• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.3
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• pp.128-132
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• 2003

This paper presents the optimum design of a Linear Induction Motor (LIM) using Genetic algorithm, Niching Genetic algorithm and Neural Network. The design variables are optimized by different optimization methods and the results are discussed.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.397-400
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• 1989

This study is to design a method of parameter estimation for a second order linear time invarient system of self-tuning controller utilizing the neural network theory proposed by Hopfield. The result is compared with the other methods which are commonly used in controller theories.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.346-349
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• 2003

For a traveling crane, various control methods such as neural network predictive control and TDOFPID(Two Degree of Freedom Proportional Integral Derivative) are studied. So in this paper, we proposed improved navigation method to reduce transfer time and sway with anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the NNPPID(Neural Network Predictive PID) controller to control the precise move and speedy navigation. The proposed predictive control system is composed of the neural network predictor, TDOFPID controller, and neural network self-tuner. We analyzed ASC(Automated Stacking Crane) system and showed some computer simulations to prove excellence of the proposed controller than other conventional controllers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1125-1128
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• 1993

We propose the new method about the neural-based pattern recognition by using Hadamard transform for the improvement of learning speed, stability and flexibility of network. We can obtain the spatial feature of pattern by Hadamard transformed pattern. We carried out an experiment to estimate the effect of Hadamard transform. We tried the learning of numeric patterns, and tried the pattern recognition with noisy pattern. As a result, the learning times of the network for the 'Hadamard' case is smaller than that of usual case. And the recognition rate of the network for the 'Hadamard' case is higher than that of usual case, too.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.285-291
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• 2008

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.17-22
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• 2021

Recently, image super-resolution techniques used in convolutional neural networks (CNN) have led to remarkable performance in the research area of digital image processing applications and computer vision tasks. Convolutional layers stacked on top of each other can design a more complex network architecture, but they also use more memory in terms of the number of parameters and introduce the vanishing gradient problem during training. Furthermore, earlier approaches of single image super-resolution used interpolation technique as a pre-processing stage to upscale the low-resolution image into HR image. The design of these approaches is simple, but not effective and insert the newer unwanted pixels (noises) in the reconstructed HR image. In this paper, authors are propose a novel single image super-resolution architecture based on synchronized depthwise separable convolution with Dense Skip Connection Block (DSCB). In addition, unlike existing SR methods that only rely on single path, but our proposed method used the synchronizes path for generating the SISR image. Extensive quantitative and qualitative experiments show that our method (SDCN) achieves promising improvements than other state-of-the-art methods.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.1 no.1
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• pp.26-39
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• 2002

This paper describes design and implementation of home network control protocol for networked home appliances. The proposed network protocol has four-layered protocol structure and device-modem interface structure for the flexibility of modems based on power line communication. The standard message set is specified to guarantee the interoperability between various home appliances The proposed protocol can be easily implemented because it has minimum network overhead.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.223-225
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• 2000

Network congestion is important problem that must be managed for active power contracts in deregulated power industry. Existing network partition technique is based on statistical method don't suggest clear network partition criterion. So in this paper we proposed a new network partition technique using the marginal cost sensitivity to fairly allocate congestion cost to network user.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.65-68
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• 2002

Distributed Generators (DG) are rapidly increasing and most of them are interconnected with distribution network to supply power into the network. Therefore, DG may make significant impacts on distribution system operation. protection, and control with respect to the voltage regulation, voltage flicker, harmonics, fault current levels, the losses of the network, etc. These impacts would be demerits for both of DG and distribution networks. And the operation of DG may be influenced by the abnormal grid condition such as disturbances occurred in the neighboring distribution feeders as well as the feeder directly connected with DG. This paper describes the influence of fault occurred in the interconnected power network on the DG operation and the impact of DG on the network load during the interruptions of utility power.