• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2013-2024
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• 2006

This paper proposes a condition classification system using wavelet transform, feature evaluation and artificial neural networks to detect faulty products on the production line of reciprocating compressors for refrigerators. The stationary features of vibration signals are extracted from statistical cumulants of the discrete wavelet coefficients and root mean square values of band-pass frequencies. The neural networks are trained by the sample data, including healthy or faulty compressors. Based on training, the proposed system can be used on the automatic mass production line to classify product quality instead of people inspection. The validity of this system is demonstrated by the on-site test at LG Electronics, Inc. for reciprocating compressors. According to different products, this system after some modification may be useful to increase productivity in different types of production lines.

• Structural Engineering and Mechanics
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• v.6 no.8
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• pp.955-969
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• 1998

Engineering problems are inherently imprecision tolerant. Biologically inspired soft computing methods are emerging as ideal tools for constructing intelligent engineering systems which employ approximate reasoning and exhibit imprecision tolerance. They also offer built-in mechanisms for dealing with uncertainty. The fundamental issues associated with engineering applications of the emerging soft computing methods are discussed, with emphasis on neural networks. A formalism for neural network representation is presented and recent developments on adaptive modeling of neural networks, specifically nested adaptive neural networks for constitutive modeling are discussed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.8
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.89-91
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• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.1
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• pp.97-122
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• 2006

The purpose of this paper is to analyze production and innovation networks of services in the long-live area of Gangwon and Jeju Provinces and to suggest a policy direction for regional development of rural areas where have been neglected in the knowledge-based information society. Four counties in the Honam Region, the long-live belt of Korea and two cities (Jeonju, Gwangju) are surveyed for the purpose of comparison with the Gangwon and Jeju areas. Production and innovation networks of research and supporting activities and tourist services are analyzed based on intensive interview surveys of the regions. The result of the analysis suggests that the innovation networks among the economic actors have considerable impacts on the innovation processes of the service activities and the service functions in the rural area are somewhat developed with local industry after the practice of local autonomy. The processes of innovation networks are progressed differently by the hierarchy of the regions as well as by the function of services such as research and supporting activities and tourist services. The direction of the rural development in the knowledge-based information society seems to be intensifying the networks among the innovative actors and developing virtual innovation networks for the development of rural innovation systems.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.62-64
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• 2001

This paper presents a forecasting method for production volume of each model manufacture d goods by using Back-Propagation technique of Neural Networks. As the learning constant and the momentum constant are respectively 0.65 and 0.94, the teaming number is the least, and the forecating accuracy is the highest. When the learning process is more than 1,000 times, the accurate forecating was possible regardless of kind of product.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.298-300
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• 2001

This paper presents a forecasting method for production volume of each model manufactured goods by using Back-Propagation technique of Neural Networks. As the learning constant and the momentum constant are respectively 0.65 and 0.94, the learning number is the least, and the forecating accuracy is the highest. When the learning process is more than 1,000 times, the accurate forecating was possible regardless of kind of product.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.47.1-47
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• 2001

The drilling process has a great importance for the production technology due to its widerspread use in the manufacturing industry. In order to enhance a maximum production rate and prevent the drill from the damage, it is important to monitor and control the drilling system. Thrust force and cutting torque are the main output variables in the design of drilling control systems. In this paper, an alternative estimation method of thrust force by using flexible neural networks is proposed. Flexible neural network uses the sigmoid activation function with adjustable parameter in order to enhance the approximation accuracy ...

• Journal of the Korean Geographical Society
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• v.38 no.2
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• pp.237-256
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• 2003

The main aim of this paper is to provide a plan for building an effective and competitive innovation system of Daeduck Valley in Korea through analysing the process of knowledge production and commercialisation of venture firms in the perspective of industry-(university) research networks. Since 1997, with the willingness of the government aimed at building' second Sillicon Valley', an ostensible innovation system cantered around the existing science technology town has been able to be built in Daeduck Valley. Nonetheless, some fundamental problems with the knowledge production and commercialisation of venture firms were appeared as the results of this study. It is led tv not only the lack of network agents and institutes that are able to facilitate and coordinate the networks of economic actors comprised of the innovation system, but also the impertinent roles of economic actors. In particular, these problems were differentiated in accordance with the growth stage of venture firms and the processes of knowledge production. Therefore, several policy implications for building innovation system are suggested in the perspective of the complement of existing innovation system of Daeduck, rather than constructing a new innovation system. At the same time, they are provided in accordance with different growth stages and knowledge production processes.