BPSO Applied to TNEP Considering Adequacy Criterion
Meisam Mahdavi,
Amir Bagheri
Issue:
Volume 4, Issue 1, June 2018
Pages:
1-7
Received:
10 November 2017
Accepted:
19 January 2018
Published:
30 January 2018
DOI:
10.11648/j.ajnna.20180401.11
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Abstract: Different methods have been proposed to solve the static transmission network expansion planning (STNEP) problem up to now. But in all of these studies, loading of transmission lines has not been studied using binary particle swarm optimization (BPSO) algorithm. BPSO is a good optimization method to solve nonlinear large-scale problems with discrete variables like STNEP. Thus, in this paper, STNEP problem is being studied considering network adequacy criterion using BPSO. The goal of this paper is obtaining a configuration for network expansion with lowest expansion cost and a specific adequacy. The proposed idea has been tested on the Garvers network. The results show that the network will possess maximum efficiency economically.
Abstract: Different methods have been proposed to solve the static transmission network expansion planning (STNEP) problem up to now. But in all of these studies, loading of transmission lines has not been studied using binary particle swarm optimization (BPSO) algorithm. BPSO is a good optimization method to solve nonlinear large-scale problems with discret...
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Architecture of the Extended-Input Binary Neural Network and Applications
Issue:
Volume 4, Issue 1, June 2018
Pages:
8-14
Received:
4 June 2018
Accepted:
20 June 2018
Published:
6 July 2018
DOI:
10.11648/j.ajnna.20180401.12
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Abstract: The proposed architecture of a binary artificial neural network is inspired by the structure and function of the major parts of the brain. Consequently it is divided into an input module that resemble the sensory (stimuli) area and an output module similar to the motor (responses) area. These two modules are single layer feed forward neural networks and have fixed weights to transform input patterns into a simple code and then to convert this code back to output patterns. All possible input and output patterns are stored in the weights of these two modules. Each output pattern can be produced by a single neuron of the output module asserted high. Similarly each input pattern produces a single input module neuron at binary 1. The training of this neural network is confined to connecting one output neuron of the input module at binary 1 that represents a code for the input pattern and one input neuron of the output module that produces the desired associated output pattern. Thus fast and accurate association between input and output pattern pairs can be achieved. These connections can be implemented by a crossbar switch. This crossbar switch acts similar to the thalamus in the brain which is considered to be a relay center. The role of the crossbar switch is generalized to an electric field in the gap between input and output modules and it is postulated that this field may be considered as a bridge between the brain and mental states. The input module encoder is preceded by the extended input circuit which ensures that the inverse of the input matrix exists and at the same time to make the derivation of this inverse of any order a simple task. This circuit mimics the processing function of the region in the brain that process input signals before sending them to the sensory region. Some applications of this neural network are: logical relations, mathematical operations, as a memory device and for pattern association. The number of input neurons can be increased (increased dimensionality) by multiplexing those inputs and using latches and multi-input AND gates. It is concluded that by emulating the major structures of the brain using artificial neural networks the performance of these networks can be enhanced greatly by increasing their speed, increasing their memory capacities and by performing a wide range of applications.
Abstract: The proposed architecture of a binary artificial neural network is inspired by the structure and function of the major parts of the brain. Consequently it is divided into an input module that resemble the sensory (stimuli) area and an output module similar to the motor (responses) area. These two modules are single layer feed forward neural network...
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An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection
Balage Don Hiroshan Lakmal,
Daisuke Oka,
Yoichi Shiraishi,
Kazuhiro Motegi
Issue:
Volume 4, Issue 1, June 2018
Pages:
15-23
Received:
3 July 2018
Accepted:
6 August 2018
Published:
1 September 2018
DOI:
10.11648/j.ajnna.20180401.13
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Abstract: This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enough to identify such differences between these two classes. Hence it is suggested to apply deep learning algorithms (DLA) as per the versatility and feature extraction power. Usually, some kinds of pre-processing are adopted before the application of DLA in order to increase the accuracy of inspection as well as to reduce the training and the application time of DLA. In this paper, the combinations of five kinds of pre-processing techniques and three types of DLAs are applied to the actual hammering sounds inspection of door strikers. Especially in two types of DLAs, the sound data have been evaluated as images. The evaluation results show that the combination of the wavelet analysis and the Convolutional Neural Network (CNN) only attained the 100% accuracy of inspection with great response time. The wavelet analysis and the CNN are independently attain the high performances comparing with others and it is likely that they are useful in this kind of hammering sound inspections.
Abstract: This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enou...
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