• 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 Journal of the Acoustical Society of Korea
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• v.17 no.5
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• pp.63-68
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• 1998

광대역 정보통신망(B-ISDN)에 적합한 직접 제공법의 스트리밍모드 비연결형 서버 는 단일 셀의 전송시간 동안 셀의 송·수신 및 룩업(lookup)을 수행해야 한다는 시간적인 제약을 받기 때문에 버스트 트래픽(Burst Traffic)이 발생했을 경우 셀 손실이 많다는 단점 을 가지고 있다. 따라서 본 논문에서는 ATM 망의 스트리밍 모드 비연결형 서버가 고속으 로 데이터를 처리하여 셀 손실을 줄일 수 있도록 하기 위해 DBLCAM을 제안하였으며, 입 력 VPI(Virtual Path Identifier)/VCI(Virtual Channel Identifier)에 대한 연결 번호를 출력하 는 기능의 포워딩 테이블 VPC맵을 제안된 DBLCAM과 이중 포트 SRAM을 이용하여 설계 하였다.

• 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 IEEK Conference
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• 2002.06b
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• pp.205-208
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• 2002

An existing ATM switch fabric uses VPI(Virtual Path Identifier) and VCI(Virtual Channel Identifier) information to route ATM cell. But AAL type 2 switch which efficiently processes delay-sensitive, low bit-rate data such as a voice routes the ATM cell by using CID(Channel Identification) field in addition to VPI and VCI. In this paper, we research the AAL type 2 switch that performs the process of CPS packet. The Receive unit extracts the CPS packet from the inputted ATM cell. The designed receive unit consists of input FIFO, r)( status table, CAM(Content Addressable Memory), new CID table and partial packet memory. Also the designed receive unit supports the PCI interface with host processor. The receive unit is implemented in Xilinx FPGA and operates at 72MHz.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.12
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• pp.894-902
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• 1988

In this paper, a method for the design of robust adaptive controller using the heuristic rules of industrial engineers is presented. This scheme works on the basis of heuristic rules and includes a supervisor, a system identifier and a detuner. The supervisor detects onsetting instability based on the analysis of the amplitude and the trend of error signal, also selects running controllers. Upon detecting instability, the controller is switched to a PID algorithm and run recursively until stability is restored. Simultaneously, new input / output data is gathered and the system identifier runs to get critical sensitivity (kc) and critical period(tc). Based on the new values(kc, tc), a GPC controller is redesigned and normal GPC is finally run. The algorithm described in this paper belongs to the supervised adaptive control category with a limited use of heuristic rules. Finally, we show the robust of this scheme by simulated example.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1403-1410
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• 1997

Because streaming mode connection server suitable to wide area ATM networks performs transmission, reception and lookup with time restriction for the transmission time of a cell, it has demerits of large cell loss incase that burst traffic occurs. Therefore, in this paper to decrease cell loss we propose a high speed CAM (Content Addressable Memory) which is capable of processing data of streaming mode connections server at a high speed. the proposed CAM is applied to forwarding table VPC map which performs function to output connection numbers about input VPI(Virtual Path Identifier)/VCI(Virtual Channel Identifier). The designed high speed CAM consist of DBL(Dual Bit Line) CAM structure performed independently write operation and match operation and two-port SRAM structure. Also, its simulation verification and full-custom layout is performed by Hspice and Composs tools in 0.8 .$\mu$m design rule.

• 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.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.62-64
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• 1996

Neural network as a controller of a nonlinear system and a system identifier has been studied during the past few years. A well trained neural network identifier can be used as a system predictor. We proposed the method to design multi-step ahead predictor and multi-step predictive controller using neural network. We used the input and out put data of B system to train the NNP and used the forecasted approximat system output from NNP as B input of NNC. In this paper we used two-step ahead predictive controller to test B heating controll system and compared with PI controller.

• Journal of Information Management
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• v.42 no.4
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• pp.39-58
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• 2011

This paper presents a meta-tag expanded from scholarly service types of OpenURL written in Key/Encoded-Value format, after analyzing new scholarly service types and DataCite metadata elements which are for research data publishing and services. So far, OpenURL Z39.88 standard, KEVFormat: Sch-Svc, supporting six scholarly service type only, the expansion of this standard is needed for a research data circulation. New eight scholarly service types were extracted, after analyzing and comparing with the Scopus, Web of Science, and NDSL services. And nine representative attributes were extracted, after analyzing intensively the DataCite's elements.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.799-806
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• 2021

In the IoT environment, event data from sensors is continuously reported over time. Event data obtained in this trend is accumulated indefinitely, so a method for efficient analysis and management of data is required. In this study, a data stream segmentation method was proposed to support the effective selection and utilization of event data from sensors that are continuously reported and received. An identifier for identifying the point at which to start the analysis process was selected. By introducing the role of these identifiers, it is possible to clarify what is being analyzed and to reduce data throughput. The identifier for stream segmentation proposed in this study is a semantic-oriented data stream segmentation method based on the event occurrence of each stream. The existence of identifiers in stream processing can be said to be useful in terms of providing efficiency and reducing its costs in a large-volume continuous data inflow environment.