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A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第2期66卷 181-200页

作者： Cetinkaya, Mehmet Bahadir Duran, Hakan Univ Erciyes Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

关键词： artificial bee colony algorithm differential evolution algorithm firefly algorithm grey wolf optimization algorithm harmony search algorithm particle swarm optimization algorithm retinal vessel segmentation teaching learning based optimization algorithm

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Intelligent optimization of Matching Layers for Piezoelectric Ultrasonic Transducer

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IEEE SENSORS JOURNAL 2021年第12期21卷 13107-13115页

作者： Sun, Xinhao Man, Jiujing Chen, Dongdong Fei, Chunlong Li, Di Zhu, Yuanbo Zhao, Tianlong Feng, Wei Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

The performance of piezoelectric ultrasonic transducer (PUT) is obviously affected by its matching layer. An intelligent optimization method for matching layer of PUT is proposed to fabricate PUT with high performance. The original data, including matching layer and performance parameters of PUT, is obtained by PiezoCAD software. Then the neural network models are established to mathematically characterize the effect of matching layer on the performance of PUT. The optimization criteria for PUT is constructed according to its performance requirements, such as center frequency (CF), bandwidth (BW) and pulse width (PW). In order to decrease the material cost, the thicknesses of matching layers are also considered in the optimization criteria. The modified particle swarm optimization algorithm is used to optimize the thicknesses of matching layers (Ag-epoxy and Parylene C). According to the designed performance, the optimized thicknesses of Ag-epoxy and Parylene C are about 54 mu m and 21 mu m, respectively. Based on the optimized thicknesses of matching layers, the simulated and experimental performances of PUT well agree with the designed ones. In addition, compared to the traditional quarter-wavelength theory, the proposed method can effectively decrease the materials cost. The -6dB BW and PW of PUT fabricated according to the optimized parameters are about 68.5% and 0.123 mu s, which are better than that fabricated according to the quarter-wavelength theory (about 50% and 0.147 mu s).

关键词： Piezoelectric ultrasonic transducer intelligent optimization matching layer particle swarm optimization algorithm

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algorithms to Model and Optimize a Stand-Alone Photovoltaic-Diesel-Battery System: An Application in Rural Libya

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 523-529页

作者： Elbaz, Abdurazaq Tahir Guneser, Muhammet Karabuk Univ Engn Fac Elect & Elect Dept TR-78100 Karabuk Turkey

This paper introduces a new optimum calculation technique for a stand-alone hybrid photovoltaic-diesel-battery system (PDBS), which meets the energy requirements of a small village in southern Libya. The bat algorithm design strategy is applied to reduce the annual cost of the system, taking into consideration the controlled electricity restriction and the optimal numbers of PV panels, diesel generators, and batteries. Comparative tests are performed using MATLAB for the bat algorithm with the grey wolf search algorithm and particle swarm optimization, demonstrating that the bat algorithm determines the optimum size of the PDBS effectively at a lower expense. Results then indicate that, taking into account the reliability characteristics, this has a significant effect on optimum capacity, load supply, and cost.

关键词： annual system cost bat algorithm grey wolf optimization algorithm particle swarm optimization algorithm photovoltaic-diesel-battery system

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MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations

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COMBUSTION SCIENCE AND TECHNOLOGY 2025年

作者： Zheng, Xuezhao Li, Peihua Cai, Guobin Guo, Jun Liu, Yin Xian Univ Sci & Technol Coll Safety Sci & Engn 58 Yanta Mid Rd Xian 710054 Shaanxi Peoples R China Shaanxi Xikuang Zhitong Technol Co Ltd Xian Peoples R China Key Lab Urban Secur & Emergency Rescue Higher Educ Xian Peoples R China

To address the issue of missing indicator gas data in coal spontaneous combustion temperature prediction models in practical applications, a novel prediction model is proposed, which integrates the MICE (Multiple Imputation by Chained Equations) method with PSO (particle swarm optimization)-optimized RF (Random Forest) algorithm. A coal spontaneous combustion heating experiment was conducted, selecting O2, CO, CO2, CH4, and C2H6 as characteristic gases for coal combustion. Five sets of data with randomly introduced missing values were designed, and four imputation methods - KNN, RF, PSO-SVR, and MICE - were used to impute the missing values. The imputed data were then used to train three temperature prediction models (BP, RF, and SVR), with parameters optimized using PSO. The imputation and prediction results were compared. The results indicate that, across five different missing data rates, the MICE method outperforms the other imputation techniques. Without parameter tuning, all three models showed overfitting. However, after PSO optimization, the PSO-RF model achieved the highest prediction accuracy and demonstrated good stability. In practical applications, the MICE-PSO-RF model yielded an average temperature prediction error of 2.69 degrees C across 15 test sets, demonstrating reliable performance in predicting coal temperature.

