• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.52-60
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• 2008

This research proposes a new strategy where documents are encoded into string vectors for text categorization and modified versions of SVM to be adaptable to string vectors. Traditionally, when the traditional version of SVM is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and apply the modified version of SVM adaptable to string vectors for text categorization.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.381-386
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• 2002

In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts for the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell for automatic test and assembling in S company.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• Journal of the korean Society of Automotive Engineers
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• v.18 no.6
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• pp.63-68
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• 1996

차량의 자동제어는 컴퓨터, 통신, 기계, 계측등의 하드웨어와 자동제어 기술, 통신망 운영, 컴퓨터 운영등의 소프트웨어 기술의 응용을 바탕으로 하는 지능형 차량은 단순히 차량 상호간, 차량과 도로에 설치된 컴퓨터와의 통신으 통한 정보교환 기능과 더불어 운전기능 자체를 스스로 행하는 기능을 포함하고 있다. 지능형 차량 개발은 운전자 인식증진 기술, 사고회피 기술, 피해확산 방지기술, ITS(Intelligent Transport System) 연계 및 관련 Infrastructure 기술, Human-Machine Interface 기술, Sensor Fusion 기술, 레이더 기술 등을 꼽을 수 있다. 이중에서 차량의 지능화를 구현하는 가장 핵심적인 세부 기술은 CCD 카메라, 레이저 레이더, 밀리파 레이더, 생체 센서등을 들 수 있다. 본 원고에서는 위와 같이 차량의 지능화, 특히 차량 거리검지 장치를 구현코자 사용되고 있는 센서의 종류와 응용에 대하여 고찰하고자 한다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.5
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• pp.523-529
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• 2009

Smart sensor is known as intelligent sensor, it is different with other conventional sensors in the case of intelligent system embedded on it. Smart sensor has many benefits e.g. low-cost in usage, self-decision and self-diagnosis abilities. This sensor consists of perception element(sensing element), signal processing and technology of communication. In this work, a bridge and structure of smart sensor has been investigated to be capable to condition monitoring routine. This investigation involves low power consumption, software programming, fast data acquisition ability, and authoritativeness warranty. Moreover, this work also develops smart sensor to be capable to perform high sampling rate, high resolution of ADC, high memory capacity, and good communication for data transfer. The result shows that the developed smart sensor is promising to be applied to various industrial fields.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.314-318
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• 2010

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (MLP) models as the likelihood for secondary structure prediction of proteins. With varying window sizes of neighboring amino acid information, the information is extracted and passed back and forth between the Neural Net and the Bayesian Inference process until the posterior probability of the secondary structure converges.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.69-73
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• 2001

The dynamic characteristics of turning processes are complex, non-linear and time-varying. Consequently, the conventional techniques based on crisp mathematical model may not guarantee surface roughness regulation. This paper presents a fuzzy controller which can regulate surface roughness in milling process using end-mill under varying cutting condition. The fuzzy control rules are established from operator experience and expert knowledge about the process dynamics. regulation which increases productivity and tool life is achieved by adjusting feed-rate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by cutting experiments in the converted CNC milling machine. The result of experiments show that the proposed fuzzy controller has a good surface roughness regulation capability in spite of the variation of cutting conditions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.60-64
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• 2001

In this paper, a new learning methodology for Kernel Methods is suggested that results in a sparse representation of kernel space from the training patterns for classification problems. Among the traditional algorithms of linear discriminant function(perceptron, relaxation, LMS(least mean squared), pseudoinverse), this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epochs. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• IE interfaces
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• v.5 no.2
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• pp.53-74
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• 1992

In order to design an FMS(Flexible Manufacturing System), an effective design tool is necessary for selecting and anlayzing the complex interacting factors of a manufacturing system. Considering most of the previous researches have been focused on evaluating the designed FMS, development of an integrated FMS designing system is needed for performing the design task such as machine selection and layout determination as well as the evaluation task of the designed FMS. In this paper, we developed a prototype of such a system called the intelligent FMS designer based on the techniques of Expert System and Simulation. By using this designing system, possible design alternatives can be analyzed and appropriate manufacturing capacity can be estimated at the designing stage. Various operating characteristics of the designed FMS can be also predicted before it is implemented. Eventually a suitable design alternative can be provided with cost details within relatively short time period. To demonstrate the performance and effectiveness of the designer, a simple example is tested. The results show a great potential for automating design processes of an FMS.