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Fundamental Relation Between Phased Array Radiation and Scattering Fields

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2023年第9期71卷 3797-3809页

作者： Li, Peng-Fa Qu, Shi-Wei Yang, Shiwen Hu, Jun Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In this article, the scattering mechanism of phased array antennas is systematically analyzed from the perspective of the scattering matrix representation. The scattering problem of an N-element phased array antenna is equivalent to an (N + 1)-port microwave network described by an (N + 1) x (N + 1) scattering matrix. The physical meanings of the coefficients in the matrix are analyzed, which imply different aspects of antenna properties like impedance matching and coupling, receiving, reradiation, and structural mode scattering. The coefficients associated with antenna receiving and reradiation are further studied to reveal the fundamental relation between phased array scattering and radiation fields. Eventually, the formulas to compute phased array scattering fields by radiation fields are deduced. Utilizing the formulas, phased array scattering under different element loads can be predicted precisely and rapidly by the precomputed data of an original phased array rather than repeating time-consuming full-wave computations. Furthermore, based on the fast scattering prediction approach, an efficient modified discrete particle swarm optimization algorithm is proposed to optimize the phased array element loads, and impressive improvement is made against the reported phased array scattering reduction approaches. The proposed scattering prediction and reduction approaches are well validated numerically and experimentally.

关键词： Scattering Phased arrays Microwave antenna arrays Loaded antennas Electromagnetic scattering Antennas Impedance particle swarm optimization algorithm phased array radiation and scattering scattering matrix representation scattering prediction scattering reduction

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Deep optimization design of 2D repetitive control systems with saturating actuators: An adaptive multi-population PSO algorithm

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ISA TRANSACTIONS 2023年 140卷 342-353页

作者： Wang, Yibing Zhang, Manli Lu, Chengda Tian, Shengnan She, Jinhua Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Hachioji 1920982 Japan

This paper presents an optimization design method for a two-dimensional (2D) modified repetitive control system (MRCS) with an anti-windup compensator. Using lifting technology, a 2D hybrid model of the MRCS considering actuator saturation is established to describe the control and learning of the repetitive control. A linear-matrix-inequality (LMI)-based sufficient condition is derived to ensure the stability of the MRCS. Two tuning parameters, the selection of which is critical to the system design, are used in the LMI to adjust the control and learning, and hence the reference-tracking performance. A new cost function, developed through time domain analysis, directly evaluates the control performance of the system without calculating control errors, thus reducing the optimization time. Based on this cost function, an adaptive multi-population particle swarm optimization algorithm is presented to select an optimal pair of tuning parameters in which multiple populations cooperatively search in non-intersecting search intervals. An anti-windup term is added between the low-pass filter and the time delay in the modified repetitive controller to mitigate the undesirable effect of actuator saturation on system performance and stability. Simulations and experiments on the speed control of a rotation control system demonstrate the validity of the approach. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： 2D modified repetitive control particle swarm optimization algorithm Adaptive multi-population framework Anti-windup compensator

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A Clustering Ensemble Method for Cell Type Detection by Multiobjective particle optimization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第1期20卷 1-14页

作者： Liu, Qiaoming Zhao, Xudong Wang, Guohua Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Northeast Forestry Univ Informat & Comp Engn Coll Harbin 150040 Heilongjiang Peoples R China

Single-cell RNA sequencing (scRNA-seq) is a new technology different from previous sequencing methods that measure the average expression level for each gene across a large population of cells. Thus, new computational methods are required to reveal cell types among cell populations. We present a clustering ensemble algorithm using optimized multiobjective particle (CEMP). It is featured with several mechanisms: 1) A multi-subspace projection method for mapping the original data to low-dimensional subspaces is applied in order to detect complex data structure at both gene level and sample level. 2) The basic partition module in different subspaces is utilized to generate clustering solutions. 3) A transforming representation between clusters and particles is used to bridge the gap between the discrete clustering ensemble optimization problemand the continuous multiobjective optimization algorithm. 4) We propose a clustering ensemble optimization. To guide the multiobjective ensemble optimization process, three cluster metrics are embedded into CEMPas objective functions in which the final clustering will be dynamically evaluated. Experiments on 9 real scRNA-seq datasets indicated that CEMP had superior performance over several other clustering algorithms in clustering accuracy and robustness. The case study conducted on mouse neuronal cells identified main cell types and cell subtypes successfully.

