• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.82-86
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• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.776_777
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.6-11
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• 2003

Recently, the modular network was proposed in a way to keep the size of the neural network small. The modular network solves the problem by splitting it into sub-problems. In this aspect, fuzzy systems act in a similar way. However, in a fuzzy system, there must be an expert rule which separates the input space. To overcome this, fuzzy-neural network has been used. However, the number of fuzzy rules grows exponentially as the number of input variables grow. In this paper, we would like to solve the size problem of neural networks using modular network with the hierarchic structure. In the hierarchic structure, the output of precedent module affects only the THEN part of the rule. Finally, the rules become shorter being compared to the rule of fuzzy-neural system. Also, the relations between input and output could be understood more easily in the Proposed modular network and that makes design easier.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.331-333
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• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.47-55
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• 2000

In this paper, a vision-based roadway detection algorithm for an automated vehicle control system, based on roadway sign information on roads, is proposed. First, in order to detect roadway signs, the color scene image is enhanced under hue-invariance. Fuzzy logic is employed to simplify the enhanced color image into a binary image and the binary image is morphologically filtered. Then, an effective algorithm of locating signs based on binary rank order transform (BROT) is utilized to extract signs from the image. This algorithm performs better than those previously presented. Finally, the inner shapes of roadway signs with curving roadway direction information are recognized by neural networks. Experimental results show that the new detection algorithm is simple and robust, and performs well on real sign detection. The results also show that the neural networks used can exactly recognize the inner shapes of signs even for very noisy shapes.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.47-49
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks(ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.88-90
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• 2007

This paper is proposed high performance speed control using AFNN controller. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. The control performance of the AFNN controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.306-310
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• 2007

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks(ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm