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International Conference on Advances in Materials and Manufacturing Processes

作者： Liu, Qinming Dong, Ming Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Anatai Coll Econom & Management Shanghai 200052 Peoples R China

ISBN: (纸本)9780878492015

This paper explores the grey model based PSO (particle swarm optimization) algorithm for fatigue strength prognosis of concrete. First, depending on concrete's testing status, fatigue life is studied. Then, one GM(1,1) based PSO algorithm is used in fatigue strength prognosis of concrete. One important advantage of the proposed algorithm is that only fewer data is in need for fatigue strength prognosis. Finally, a case study is given to illustrate effectiveness and efficiency of the proposed approach.

关键词： Fatigue strength Grey model particle swarm optimization algorithm Building materials

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Factor analysis and forecasting of CO₂ emissions in Hebei, using extreme learning machine based on particle swarm optimization

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JOURNAL OF CLEANER PRODUCTION 2017年 162卷 1095-1101页

作者： Sun, Wei Wang, Caifei Zhang, Chongchong North China Elect Power Univ Dept Business Adm Baoding 071000 Peoples R China

In the prevailing low-carbon economy, China is under enormous pressure to control CO2 emissions, therefore, of great significance is the study to analyze what influential factors mainly contribute to emissions, so as to forecast emissions accurately and harness the growth from the source. In this paper, basing on 22 influencing factors identified by bivariate correlation analysis, factor analysis is then adopted to extract the latent factors which essentially affect emissions and 8 special factors transformed by scoring coefficients are acquired. Extreme learning machine (ELM) whose input weights and bias threshold were optimized by particle swarm optimization (PSO), hereafter referred as PSO-ELM, is established to predict CO2 emissions and testify the availability of the factor analysis. Case studies reveal that the factor analysis which generates 8 factors as input can highly improve prediction accuracy. And the simulation results demonstrate that the built model PSO-ELM outperforms the compared ELM and back propagation neural network in forecasting CO2 emissions. Eventually, the analysis made in this study can provide valuable policy implications for Hebei's CO2 emissions reduction and strategic low carbon development. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： CO2 emissions Extreme learning machine particle swarm optimization algorithm Factor analysis

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A new Combined particle swarm optimization algorithm Based Golden Section Strategy

A new Combined Particle Swarm Optimization Algorithm Based G...

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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： Hui, Fan Shanghai Dianji Univ Sch Elect Engn Shanghai Peoples R China

ISBN: (纸本)9783037852132

Based the defects of global optimal model falling into local optimum easily and local model with slow convergence speed during traditional PSO algorithm solving a complex high-dimensional and multi-peak function, a two sub-swarms particle optimization algorithm is proposed. All particles are divided into two equivalent parts. One part particles adopts global evolution model, while the other part uses local evolution model. If the global optimal fitness of the whole population stagnates for some iteration, a golden rule is introduced into local evolution model. This strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model. So, some particles with advantage are joined into the whole population to make the algorithm keep active all the time. Compared with classic PSO and PSO-GL(A dynamic global and local combined particle swarm optimization algorithm, PSO-GL), the results show that the proposed PSO in this paper can get more effective performance over the other two algorithm in the simulation experiment for four benchmark testing function.

关键词： particle swarm optimization algorithm golden rule diversity model optimization

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particle swarm optimization method for small retinal vessels detection on multiresolution fundus images

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JOURNAL OF BIOMEDICAL OPTICS 2018年第5期23卷 056004-056004页

作者： Khomri, Bilal Christodoulidis, Argyrios Djerou, Leila Babahenini, Mohamed Chaouki Cheriet, Farida Univ Biskra LESIA Lab Biskra Algeria Polytech Montreal Lab LIV4D Montreal PQ Canada

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high-and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two lowr-esolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： retinal blood vessel segmentation fundus imaging multiscale line detection multiobjective optimization particle swarm optimization algorithm image segmentation

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Research on Multi-objective Adaptive Cruise Control Method

Research on Multi-objective Adaptive Cruise Control Method

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2020中国汽车工程学会年会暨展览会

作者： Yang Lei Mao Jin Liu Kai Du Jinfu School of Mechanical and Precision Instrument Engineering Xi'an University of Technology

In order to improve ACC's ability to coordinate various targets during the following process,a multi-target adaptive cruise control algorithm was *** a longitudinal kinematics model of the workshop and introduce a variable spacing strategy. Design objective functions and constraints,which comprehensively consider factors such as safety,comfort,fuel economy,and vehicle limitations,And introduce relaxation factor vectors to soften different hard constraint boundaries to solve the problem of no feasible solution,and introduce reference trajectory. Based on the model predictive control theory,the problem is transformed into a multiconstrained quadratic programming problem,which is solved using an improved particle swarm optimization *** simulation results show that the multi-objective adaptive cruise control algorithm based on particle swarm optimization algorithm can greatly improve the vehicle's driving comfort and fuel economy.

关键词： multi-target adaptive cruise control spacing strategy model predictive control particle swarm optimization algorithm

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Fault Diagnosis Approach for Pneumatic Control Valves Based on Modified Expert System

Fault Diagnosis Approach for Pneumatic Control Valves Based ...

