Neural Dynamics in Real-Time for Large Scale Biomorphic Neural Networks

1998 ◽  
pp. 481-486 ◽  
Author(s):  
M. Rossmann ◽  
C. Burwick ◽  
A. Bühlmeier ◽  
G. Manteuffel ◽  
K. Goser
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Large Scale ◽  
Neural Dynamics

Real-Time Smart Grids Control for Preventing Cascading Failures and Blackout using Neural Networks: Experimental Approach for N-1-1 Contingency

2016 ◽  
Vol 17 (6) ◽  
pp. 703-716 ◽  
Author(s):  
Sina Zarrabian ◽  
Rabie Belkacemi ◽  
Adeniyi A. Babalola
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Smart Grids ◽  
Intelligent Systems ◽  
Large Scale ◽  
Frequency Control ◽  
Test System ◽  
Selection Strategy ◽  
Control Approach ◽  
Contingency Condition

Abstract In this paper, a novel intelligent control is proposed based on Artificial Neural Networks (ANN) to mitigate cascading failure (CF) and prevent blackout in smart grid systems after N-1-1 contingency condition in real-time. The fundamental contribution of this research is to deploy the machine learning concept for preventing blackout at early stages of its occurrence and to make smart grids more resilient, reliable, and robust. The proposed method provides the best action selection strategy for adaptive adjustment of generators’ output power through frequency control. This method is able to relieve congestion of transmission lines and prevent consecutive transmission line outage after N-1-1 contingency condition. The proposed ANN-based control approach is tested on an experimental 100 kW test system developed by the authors to test intelligent systems. Additionally, the proposed approach is validated on the large-scale IEEE 118-bus power system by simulation studies. Experimental results show that the ANN approach is very promising and provides accurate and robust control by preventing blackout. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN approach showed more accurate and robust response than the MAS algorithm.

A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks

2012 ◽  
Author(s):  
Ken'ichi Morooka ◽  
Tomoyuki Taguchi ◽  
Xian Chen ◽  
Ryo Kurazume ◽  
Makoto Hashizume ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Large Scale

scholarly journals Segmentation of Illuminated Areas of Light Using CNN and Large-Scale RGB+D Dataset for Augmented and Mixed Reality Systems

2020 ◽  
pp. short58-1-short58-7
Author(s):  
Maksim Sorokin ◽  
Dmitriy Zhdanov ◽  
Andrey Zhdanov
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Mixed Reality ◽  
Depth Map ◽  
Light Sources ◽  
Realistic Rendering ◽  
Visual Difference

This work is devoted to the problem of restoring realistic rendering for augmented and mixed reality systems. Finding the light sources and restoring the correct distribution of scene brightness is one of the key parameters that allows to solve the problem of correct interaction between the virtual and real worlds. With the advent of such datasets as, "LARGE-SCALE RGB + D," it became possible to train neural networks to recognize the depth map of images, which is a key requirement for working with the environment in real time. Additionally, in this work, convolutional neural networks were trained on the synthesized dataset with realistic lighting. The results of the proposed methods are presented, the accuracy of restoring the position of the light sources is estimated, and the visual difference between the image of the scene with the original light sources and the same scene. The speed allows it to be used in real-time AR/VR systems.

Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks

2019 ◽  
Vol 49 (7) ◽  
pp. 2490-2503 ◽  
Author(s):  
Shuangming Yang ◽  
Jiang Wang ◽  
Bin Deng ◽  
Chen Liu ◽  
Huiyan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Large Scale ◽  
Spiking Neural Networks ◽  
Neuromorphic System

A Fault Diagnosis Method Based on Combination of Neural Network and Fault Dictionary

2013 ◽  
Vol 765-767 ◽  
pp. 2078-2081
Author(s):  
Ya Feng Meng ◽  
Sai Zhu ◽  
Rong Li Han
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Real Time ◽  
Large Scale ◽  
Fault Dictionary ◽  
Diagnosis Method ◽  
The Neural Networks ◽  
Trained Neural Network ◽  
Complex Circuits

Neural network and Fault dictionary are two kinds of very useful fault diagnosis method. But for large scale and complex circuits, the fault dictionary is huge, and the speed of fault searching affects the efficiency of real-time diagnosing. When the fault samples are few, it is difficulty to train the neural network, and the trained neural network can not diagnose the entire faults. In this paper, a new fault diagnosis method based on combination of neural network and fault dictionary is introduced. The fault dictionary with large scale is divided into several son fault dictionary with smaller scale, and the search index of the son dictionary is organized with the neural networks trained with the son fault dictionary. The complexity of training neural network is reduced, and this method using the neural networks ability that could accurately describe the relation between input data and corresponding goal organizes the index in a multilayer binary tree with many neural networks. Through this index, the seeking scope is reduced greatly, the searching speed is raised, and the efficiency of real-time diagnosing is improved. At last, the validity of the method is proved by the experimental results.

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