Flow return Activated Sludge oxygen transfer coefficient particle swarm optimisation algorithm

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Hybrid firefly particle swarm optimisation algorithm for feature selection problems

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EXPERT SYSTEMS 2024年第7期41卷 e13363-e13363页

作者： Ragab, Mahmoud King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah Saudi Arabia King Abdulaziz Univ Ctr Excellence Smart Environm Res Jeddah Saudi Arabia King Abdulaziz Univ Univ Oxford Ctr Artificial Intelligence Precis Med Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah Saudi Arabia

Feature selection techniques play a vital role in the processes that deal with enormous amounts of data. These techniques have become extremely crucial and necessary for data mining and machine learning problems. Researchers have always been in a race to develop and provide libraries and frameworks to standardise this procedure. In this work, we propose a hybrid meta-heuristic algorithm to facilitate the problem of feature selection for classification problems in machine learning. It is a python based, lucid and efficient algorithm geared towards optimising and striking a balance between the number of features selected and accuracy. The proposed work is a binary hybrid of existing meta-heuristic algorithms, the particle swarm optimisation (PSO) algorithm, and the firefly algorithm (FA) such that it blends the best of each algorithm to provide an optimised and efficient way of solving the said problem. The suggested approach is assessed against six datasets from different domains that are publicly available at the UCI repository to demonstrate its validity. The datasets are Breast cancer, Iris, WBC, Mushroom, Glass ID, and Abalone. This approach has also been evaluated against similar, such evolutionary-based approaches to prove its superiority. Various metrics such as accuracy, precision, recall, f1 score, number of selected features, and run time have been analysed, measured, and compared. The hybrid firefly particle swarm optimisation algorithm is found to be suitable for feature selection problems.

关键词： classification evolutionary algorithm feature selection firefly algorithm hybrid algorithm nature-inspired particle swarm optimisation algorithm

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Parameter calibration method of clustered-particle logic concrete DEM model using BP neural network-particle swarm optimisation algorithm (BP-PSO) inversion method

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ENGINEERING FRACTURE MECHANICS 2023年 292卷

作者： Pan, Xupeng Niu, Yanwei Zhao, Yu Huang, Pingming Wu, Yizhen Chang An Univ Sch Highway Xian 710064 Shaanxi Peoples R China Chang An Univ Coll Future Transportat Xian 710064 Shaanxi Peoples R China

A high precision back propagation neural network-particle swarm optimization (BP-PSO) algorithm inversion method is proposed to calibrate the microparameters of the clustered-particle logic concrete discrete element method model. The calibration targets include initial damage, failure mode, and mechanical properties of the material. The research utilise 243 training datasets generated through orthogonal experimental design and conducted simulations to train the BP neural network. In addition, a parameter sensitivity analysis is employed on the trained BP neural network to quantify the impact level of each microparameter and guide future macroparameter fine-tuning. The results indicate that the mean absolute percentage error of the BP-PSO inversion method is only 3.79%. This research also study on the concrete failure mechanism by using a mesoscale model based on clustered-particle logic, which considered concrete as a three-phase composite composed of mortar matrix, aggregates, and interfacial transition zone. The crack initiation, propagation and coalescence of DEM model show a good agreement with the experimental results.

关键词： Discrete element method Initiation damage BP neural network particle swarm optimisation algorithm Parameter sensitivity analysis Concrete failure mechanism

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Global Source optimisation Based on Adaptive Nonlinear particle swarm optimisation algorithm for Inverse Lithography

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2021年第4期13卷 1页

作者： Sun, Haifeng Du, Jing Jin, Chuan Feng, Jinhua Wang, Jian Hu, Song Liu, Junbo Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Source optimisation (SO) is an approved approach to improve the imaging quality in inverse lithography techniques. It is critical to apply an optimisation approach with high convergence efficiency and minimum errors in pixel-based SO. To improve the convergence efficiency of the pixel-based SO, a route of particle swarm optimiser (PSO) combined with the adaptive nonlinear control strategy (ANCS) is proposed in this study. As a global optimisation algorithm, ANCS-PSO has the attributes of breaking away from the local optimum by adjusting the particle learning factor adaptively. In addition, the nonlinear control approach can broaden the search range and speed up the convergence of the iteration operation. The proposed approach also is compared with the linear decreasing inertia weight strategy and the simulated annealing strategy. The performance verification simulation displays the validity of PSO-ANCS and its potentials in SO with high convergence efficiency and optimisation capacity, by comparing the linear decreasing inertia weight strategy and the simulated annealing strategy.