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第十九届分布式计算及其应用国际学术研讨会

作者： Daoming Zhang Jixing Hao Liming Chen Linna Zhou School of Information and Control Engineering China University of Mining and Technology

This paper studies the fault diagnosis method of pneumatic control valve. Firstly, the faults characteristics of pneumatic control valves are analyzed according to the operating principle and status of pneumatic control valves;secondly, the expert experience of the fault diagnosis of pneumatic control valves is summarized, which is verified according to the operating mechanism;thirdly, a fault diagnosis approach for pneumatic control valves based on modified expert system is proposed, by combining particle swarm optimization(PSO) algorithm with expert rules. Finally, the availability and advantages of the proposed approach is verified by the designed valve experimental system platform. The results show that compared with the basic expert system-based method, the modified method improves the accuracy and reduces the false negative rate effectively.

关键词： pneumatic control velve fault diagnosis expert system particle swarm optimization algorithm

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Prediction of Remaining Useful Life of Lithium-ion Battery based on Multi-kernel Support Vector Machine with particle swarm optimization

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JOURNAL OF POWER ELECTRONICS 2017年第5期17卷 1288-1297页

作者： Gao, Dong Huang, Miaohua Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan Hubei Peoples R China

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

关键词： Lithium-ion battery RUL Multi-kernel support vector machine particle swarm optimization algorithm RUL prediction

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A particle swarm optimization Technique-Based Parametric Wavelet Thresholding Function for Signal Denoising

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2017年第1期36卷 247-269页

作者： Zhang, Xun Li, Juelong Xing, Jianchun Wang, Ping Yang, Qiliang He, Can PLA Univ Sci & Technol Coll Def Engn Nanjing 210007 Jiangsu Peoples R China Res Ctr Coastal Def Engn Beijing 100841 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

The determination of threshold and the construction of thresholding function would directly affect the signal denoising quality in wavelet transform denoising techniques. However, some deficiencies exist in the conventional methods, such as fixed threshold value and the inflexible thresholding functions. To overcome the defects of the traditional wavelet thresholding techniques, a modified particle swarm optimization (MPSO) algorithm-based parametric wavelet thresholding approach is proposed for signal denoising. Firstly, a kind of parametric wavelet thresholding function construction method is proposed on the basis of conventional thresholding functions. With mathematical derivation, the properties of the constructed function are proved. Three dynamic adjustment strategies are then employed to modify the PSO algorithm. The mean square error (MSE) between the original signal and the reconstructed signal is minimized by the MPSO algorithm. Finally, the performances of the proposed approach and the existing methods are simulated by denoising four benchmark signals with different noise levels. The simulation results show that the proposed MPSO-based parametric wavelet thresholding can obtain lower MSE, higher signal-to-noise ratio, and noise suppression ratio compared to the other algorithms. Besides, the denoising visual results also indicate the superiority of the proposed approach in terms of the signal denoising capability.

关键词： Signal denoising Wavelet transform particle swarm optimization algorithm Thresholding function

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Heuristic particle swarm optimization approach for test point selection with imperfect test

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第1期28卷 37-50页

作者： Deng, Sen Jing, Bo Zhou, Hongliang Air Force Engn Univ Aeronaut & Astronaut Engn Coll Xian 710038 Peoples R China

The problem of near-optimal test point set selection with imperfect test is solved by using the heuristic particle swarm optimization (HPSO) algorithm. First, to describe the uncertainty of each test, the testability analysis model and such indexes as fault detection rate, fault isolation rate, and false alarm rate are redefined. A heuristic function is then established to evaluate the detection isolation capability and uncertainty of the test point, which can provide heuristic information to improve the searching efficiency of particle swarm optimization (PSO). The heuristic function and least test cost principle are used as bases to design a fitness function of PSO algorithm for test point selection. Finally, the HPSO algorithm is proposed to select the optimal test point set for two practical systems. Simulation and experiment results show that the method can determine the global optimal test point accurately and effectively while meeting the requirements of testability indexes with least cost.

关键词： Imperfect test Test point selection particle swarm optimization algorithm Heuristic function

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optimization of Reservoir Operation using New Hybrid algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第11期22卷 4668-4680页

作者： Yaseen, Zaher Mundher Karami, Hojat Ehteram, Mohammad Mohd, Nuruol Syuhadaa Mousavi, Sayed Farhad Hin, Lai Sai Kisi, Ozgur Farzin, Saeed Kim, Sungwon El-Shafie, Ahmed Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Semnan Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Semnan Iran Univ Malaya Civil Engn Dept Fac Engn Kuala Lumpur 50603 Malaysia Ilia State Univ Sch Nat Sci & Engn Tbilisi Georgia Dongyang Univ Dept Railrd Construct & Safety Engn Yeongju 36040 South Korea

Due to the scarcity of fresh water resources, exploiting dams' reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers' water needs with high reliability. In this research, a new hybrid approach of Artificial Fish swarm algorithm (AFSA) and particle swarm optimization algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant's objective function.

关键词： optimization of reservoir operation artificial intelligence artificial fish algorithm particle swarm optimization algorithm

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