关键词： Clustering ensemble RNA-seq single-cell analysis particle swarm optimization algorithm multi-objective optimization

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Test-Case Generation for Data Flow Testing of Smart Contracts Based on Improved Genetic algorithm

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IEEE TRANSACTIONS ON RELIABILITY 2023年第1期72卷 358-371页

作者： Ji, Shunhui Zhu, Shaoqing Zhang, Pengcheng Dong, Hai Yu, Jianan Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Coll Comp & Informat Nanjing 211100 Peoples R China RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia

Smart contracts are commonly deployed for safety-critical applications, the quality assurance of which has been a vital factor. Test cases are standard means to ensure the correctness of data flows in smart contracts. To more efficiently generate test cases with high coverage, we propose an improved genetic algorithm-based test-case generation approach for smart contract data flow testing. Our approach introduces the theory of particle swarm optimization into the genetic algorithm, which reduces the influence brought by the randomness of genetic operations and enhances its capability to find global optima. A set of 30 real smart contracts deployed on Ethereum and GitHub is collected to perform the experimental study, on which our approach is compared with three baseline approaches. The experimental results show that, in most cases, the coverage of the test cases generated by our approach is significantly higher than the baseline approaches with relatively lower numbers of iterations and lower execution time.

关键词： Testing Smart contracts Genetic algorithms Blockchains Genetics Statistics Sociology Data flow testing genetic algorithm particle swarm optimization algorithm smart contract test-case generation

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CSP-free adaptive Kriging surrogate model method for reliability analysis with small failure probability

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 243卷

作者： Li, Wenxiong Geng, Rong Chen, Suiyin South China Agr Univ Coll Water Conservancy & Civil Engn Guangzhou 510642 Peoples R China

In the field of reliability engineering, the active learning reliability method that amalgamates Kriging model and Monte Carlo simulation has been devised and proven to be efficacious in reliability analysis. Nevertheless, the performance of this method is sensitive to the magnitude of candidate sample pool, particularly for systems with small failure probabilities. To surmount these limitations, this paper proposes an active learning method that obviates the need for candidate sample pools. The proposed method comprises two stages: construction of surrogate model and Monte Carlo simulation for failure probability estimation. During the surrogate model construction stage, the surrogate model is iteratively refined based on the representative samples selected by solving the optimization problem facilitated by the particle swarm optimization algorithm. To achieve an optimal balance between solution accuracy and efficiency, the penalty intensity control and the density control for the experimental design points are incorporated to modify the objective function in optimization. The per-formance of the proposed method is evaluated using numerical examples, and results indicate that by leveraging an optimization algorithm to select representative samples, the proposed method overcomes the limitations of traditional active learning methods based on candidate sample pool and exhibits exceptional performance in addressing small failure probabilities.

关键词： Reliability analysis Kriging surrogate model particle swarm optimization algorithm Learning function Candidate sample pool

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Explainable efficient and optimized feature fusion network for surface defect detection

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年 1-18页

作者： Sundarrajan, Kavitha Rajendran, Baskaran Kuttuva Kumaraguru Coll Technol Dept Informat Technol Coimbatore 641049 India Kumaraguru Coll Technol Dept Comp Sci & Engn Coimbatore 641049 India

The quality of the surface and plate form of hot-rolled strip steel, a crucial raw material to produce automobiles, household appliances, and other goods greatly influences the final products that end users make. The identification of surface flaws is crucial to the manufacture of steel strips. Furthermore, typical fault identification techniques have issue of poor detecting reliability, and lower accuracy is obtained by the explainable single pre-trained networks which led to the development of the feature fusion network (FFN). The major objective of the work is to design a traditional deep network model is enhanced by the application of a transfer learning model to detect surface flaws in steel strips. The use of pre-trained models reduces negative effects by drastically reducing training time and improving the accuracy of image classification. Transfer learning models such as VGG16, InceptionV3, and ResNet50 are used to train the Northeastern University-DETection (NEU-DET) Dataset which significantly reduces the time for the training. Generative adversarial network is used for data augmentation to increase the input images. An explainable artificial intelligence (XAI) classifier is applied to the pre-trained networks to understand the classification of the surface defects. A hybrid FFN (HFFN) is proposed which combines the features of pre-trained networks (VGG16, InceptionV3, and ResNet50) to accurately classify flaws in the hot-rolled strips surface. To reduce the features in the HFFN, particle swarm optimization (PSO) algorithm (PFFN) is used. On the NEU-DET, FFN by three-pre-trained model achieves 98.65%, 98.42%, 98.51%, and 98.54% for precision, recall, f-score, and accuracy respectively.

关键词： Hot-rolled strip steel Transfer learning Deep learning model Feature fusion network (FFN) Vgg16 Inceptionv3 Resnet50 Feature extraction Image classification Explainable artificial intelligence (XAI) particle swarm optimization algorithm

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Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第32期30卷 78849-78864页

作者： Liu, Bo Chang, Haodong Li, Yan Zhao, Yipeng Chengdu Univ Technol Coll Management Sci Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Math & Phys Chengdu 610059 Peoples R China

The "14th Five-Year Plan" period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the " double carbon"). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization-extreme learning machine (PSO-ELM) model together. Based on the PSO- ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing's low-carbon development during the 14th Five-Year Plan period.

