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UK, Europe, China Millimeter Waves and THz Technology Workshop (UCMMT)

作者： Zhijie Sun Weiye Zhong Liu Cong Weiping Qin Nanjing University of Posts and Telecommunications Nanjing China CAS Key Laboratory of Radio Astronomy Nanjing Jiangsu China CAS Shanghai Astronomical Observatory Shanghai China

ISBN: (数字)9781538674970

ISBN: (纸本)9781538674987

A high performance wideband quad-ridged flared horn (QRFH) feed has been designed which operates from 3.9 -15.3 GHz. In order to find the balance between spillover efficiency and aperture efficiency to ensure the high sensitivity, spline-profiled ridges and horn have been optimized with the help of particle swarm optimization (pso) algorithm. Through computation and optimization, the average sensitivity of the QRFH is greater than 4.6 m 2 /K, the average aperture efficiency is more than 65% and the return losses of two port are greater than 14 dB within the working frequency band.

关键词： QRFH pso algorithm splines high sensitivity high aperture efficiency aperture efficiency splines (mathematics) RETURN LOSS feed horn Horns parametric subharmonic oscillator broadband Frequency bands HIGH SENSITIVITY

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Numerical Investigation on the Transmission Loss of Skin Panels Based on the Intelligent pso-CGA algorithm

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IEEE ACCESS 2018年 6卷 26121-26130页

作者： Zhang, Hui Xu, Guo-Liang Yin, Wei Wang, Hong-Bo Ge, Peng Beijing Inst Struct & Environm Engn Beijing 100076 Peoples R China

In the reported research, they only analyzed the impacts of restraint damping parameters on acoustic characteristics of skin panels, but failed to adopt advanced optimization algorithms to conduct an optimization design on restraint damping effects. In addition, they did not conduct further research on noises in cabins with skin panels as the basis. Aiming at these questions, according to the acoustic model and the pso-CGA algorithm, the acoustic characteristics of the skin panel were conducted a multi-objective optimization, and a better structure with low noise and light weight was finally obtained. The computational model was also validated by its experimental test. In order to verify the effectiveness of the pso-CGA algorithm after parameter selection, it was compared with the traditional CGA and pso algorithm. Three kinds of algorithms adopted the same population to conduct a multi-objective optimization of transmission loss of skin panels. Optimized results showed that: the pso-CGA model in the paper had a high clustering degree. Transmission loss of most iteration points was obviously more than those of other two kinds of algorithms. Noises in the cabin could be improved effectively.

关键词： Skin panels transmission loss CGA algorithm pso algorithm novel pso-CGA algorithm

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Study on Resources Scheduling Based on ACO algorithm and pso algorithm in Cloud Computing

Study on Resources Scheduling Based on ACO Algorithm and PSO...

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第11届分布式计算及其应用国际学术研讨会

作者： Xiaotang Wen Minghe Huang Jianhua Shi School of Software Jiangxi Normal University

It improves the algorithm because of the shortcoming that the ACO algorithm is easy to fall into local optimal solution in the cloud computing resource scheduling. The improved algorithm makes particle optimization inosculated into ant colony algorithm, which first finds out several groups of solutions using ACO algorithm according to the updated pheromone, and then gets more effective solutions using pso algorithm to do crossover operation and mutation operation so as to avoid the algorithm prematurely into the local optimal solution.

关键词： ACO algorithm pso algorithm Cloud Computing Resources Scheduling

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Thermal circuit model parameters identification of oil-immersed transformer based on pso algorithm

Thermal circuit model parameters identification of oil-immer...

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2015 4th International Conference on Mechatronics,Materials,Chemistry and Computer Engineering(ICMMCCE 2015)

作者： Linli Zhang Lisheng Li Bin Jiang Rong Li Hongmei Li Xiao Dong State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power Company

ISBN: (纸本)9781510817371

The thermal circuit model of oil-immersed transformer needs to improve its accuracy in predicting the winding hottest-spot temperature, especially in the case of overload and different cooling modes. In the case of overload condition, the estimate values obtained from most existing models are smaller than the actual measurement values, which increase the potential of transformer overheating fault because of the estimate shortage. These limitations are mainly due to the thermal circuit parameters which are deviating when overloaded or having different cooling modes. In order to overcome these limitations, a parameter identification and correction approach based on pso algorithm is proposed. The approach works on daily load pattern, permits better accuracy and improves the estimation security margin of thermal circuit in the presence of overload condition and different cooling modes.

关键词： power transformer hot spot thermal circuit model pso algorithm parameter identification

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Tolerance Optimization Design based on Physical Programming Methods and pso algorithm

Tolerance Optimization Design based on Physical Programming ...

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The 2011 International Conference on Smart Materials and Nanotechnology in Engineering (ICSMCE2011)

作者： H.B.Qiu Y.Y.Dong Y.Wang L.Gao The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

Product tolerance is one of the key factors which can determine the good or bad performance of mechanical productsIts size not only affects the manufacturing and assembly process, but also affects product features [1]Thus tolerance optimization design gets more and more attentionIn this paper, an improved physical programming method is used to make mathematical modeling for tolerance allocation problem of assembly dimensional chain, and pso algorithm is also used to improve solving abilityAnd the effective solution for tolerance optimization is designed.

关键词： Tolerance optimization Tolerance allocation Physical programming Assembly dimension chain pso algorithm

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A self-adaptive harmony pso search algorithm and its performance analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第21期42卷 7436-7455页

作者： Zhao, Fuqing Liu, Yang Zhang, Chuck Wang, Junbiao Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech Xian 710072 Peoples R China

Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHpsoS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the pso algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHpsoS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHpsoS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHpsoS outperforms original HS and the other related algorithms in terms of the solution quality and the stability. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Harmony Search algorithm pso algorithm Self-adaptive scheme Mutation strategy Markov model

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Study on Improved Expert PID Controller Based on pso algorithm for Maglev Transportation System with Time Delay

Study on Improved Expert PID Controller Based on PSO Algorit...

