A Prediction Method of Power Energy Saving Potential Based on Rough Set Neural Network

2010 ◽  
Vol 44-47 ◽  
pp. 3795-3799
Author(s):  
Jin Ying Li ◽  
Ya Jun Wei ◽  
Jin Chao Li ◽  
Yu Zhi Zhao
Keyword(s):  
Neural Network ◽  
Energy Saving ◽  
Rough Set ◽  
Reduction Method ◽  
Prediction Method ◽  
Pollutant Emission ◽  
Resource Saving ◽  
Environment Friendly ◽  
Knowledge Reduction ◽  
Energy Saving Potential

Power industry is the key field of implementing energy saving and pollutant emission reduction in china, strengthen power energy saving is helpful to establish a resource-saving and environment-friendly society and promote a sustainable development of economic society. This paper synchronizes respective advantages of rough set and neural network, puts forward a prediction model-RSBPNN which uses rough set knowledge reduction method to prune the redundant and neural network to build a forecasting model.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

scholarly journals Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaolei Chen ◽  
Baoning Cao ◽  
Ishfaq Ahmad
Keyword(s):  
Neural Network ◽  
Sensor Network ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Video Data ◽  
Wireless Video ◽  
Resource Saving ◽  
Movement Data ◽  
Unit Module ◽  
Video Sensor

Live virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be deployed more widely. To solve the bandwidth and energy challenges, VR video viewport prediction has been proposed as a feasible solution. However, the existing works mainly focuses on the bandwidth usage and prediction accuracy and ignores the resource consumption of the server. In this study, we propose a lightweight neural network-based viewport prediction method for live VR streaming in WVSN to overcome these problems. In particular, we (1) use a compressed channel lightweight network (C-GhostNet) to reduce the parameters of the whole model and (2) use an improved gate recurrent unit module (GRU-ECA) and C-GhostNet to process the video data and head movement data separately to improve the prediction accuracy. To evaluate the performance of our method, we conducted extensive experiments using an open VR user dataset. The experiments results demonstrate that our method achieves significant server resource saving, real-time performance, and high prediction accuracy, while achieving low bandwidth usage and low energy consumption in WVSN, which meets the requirement of live VR streaming.

Research on the Improved Apriori Based on Rough Set Method

2011 ◽  
Vol 467-469 ◽  
pp. 306-311
Author(s):  
Xian Wen Luo
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Apriori Algorithm ◽  
Knowledge Reduction ◽  
Improved Algorithm

The paper adopts the knowledge reduction method in Rough Set theory to adjust Apriori Algorithm and proposes “Itemset Reduction Method”to reduce the amount of the candidate sets and improve the effeciency of the algorithm. In the experiments of the research, the results of both the improved algorithm and Apriori Algorithm are compared, and ideal results are gained.

ROUGH FEATURE SELECTION FOR NEURAL NETWORK BASED IMAGE CLASSIFICATION

2002 ◽  
Vol 02 (04) ◽  
pp. 541-555 ◽  
Author(s):  
CHANGJING SHANG ◽  
QIANG SHEN
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Image Classification ◽  
Rough Set ◽  
Reduction Method ◽  
Feature Reduction ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Study Results ◽  
Selection For

Effective feature selection is essential to the development of any intelligent classifier which is intended for use in high-dimension domains. This paper presents an approach that incorporates a rough set-assisted feature reduction method and a neural network-based classifier for image classification. The approach minimises the need for feature extraction without altering the underlying semantics of the features chosen. Through the proposed integration the size of the neural network classifier, which is sensitive to the dimensionality of the dataset, becomes manageable and the network is able to classify images that would otherwise require many more features to represent. Comparative study results from realistic applications demonstrate the success of this work.

Knowledge Push Technology for Complex Mechatronic Products Design Based on Ontology and Variable Precision Rough Set

2015 ◽  
Vol 799-800 ◽  
pp. 1107-1112
Author(s):  
Fa Lin Wang ◽  
Yu Guo ◽  
Wen He Liao ◽  
Bao Sheng Wu
Keyword(s):  
Rough Set ◽  
Development Process ◽  
Reduction Method ◽  
Product Development Process ◽  
Design Knowledge ◽  
Knowledge Reduction ◽  
Knowledge Repository ◽  
Variable Precision ◽  
Push Technology ◽  
Variable Precision Rough Set

In this paper, we present a new knowledge push technology for complex mechatronic products design based on ontology and variable precision rough set (VPRS). Ontology can explicitly represent knowledge semantics and let designers exchange knowledge about design and the product development process; while using the knowledge reduction method based on the VPRS method, the design knowledge repository is simplified and design rules also be distilled from the reduct design knowledge repository. On the basis of the above approach, multiple designers can efficiently share design knowledge and can obtain appropriate design knowledge during all design processes. Finally, a case is employed to validate the proposed method of this paper.

scholarly journals DISASSEMBLY TECHNOLOGY

2021 ◽  
Vol 81 (3) ◽  
pp. 138
Author(s):  
T. Тolkynbayev ◽  
◽  
L. Sivachenko ◽  
L. Utepbergenova ◽  
G. Abdukalikova ◽  
...  
Keyword(s):  
Energy Saving ◽  
Raw Materials ◽  
Industry Analysis ◽  
Resource Saving ◽  
Human Needs ◽  
Energy Saving Potential ◽  
Fixed Assets ◽  
Scientific Justification ◽  
High Level ◽  
Comprehensive Processing

The article provides a scientific justification of energy and resource-saving reserves that are not taken into account and are available for the implementation of industry, modernization of fixed assets and mastering the production of high-level products. They were evaluated on the basis of systematic industry analysis of technological divisions of processing of raw materials and materials. The energy-saving potential of the most expensive technological complexes is scientifically justified. The main result of the article is a description of the improvement of technical devices by developing innovative art achievements for the comprehensive processing of various materials and obtaining the necessary products to meet human needs.

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