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Application of Fuzzy Predictive Control for Smoke Sensing Alarm in Automatic Train Driving Control

IEEE ACCESS 2024年 12卷 150719-150738页

作者： Huang, Peili Zhengzhou Police Univ Sci Publ Order Dept Zhengzhou 450000 Peoples R China

The efficacy of traditional smoke detection methods is often constrained by the use of fixed threshold judgments, which impedes the ability of these methods to accurately and timely detect smoke, thereby increasing the risk of train operation. The objective of this study is to enhance the precision and reliability of automated driving control, thereby enabling vehicles to adapt to diverse and challenging environments more effectively. Based on fuzzy predictive control and granular computing, the algorithm is improved to solve the problem of rule explosion. Combining the annealing algorithm and particle swarm optimization algorithm to optimize the system parameters, a new fuzzy predictive control system for smoke sensing alarms is designed. The experimental results showed that the proposed algorithm on the Rosenbrook algorithm was concentrated between 45 and 51, demonstrating good stability and GSC, and having an excellent ability to jump out of local optima. Furthermore, the model demonstrated a rapid and stable convergence of the prediction accuracy curve, with an average prediction accuracy of 95.1% and a final value of 97.8%. The proposed smoke sensing alarm fuzzy predictive control has the advantages of being less prone to local optima, high accuracy, short time consumption, and fast response in automatic driving control. It provides new solutions and methods for other similar problems in the field of intelligent transportation.

关键词： Prediction algorithms Accuracy Fuzzy logic Safety optimization Predictive control Temperature sensors Reliability Predictive models Sensors particle swarm optimization Smoke detectors Fuzzy predictive control annealing algorithm particle swarm optimization algorithm smoke alarm metropolis guidelines

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Automatic History Matching Method and Application of Artificial Intelligence for Fractured-Porous Carbonate Reservoirs

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PROCESSES 2024年第12期12卷 2634页

作者： Tong, Kaijun Song, Wentong Chen, Han Guo, Sheng Li, Xueyuan Sun, Zhixue CNOOC Int Ltd Beijing 100028 Peoples R China China Univ Petr Huadong Sch Petr Engn Qingdao 266580 Peoples R China

Fractured-porous carbonate reservoirs, mainly composed of dolomites and crystalline rocks with various rock types and extremely poor initial porosity and permeability, are dominated by tectonic fractures and exhibit extreme heterogeneity. The fracture system plays a predominant role in hydrocarbon fluid transport. Compared with conventional sandstone reservoirs, fracture geometry and topological structure parameters are key factors for the accuracy and computational efficiency of numerical simulation history matching in fractured reservoirs. To address the matching issue, this paper introduces an artificial intelligence history matching method combining the Monte Carlo experimental planning method with an artificial neural network and a particle swarm optimization algorithm. Taking reservoir geological parameters and phase infiltration properties as the objective function, this method performs reservoir production history matching to correct the geological model. Through case studies, it is verified that this method can accurately correct the geological model of fractured-porous reservoirs and match the observed production data. This research represents a collaborative effort among multiple disciplines, integrating advanced algorithms and geological knowledge with the expertise of computer scientists, geologists, and engineers. Currently the world's major oilfields history fitting is mainly based on reservoir engineers' experience to fit;the method is applicable to major oilfields, but the fitting accuracy and fitting efficiency is severely limited, the fitting accuracy is less than 75%, while the artificial intelligence history fitting method shows a stronger applicability;intelligent history fitting is mainly based on the integrity of the field data, and as far as the theory is concerned, the accuracy of the intelligent history fitting can be up to 100%. Therefore, AI history fitting can provide a significant foundation for mine field research. Future research cou

关键词： fractured-porous reservoir Monte Carlo test planning method particle swarm optimization algorithm artificial neural network artificial intelligence history matching

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Application of NIPOS-SVM Model for Evaluation of Water Richness of Coal Seam Roof Aquifer-A Case Study of the Xinhu Coal Mine in Huaibei, China

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WATER 2024年第24期16卷 3670-3670页

作者： Xing, Maolin Wang, Qiqing Xu, Jie Li, Wenping China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China CCTEG Xian Res Inst Grp Co Ltd Xian 710077 Peoples R China State Key Lab Coal Mine Disaster Prevent & Control Xian 710077 Peoples R China Shaanxi Key Lab Prevent & Control Technol Coal Min Xian 710077 Peoples R China

The sandstone roof of coal seams, with its high porosity and developed fissures, serves as a favorable reservoir for groundwater. Predicting and assessing the water-bearing capacity of the sandstone roof in coal seams is crucial for the rational development of coal tunnels, ensuring safe and efficient production in mining areas. This study targets the Cenozoic bottom aquifer of the No. 81 mining area of the Xinhu Coal Mine. By analyzing the geological and hydrogeological conditions of the mining area, it was found that the primary water-bearing strata of the coal seam roof are the Permian sandstone fracture waters. Key factors for evaluating the water richness of the sandstone aquifer were identified as aquifer thickness, aquifer depth, core recovery rate, coal seam dip angle, brittleness-plasticity ratio, and the sand-mud interlayer index. A novel particle swarm optimization algorithm incorporating improved sine chaos mapping (NIPSO) to enhance the support vector machine (SVM), thereby constructing the NIPSO-SVM model, was applied for quantitative evaluation of water richness in the study area. Experimental results indicated that the NIPSO-SVM model has high accuracy and practical engineering application value in predicting water richness, which is significant for ensuring the safe production of coal mines.

关键词： water-richness support vector machine model particle swarm optimization algorithm NIPSO GIS

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ALLOCATION OF RES AND DSTATCOM FOR THE OPTIMAL DISTRIBUTION SYSTEM PLANNING 22

ALLOCATION OF RES AND DSTATCOM FOR THE OPTIMAL DISTRIBUTION ...

