• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.2237-2240
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• 2003

하나의 물리 망 위에 있는 두 시스템은 상대방의 물리 주소를 알고 있어야만 통신을 할 수 있고. 물리 주소는 통신비용 절감을 위해 ARP를 사용하는 HOST의 ARP cache에 Internet-to-Ethernet Mapping형태로 저장한다. 이러한 ARP cache 구조는 Modification의 많은 취약성을 가진다. 그 중 취약성을 이용한 공격 중 하나인 ARP Redirect Attack은 물리 망 위의 Target Host 패킷이 공격자의 시스템을 통해 게이트웨이까지 가도록 한다. 본 논문은 게이트웨이 및 일반 HOST 시스템으로 구성된 Local Area Network 기반 구조를 내부 공격자 시스템으로부터 다른 내부 시스템의 사용자 정보를 안전하게 게이트웨이까지 보내기 위한 대응 모델을 제안하고자 한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.37-45
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• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.469-474
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• 2003

RFC2016bis에 기술된 프로토콜인 ENUM( tElphon/E. 164 NUmber Mapping)은 E. 164 전화번호에 전자메일주소, URL, LDAP 등 인터넷상의 다양한 서비스 에 대한 URI들을 매핑한 후 DNS에 등록함으로서 등록된 전화번호에 대응되는 가용한 서비스를 탐색할 수 있도록 하는 프로토콜이다. 본 논문은 현재진행 중에 있는 ENUM 표준화 작업에 있어 서비스 관련 표준화 동향을 소개한다. 본 논문은 현재 진행되고 있는 국내외 ENUM 서비스 표준화 및 ENUM 시험시스템 구축에 있어 서비스 등록 표준화 내용을 기술함으로써 ENUM의 확산에 기여할 수 있으리라 사료된다.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.939-942
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• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.131-140
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• 2004

With a rapid development of DWDM and photonic switching, $MP{\lambda}S$ network can realize DiffServ like QOS services using optical wavelength mapping to the traffic flows. In this study, we describe the service connection architecture for providing QOS service between $MP{\lambda}S$ network and network users. We also describe a methodology, which is similar to DiffServ mechanism, to build traffic conditioning blocks that can be used to provide the negotiated QOS services using traffic control components defined by IETF

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.1
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• pp.1-5
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• 1998

This study presents intelligent deburring system which can transfer the exper's skill to deburring robot through neural network. The expert's skill is expressed as associate mapping between the characteristics of the burr and human expert's action. Under the fundamental idea that the state of the deburring process can be extracted via the visual sense of the human, we employ vision system for the perception and identification of the changing burr. From the demonstration of human experts, force data are measured and fitted impedance model. Finally the characteristics of the burr and coressponding force are associated by the neural network which is trained through many demonstrations. The proposed method is verified in the deburring process of welding burr.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.186-198
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• 2021

Security control using surveillance cameras is established when people observe all surveillance videos directly. However, this task is labor-intensive and it is difficult to detect all abnormal situations. In this paper, we propose a deep neural network model, called AT-Net, that automatically detects abnormal situations in the surveillance video, and introduces an automatic video surveillance system developed based on this network model. In particular, AT-Net alleviates the ambiguity of existing abnormal situation detection methods by mapping features representing relationships between people and objects in surveillance video to the new tensor structure based on sparse coding. Through experiments on actual surveillance videos, AT-Net achieved an F1-score of about 89%, and improved abnormal situation detection performance by more than 25% compared to existing methods.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.1
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• pp.77-88
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• 1993

In this paper, we propose a stereo vision system to solve correspondence problem with large disparity and sudden change in environment which result from small distance between camera and working objects. First of all, a specific feature is divided by predfined elementary feature. And then these are combined to obtain coded data for solving correspondence problem. We use Neural Network to extract elementary features from specific feature and to have adaptability to noise and some change of the shape. Fourier transformation and Log-polar mapping are used for obtaining appropriate Neural Network input data which has a shift, scale, and rotation invariability. Finally, we use associative memory to obtain coded data of the specific feature from the combination of elementary features. In spite of specific feature with some variation in shapes, we could obtain satisfactory 3-dimensional data from corresponded codes.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.323-333
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• 2023

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.

• Steel and Composite Structures
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• v.46 no.3
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• pp.435-449
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• 2023

Time domain method and frequency domain method are commonly used in the current fatigue life calculation theory. The time domain method has complicated procedures and needs a large amount of calculation, while the frequency domain method has poor applicability to different materials and different spectrum, and improper selection of spectrum model will lead to large errors. Considering that artificial neural network has strong ability of nonlinear mapping and generalization, this paper applied this technique to random fatigue life prediction, and the effect of average stress was taken into account, thereby achieving more accurate prediction result of random fatigue life.