A New Method for the Nonintrusive Load Monitoring Based on BP Neural Network

2017 ◽  
Author(s):  
TiangYang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
New Method ◽  
Load Monitoring

scholarly journals Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1385
Author(s):  
Sheng Wu ◽  
Kwok L. Lo
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Bp Neural Network ◽  
Distribution Curve ◽  
Electrical Characteristics ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Current Frequency ◽  
Operation Pattern ◽  
Load Monitoring

Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

Detection of Violent Video with Audio-Visual Features Based on MPEG-7

2013 ◽  
Vol 411-414 ◽  
pp. 1002-1007 ◽  
Author(s):  
Yu Qing Peng ◽  
Wei Liu ◽  
Cui Cui Zhao ◽  
Tie Jun Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Color Space ◽  
New Method ◽  
Visual Features ◽  
Fusion Model ◽  
Data Redundancy ◽  
Dominant Color ◽  
Time Motion

In order to solve the problem that there isn’t an effective way to detect the violent video in the network, a new method using MPEG-7 audio and visual features to detect violent video was put forward. In feature extraction, the new method targeted chosen the features about audio, color, space, time, motion. Parts of MPEG-7 descriptors were added and improved: instantaneous feature of audio was added, motion intensity descriptor was customized, and a new method to extract dominant color of video was proposed. BP neural network optimized by GA was used to fuse the features. Experiment shows that these selected features are representative, discriminative and can reduce the data redundancy. Fusion model of neural network is more robust. And the method of fusing audio and visual features improves the recall and precision of video detecting.

Fault Diagnosis of Analog Circuit Based on Multi-Test Points and Multi-Feature Information

2013 ◽  
Vol 313-314 ◽  
pp. 277-280
Author(s):  
Qiang Pan ◽  
Chao Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
New Method ◽  
Feature Information ◽  
Simulation Results ◽  
Test Points ◽  
Sample Set

In the process of use BP neural network to fault diagnosis of analog circuits, the network input which represents fault signature was very important. A given new method which base on multi-points and multi-feature information is taken to construct the original sample set. With this method to construct the original fault signature set, then as the input of BP neural network and train the network. Simulation results show that, the network train with sample set which constructed by this method use in fault diagnosis of analog circuits is better accuracy than traditional methods. Proved the feasibility of this method in fault diagnosis of analog circuits, and offer a new method for fault diagnosis of analog circuits.

scholarly journals A New Method for Predicting Crosstalk of Random Cable Bundle Based on BAS-BP Neural Network Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20224-20232 ◽  
Author(s):  
Chao Huang ◽  
Yang Zhao ◽  
Wei Yan ◽  
Qiangqiang Liu ◽  
Jianming Zhou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
New Method ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Cable Bundle
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