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12th International Conference on Applications and Technologies in Information Security (ATIS)

作者： Pan, Shaowei Mou, Yuhui Zheng, Zechen Xian Shiyou Univ Xian 710065 Shaanxi Peoples R China

ISBN: (纸本)9789811911668;9789811911651

The moisture content identification of a water drive oilfield is related to how to formulate and adjust the development plan of the oil field. However, traditional prediction methods for the water cut usually have problems such as slow recognition speed and limited by the specific conditions of the oil well. Therefore, in order to avoid the influence of the above problems, a machine algorithm model of water cut based on hybrid optimization is proposed, which is based on the support vector regression (SVR) model. First, the data is constructed by time sliding window;secondly, on the basis of the fundamental SVR model, this paper combines the particle swarm optimization (PSO) and the Artificial Fish swarm algorithm (AFSA) to optimize the hyperparameters of the SVR prediction model to achieve better experimental results;finally, if the hybrid model proposed in this article has some good experimental results, then it can be applied to the actual water cut prediction of the oilfield. After comparing four different models, the prediction model based on the hybrid optimization algorithm proposed in this paper has some good experimental results. The prediction curve and the real curve have the same trend as a whole, and the subtle errors are also the smallest. Thus, it performs better than the SVR prediction model optimized by the differential evolution algorithm, the SVR prediction model optimized by the genetic algorithm, and the SVR prediction model optimized by the PSO.

关键词： Water cut identification particle swarm optimization algorithm Artificial Fish swarm algorithm SVR Prediction

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optimization-based design of sliding sector control for active seismic protection of structures

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2024年第3期33卷 e2073-e2073页

作者： Saadatfar, Saman Emami, Fereshteh Khatibinia, Mohsen Eliasi, Hussein Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Univ Birjand Dept Civil Engn Birjand Iran Univ Birjand Dept Elect Engn Birjand Iran

The active tuned mass damper (ATMD) is a reliable energy-dissipating device to effectively protect structures from serious damages due to earthquake excitations. This study proposes the optimal design of sliding sector control (SSC) for the seismic protection of an 11-story shear building structure equipped with ATMD. First, the SSC controller is optimally designed for the seismic control of the structure subjected to an artificial earthquake. Then, the effectiveness of the optimized SSC (OSSC) is assessed in reducing the seismic responses of the structure subjected to four near- and far-fault earthquake excitations. The efficient performance of the OSSC technique is also validated and compared with that of a number of the control techniques such as linear quadratic regulator (LQR), fuzzy logic control (FLC), proportional-integral-derivative (PID), and optimal sliding mode control (OSMC). Comparative results demonstrate the efficiency and robustness of the proposed OSSC in comparison with those of the other controllers.

关键词： active tuned mass damper optimum design particle swarm optimization algorithm sliding sector control structural control

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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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Structures and stabilities of UPbn (n ≤ 18) clusters: A first-principles global optimization calculation

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MATERIALS TODAY COMMUNICATIONS 2023年 36卷

作者： Gao, Chun Guo, Guina Hu, Shuxian Liu, Haitao Zou, Wenli Zhang, Ping Yan, Jun Inst Appl Phys & Computat Math Lab Computat Phys Beijing 100088 Peoples R China Northwest Univ Inst Modern Phys Xian 710127 Shaanxi Peoples R China Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China Shaanxi Key Lab Theoret Phys Frontiers Xian 710127 Shaanxi Peoples R China Peking Univ Ctr Appl Phys & Technol Beijing 100871 Peoples R China

Endohedrally doped clusters have received much attention because they can act as superatoms and have great potential as the building blocks for cluster-assembled materials. We have carried out a comprehensive study based on the combination of the particle swarming optimization algorithm and the relativistic density functional theory, to obtain geometric structures and stabilities of lead (Pb) clusters doped with one uranium (U) atom. A gradual evolution pattern was observed with the increasing number of Pb atoms, from exohedrally doped structures to quasi-endohedral structures, and finally to endohedrally doped structures. At least 12 and 11 Pb atoms were necessary to encapsulate the U atom completely in the calculation without and with the spin-orbit coupling (SOC) effect respectively. The UPb16 cluster was represented as a highly stable endohedral cage structure with the U atom around its center. In addition, the crucial role of the SOC effect was emphasized. We hope that our findings will provide a fundamental understanding of actinide-doped lead clusters, which may be quite different from their light element analogues.

关键词： Intermetalloid cluster Endohedrally doped cluster particle swarm optimization algorithm Density functional theory Relativistic effect

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

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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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