scholarly journals An Interval Reliability Demand Prediction Method Combined with XGBoost and D-S Evidence Theory in Film Preparation Period

2021 ◽  
Vol 2025 (1) ◽  
pp. 012022
Author(s):  
Zhongjun Tang ◽  
Lang Ni
Keyword(s):  
Evidence Theory ◽  
Prediction Method ◽  
Demand Prediction ◽  
Interval Reliability ◽  
Preparation Period ◽  
Film Preparation

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Wen Tian ◽  
Huiqing Xu ◽  
Yixing Guo ◽  
Bin Hu ◽  
Yi Yao
Keyword(s):  
Traffic Congestion ◽  
Southern China ◽  
Probabilistic Method ◽  
Prediction Method ◽  
Weather Conditions ◽  
Error Distribution ◽  
Time Interval ◽  
Distribution Characteristics ◽  
Traffic Demand ◽  
Demand Prediction

In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.

scholarly journals A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yajun Zhou ◽  
Lilei Wang ◽  
Rong Zhong ◽  
Yulong Tan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Prediction Method ◽  
Chain Model ◽  
Daily Production ◽  
Forecasting Accuracy ◽  
Traffic Demand ◽  
Demand Prediction ◽  
Proposed Model ◽  
Bike Sharing

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

scholarly journals Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259284
Author(s):  
Hailan Ran
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Inventory Management ◽  
Management System ◽  
Prediction Method ◽  
Spare Parts ◽  
Collaborative Computing ◽  
Demand Prediction ◽  
Industrial Enterprises ◽  
Inventory Management System

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

scholarly journals DEVELOPMENT OF A SHORT-TERM PREDICTION SYSTEM FOR ELECTRICITY DEMAND

10.6036/9894 ◽  
2021 ◽  
Vol 96 (3) ◽  
pp. 285-289
Author(s):  
STEVEN VAN VAERENBERGH ◽  
ALBERTO SALCINES MENEZO ◽  
OSCAR COSIDO COBOS
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Prediction Method ◽  
Electrical Energy ◽  
Data Availability ◽  
Simple Method ◽  
Demand Prediction ◽  
Power Machine ◽  
Short Term Prediction ◽  
Learning Data

This article describes the development of a prediction method for the demand for electrical energy of a marketer's customer portfolio. The project is motivated by the economic benefit produced when the entity has accurate estimates of energy demand when buying energy in an electricity auction. The developed system is based on time series analysis and machine learning. As this system was part of a real-world project with data from a real environment, the article focuses on practical aspects of the design and development of system of these characteristics, such as the heterogeneity of data sources, and the delay in data availability. The predictions obtained by the developed system are compared with the results of a simple method used in practice. Keywords: energy demand prediction, electric power, machine learning, data-driven prediction.

Application of Grey Least Square Support Vector Machine in New Equipment Materiel Demand Prediction

2014 ◽  
Vol 602-605 ◽  
pp. 3333-3337
Author(s):  
Shuang Shuang Yu ◽  
Tie Ning Wang ◽  
Ning Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Original Data ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Grey Prediction ◽  
Demand Prediction ◽  
Efficient Prediction ◽  
General Demand

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.

Information Fusion Algorithm for Electromechanical Equipment Based on DS Evidence Theory

2013 ◽  
Vol 380-384 ◽  
pp. 1125-1128 ◽  
Author(s):  
Yao Hui Zhang ◽  
Jun Xu ◽  
Kang Du
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Prediction Method ◽  
Electrical Equipment ◽  
Fusion Algorithm ◽  
State Prediction ◽  
Level Information ◽  
The Difference ◽  
Equipment State ◽  
Electromechanical Equipment

According to the problem that the difference of test mode, mixed quantitative and qualitative information of electromechanical equipment state prediction, a state prediction method based on information fusion was proposed in this paper. It was used DS evidence theory to fuse decision level information of electromechanical equipments at this method. Simulation results showed that it is feasible and effective that information fusion technology is applied on the state prediction for mechanical and electrical equipment. Information for decision-making integrated repeatedly by different forecasting methods, can greatly reduce the blindness of judgment and improve the accuracy of state prediction.

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