• 한국정밀공학회지
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• 제21권8호
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• pp.83-88
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• 2004

The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

• 한국정보전자통신기술학회논문지
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• 제5권1호
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• pp.7-12
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• 2012

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.414-421
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• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• 한국패션뷰티학회지
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• 제6권1호
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• pp.16-20
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• 2008

Ubiquitous computing which is called, the innovation of the 21st Century is a core technology of next generation. RFID technology is noticed, and physical distribution, home network, telematics, like industry automatization are developed by using various technique. Particulary, They use RFID technology for fashion and beauty industry, So, supplying network management, physical distribution management and warehouse management are investigated for the automatization. What we have investigated so far, a traceability system is made of ubiquitous sensor network for monitoring fashion and beauty industry environment. The proposed system using EPC network can provide information of industrial products traceability quickly and efficiently. Moreover, this system with RFID Technology gives intelligence of distribution and record. In this paper, We recommend the vision of Ubiquitous convergence of fashion and beauty industry.

• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.540-545
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• 2012

In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2005년도 춘계 종합학술대회 논문집
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• pp.502-506
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• 2005

기존 유선 방식의 홈네트워크 시스템에 무선 방식이 적용됨에 따라 사용자들은 댁내 어디에서나 편리하게 홈네트워크 서비스를 이용할 수 있게 되었다. 이러한 홈네트워크 시스템에서 안전한 네트워크를 구성하기 위해서는 사용자의 인증과 더불어 무선을 사용하는 기기들에 대한 양방향 인증이 필요하다. 본 논문에서는 안전한 무선 홈네트워크 환경을 구축하기 위한 양방향 인증 메커니즘을 제안한다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2002년도 전력전자학술대회 논문집
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• pp.693-696
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• 2002

In this paper, network management system(NMS) was developed using Ethernet network for several devices. Recently, due to the development of the information communication, network has been constructed several place. And management system using network has been studied due to the increment of necessity of remote control for industrial device. Agent board that necessity of NMS, was developed using general micro-controller, it operates like stand-alone network device, supports TCP/IP protocol suite, has the ability to connect to industrial device and communicates each other. Also manager base on MMI was developed, it operates with agent board and supports effective management. To prove this system UPS(uninterruptible power supply) is selected as the example of industrial device. Finally, experimental result verifies the communication between agent board and manager.

• 한국통신학회논문지
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• 제12권4호
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• pp.357-364
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• 1987

본 논문에서는 다중프로세서 시스템을 구성하는 중요한 요소인 다단상호결합 네트웍의 성능을 고찰한다. Baseline 네트웍을 이용한 Star네트웍 시스템의 Hardware적인 구성을 고찰하며 Analytical Model로 Request가 받아들여질 확률과 Clustering 확률을 구한다. 그리고 Baseline네트웍 대신에 Delta네트웍을 이용하여 위에서 언급한 확률을 구한 다음 네트웍의 성능을 비교한다.