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22nd National Power Systems Conference (NPSC)

作者： Shukla, Madhu Jain, Prerna MNIT Jaipur Dept Elect Engn Jaipur India

ISBN: (纸本)9781665462020

The increasing demand of the load causes the voltage imbalance and increase in power loss. This is forcing the distribution companies to take the proper actions in the planning of distribution system. The allocation of Renewable Energy Source (RES) and Distribution Static Compensator (DSTATCOM) are considered in the distribution system planning to reduce the losses and maintain the voltage of the system within the required limit with the growing load demand. Due to the environmental concerns, the RES is becoming the must to consider as a part of the power system whereas the use of DSTATCOM is for the reactive power compensation to improve the power quality and the voltage profile. For the simultaneous allocation of RES and DSTATCOM in balanced Radial Distribution System (RDS), Loss Sensitivity Factor (LSF) used to get the candidate buses and particle swarm optimization (PSO) algorithm is used for optimal sizes. For the calculation of load flow of the system an efficient Backward-Forward Sweep Load Flow algorithm is used. The objective of the proposed methodology is to minimize the total cost which is the sum of total investment cost of RES and DSTATCOM, power purchase cost and pollution emission cost, keeping the voltage of each bus of the system within the limit. From the result obtained for the 38-bus balanced radial distribution system (RDS), it is clearly observed that the total active and reactive power losses are reduced and voltages of the system is improved with the optimal allocation of RES and DSTATCOM at minimum cost.

关键词： Renewable Energy Source Distribution Static Compensator Loss Sensitivity Factor particle swarm optimization algorithm Balanced RDS

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Research on Education Cost Prediction Based on Improved BP Neural Network 6

Research on Education Cost Prediction Based on Improved BP N...

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6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Wei, Ping Lanzhou Resources & Environm Voc Tech Univ Coll Finance & Business Yellow River Basin Ecotope Integrat Ind & Educ Re 1173 Jiatan Lanzhou City 730000 Gansu Peoples R China

ISBN: (纸本)9781665454889

In order to better predict the education cost and improve the utilization rate of university funds, an education cost prediction model based on K-means clustering improved PSO-BP neural network was proposed. Firstly, aiming at the problem that BP neural network is prone to trap into local optimization and its convergence speed is slow, PSO was proposed to optimize the parameters of BP. Considering the limitations of PSO, the K-means clustering algorithm was added to cluster particle swarms, thereby enriching the diversity of the population. Finally, the improved particle swarm optimization algorithm was used for the education cost prediction of BP neural network. The results showed that the optimization performance of the improved particle swarm optimization algorithm is more stable and the convergence speed is faster. At the same time, in terms of prediction accuracy, the education cost prediction accuracy of the improved PSO-BP is higher.

关键词： BP neural network particle swarm optimization algorithm K-means clustering education cost prediction

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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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Prediction of per capita water consumption for 31 regions in China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第23期28卷 29253-29264页

作者： Meng, Xiangmei Wu, Lifeng Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China

Considering the shortage of per capita water resources in China, the paper established a fractional order accumulated grey prediction model (FGM(1,1)) to predict per capita water consumption of 31 regions (provinces, municipalities, and autonomous regions) in China from 2019 to 2024. The results show that per capita water consumption varies greatly across the different regions. Among them, per capita water consumption of nine regions (i.e., Beijing, Tianjin, Inner Mongolia, Jiangsu, Henan, Hubei, Guizhou, Yunnan, and Shaanxi) shows an increasing trend, whereas per capita water consumption in other 22 regions shows a downward trend. The predictive results can provide a basis for water resource management in China.

关键词： Per capita water consumption Water resources Fractional order accumulated gray prediction model particle swarm optimization algorithm

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Forecasting the renewable energy consumption of the European countries by an adjacent non-homogeneous grey model

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APPLIED MATHEMATICAL MODELLING 2021年第Part2期89卷 1932-1948页

作者： Liu, Lianyi Wu, Lifeng Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China

In this paper, a new adjacent non-homogeneous grey model was proposed to predict renewable energy consumption in Europe. Based on the principle of adjacent accumulation, the proposed model emphasizes the weight relationship between the latest value and the historical data. Meanwhile, the heuristic algorithm is used to optimize the parameters of the proposed model. The model's performance was tested by the examples from four central European countries, including Austria, the Czech Republic, Hungary and Poland. In addition, the consumption of renewable energy in Europe and the world was predicted respectively, which showed that Europe's proportion of the world's renewable energy consumption will gradually decline. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Renewable energy consumption Adjacent accumulation Grey model European countries particle swarm optimization algorithm

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Multi-model fusion stacking ensemble learning method for the prediction of berberine by FT-NIR spectroscopy

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INFRARED PHYSICS & TECHNOLOGY 2024年 137卷

作者： Li, Xiaoyu Chen, Huazhou Xu, Lili Mo, Qiushuang Du, Xinrong Tang, Guoqiang Guilin Univ Technol Sch Math & Stat Guilin 541004 Peoples R China Guilin Univ Technol Ctr Data Anal & Algorithm Technol Guilin 541004 Peoples R China Beibu Gulf Univ Coll Marine Sci Qinzhou 535011 Peoples R China Guilin Univ Technol Sch Math & Stat 12 Jiangan Rd Guilin 541004 Peoples R China

Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSOXGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi -model fusion stacking ensemble learning are superior to the single -model regression methods. Therefore, the stacking ensemble learn

关键词： FT-NIR spectroscopy Berberine Stacking ensemble learning particle swarm optimization algorithm Adaptive inertia weight

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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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