关键词： Coal spontaneous combustion temperature prediction data imputation multiple imputation method random forest particle swarm optimization algorithm

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Constant Force Control Method of Grinding Device 15th

Constant Force Control Method of Grinding Device

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15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Duo, Jia Wen Feng, Jiang Zi Yu, Dai Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9783031138355;9783031138348

At present, the traditional robot grinding has some shortcomings in output constant force control. As a result, the output force on the grinding object is frequently instable. Improper force can damage the object during grinding and lead huge economic loss. Therefore, how to improve the accuracy of the output force of robot grinding, has become an urgent problem to be solved. In this paper, aim to improve the grinding force control accuracy, a new control framework which is suitable for cylinder driven grinding device is proposed. The control framework is applied to control the cylinder output force of the grinding device, thereby improving the control ability of the high-precision grinding process robot. In the framework, a PID controller with nonlinear differential gain parameters is used, and parameters are optimized by using the particle swarm optimization algorithm (PSO). The proposed control method, based on the model of the actual cylinder driven grinding device, is verified in MATLAB. The results show that it controls the actual force of the grinding object near the ideal force accurately. The overshoot of the output force on the grinding object is zero and the system stability is very good.

关键词： Grinding device Nonlinear differential gain PID controller particle swarm optimization algorithm

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A Novel GASEN-PSOELM-based Ensemble Method for Quality Variable Prediction in Chemical Processes 41

A Novel GASEN-PSOELM-based Ensemble Method for Quality Varia...

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第41届中国控制会议

作者： Yongqiang Yin Li Zhu Zhuohan Li Department of Control Science and Engineering Dalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Data-driven soft sensor technology is an effective way to realize the online prediction of those important yet difficultto-measure quality and component variables in chemical *** the characteristics of strong nonlinearity,timevariability and multi-working conditions,the soft sensor models based on a single network are difficult to guarantee global-scale prediction accuracy,while the traditional integration methods usually lead to a decrease in prediction speed and an increase in storage *** cope with these issues,this paper proposes a novel ensemble soft sensor method using genetic algorithm to selectively integrate an extreme learning machine optimized by particle swarm algorithm,named GASEN-PSOELM in ***,the sampling data are used to generate the training samples of the sub-models by Bootstrap ***,the candidate sub-models are established via the extreme learning machine optimized by particle swarm algorithm,and then selected by using genetic ***,the prediction outputs are obtained by simple averaging *** validity and effectiveness of the proposed method is verified by the chemical equipment and chemical process,*** experimental results show that the proposed GASEN-PSOELM method can not only improve the prediction speed,but also ensure the good prediction accuracy and generalization performance.

关键词： Soft Sensor Extreme Learning Machine particle swarm optimization algorithm Genetic algorithm Selective Integration

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Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering

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MOBILE NETWORKS & APPLICATIONS 2021年第1期26卷 473-487页

作者： Wang, Honggang Fu, Weina Fuyang Normal Univ Coll Marxism Fuyang 236032 Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language In Changsha 410000 Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410000 Peoples R China Hunan Xiangjiang Artificial Intelligence Acad Changsha 410000 Peoples R China

This paper proposes a personalized learning resource recommendation method based on dynamic collaborative filtering algorithm. Pearson correlation coefficient is used to calculate the data similarity between learning users or project resources in the network, and the unscored value is obtained. In order to solve the problems of sparse data and poor scalability in collaborative filtering algorithm, dynamic k-nearest-neighbor and Slope One algorithm are used to optimize it, and the sparsity of learning resource data in the network is analyzed according to the result of neighbor selection. The bidirectional self-equalization of stage evolution is used to improve the personalized recommendation of resource push, and the fuzzy adaptive binary particle swarm optimization algorithm based on the evolution state judgment is used to solve the problem of the optimal sequence recommendation, so as to realize the personalized learning resource recommendation. The experimental results show that the proposed method has higher matching degree and faster recommendation speed.

