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ANALYSIS AND RESEARCH ON OPTIMAL ECONOMIC OPERATION OF COGENERATION MICRO-GRID BASED ON HEAT PUMP CONTROL

THERMAL SCIENCE 2021年第4期25卷 2871-2879页

作者： Xia, Wei Hua Henan Inst Econ & Trade Intelligent Finance Zhengzhou Henan Peoples R China Huazhong Univ Sci & Technol Hust Sch Econ Wuhan Hubei Peoples R China

When the micro-grid is connected to the grid, the influence of the load and the micropower supply's operating characteristics may cause the power exchange between the micro-grid and the large grid to cause unnecessary losses. The objective function of power supply satisfaction of large power grids. The article takes the cogeneration micro-grid as the research object. It proposes establishing an objective function including the satisfaction of micro-grid decision-makers, the satisfaction of social benefits, and the satisfaction of large-scale power supply using the analytic hierarchy process. After that, the article uses the particle swarm optimization algorithm combined with the typical dispatching strategy of the micro-grid to solve the aforementioned model with MATLAB as the primary tool, and obtains the 24 hour output optimization results of the micro-grid, and analyzes the results in detail. The calculation examples show that the model can achieve economic optimization based on satisfying the satisfaction of different groups, which verifies the scientificity and effectiveness of the proposed model.

关键词： heat pump control economic operation cogeneration type particle swarm optimization algorithm micro-grid

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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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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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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach 34

The State of Charge Estimation of Lithium-ion Batteries Usin...

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34th Chinese Control and Decision Conference (CCDC)

作者： He, Wei Ma, Hongyan Zhang, Yingda Wang, Shuai Dou, Jiaming Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Natl Virtual Simulat Expt Ctr Smart City Educ Beijing 100044 Peoples R China

ISBN: (纸本)9781665478960

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a particle swarm optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge particle swarm optimization algorithm Extreme Learning Machine

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

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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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A novel grey multivariate model for forecasting landslide displacement

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 103卷 104297-104297页

作者： Li, S. H. Zhu, L. Wu, Y. Lei, X. Q. Southwest Jiaotong Univ Dept Geol Engn Chengdu 610031 Sichuan Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Surface Proc Chengdu 610041 Sichuan Peoples R China

Forecasting landslide displacement is an important issue in engineering geology. In this field, it is difficult to accurately forecast the displacement of the step point of a step-type landslide. In this study, a novel grey multivariate model is proposed for forecasting landslide displacement. The proposed model transforms the original sequence by using the Hausdorff derivative operator, determines the model parameters by using the particle swarm optimization algorithm, and uses the trapezoidal integral formula to calculate the predicted value. Two numerical examples show that the average absolute relative error and mean squared error of the proposed model are smaller than those of the recursive discrete multivariate grey model and the multivariable grey model with structure compatibility. The proposed model is used to forecast the displacement of the Bazimen landslide in the Three Gorges Reservoir area of China. The previous month's displacement, precipitation, and change in the reservoir water level are used as input variables. The results show that the performance of the proposed model is superior to that of the extreme learning machine model. This paper provides an effective method for forecasting displacement of the step point of a step-type landslide.

关键词： Landslide displacement Forecast Grey multivariate prediction model Hausdorff derivative particle swarm optimization algorithm

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optimization of visible transmission and NIR reflection in multilayer via PSO: Simulation and experimental validation

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RENEWABLE ENERGY 2024年 237卷

作者： Zhang, Kaihua Chen, Zhiying Guo, Jinyang Gao, Shuai Yu, Kun Zhang, Xiansheng Liu, Yufang Wu, Xiaohu Henan Normal Univ Sch Phys Henan Key Lab Infrared Spectrum Measures & Applica Xinxiang 453007 Peoples R China Shandong Inst Adv Technol Jinan 250100 Shandong Peoples R China Qingdao Univ Qingdao 266071 Shandong Peoples R China

