• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.291-299
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• 1996

A new identification method using a higher order multilayer neural network is proposed for identifying a complex dynamic system such as a robotic manipulator. The input torque data for learning of the neural identifier are generated for producing effective output trajectories by a minimization process of a specific performance index function which indicates the difference between the reference points and the present joint positions and their velocities of the robotic manipulator. Computer simulation results show that the proposed identification method is very effective for identifying the systems with complex dynamics and large moment of inertia.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2020-2027
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• 2000

The hypertext document is used for information exchange in the Web environments. Its structure is considered as having graph structures with links, which makes nonlinear processing of documents possible. This paper proposes an indexing method for information retrieval system using XML links. We define new attributes that control links of a remote document and assign an unique identifier for the attribute of each link. Each identifier has a different weight according to its occurrence position that is local or remote documents. We index a word not only from a local document but a remote document based on the given weight. Experimental results show that the proposed method outperforms conventional retrieval systems that ignore links.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1231-1238
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• 1999

This paper presents an approach for the optimal heating load Identification using Diagonal Recurrent Neural Networks(DRNN). In this paper, the DRNN captures the dynamic nature of a system and since it is not fully connected, training is much faster than a fully connected recurrent neural network. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself. In this study, A dynamic backpropagation (DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. Delta-bar-delta learning method is an empirical method to adapt the learning rate gradually during the training period in order to improve accuracy in a short time. The simulation results based on experimental data show that the proposed model is superior to the other methods in most cases, in regard of not only learning speed but also identification accuracy.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.588-591
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• 1998

In this paper, we present a robust adaptive backstepping output feedback controller for nonlinear systems perturbed by unmodelled dynamics and disturbances. Especially, backstepping technique with modular approach is used to separately design controller and identifier. The design of identifier is based on the observer-based scheme which possesses a strict passivity property of observer error system. We will use Switching-${\sigma}$ modification at the update law and the modified control law to attenuate the effects of undodelled dynamics and disturbances for nonlinear systems.

• Journal of the Semiconductor & Display Technology
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• v.16 no.1
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• pp.6-10
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• 2017

Recent enhancement in the still image acquisition devices has been widely perpetrated into the daily life of the common people. Due to this trend, the voluminous still images, that are produced and shared in the personal or the massive storage, need to controlled with effective and efficient management. The human-devised or system-generated still image identifiers used for the identification of the images are at risk in the situation of unexpected changing or eliminating of the identifiers. In this paper, we propose a parallel processing based method for still image identifier generation by utilizing the still image internal features.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.17-22
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• 2004

지형지물 유일식별자(UFID: Unique Feature Identifier)는 주민등록번호처럼 우리나라의 국토를 구성하고 있는 도로, 건물 및 하천 등의 모든 인공적 및 자연적 지형지물에 단일식별자를 부여함으로써 해당 지형지물을 관리하는 기관은 물론, 물류, 금융 등 각종 산업 분야에 매우 중요한 역할을 하게 된다. 따라서 본 논문에서는 국가기본지리정보에 지형지물 유일식별자를 생성 및 소멸, 검수 그리고 부여하는 방법을 제시하였다. 그리고, 국가기본지리정보를 위한 지형지물 유일식별자 관리시스템을 설계하고 구현하였으며, 서로 다른 두 데이터베이스간에 이루어지는 지형지물의 삽입, 삭제 그리고 갱신 등을 지형지물 유일식 별자를 통하여 연계하는 방안을 설계하여 제시하였다. 본 논문에서 제안한 시스템은 통신, 가스, 전략 등의 기관에 확대하여 적용이 가능할 것이다.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.93-96
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• 2021

The desire for a technology that can mechanically acquire 2D images starting with the manual method of drawing has been making possible a wide range of modern image-based technologies and applications over a period. Moreover, this trend of the utilization of image-related technology as well as image-based information is likely to continue. Naturally, as like other technology areas, the function that humans produce and utilize by using images needs to be automated by using computing-based technologies. Surprisingly, technology using images in the future will be able to discover knowledge that humans have never known before through the information-related process that enables new perception, far beyond the scope of use that human has used before. Regarding this trend, the manipulation and configuration of massively distributed image database system is strongly demanded. In this paper, we discuss image identifier production methods based on the utilization of the image hashing technique which especially puts emphasis over an entropy operator.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1017-1021
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• 1991

This paper is to explore the potential use of the modeling capacity of neural networks for control applications. The tasks are carried out by two neural networks which act as a plant identifier and a system controller, respectively. Using information stored in the identification network control action has been developed. Without supervising control signals are generated by a gradient type iterative algorithm.