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哈尔滨工程大学学报（英文版） 2023年第4期22卷 837-849页

作者：曲金博冯永明吴云金朱元清邬斌扬肖忠旭 College of Power and Energy Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this ***,a 0-D model of SOFC and a 1-D model of ICE are built as agent ***,parameter analysis of the system is conducted based on SOFC and ICE *** show that the number of cells,current density,and fuel utilization can influence SOFC and ***,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this *** results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power ***,promising improvements in efficiency of 5.1%are achieved compared to the original ***,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.

关键词： Combined system SOFC-ICE integrated cycle Data-driven model particle swarm optimization algorithm

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Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Feng, Hao Jiang, Jinye Ding, Nan Shen, Fangping Yin, Chenbo Cao, Donghui Li, Chunbiao Liu, Tao Xie, Jiaxue Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Wuxi Cosmo Suspended Platform Co Ltd Wuxi 214128 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Changwang Sch Honors Nanjing 210044 Peoples R China Nanjing Tech Univ United Inst Excavator Key Technol Nanjing 211816 Peoples R China SANY Grp Co Ltd Suzhou 215300 Peoples R China

Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.

关键词： Excavator Hydraulic system Trajectory planning particle swarm optimization algorithm

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Traveling wave inversion method of power line faults

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期149卷

作者： Xia, Yixiang Li, Zewen Wang, Shuai Sun, Jian Guo, Xinyu Changsha Univ Sci & Technol Elect & Informat Engn Dept Changsha Peoples R China

In substations, the fault signal at the measuring point includes a variety of signal aliasing. This is partly due to the short length of the substation lines and the exitance of multiple types of equipment that cause the reflection and refraction of the traveling waves. Compared with the incident wave, the distortion in the fault signal is often significant hence interfering with the fault identification and location. To address this issue, we propose an inversion method of the incident wave. To do this, we first investigate the impact of multiple outlets of the substation bus, equivalent stray capacitance of the bus to the ground, transformers, line trap, and other types of equipment on traveling wave transmission. Modeling the aliasing effect of the traveling wave measuring point signal we then propose a fault signal filtering algorithm based on the improved Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT). Using this algorithm we then formulate an incident wave inversion model based on substation component parameters. The component parameters in the equivalent circuit model of substation are then accurately identified to achieve an accurate inversion of the incident waves. Identification of parameters is based on an optimization model of substation component parameters based on the particle swarm optimization algorithm (PSO). Our results show that compared with the measured mixed wave signal, the fault characteristics of the inverted incident waves are more accurate, hence improving the fault location accuracy.

关键词： Waveform inversion Incident wave Substation particle swarm optimization algorithm

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A tristable nonlinear energy sink

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2023年 238卷

作者： Zeng, You-cheng Ding, Hu Shanghai Univ Shanghai Inst Appl Math & Mech Sch Mech & Engn Sci Shanghai Key Lab Mech Energy Engn Yangchang Rd 149189 Shanghai 200444 Peoples R China

The tristable characteristics are mostly used in the field of energy harvesting and vibration isolator, but are rarely devoted to in the field of vibration absorption. In this paper, a tristable nonlinear energy sink (TNES) model is proposed for vibration absorption. The TNES is constructed by fixing a magnet in the middle of the clamping pre -pressure beam, and setting a magnet on both sides of the fixed magnet. By adjusting the magnet position, the proposed TNES can be transformed into bistable NES (BNES) and monostable NES (MNES). The TNES is coupled with the linear oscillator (LO), and the differential equation of motion of the coupled system is obtained with the Hamiltonian principle. The approximate analytical method and the numerical method are mutually verified. The vibration reduction efficiency of the TNES under harmonic excitation is presented. The influence of the pa-rameters of the TNES on the dynamics and vibration reduction efficiency is studied. The optimal parameters of the TNES are obtained by the particle swarm optimization (PSO) algorithm. Compared with the corresponding bistable NES and monostable NES, it shows that the tristable NES has obvious advantages in vibration sup-pression efficiency. The effects of the TNES barrier depths and the excitation amplitude on the vibration reduction efficiency are revealed. The TNES can dissipate energy through chaotic inter-well oscillation between three stable positions, and achieve the purpose of effectively suppressing vibration.