关键词： Dynamic collaborative filtering algorithm Learning resource recommendation Bidirectional self-equalization particle swarm optimization algorithm

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Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

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ENERGIES 2021年第20期14卷

作者： Yi, Min Xie, Wei Mo, Li Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

关键词： electricity price forecast maximum information number Pearson coefficient BP neural network particle swarm optimization algorithm simulated annealing algorithm

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High-Precision Demodulation Method for Surface Acoustic Wave Strain Sensor Based on Hybrid Changing Area Niche Genetic algorithm

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IEEE SENSORS JOURNAL 2021年第21期21卷 24246-24253页

作者： Cheng, Lei Ma, Xiaoxin Fan, Yanping Ji, Xiaojun Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

The echo signal energy of surface acoustic wave (SAW) strain sensor is weak, resulting in low demodulation accuracy. In order to improve the measurement result, a SAW sensor demodulation method by combining hybrid changing area niche genetic algorithm and MUSIC algorithm was proposed. The proposed method was based on the MUSIC algorithm to construct the power spectrum function of SAW echo signal. A hybrid changing area niche genetic algorithm (CANGA) and particle swarm optimization (PSO) algorithm was developed to improve the spectrum peak search accuracy and reduce the amount calculations of MUSIC algorithm. The power spectrum function of the MUSIC algorithm was used as the fitness function of the hybrid algorithm. The echo frequency was estimated by the variable value corresponding to the optimal solution. The proposed hybrid PSO-CANGA has the characteristics of strong global search ability and fast convergence speed to improve the performance of MUSIC algorithm. The numerical comparisons were performed. The results showed that the hybrid PSO-CANGA gives fastest convergence and the highest estimation accuracy compared to original PSO and NGA. In addition, compared with traditional spectrum estimation algorithms, the proposed algorithm had a minimum estimation error with standard deviation of 0.27KHz. The proposed demodulation method was used to SAW strain system. The nonlinearity of the strain measurement was improved, with a nonlinearity of 0.55%. Simulation and experiment results verified the effectiveness of the proposed algorithm for the demodulation of the SAW strain sensor system. It helps to improve the accuracy of SAW strain measurement.

关键词： Surface acoustic waves Sensors Strain Frequency estimation Multiple signal classification Signal processing algorithms Resonant frequency Surface acoustic wave strain system variable area search niche genetic algorithm particle swarm optimization algorithm MUSIC algorithm frequency estimation

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A Link Quality Estimation Method Based on Improved Weighted Extreme Learning Machine

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IEEE ACCESS 2021年 9卷 11378-11392页

作者： Liu, Linlan Lv, Hui Xu, Jiangbo Shu, Jian Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China Nanchang Hangkong Univ Sch Software Nanchang 330063 Jiangxi Peoples R China

The link quality of wireless sensor networks is the basis for selecting communication links in routing protocols. Effective link quality estimation is helpful to select high-quality links for communication and to improve network stability. The correlation of link quality parameter and packet reception rate (PRR) is calculated by the Pearson correlation coefficient. According to Pearson coefficient values, the averages of the link quality indication, received signal strength indication, and signal-to-noise are selected as the parameters of the link quality. The link quality grade is taken as a metric of the link quality estimation. particle swarm optimization (PSO) algorithm is used to optimize the parameters of the weighted extreme learning machine (WELM), including the number of hidden nodes, weights, and the normalization factor. A link quality estimator (LQE) based on the improved weighted extreme learning machine (LQE-IWELM) is constructed. In different scenarios, experiment results show that the improved weighted extreme learning machine (IWELM) is more effective than extreme learning machine (ELM) and WELM. Compared with the other three link quality estimation models, LQE-IWELM has better precision and G_mean.

关键词： Estimation Extreme learning machines Signal to noise ratio Wireless sensor networks particle swarm optimization Software Measurement Wireless sensor networks link quality estimation weighted extreme learning machine particle swarm optimization algorithm

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