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RETRACTED: Bottleneck prediction of urban road network based on improved pso algorithms and fuzzy control (Retracted Article)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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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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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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Optimal test-control block designs with unequal block sizes and correlated errors

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年第9期53卷 4519-4536页

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

The purpose of the present article is to introduce theoretical and algorithmic approaches to address the problem of finding optimal test-control incomplete block designs with unequal block sizes where intra-block observations are correlated. Theoretical approach is used to find E-tc-optimal designs analytically. In addition, due to the computational complexity of theoretical methods, in this article a two-phase optimization algorithm is proposed to construct phi-optimal or nearly phi-optimal designs. The effectiveness of the proposed algorithm is validated by comparing our results with optimal designs presented in several prior studies. Our algorithm has the advantages of being independent from the sizes of blocks, structure of correlation, and the optimality criteria. Moreover, it takes only a few minutes to obtain the optimal designs.

关键词： Optimal designs Unequal block sizes Correlated errors pso algorithms GA algorithms

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Improvements to solutions for normal depth in multiple sections of tunnels

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FLOW MEASUREMENT AND INSTRUMENTATION 2020年 73卷 101723-101723页

作者： Shang, Haixin Xu, Song Zhang, Kuandi Northwest A&F Univ Key Lab Agr Soil & Water Engn Minist Educ Arid Areas Weihui Rd Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ State Key Lab Soil Eros & Dryland Farming Loess P Weihui Rd Yangling 712100 Shaanxi Peoples R China

The normal depth is an essential parameter for the design, operation, and maintenance of open channels. The circular, city-gate crossing and egg-shaped sections are often used in non-pressure tunnels in hydraulic engineering, agricultural irrigation, and sewerage works. However, the equations governing the normal depth in various sections are complicated implicit transcendental equations. In order to improvements the solutions for normal depth in multiple sections of tunnels, a function model is first established for the geometric features of various sections using the mathematical transform method and while considering non-dimensional parameters. Then implement the revised pso algorithms in MATLAB, and establish three right solution's formulas for the normal depths in different non-pressure tunnel sections through optimization. The error analysis results and project cases show that the established formula has broad applicability. The maximum relative errors of the formula for normal depths are less than 0.07%, 0.04%, and 0.07% in circular, city-gate crossing, and egg-shaped sections, respectively, which are more accurate than the existing formulas. The results of this research may be useful in design, operation, and maintenance in tunnel engineering.

关键词： Normal depth Non-pressure tunnel pso algorithms Computing model Computing formula

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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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Research of Teacher's Performance Evaluation Model Based on AHP and Improved pso-BP Neural Network

Research of Teacher's Performance Evaluation Model Based on ...

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2019 International Conference on Applied Mathematics, Modeling, Simulation and Optimization(AMMSO 2019)

作者： Jia XU Lang PEI College of Computer Science Wuhan Qingchuan University

The teacher's performance evaluation is an important guarantee for the development of higher education. In view of the limitations of traditional analytic hierarchy process in the teachers' performance comprehensive evaluation and the shortcomings of BP neural network in the teachers' performance comprehensive evaluation, such as non-convergence and large prediction error, the paper proposed an evaluation index system based on analytic hierarchy process as input of BP neural network, and used dynamic inertial weight and multiple empirical particles to improve pso algorithm and optimize the weights and thresholds of BP network, established teacher's performance evaluation model. The simulation results show that the model effectively reduces the number of network iterations, improves the prediction accuracy, and has a good application prospect in the teacher's performance evaluation.

关键词： Analytic hierarchy process BP neural network pso algorithms

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Experimental study and optimisation in turning process of EN8 steel using RSM with hybrid algorithm approach

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第4期13卷 242-256页

作者： Thangarasu, S. K. Shankar, S. Prasath, R. Navin Kongu Engn Coll Dept Mechatron Engn Erode 638060 India

The effects of cutting speed, cutting feed, depth of cut on the output responses in turning were investigated for different tool conditions. Three-factor and three-level fractional experiment designs completed with statistical analysis of variance (ANOVA) were performed. Mathematical models for output responses were developed using response surface methodology (RSM). EN8 steel is work piece material and TiN coated cemented carbide is cutting tool. The experiments were conducted for fresh, worn out tools and the output responses are measured. The responses (cutting force, tool wear, surface roughness) are to be minimised. A quadratic model is developed along with combined optimisation of the response using RSM. For each test the output responses was measured for both tools. Finally an optimum cutting speed of 90 mm/min for fresh tool and 270 mm/min for worn out tool was obtained. The results concluded that pso algorithm produces better optimisation compared to firefly, cuckoo search algorithms.

关键词： firefly algorithm response surface methodology RSM coated carbide inserts design of experiments swarm intelligent technique grey relational analysis cuckoo search algorithm pso algorithms cutting force tool wear surface roughness

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Explicit Solution for Critical Depth in Closed Conduits Flowing Partly Full

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WATER 2019年第10期11卷 2124-2124页

作者： Shang, Haixin Xu, Song Zhang, Kuandi Zhao, Luyou Northwest A&F Univ Minist Educ Arid Areas Key Lab Agr Soil & Water Engn Weihui Rd Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China

Critical depth is an essential parameter for the design, operation, and maintenance of conduits. Circular, arched, and egg-shaped sections are often used in non-pressure conduits in hydraulic engineering, irrigation, and sewerage works. However, equations governing the critical depth in various sections are complicated implicit transcendental equations. The function model is established for the geometric features of multiple sections using the mathematical transform method and while considering non-dimensional parameters. Then, revised pso algorithms are implemented in MATLAB, and the right solution's formula for the critical depths in various non-pressure conduit sections is established through optimization. The error analysis results show that the established formula has broad applicability. The maximum relative errors of the formula for critical depths are less than 0.182%, 0.0629%, and 0.170% in circular, arched, and egg-shaped sections, respectively, which are more accurate than those of existing formulas;the form of the formula proposed in this work is also more compact than that of the existing formulas. The results of this research may be useful in design, operation, and maintenance in conduit engineering.

关键词： circular sections arched sections egg-shaped sections critical depth pso algorithms computing model

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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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