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Hybrid Structured Artificial Network For Compressive Strength Prediction Of HPC Concrete

淡江理工学刊 2023年第7期26卷 989-999页

作者： Liang Chen

This study aims to develop a surrogate artificial neural network-based technique for predicting the compressive strength of concrete, which is the most significant factor in the life service of concrete and its durability in civil construction projects of civil engineering. For this goal, a data set for high-performance concrete is gathered from the literature repository, which includes a different percentage of fly ash, silica fume, and superplasticizer in the mix designs. The data set is applied to train and validate the optimal structured artificial neural network optimized by innovative equilibrium optimization and particle swarm optimization algorithms. The results showed that the EOANN-I and PANN-I model with the R2 and RMSE values of 0.9889, 1.771, 0.9881, and 1.8813, stood at the first and second stage of the most capable models in the prediction of HPC compressive strength. Although the PANN-I model's complexity is lower than the EOANN-I model, the rate of its prediction accuracy covers its lack of complexity.

关键词： HPC concrete compressive strength artificial neural network equilibrium optimization algorithm particle swarm optimization algorithm

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Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Xu, Lei Rongke Digital Energy Informat Technol Beijing Co Beijing Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

关键词： Energy hub systems Renewable energy integration Multi-objective optimization particle swarm optimization algorithm System reliability

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Optimizing Thermal-Elastic Properties of C/C-SiC Composites Using a Hybrid Approach and PSO algorithm

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MATERIALS 2016年第4期9卷 222-222页

作者： Xu, Yingjie Gao, Tian Northwestern Polytech Univ ESAC Xian 710072 Peoples R China

Carbon fiber-reinforced multi-layered pyrocarbon-silicon carbide matrix (C/C-SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C-SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C-SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method.

关键词： C/C-SiC composites micromechanical modeling BP neural network particle swarm optimization algorithm thermal-elastic properties

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Performance Study of Random Layout Light Source for Visible Light Communication System

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PHOTONICS 2024年第12期11卷 1127-1127页

作者： Ke, Xizheng Zheng, Yuwei Liang, Jingyuan Qin, Huanhuan Xian Univ Technol Fac Automat & Informat Engn Xian 710048 Peoples R China Shaanxi Civil Mil Integrat Key Lab Intelligence Co Xian 710126 Peoples R China

In indoor visible light communication, a rational layout of light sources is required to ensure that multiple users at different locations in the room can obtain better communication quality and achieve uniform coverage of optical power on the receiving surface. The article investigates the performance of an indoor visible light communication system when the random layout of the Matern hardcore point process is used and compares it with the communication performance under the Poisson point process and the binomial point process. A particle swarm optimization algorithm is introduced based on the Matern hardcore point process, where the points generated under the Matern hardcore point process are used as the initial positions of the particles, and optimization adjustments are made according to the objective function to find the optimal layout. The results show that compared with the Poisson point process and the binomial point process, the use of the Matern hardcore point process to randomly lay out the LEDs makes the light intensity in the system more uniform and the numerical fluctuation of the received power is smaller. The uniformity of the indoor illumination after the combination of the Matern hardcore point process and the particle swarm optimization algorithm reaches 0.84, the deviation of the peak power is reduced by 20%, and the average signal-to-noise ratio value is 0.86, which is an increase in the average signal-to-noise ratio compared to the average signal-to-noise ratio before optimization.

关键词： visible light communication light source layout matern hardcore point process particle swarm optimization algorithm signal-to-noise ratio

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Error analysis and modeling of the racetrack magnetohydrodynamic linear motion sensor

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第4期35卷 046118-046118页

作者： Xu, Mengjie Mo, Jiahui Xia, Chenyan Yang, Lei Ji, Feng Chen, Jingjing Li, Xingfei Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Hefei Univ Technol Anhui Prov Key Lab Measuring Theory & Precis Instr Hefei 230009 Anhui Peoples R China Tianjin Univ State Key Lab Precise Measurement Technol & Instru Tianjin 300072 Peoples R China

Micro-vibration measurement methods for spacecraft structures mainly include the use of an accelerometer, laser, and magnetohydrodynamic (MHD) measurement methods. The micro-vibration measurement sensor developed based on the MHD measurement method has no mechanical wear between internal components, a fast high-frequency vibration response, and strong anti-interference properties. To reduce the measurement error of the racetrack MHD linear motion sensor developed in the laboratory, this paper investigated the sensor error, analysed the error source, combined the BP neural network optimized by particle swarm optimization (PSO) with variational mode decomposition (VMD), used the VMD-PSO-BP neural network to establish the error compensation model of the racetrack MHD linear motion sensor, and combined the PSO-BP neural network with wavelet threshold de-noising (WTD). The WTD-PSO-BP and RBF neural networks were used to develop the error compensation model of the racetrack MHD linear motion sensor. Comparing the three models, the experimental results show that the VMD-PSO-BP model has the best compensation effect. The mean absolute error of the output signal of the racetrack MHD linear motion sensor compensated by the VMD-PSO-BP neural network model was 1-2 times lower than that before compensation, the signal-to-noise ratio was 10 times higher on average, and the correlation coefficient was more than 0.95.

关键词： magnetohydrodynamic linear motion sensor error analysis particle swarm optimization algorithm variational mode decomposition

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A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

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WATER 2023年第23期15卷 4117-4117页

作者： Dai, Xue Li, Xiaoqin Zhang, Yuguang Li, Wenping Meng, Xiangsheng Li, Liangning Han, Yanbo China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China State Investment Hami Energy Dev Co Ltd Hami 839000 Peoples R China

With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 x 10-4, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference

关键词： water abundance particle swarm optimization algorithm genetic algorithm BP neural network FAHP

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A novel blind deconvolution method and its application to fault identification

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JOURNAL OF SOUND AND VIBRATION 2019年 460卷 114900-000页

作者： Cheng, Yao Chen, Bingyan Mei, Guiming Wang, Zhiwei Zhang, Weihua Southwest Jiaotong Univ State Key Lab Tract Power 111First SectNorth Second Ring Rd Chengdu 610031 Sichuan Peoples R China

Blind deconvolution is a method for enhancing the fault feature of rolling element bearings. Based on different maximization criteria, including kurtosis, correlated kurtosis, D-norm, multi-D-norm, and cyclostationarity indicator, different blind deconvolution algorithms have been proposed as powerful tools for fault feature extraction. However, kurtosis and D-norm are susceptible to extreme values, while the other three criteria strongly rely on prior knowledge of the fault period. To overcome the shortcomings of the existing criteria, this study proposes a new criterion called impulse-norm. It is a time-domain parameter defined as the ratio of the average amplitude of the first several maximum energy points to the energy of the entire signal. As opposed to kurtosis and D-norm, the impulse-norm is not affected by strong random impulses. Unlike correlation kurtosis, multi-D-norm and cyclostationarity indicator, it is also independent from the fault period. Based on impulse-norm, we also propose a new deconvolution algorithm called particle swarm optimization-based maximum impulse-norm deconvolution. This blind deconvolution algorithm employs generalized sphere coordinate transformation and adopts the PSO algorithm to optimally solve the filter coefficients by maximizing the impulse-norm of the signal being filtered. The proposed method was validated using simulated signals and high-speed train axle-box bearing experimental signals. The simulation and experimental results indicated that the proposed PSO-MIND method can effectively identify the weak impulse fault feature of rolling element bearings. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Blind deconvolution particle swarm optimization algorithm Fault identification Railway Rolling element bearing

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

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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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