关键词： Source optimisation particle swarm optimisation algorithm adaptive nonlinear control strategy inverse lithography techniques

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Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第4期10卷 275-282页

作者： Sadeghiram, Soheila Univ Mohaghegh Fac Engn Dept Informat Technol Ardabili Iran

Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimisation problems, and granted results have been achieved. particle swarm optimisation (PSO) algorithm is one of the most utilised algorithms in recent years, which has indicated acceptable efficiency. On the other hand, bacterial foraging optimisation algorithm (BFOA) is relatively new compared to other meta-heuristic algorithms, and like PSO has shown a good ability to solve different optimisation problems. Genetic algorithms (GAs) are a well-known group of meta-heuristic algorithms which have been in use earlier than the other in various research fields. In this paper, we compare the efficiency of BFOA and PSO algorithms in an identical condition by minimising different test functions (from two to 20 dimensional). In this experiment, GA is used as a basic method in comparing the two algorithms. The methodology and results are presented. Although results verify the accurate convergency of both algorithms, the efficiency of BFOA on high-dimensional functions is dramatically better than that of PSO.

关键词： particle swarm optimisation algorithm bacterial foraging optimisation algorithm BFOA genetic algorithms high-dimensional functions

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An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第3期45卷 337-350页

作者： Hu, Yifan Ding, Yongsheng Hao, Kuangrong Ren, Lihong Han, Hua Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China Key Lab Comp Network Shandong Prov Jinan 250014 Peoples R China

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.

关键词： wireless sensor networks mobile sink routing recovery orthogonal learning strategy immune algorithm particle swarm optimisation algorithm

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particle swarm algorithm for Smart Contract Vulnerability Detection Based on Semantic Web

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2024年第1期20卷 1页

作者： Feng, Tao Cui, Yuyang Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

In recent years, smart contracts have risen rapidly in the blockchain field, but security issues have also become increasingly prominent. Due to the lack of unified evaluation standards, the security analysis of smart contracts mainly relies on complex and not easily scalable expert rules. To address these issues, we employ slicing techniques to reduce the interference of extraneous code on the detection process, apply normalisation techniques to eliminate the differences between different compiler versions and use particle swarm optimisation algorithms to determine the similarity between contracts, thus improving the accuracy and efficiency of detection. In addition, we combine a variety of features such as static analysis, dynamic analysis and symbolic execution to gain a more comprehensive understanding of contract characteristics and behaviours for more accurate vulnerability identification. Experimental results show that the scheme significantly improves the detection capability and provides a new solution for the security detection of smart contracts.

关键词： Graph Embedding algorithm Multimodal Feature Fusion particle swarm optimisation algorithm Smart Contracts Vulnerability Detection

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optimisation method of residential building energy conservation in hot summer and cold winter areas: particle swarm optimisation

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2025年第3期47卷 302-321页

作者： Cao, Wen Hunan City Univ Design & Res Inst Co LTD Yiyang Hunan Peoples R China

In this paper, an optimisation method of residential building energy conservation in hot summer and cold winter areas based on particle swarm optimisation algorithm is studied. First, considering the influence of external and internal factors of the residential environment and the change of energy consumption, the energy-conservation parameters of residential buildings are selected. Then, the particle swarm optimisation algorithm is introduced to build the optimisation model of building energy conservation, and the optimisation results are corrected by inertia weight to complete the design. The test results show that the energy consumption of this method is 2796 KWh, the correlation coefficient is higher than 0.95, and the optimisation time is 1.27 s. This method can effectively reduce the energy consumption of residential buildings, and the optimisation speed is faster.

关键词： particle swarm optimisation algorithm hot summer and cold winter areas residential building energy-saving optimisation particle fitness

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