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2014 International Conference on Mechanical Design, Manufacture and Automation Engineering(MDMAE 2014)

作者： Rong-rong SONG Zi-li CHEN Wei-hua MA College of Electrical Engineering Southwest Jiaotong University College of Computer Science and Technology Southwest University for Nationality College of Maths Southwest Jiaotong University Traction power state key laboratory Southwest Jiaotong University

Time delay is one of the most important aspects of the suspension control performance impact in maglev transportation system(MTS).By using the traditional PID control,the problem is that it is difficult to confirm the PID parameters and have long settling time,even appear the chaotic ***,using Expert PID(EPID)control method produced over-fitting to initial assignments,local optimum induced easily and slow convergence rate *** on the global optimization feature of pso algorithm,it has been adopted to optimize the initial values of Expert PID control in this *** intelligent control algorithm for the maglev transportation system was put forward based on Expert PID optimized by Particle Swarm Optimization algorithm(EPID-pso).The expert rule is that if the absolute value of the error trended to decrease,the PID current control kept its maintenance;if not,then the PID current control applied strong *** this rule,the dynamic error is reduced,and the performance of time delay is *** results by MATLAB proved that the control scheme compared with EPID control and pso-PID control has good robustness,shorter adjustment time,faster response time,achieving better quality of control.

关键词： Maglev transportation system Expert PID control pso algorithm Time delay MATLAB simulation

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The Computation of Concept Similarity Based on pso algorithm

The Computation of Concept Similarity Based on PSO Algorithm

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2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Jia Liu School of Information Science and Engineering Shandong Normal University Ji Nan,China HuaYu Wang School of Information Science and Engineering Shandong Normal University Ji Nan,China

Ontology heterogeneity restricts the application of *** key of ontology mapping is to compute concept *** present,the method of similarity computation is *** the computation quantity is *** order to decrease computation quantity and completely compute concept similarity,the influences of relations and attributes is considered and use candidate *** this paper,a computation method of similarity based on pso algorithm is *** first,the semantic similarity and descriptive similarity of concepts are *** the attribute priority is specified by pso *** last,the attribute with high priority is chosen according to the user *** two ontologies as example and compute concept *** results show that this calculative method can improve the precision of similarity and reduce the calculated quantity.

关键词： Ontology mapping Concept similarity pso algorithm Priority

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Well logs reconstruction of petroleum energy exploration based on bidirectional Long Short-term memory networks with a pso optimization algorithm

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Zhang, Haoyu Wu, Wensheng Chen, Zhangxin Jing, Jin China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China Eastern Inst Technol Ningbo 315100 Peoples R China Univ Calgary Dept Chem & Petr Engn Calgary AB T2N 1N4 Canada Taiyuan Emergency Management Bur Hebi 030001 Peoples R China

During petroleum energy exploration, estimating missing well logs from existing logging data is very meaningful. Due to a highly nonlinear relationship between various logging data and a challenge to express the complexity of underground geological conditions by deterministic functions, traditional methods are difficult to meet the need of accurate interpretations of log data and fine descriptions of reservoirs. Nevertheless, deep learning method provides an advanced means for reconstructing well logs, and can directly map existing logging data to missing well logs. In this paper, we adopt the pso-BiLSTM network that combines the BiLSTM (Bidirectional Long Shortterm Memory) network with the pso (Particle Swarm Optimization) algorithm. BiLSTM is an excellent datadriven method that can extract bidirectional temporal well logging data. pso algorithm enhances the performance of BiLSTM model in predicting missing well logs through hyperparameter optimization, global search capability, and adaptive learning. At the same time, RMSE, MAE, and MAPE are used as indicators to evaluate the performance of pso-BiLSTM model. The results show that when a pso-BiLSTM model is applied to well logs reconstruction experiments, the reconstructed value of a log curve is in good agreement with its real value. Compared to LSTM and BiLSTM models, a pso-BiLSTM model provides the best accuracy and stability in predicting missing well logs. The pso-BiLSTM model strengthens the extraction of relevant logging information and reduces the error of parameter adjustment. It has an important reference significance for the reconstruction of well logs in complex formations.

关键词： Oil-gas energy exploration Well logs reconstruction Bidirectional long short-term memory neural network pso algorithm

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A pso-based algorithm for optimal multiple chiller systems operation

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APPLIED THERMAL ENGINEERING 2012年第1期32卷 31-40页

作者： Beghi, Alessandro Cecchinato, Luca Cosi, Giovanni Rampazzo, Mirco Univ Padua Dipartimento Ingn Informaz I-35131 Padua Italy Univ Padua Dipartimento Fis Tecn I-35131 Padua Italy

In Heating, Ventilation and Air Conditioning (I-IVAC) plants of medium-high cooling capacity, multiple chiller systems are often employed. System performance is evaluated in terms of user comfort, energy use, and financial costs. The problem of efficiently managing multiple chiller systems is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. Therefore significant energy savings can be achieved by optimizing chiller operation. In this paper an unified method for efficient management of multiple chiller systems, by means of a Particle Swarm Optimization (pso) based algorithm, is presented. In particular, the results show that it is possible to achieve substantial energy savings while granting good load profile tracking with respect to standard approaches. The performance of the algorithm is evaluated by resorting to a simulation environment, where the plant dynamics are accurately described. The results show that pso satisfactorily deals with such kind of nonlinear constrained optimization problem, while offering advantages, with respect to other optimization techniques, in terms of ease of implementation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multiple chiller Optimal chiller loading Optimal chiller sequencing pso algorithm

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