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RETRACTED: Bottleneck prediction of urban road network based on improved pso algorithms and fuzzy control (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5833-5843页

作者： Xing, Xue Jilin Inst Chem Technol Coll Informat & Control Engn Jilin 132022 Jilin Peoples R China

Traffic bottleneck refers to the road section or node that often causes the propagation or spread of traffic congestion in the road network, which is the source of the whole road network congestion. In this paper, the author analyzes the bottleneck prediction of urban road network based on improved pso algorithms and fuzzy control. This paper analyzes the factors and characteristics of the main road of the system, proposes the traffic coordination control of the main road based on the delay model, and carries on the statistical simulation to the actual traffic data, develops the basic theory of the traffic coordination control which is more effective than the traditional timing control strategy. Compared with the traditional model, the algorithm considers the waiting time of the red light at the intersection. For the congested road section, it can better calculate the travel time of the vehicle, making the results more accurate and more applicable. The results of this study can provide a strong theoretical basis and prediction scheme for the traffic management and control of the road network in the target area.

关键词： Traffic bottleneck pso algorithms fuzzy control Road network planning

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Study on Rotor Speed Identification of DTC System Based on pso algorithms of BP Neural Network

Study on Rotor Speed Identification of DTC System Based on P...

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IITA International Conference on Services Science, Management and Engineering

作者： Cao, Chengzhi Lu, Yuanyuan Wang, Fang Zheng, Haiyin Shenyang Univ Technologyorganizat Dept Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9780769537290

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(pso) algorithms, two new particle's position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified pso algorithms (called Leading pso algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified pso algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.

关键词： pso algorithms DTC system rotor speed identification BP neural network

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Process parameter optimisation for plasma sprayed nanostructured ZrO₂-7%Y₂O₃ coatings based on pso algorithm

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2015年第3期51卷 220-227页

作者： Yang, Bin Zhao, Hongwei Zhang, Yingfu Tianjin Univ Technol & Educ Sch Automobile & Transportat Tianjin 300222 Peoples R China Great Wall Motor Co Ltd Baoding 071000 Hebei Peoples R China Jilin Univ Coll Machinery & Engn Changchun 130025 Peoples R China

The process parameters of plasma sprayed nanostructured ZrO2-7%Y2O3 coatings were optimised based on a particle swarm optimisation (pso) algorithm. A BP neural network was applied to compute the fit of the pso algorithm, and was assembled with four process parameters which included spraying distance, spraying electric current, primary gas pressure, and secondary gas pressure as the inputs;the bonding strength of the coating was the output. The results of the pso algorithm and BP neural network show that the maximal bonding strength of the coatings was 42.5822 MPa. And the optimal process parameters discovered in this research for the plasma sprayed nanostructured ZrO2-7%Y2O3 coatings are a spraying distance of 80 mm, spraying electric current of 994.3707 A, primary gas pressure of 0.2575 MPa, and secondary gas pressure 1.1611 MPa.

关键词： plasma spraying nanostructured coating neural networks pso algorithms process parameters optimisation

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Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/pso

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IET BIOMETRICS 2018年第2期7卷 153-162页

作者： Ahmadi, Neda Akbarizadeh, Gholamreza Shahid Chamran Univ Ahvaz Fac Engn Dept Comp Sci Ahvaz Iran Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Ahvaz Iran

Computational intelligence is employed to solve factual and complicated global problems, though neural networks (NNs) and evolutionary computing have also affected these issues. Biometric traits are applicable for detecting crime in security systems because they offer attractive features such as stability and uniqueness. Although various methods have been proposed for this objective, feature shortcomings such as computational complexity, long run times, and high memory consumption remain. The current study proposes a novel human iris recognition approach based on a multi-layer perceptron NN and particle swarm optimisation (pso) algorithms to train the network in order to increase generalisation performance. A combination of these algorithms was used as a classifier. A pre-processing step was performed on the iris images to improve the results and two-dimensional gabor kernel feature extraction was applied. The data was normalised, trained, and tested using the proposed method. A pso algorithm was applied to train the NN for data classification. The experimental results show that the proposed method performs better than many other well-known techniques. The benchmark Chinese Academy of Science and Institute of Automation (CASIA)-iris V3 and Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) machine learning repository datasets were used for testing and comparison.