关键词： Carbon emissions Gray correlation method particle swarm optimization algorithm Extreme learning machine model PSO-ELM combined prediction model Decoupling analysis

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Investigation on dynamic mathematical model and control method of flue gas heat exchange system

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ENGINEERING COMPUTATIONS 2023年第2期40卷 434-456页

作者： Ma, Hongqiang Xie, Yue Song, Xingpeng Liu, Yu Luo, Xinmei Wang, Shengxun East China Jiaotong Univ Nanchang Peoples R China Lanzhou Univ Technol Lanzhou Peoples R China

PurposeThe purpose of this paper is to recover the waste heat of flue gas heat exchanger (FGHE) as efficiently as possible and avoid the acid dew corrosion of ***/methodology/approachA novel flue gas waste heat recovery system was proposed in the paper. The dynamic mathematical models of key equipment in that were established based on theory and experiment method. The proportion integration differentiation-differentiation (PID-P) cascade control method based on particle swarm optimization algorithm was used to control the outlet temperature of FGHE. The dynamic characteristics of the flue gas heat exchange system were simulated by the particle swarm optimization algorithm with different fitness *** PID-P temperature controller parameters can be quickly and effectively obtained by the particle swarm optimization algorithm based on the fitness function of integral time absolute error (ITAE). The overshoot, rise time and adjusting time of the novel system are 2, 83 and 105s, respectively. Compared with the traditional two-step tuning (T-ST) method, the novel system is better in dynamic and steady-state performance. The overshoot and the adjustment time of the system are reduced by 44% and 328s, respectively. ITAE is a performance evaluation index for control system with good engineering practicability and ***/valueThe dynamic mathematical model of key equipment in the new flue gas waste heat recovery system is established and the system's control strategies and methods are explored.

关键词： Dynamic mathematical model The PID-P cascade Control particle swarm optimization algorithm Performance evaluation index

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High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter optimization Model of a Cascaded Stochastic Resonance System

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SENSORS 2023年第9期23卷 4429-4429页

作者： Lai, Zhihui Huang, Zhangjun Xu, Min Wang, Chen Xu, Junchen Zhang, Cailiang Zhu, Ronghua Qiao, Zijian Shenzhen Univ Coll Mechatron & Control Engn Shenzhen Key Lab High Performance Nontradit Mfg Shenzhen 518060 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Guangdong Key Lab Electromagnet Control & Intellig Shenzhen 518060 Peoples R China China Tobacco Zhejiang Ind Co Ltd Ningbo Cigarette Factory Ningbo 315040 Peoples R China Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Ningbo Univ Sch Mech Engn & Mech Ningbo 315211 Peoples R China

Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, a global parameter optimization (GPO) model of a cascaded SR system is proposed in this work. The cascaded SR systems, which involve multiple multi-parameter-adjusting SR systems with both bistable and tri-stable potential functions, are first introduced. The fixed-parameter optimization (FPO) model and the GPO models of the cascaded systems to achieve optimal SR outputs are proposed based on the particle swarm optimization (PSO) algorithm. Simulated results show that the GPO model is capable of achieving a better SR output compared to the FPO model with rather good robustness and stability in detecting low signal-to-noise ratio (SNR) weak signals, and the tri-stable cascaded SR system has a better weak signal detection performance compared to the bistable cascaded SR system. Furthermore, the weak fault diagnosis approach based on the GPO model of the tri-stable cascaded system is proposed, and two rolling bearing weak fault diagnosis experiments are performed, thus verifying the effectiveness of the proposed approach in high-performance adaptive weak fault diagnosis.

关键词： weak fault diagnosis stochastic resonance cascaded system global parameter optimization particle swarm optimization algorithm

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Building a consensus for the best-worst method in group decision-making with an optimal allocation of information granularity

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INFORMATION SCIENCES 2023年 619卷 630-653页

作者： Qin, Jindong Ma, Xiaoyu Liang, Yingying Wuhan Univ Technol Sch Management Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Res Ctr Data Sci & Intelligent Decis Making Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Res Inst Digital Governance & Management Decis Inn Wuhan 430070 Hubei Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Sichuan Peoples R China Beijing Inst Technol Sch Management Beijing 100081 Peoples R China

In a multiple-criteria group decision-making process, it may be unrealistic to assume that all experts are specialized in all aspects of the entire problem and can reach full agreement. To obtain a suitable result with more flexibility and robustness in group decision-making, this study concerns the introduction of information granules to the best-worst method model, regarded as an essential design asset to reach a consensus in a group decisionmaking scenario. More specifically, each pairwise comparison is performed through information granules instead of single numbers, by using an optimal allocation of information granularity. Further, we develop an optimization model that considers both consistency and consensus in one problem. The particle swarm optimization algorithm is used as an optimization method to find the optimal particle of the granular decision model. Furthermore, a new convergent iterative algorithm is developed to obtain the desired decision matrix. Next, an experimental study is presented to demonstrate the effectiveness, flexibility, and essence of the proposed model. Finally, a case study of *** is conducted to select suitable automobiles for a company based on online reviews. The solving procedures are demonstrated, to further illustrate the feasibility of the proposed method.

关键词： Multiple-criteria group decision making Best-worst method Information granules particle swarm optimization algorithm Consensus reaching process

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