Real-time intelligent control and cascading failure prevention in microgrid systems based on neural network algorithm: an experimental approach

2016 ◽  
Vol 7 (3) ◽  
pp. 292 ◽  
Author(s):  
Rabie Belkacemi ◽  
Sina Zarrabian
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Intelligent Control ◽  
Experimental Approach ◽  
Cascading Failure ◽  
Failure Prevention ◽  
Network Algorithm ◽  
Neural Network Algorithm

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Research of BP Neural Network Algorithm Testing Platform Based on OPC Communication

2014 ◽  
Vol 668-669 ◽  
pp. 575-580
Author(s):  
Bing Wu Chen ◽  
Xing Chen ◽  
Hui Peng Shu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Bp Neural Network ◽  
Data Exchange ◽  
Communication Process ◽  
Effective Control ◽  
Testing Platform ◽  
Network Algorithm ◽  
Neural Network Algorithm

Effective control algorithm can be applied to the real-time control system by the data exchange between MCGS and MATLAB with OPC technology, as a result, a testing platform for advanced control algorithms is established. The paper presents a second-order liquid level control system for research example; the BP neural network algorithm is applied to the control system. The communication process verifies that the data exchange is reliable and the simulation results show the control of the BP neural network algorithm for real-time optimized control process is effective.

scholarly journals The implementation of an object detection algorithm on the FT processor

2018 ◽  
Vol 173 ◽  
pp. 03081
Author(s):  
Bin Li ◽  
Shuming Chen ◽  
Chao Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Detection Algorithm ◽  
Utilization Rate ◽  
Time Requirement ◽  
Processor Performance ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the continuous development of automatic drive and neural networks, it is possible to use neural network algorithm to carry out object detection in unmanned driving. Usually, the computation of neural network algorithm is huge. How to efficiently compute the algorithm and meet the real-time requirement is a challenge. In this paper, a sparse neural network algorithm is proposed, which can improve the utilization rate of processors. The object detection algorithm YOLO is implemented on the processor. Its performance is equivalent to the current best processor performance.

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