关键词： feature extraction neural nets iris recognition computational complexity particle swarm optimisation learning (artificial intelligence) UCI machine learning repository datasets data classification 2D Gabor kernel feature extraction generalisation performance pso algorithms particle swarm optimisation multilayer perceptron NN human iris recognition computational complexity security systems biometric traits evolutionary computing neural networks complicated global problems computational intelligence multilayer perceptron neural network/pso 2D Gabor features iris image pre-processing hybrid robust iris recognition

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Fast and accurate synthesis of electronically reconfigurable annular ring monopole antennas using particle swarm optimisation and artificial bee colony algorithms

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第4期10卷 362-369页

作者： Brito Rodrigues, Eduardo Jorge Castro Lins, Hertz Wilton D'Assuncao, Adaildo Gomes Univ Fed Rio Grande do Norte UFRN Dept Commun Engn Caixa Postal 1655 BR-59072970 Natal RN Brazil Anatel Natl Agcy Telecommun BR-58043010 Joao Pessoa Paraiba Brazil

The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (pso) and artificial bee colony (ABC) algorithms is proposed. Several antenna dimensions are selected as objective functions of pso and ABC and their best solutions are considered as the optimised dimensions of the antenna geometry. Two radio-frequency p-i-n diodes, connecting the antenna feeding line to two microstrip stubs are used to change the antenna frequency response from ultra-wideband (UWB), from 3.1 to 10.6 GHz, to narrowband (NB) operation about 5.8 GHz. The antenna design and simulation are performed using Ansoft high-frequency structure simulator software. pso and ABC algorithms are written in Java language. Thereafter, antenna prototypes are fabricated and measured for validation purposes. Simulation and measurement results are obtained showing good agreement. The measured optimised impedance bandwidths of the UWB and NB bands are up to 128 and 23%, respectively. Additionally, simulated and measured radiation patterns are very similar when the reconfigurable antenna is operating in OFF-state (UWB sensing antenna) and in ON-state (NB transmitting antenna) modes, indicating the proposed antenna geometry as a promising candidate for applications such as UWB and cognitive radio.

关键词： monopole antennas particle swarm optimisation ultra wideband antennas frequency response Java antenna radiation patterns electrical engineering computing electronically reconfigurable annular ring monopole antenna synthesis particle swarm optimisation artificial bee colony bioinspired algorithms antenna dimensions objective functions antenna geometry radiofrequency p-i-n diodes antenna feeding line microstrip stubs frequency response ultrawideband antenna antenna design pso algorithms ABC algorithms Java language ANSOFT high-frequency structure simulator software antenna prototypes validation purposes impedance bandwidths antenna radiation patterns OFF-state modes ON-state modes cognitive radio NB transmitting antenna UWb sensing antenna

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Defect detection and classification of offshore wind turbine rotor blades

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第4期39卷 954-975页

作者： Deng, Liwei Liu, Shanshan Shi, Wei Xu, Jiazhong Harbin Univ Sci & Technol Sch Automation Heilongjiang Prov Key Lab Complex Intelligent Syst Harbin Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents challenges in detecting defects in Wind Turbine Rotor Blades (WTRB) and repairing them. To address this issue, this paper proposes an improved wavelet-based S-U-Net network to denoise WTRB images followed by a weakly supervised CNN method for removing background parts that could affect defective feature extraction. Defective features are then extracted using VGG16 network on a deep learning server platform, while an enhanced Particle Swarm Optimization (pso) algorithm combined with K-means is used to classify defect features of WTRBs. Experimental results demonstrate that classification accuracy of unlabelled blade defect datasets improved significantly from 62.6% using only Kmeans clustering method up to 96.4% using our proposed algorithm approach. This study applies improved pso and Kmeans algorithms towards offshore Wind Turbine Rotor Blade condition monitoring with precise detection test results enabling early-stage detection of Wind Turbine Rotor Blades defects leading to timely repairs.

关键词： Wind turbine rotor blades S-U-net VGG16 network deep learning pso algorithms K-means

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Improving the Intersection's Throughput using V2X Communication and Cooperative Adaptive Cruise Control

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IFAC-PapersOnLine 2016年第5期49卷 359-364页

作者： Liu, Bing Sun, Qi El Kamel, Abdelkader Ècole Centrale de Lille Villeneuve d'Ascq 59651 France

In this paper, a decentralized cooperative adaptive cruise control algorithm for vehicles in the vicinity of intersections (CACC-VI) is proposed. This algorithm is designed to make use of the road capacity to let more vehicles get through the intersection within a limited traffic signal period in consideration of safety, fuel consumption, speed limit and heterogeneous features of vehicles, and passenger comfort. Within a platoon, vehicles try to find the optimal control input by a distributed pso algorithm in order to reduce tracking errors while respecting different constraints. A concept of opportunity space is proposed to facilitate platoon reorganization in which a sub-platoon or an individual vehicle can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. It is found that the throughput of the intersection during the green phase can be improved, and an optimal control input can be found for each vehicle within the defined constraints. © 2016

