• Biotechnology and Bioprocess Engineering:BBE
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• 제5권4호
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• pp.234-241
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• 2000

The Saccharomyces cerevisiae PMR1 gene encodes a Ca2+-ATPase localized in the Golgi. We have investigated the effects of PMR1 disruption in S. cerevisiae on the glycosylation and secretion of three heterologous glycoproteins, human ${\alpha}$1-antitrypsin (${\alpha}$1-AT), human antithrombin III (ATHIII), and Aspergillus niger glucose oxidase (GOD). The pmr1 null mutant strain secreted larger amounts of ATHIII and GOD proteins per a unit cell mass than the wild type strain. Despite a lower growth rate of the pmr1 mutant, two-fold higher level of human ATHIII was detected in the culture supernatant from the pmr1 mutant compared to that of the wild-type strain. The pmr1 mutant strain secreted ${\alpha}$1-AT and the GOD proteins mostly as core-glycosylated forms, in contrast to the hyperglycosylated proteins secreted in the wild-type strain. Furthermore, the core-glycosylated forms secreted in the pmr1 mutant migrated slightly faster on SDS-PAGE than those secreted in the mnn9 deletion mutant and the wild type strains. Analysis of the recombinant GOD with anti-${\alpha}$1,3-mannose antibody revealed that GOD secreted in the pmr1 mutant did not have terminal ${\alpha}$1,3-linked mannose unlike those secreted in the mnn9 mutant and the wild type strains. The present results indicate that the pmr1 mutant, with the super-secretion phenotype, is useful as a host system to produce recombinant glycoproteins lacking high-mannose outer chains.

• 한국산학기술학회논문지
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• 제8권4호
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• pp.781-787
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• 2007

NEMO(Network Mobility) 기본 지원 프로토콜은 경로 최적화 과정을 수행하고 있지 않으며 MR(Mobile Router)과 HA(Home Agent) 사이의 양방향 터널 구간을 제외한 다른 구간에서는 특별한 보안 메커니즘을 제시하고 있지 않다. 따라서 본 논문에서는 MR과 MNN(Mobile Network Node) 사이의 양방향 터널을 통해 위임 권한 프로토콜을 수행하고 위임 권한을 획득한 MR과 CN (Correspondent Node) 사이에 인증된 바인딩 갱신 프로토콜을 통해 경로를 안전하게 최적화한다. 각 노드의 주소는 주소 소유권 증명을 위해 CGA(Cryptographically Generated Address)방식을 통해 생성한다. 끝으로 NEMO에서의 보안 요구사항과 기존에 알려진 공격을 통해 안전성을 분석하고 NEMO 지원 프로토콜과 연결성 복구력(connectivity recovery)과 종단간 패킷 전송 지연 시간율(end-to-end packet transmission delay time)을 비교하여 효율성을 분석한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.694-697
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• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with Multi-Layer Neural Network(MLNN). The structure of Modular Neural Network(MNN) in researched by Jacobs and jordan is selected in this paper. Modular network consists of several Expert Networks(EN) and a Gating Network(CN) which is composed of single-layer neural network(SLNN) or multi-layer neural network. We propose modular network structure using Recurrent Neural Network(RNN), since the state of the whole network at a particular time depends on aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• 한국통신학회논문지
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• 제17권5호
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.1187-1190
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• 2007

핸드오버가 발생하는 NEMO(NEtwork MObility) 환경에서 주요 이슈 중 하나는 MR(Mobile Router)이 핸드오버 하는 동안에도 NEMO 네트워크 내의 MNN(Mobile Network Node)들에게 지속적인 QoS(Quailty of Service)를 보장해주는 것이다. 이를 위해 MR의 등록과 자원예약 프로세스가 완료되기까지의 지연시간을 최소화하는 것이 중요하다. 또한 MR이 핸드오버 하는 동안 네트워크상에 전달되고 있는 데이터 패킷들의 손실을 최소화해야 한다. 이에, 본 논문에서는 NEMO 네트워크에서의 자원예약 트리거와 버퍼링 방안을 제안한다. 제안하는 방안에서는 MR이 새로운 경로와 이전 경로가 만나는 지점의 노드인 CRN(CRossover Node)에게 NOTIFY 메시지를 보내 새로운 경로 상에 자원예약 셋업이 빨리 시작하도록 트리거 하였고, 자원예약이 수행되고 있는 동안 인터넷상에 전송중인 데이터 패킷들의 손실을 줄이기 위해 CRN에서 버퍼링하도록 하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• 한국지능시스템학회논문지
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• 제8권6호
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• pp.12-20
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• 1998

제어 환경의 변화에 강건하게 대처할 수 있는 제어 시스템을 개발하기 위해서, 본 논문에서는 자연계의 면역 시스템과 다층 신경망을 결합한 제어 시스템을 제안한다. 제안한 제어 시스템은 면역 알고리즘을 이용하여 다층 신경망의 가중치를 조절한다. 면역 알고리즘은 초기 방어 단계인 선천성 면역 알고리즘과 적응 단계인 적응 면역 알고리즘으로 구성되어 있다. 과거에 학습한 경험이 있는 환경과 유사한 환경에 대해서 선천성 면역 알고리즘이 동작하고, 학습한 경험이 없는 새로운 제어 환경의 변하에 대해서는 적응 면역 알고리즘이 동작한다. 면역 알고리즘을 이용한 제어 시스템을 로봇 매니퓰레이터의 궤적 추종 제어에 적용하였으며, 컴퓨터 모의 실험을 통해 제어 시스템의 성능을 평가한다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제11권4호
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• pp.9-17
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• 2007

Network Mobility basic support protocol (NEMO Basic) extends the operation of Mobile IPv6 to provide uninterrupted Internet connectivity to the communicating nodes of mobile networks. The protocol uses a mobile router (MR) in the mobile network to perform prefix scope binding updates with its home agent (HA) to establish a bi-directional tunnel between the HA and MR. This solution reduces location-update signaling by making network movements transparent to the mobile nodes (MNs) behind the MR. However, delays in data delivery and higher overheads are likely to occur because of sub-optimal routing and multiple encapsulation of data packets. To manage the mobility of the mobile network, it is important to minimize packet overhead, to optimize routing, and to reduce the volume of handoff signals over the nested mobile network. This paper proposes en aggregate router-assisted route optimization (ARARO) scheme for nested mobile networks support which introduces a local anchor router in order to localize handoff and to optimize routing. With ARARO, a mobile network node (MNN) behind a MR performs route optimization with a correspondent node (CN) as the MR sends a binding update message (BU) to aggregate router (AGR) via root-MR on behalf of all active MNNs when the mobile network moves. This paper describes the new architecture and mechanisms and provides simulation results which indicate that our proposal reduces transmission delay, handoff latency and signaling overhead. To evaluate the scheme, we present the results of simulation.