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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Energy Efficiency Data Mining And Scheduling Optimization Of Discrete Workshop, Yugu Lin, Wang Yan
Energy Efficiency Data Mining And Scheduling Optimization Of Discrete Workshop, Yugu Lin, Wang Yan
Journal of System Simulation
Abstract: This paper addresses the optimization of energy consumption in discrete workshops and establishes the energy efficiency optimization model of discrete workshops. The relationship between data mining and knowledge discovery is established. Through scheduling data preprocessing and C4.5 decision tree learning algorithm, the discovery of scheduling knowledge is realized. Energy efficiency optimization calculation is achieved in discrete workshops by the combination of scheduling knowledge and improved differential evolution algorithm (IDE). By comparing with TLBO, GA and PSO, the feasibility of IDE algorithm is verified.
Study On Data-Driven Orca Preference Velocity, Wang Jie, Bin Chen, Yuan Peng, Ma Liang, Xiaogang Qiu
Study On Data-Driven Orca Preference Velocity, Wang Jie, Bin Chen, Yuan Peng, Ma Liang, Xiaogang Qiu
Journal of System Simulation
Abstract: Crowd evacuation is an important aspect of emergency management. Because of economic and safety reasons, emergency evacuation drill cannot be carried out repeatedly, however modeling and simulation has the obvious advantages of low cost, high repeatability, etc. Optimal Reciprocal Collision Avoidance (ORCA) is a velocity-based Collision Avoidance method, which has fine granularity and high computational efficiency, and is widely used in robot collision avoidance and other domains. In this paper, ORCA is introduced into crowd evacuation simulation, and the optimal parameters of ORCA under different kinds of speed strategies are identified by data-driven method. The optimal strategy …
Collaborative Optimal Scheduling Method For Production And Energy Consumption In Discrete Manufacturing Process, Wenjie Chen, Wang Yan
Collaborative Optimal Scheduling Method For Production And Energy Consumption In Discrete Manufacturing Process, Wenjie Chen, Wang Yan
Journal of System Simulation
Abstract: The collaborative optimization of manufacturing process and energy consumption is one of the hot research issues in intelligent optimization manufacturing. To solve the scheduling optimization problem of discrete manufacturing process, a coordinated scheduling optimization of production and energy consumption with the shortest processing time and lowest energy consumption is established. The model proposes an improved differential evolution algorithm based on adaptive mutation and crossover probability factor to solve the optimal scheduling problem. By establishing the operation-based coding method, the application of continuous algorithm in discrete optimization scheduling problem is realized by using ascending ordering rules. The effectiveness of …
Prediction Model Of Coke Quality Based On De-Bp Neural Network, Wenhua Tao, Zhengbo Yuan
Prediction Model Of Coke Quality Based On De-Bp Neural Network, Wenhua Tao, Zhengbo Yuan
Journal of System Simulation
Abstract: The quality of coke has a great effect on the furnace process. In order to solve the problem of large amount of calculation and inspection of coke quality prediction linear method, the coke quality prediction model based on DE-BP neural network is established on the basis of analyzing the factors affecting coke quality, which uses principal component analysis method to determine the parameters of the input vector with the coal and coke ash quality indicators Ad, sulfur Std, crushing strength M40, abrasion resistance M10 as the output vector prediction. The result of …
Prediction Model For Breakdown Voltage Of Transformer Oil Based On Relative Transformation And Kernel Principal Component Analysis, Yinguo Xiong
Journal of System Simulation
Abstract: Aiming at the difficulty of measuring the breakdown voltage of transformer oil on line, a new prediction model for breakdown voltage of transformer oil is proposed based on relative transformation (RT) and kernel principal component analysis (KPCA). By analyzing the factors that are closely related to the breakdown voltage, the original data space is converted to the relative data space by relative transformation to improve the distinguishability between data. KPCA is employed in the relative space for the purpose of data dimension reduction, denoising and extracting nonlinear features. Kernel principal components extracted by KPCA are used as the input …