关键词： Adaptive cruise control Cooperative communication Traffic signals Vehicles Cooperative adaptive cruise control Eco driving Heterogeneous features Optimal controls Passenger comfort pso algorithms pso with constraints Tracking errors

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Distributed Path Planning for Controlling a Fleet of UAVs: Application to a Team of Quadrotors

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IFAC-PapersOnLine 2017年第1期50卷 15983-15989页

作者： Belkadi, A. Abaunza, H. Ciarletta, L. Castillo, P. Theilliol, D. Universite de Lorraine CRAN CNRS UMR 7039 BP 70239 Vandoeuvre Cedex54506 France Mines Nancy Universite de Lorraine UMR 7503 Loria France Sorbonne Universites Universit de Technologie de Compigne CNRS UMR 7253 Compiegne France

In this paper, a distributed trajectory generation strategy is proposed to control a group of Unmanned Aerial Vehicles (UAVs). The issue, treated as an online optimization problem, is solved using a Particle Swarm Optimization (pso) algorithm. The proposed pso is implemented independently in each vehicle in order to determine, by minimizing a cost function, the best paths that ensure the fleet formation control, target tracking and collision and obstacle avoidance. The method illustrated in this paper offers solutions to several questions related to the control of a group of UAVs and could be applied to solve problems such as covering large search areas for surveillance, inspection and rescue. Firstly, experiments on one quadrotor are implemented to illustrate the effectiveness of the proposed algorithm. Large disturbances and obstacles are considered to illustrate the robustness of the presented approach. The method is tested in a real environment with a group of three quadrotors. © 2017

关键词： Particle swarm optimization (pso) Antennas Cost functions Fleet operations Motion planning Optimization Target tracking Fleet control Flight training Formation control Large disturbance Online optimization Path generation pso algorithms Trajectory generation

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An algorithm for generating efficient block designs via a novel particle swarm approach

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COMPUTATIONAL STATISTICS 2024年第5期39卷 2437-2449页

作者： Pooladsaz, Saeid Doosti-Irani, Mahboobeh Isfahan Univ Technol Dept Math Sci Esfahan *** Iran

The problem of finding optimal block designs can be formulated as a combinatorial optimization, but its resolution is still a formidable challenge. This paper presents a general and user-friendly algorithm, namely Modified Particle Swarm Optimization (Mpso), to construct optimal or near-optimal block designs. It can be used for several classes of block designs such as binary, non-binary and test-control block designs with correlated or uncorrelated observations. In order to evaluate the algorithm, we compare our results with the optimal designs presented in some published papers. An advantage of our algorithm is its independency to the sizes of blocks and the structure of correlations.

关键词： Optimal designs Connected block designs Non-binary designs Test-control designs Correlated errors pso algorithms

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Model of coal product structure based on particle swarm optimization algorithm

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Procedia Earth and Planetary Science 2009年第1期1卷 640-647页

作者： Zhang-guo, Wang Ya-li, Kuang Zhe, Lin Chang-sheng, Shi School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou 221116 China Environmental Engineering Department North China Institute of Science and Technology Beijing 101601 China

Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparation product structure, such as raw coal quality, separating methods, coal price, processing cost, product quality demands and equipment performance, etc. The research focuses on the optimization of product structure under the Multi-factor influences. In order to maximizing the economic benefit, the algorithm model of product structure is established, and the multiple influence factors are transformed as model parameters and constraint conditions. Then the particle swarm optimization (pso) algorithm is used to search the optimal scheme of product structure. According to the actual requirement, the model was divided into several child models during the calculation. A set of practical software has been developed based on the research. The result shows that using pso algorithm can get better convergence effect and avoid the local optimization for the Multi-factor model and that the optimal scheme of product structure from the model accord with the practical situation. © 2009.

关键词： Particle swarm optimization (pso) algorithms Coal deposits Coal industry Coal preparation Coal research Convergence of numerical methods Energy resources Industrial research Shape optimization Structural optimization Algorithm model Child models Coal products Constraint conditions Economic benefits Equipment performance Gravity separation Influence factors Local optimizations Model optimization Model parameters Multi factor Optimal scheme Particle swarm optimization algorithm Processing costs Product quality Product structure pso algorithms Raw coal Save energy

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