关键词： Nonlinear energy sink Tristable Vibration control Harmonic balance method particle swarm optimization algorithm

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Design and optimization of Personalized Education System Based on Intelligent algorithms

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Procedia Computer Science 2024年 243卷 514-522页

作者： Jie Zhu Yahong Liao Danni Lu Wuhan Railway Vocational College of Technology Wuhan 430205 Hubei China Xiamen Jin Shang Middle School Xiamen 361000 Fujian China Hubei Vocational College of Railway Transportation Wuhan 430070 Hubei China

The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.

关键词： Intelligent algorithm Decision Tree algorithm Personalized Education System particle swarm optimization algorithm

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An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第3期10卷 841-850页

作者： Qian, Bin Wang, Qian-Qian Hu, Rong Zhou, Zhi-Jie Yu, Chuan-Qiang Zhou, Zhi-Guo Kunming Univ Sci & Technol Coll Informat Engn & Automat Dept Automat Kunming 650500 Yunnan Peoples R China Rocket Force Univ Engn Xian 710025 Shanxi Peoples R China High Tech Inst Xian Xian 710025 Shanxi Peoples R China Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA

This paper proposes a soft computing technology based on belief rule base (BRB) system for the tipping paper permeability measurement in tobacco factory. In current studies about BRB, both the referential values of the antecedent attributes and the utilities of the consequents are given in advance and are not trained by using the dedicated optimal algorithms. The limitations of expert knowledge may lead to error of BRB because both the referential values and the utilities make a real difference on the structure of BRB, and the appropriate structure is helpful for tuning parameters more accurately. Therefore, this paper focuses on the structure and parameters optimization of BRB (SPO-BRB) by taking the referential values of the antecedent attributes and the utilities of the consequents into account to improve the input-output modeling ability of BRB. However, SPO-BRB is a nonlinear nonconvex optimization problem (NNOP). To deal with the NNOP of SPO-BRB, a particle swarm optimization algorithm with improved velocity update way and repair methods (PSO_VR) is proposed. A case study based on the data collected from a tobacco factory of china is carried out. The test results demonstrate the functionality of SPO-BRB and the effectiveness of PSO_VR.

关键词： Soft computing technology Belief rule base Parameter and structure identification particle swarm optimization algorithm Porosity measuring

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Logistics Distribution Location Based on particle swarm optimization algorithm

Logistics Distribution Location Based on Particle Swarm Opti...

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World Automation Congress (WAC)

作者： Chen, Xichun Wang, Junli Lanzhou Jiaotong Univ Sch Traff & Transportat Engn Lanzhou 730070 Peoples R China Gansu Prov Supervis & Inspect Ctr Product Qual Lanzhou Peoples R China

ISBN: (纸本)9781467344975

The aim of logistics distribution center location is to improve the logistics service quality and reduce the transportation cost of goods. The problems are related to both the classical location problem and the vehicle routing problem. This paper proposes logistics distribution center location based on particle swarm optimization algorithm. It models the logistics distribution center location problem considering the user demand and operating cost, then it uses modified particle swarm optimization algorithm to give solution. Experimental result shows that the proposed model and algorithm is effective.

关键词： distribution center location particle swarm optimization algorithm optional point

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particle swarm optimization-Based Deep Neural Network for Digital Modulation Recognition

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IEEE ACCESS 2019年 7卷 104591-104600页

作者： Shi, Wenzhe Liu, Dejun Cheng, Xing Li, Yang Zhao, Yang China Univ Petr Coll Informat Sci & Engn Beijing 102249 Peoples R China