Transparent heat reflective windows (THRW) effectively manage solar energy by reflecting near-infrared (NIR) heat while maintaining high visible (VIS) light transmittance, thus improving building energy efficiency. Conventional THRW designs using dielectric/metal/dielectric (D/M/D) structures often increase metal thickness for better NIR reflectance, which compromises VIS transmittance. In this work, we present a dielectric/metal/ dielectric/metal/dielectric (D/M/D/M/D) multilayer structure, utilizing TiO2 as the dielectric and Ag as the metallic component. The film thicknesses were systematically optimized using the particle swarm optimization algorithm to achieve an optimal balance between high VIS transmittance ((TVIS = 87 %) and high NIR reflectance (RNIR = 95 %). Additionally, the structure shows only 15 % UV transmittance due to absorption by TiO2 and reflection by Ag. The multilayer films are fabricated using electron beam evaporator, with the experimental results show that the multilayers maintain good VIS transmittance as well as high NIR reflectance. This highperformance THRW design demonstrates significant potential for energy-saving applications in hot climates, offering a viable solution for enhancing building energy efficiency and occupant comfort in the future.

关键词： Transparent-heat reflective windows Building energy efficiency Spectrally selective regulation particle swarm optimization algorithm

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Modeling and Performance optimization of Wireless Sensor Network Based on Markov Chain

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IEEE SENSORS JOURNAL 2021年第22期21卷 25043-25050页

作者： Liu, Yutang Zhang, Qin Henan Inst Technol Sch Sci Xinxiang 453002 Henan Peoples R China Xinxiang Univ Sch Math & Stat Xinxiang 453003 Henan Peoples R China

Wireless sensor networks are usually deployed in areas with relatively harsh natural environments, and the collection node transmits data to the destination node through a multi-hop route. Therefore, how to effectively plan the transmission path is an important issue. This paper combines the unbiased gray model with the Markov chain model to establish an unbiased gray Markov chain model, and points out that the unbiased gray Markov chain model also has shortcomings in parameter selection. The particle swarm algorithm is used to improve it, and the mathematical model, calculation principle and various parameters of the particle swarm optimization algorithm are introduced, and the implementation flow chart of the particle swarm algorithm is given. Aiming at the shortcomings of the unbiased gray Markov chain model, the particle swarm algorithm and the unbiased gray Markov chain model are combined to form the particle swarm unbiased gray Markov chain model. The simulation environment and the training environment of the particle swarm unbiased grey Markov chain model were designed in the experiment. The node scheduling optimization experiment proves that the scheduling method based on the particle swarm unbiased grey Markov chain model has achieved better results in coverage and energy consumption balance than the random and shortest distance method. In the routing experiment, the experimental analysis of the node's Q value proved the convergence of the algorithm, and compared with other protocols, it proved that the routing algorithm can effectively extend the network life cycle and achieve load balancing.

关键词： Sensors Wireless sensor networks Markov processes Mathematical model Routing particle swarm optimization Monitoring Wireless sensor network Markov chain particle swarm optimization algorithm routing protocol unbiased gray model

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Reliability evaluation of concrete-filled steel tube arch bridge based on DBN-PSOSA hybrid algorithm

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STRUCTURAL CONCRETE 2021年第4期22卷 2309-2321页

作者： Lu, Pengzhen Xu, Zijie Hong, Tao Wu, Ying Li, Dengguo Zhejiang Univ Technol Coll Civil Engn Hangzhou 310014 Zhejiang Peoples R China Jiaxing Nanhu Univ Jiaxing Peoples R China

When traditional response surface method is used to evaluate the reliability of concrete-filled steel tubular arch bridge, due to its complex structure and highly nonlinear implicit function, the response surface fitting accuracy is not high, and the reliability accuracy is difficult to meet the requirements of design specifications. In order to solve the above problems, this paper chooses dynamic Bayesian networks (DBN) which is suitable for solving the problem of multiple state unit or system uncertainty to build implicit function of the response surface function. And this paper combines DBN and particle swarm optimization algorithm based on simulated annealing algorithm (PSOSA) to improve efficiency of model parameter optimization. It can make the construction of implicit function simulate the real structure of the limit state function. Then this paper verifies the suitability for this kind of complex structure reliability assessment of DBN-PSOSA hybrid algorithm. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of a concrete filled steel tube arch bridge project is evaluated. The results show that this method improves the calculation accuracy and efficiency.

关键词： annealing algorithm bridge engineering dynamic Bayesian networks particle swarm optimization algorithm reliability analysis the response surface

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