• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.115-118
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• 2000

In order to approximate a nonlinear function, modular wavelet networks combining wavelet theory and modular concept based on single layer neural network have been proposed as an alternative to conventional wavelet neural networks and kind of modular network. Modular wavelet networks provide better approximating performance than conventional one. In this paper, we propose an effective method to construct an optimal modualr wavelet network using genetic algorithm. This is verified through experimental results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.253-258
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• 2010

The causal network represents the knowledge related to the dependency relationship between all attributes. If the causal network is available, the dependency relationship can be employed to estimate the missing values for improving the estimation performance. However, the previous method had a limitation in that it did not consider the bidirectional characteristic of the causal network. The proposed method considers the bidirectional characteristic by applying prior and posterior conditions, so that it outperforms the previous method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2747-2749
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• 2005

This paper presents an intelligent control system for an ac servo motor dirve to track periodic commands using a neural network. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.15-19
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• 2002

Automatic detection of interstitial lung disease using Neural Network is presented. The rounded opacities in the pneumoconiosis X-ray photo are picked up quickly by a back propagation (BP) neural network with several typical training patterns. The training patterns from 0.6 mm ${\O}$ to 4.0 mm ${\O}$ are made by simple circles. The total evaluation is done from the size and figure categorization. Mary simulation examples show that the proposed method gives much reliable result than traditional ones.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.95-98
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• 2002

Monitoring of the cutting trouble is necessarily required to do Factory Automation and Intelligent manufacturing system. Therefore, we constructed a monitoring system using neural network in order to monitor of the cutting trouble. From obtained result, it is shown that the cutting trouble can be monitored effectively by neural network

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• 한국정밀공학회지
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• 제28권4호
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• pp.436-445
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• 2011

As automation systems become intelligent and autonomous for productibility, industrial networks (fieldbuses) and network-based devices are essential components of intelligent manufacturing systems. However, there are obstacles for the wide acceptance of the network-based devices such as smart sensor and network-based actuator. First, there exist numerous fieldbus protocols that a network-based device should be able to support. Second, the whole network-based device has to be replaced when only the sensor of the module fails. In order to overcome these obstacles, a smart sensor/actuator is implemented as two units; one responsible for network communication and the other for sensor/actuator operations using IEEE 1451.0 standard. This paper presents a structure of the 1451.0-based smart sensor with multiple connectivity function designed by CANopen.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2266-2270
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• 2005

In this paper, an internet based control application for dynamical systems is implemented. This implementation is maily targeted for the part of advanced control education. Intelligent control algorithms are implemented in a PC so that a client can remotely access the PC to control a dynamical system through the internet. Neural network is used as an on-line intelligent controller. To have on-line learning and control capability, the reference compensation technique is implemented as intelligent control hardware of combining a DSP board and an FPGA chip. GUIs for a user are also developed for the user's convenience. Actual experiments of motion control of a DC motor have been conducted to show the performance of the intelligent control though the internet and the feasibility of advanced control education.