Modulation recognition is a major task in many wireless communication systems including cognitive radio and signal reconnaissance. The diversification of modulation schemes and the increased complexity of the channel environment put higher requirements on the correct identification of modulated signals. Deep learning (DL) is considered as a potential solution to solve these problems due to the superior big data processing and classification capabilities. This paper proposes an efficient digital modulation recognition method based on deep neural network (DNN) model. Furthermore, we present the particle swarm optimization (PSO) algorithm to optimize the number of hidden layer nodes of the DNN so as to solve the problem that the traditional DNN is trapped in local minimum values and the number of hidden layer nodes needs selecting manually. In this paper, we utilize the proposed PSO-DNN method to learn characteristics extracted from the modulated signal added by additive white Gaussian noise (AWGN) and to train the network, which can improve the performance of recognition under the condition of low signal-to-noise ratio (SNR). The experimental results demonstrate that the recognition rate on this algorithm has improved by 9.4% and 8.8% compared with methods that adopt conventional DNN and support vector machine (SVM) when SNR equals 0 and 1 dB, respectively. Besides, another experiment compared with the genetic algorithm (GA) also proves that our proposed algorithm is more effective in optimizing the DNN. The proposed method is easy to be implemented so that it has a broad development prospect in modulation recognition.

关键词： Additive white Gaussian noise deep neural network digital modulation recognition particle swarm optimization algorithm

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General metaheuristic-based methodology for computation and decomposition of LMPs

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ELECTRICAL ENGINEERING 2021年第2期103卷 793-811页

作者： Saraiva, Felipe Oliveira Silva Paucar, Vicente Leonardo Fed Univ Maranhao UFMA Dept Elect Engn BR-65085580 Sao Luis MA Brazil

Locational marginal prices (LMPs) are economic signals paramount to the deliberations, policy designs, and planning judgments of most electricity markets. This paper presents a general metaheuristic-based methodology applied in the scope of two subproblems to compute and decompose the LMPs considering the incorporation of a distributed slack bus model in the power flow formulation. The deterministic core of the methodology is governed by a process of sequential setting of maximum number of metaheuristic iterations based on load disharmony indices (LDIs) in the context of the initial subproblem. The metaheuristic crux of the methodology is anchored in a random initialization strategy based on time-traveling parameters in the environment of both subproblems. In the introductory arrangement of the optimization sequence, the chain order of the operating horizon periods is defined based on the LDIs. Due to its notorious merits and the breadth of its applications in power system problems, a particle swarm optimization (PSO) algorithm model is used to solve the mentioned subproblems. For the practical purposes of this paper, in the PSO algorithm model instance the particle coordinates are adopted as time-traveling metaheuristic parameters. Numerical simulations on a 3-bus system and on the IEEE 30-bus test system corroborate the efficiency and adequacy of the proposed methodology.

关键词： Electricity market LMP decomposition Load disharmony index Locational marginal price particle swarm optimization algorithm

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Spatial accessibility analysis and location optimization of emergency shelters in Deyang

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GEOMATICS NATURAL HAZARDS & RISK 2023年第1期14卷

作者： Zhang, Zuopei Hu, Yunfeng Lu, Wei Cao, Wei Gao, Xing Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing Peoples R China

The selection and planning of the location of emergency shelters have a crucial impact on the safety of residents and cities. In this paper, based on multivariate open geographic data, the Gaussian two-step floating catchment area method, K-means clustering, and particle swarm optimization algorithm methods are utilized to carry out a spatial accessibility analysis and location optimization of emergency shelters in Deyang City, Sichuan Province, China. The study shows that: (1) Deyang City's emergency shelters are higher than the government's relevant standard requirements in terms of major indicators such as single building area, total area and per capita shelter area. (2) The spatial distribution of emergency shelters in the study area is uneven and unreasonable, with accessibility from the urban center outwards, exhibiting a "high-low" distribution pattern. (3) The study suggests that 10 new emergency shelters can reduce the number of accessible blind areas by 43.31%. The study recommends that the assessment and construction of emergency shelter facilities in rural areas in China and globally should be emphasized. Reliable recommendations for improvement of emergency shelter planning in Deyang city are provided in the study results.

关键词： Spatial accessibility Gaussian two-step floating catchment area method K-means clustering particle swarm optimization algorithm location optimization

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