• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.1062-1065
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• 2002

We have evaluated the ability of a time-delayed artificial neural network (TDANN) to predict muscle forces using only eletromyographic(EMG) signals. To achieve this goal, tendon forces and EMG signals were measured simultaneously in the gastrocnemius muscle of a dog while walking on a motor-driven treadmill. Direct measurements of tendon forces were performed using an implantable force transducer and EMG signals were recorded using surface electrodes. Under dynamic conditions, the relationship between muscle force and EMG signal is nonlinear and time-dependent. Thus, we adopted EMG amplitude estimation with adaptive smoothing window length. This approach improved the prediction ability of muscle force in the TDANN training. The experimental results indicated that dynamic tendon forces from EMG signals could be predicted using the TDANN, in vivo.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.395-401
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• 1996

In this paper a new neural network model, time delayed counterpropagation neural networks (TDCPN) which have high recognition rate and short total learning time, is proposed for electromyogram(EMG) recognition. Signals the proposed model increases the recognition rates after learned the regional temporal correlation of patterns using time delay properties in input layer, and decreases the learning time by using winner-takes-all learning rule. The ouotar learning rule is put at the output layer so that the input pattern is able to map a desired output. We test the performance of this model with EMG signals collected from a normal subject. Experimental results show that the recognition rates of the suggested model is better and the learning time is shorter than those of TDNN and CPN.

• Smart Structures and Systems
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• 제7권1호
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• pp.59-82
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• 2011

In this study, an output-only modal identification approach is proposed for decentralized wireless sensor nodes used for linear structural systems. The following approaches are implemented to achieve the objective. Firstly, an output-only modal identification method is selected for decentralized wireless sensor networks. Secondly, the effect of time-unsynchronization is assessed with respect to the accuracy of modal identification analysis. Time-unsynchronized signals are analytically examined to quantify uncertainties and their corresponding errors in modal identification results. Thirdly, a modified approach using complex mode shapes is proposed to reduce the unsynchronization-induced errors in modal identification. In the new way, complex mode shapes are extracted from unsynchronized signals to deal both with modal amplitudes and with phase angles. Finally, the feasibility of the proposed approach is evaluated from numerical and experimental tests by comparing with the performance of existing approach using real mode shapes.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.641-642
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• 1986

We present a method for the estimation of intramuscular electromyographic(EMG) signals from the given surface EMG signals. This method is based on representing the surface EMG signal as an autoregressive(AR) time model with a delayed intramuscular EMG signal as an input. The parameters of the time series model that transforms the intramuscular signal to the surface signal are identified. The identified model is then used in estimating the intramuscular signal from the surface signal.

• 한국정보통신학회논문지
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• 제17권12호
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• pp.2818-2825
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• 2013

본 논문에서는 MIMO-OFDM 시스템에서 시간영역 훈련신호의 직교화를 통한 채널추정 방법을 제안하였다. 본 논문에서는 Jeon[8]이 제안한 방법을 그대로 송신 안테나 개수가 4개인 MIMO-OFDM 시스템으로 확장하였을 때 수신기에서 다중경로 지연신호로 인하여 훈련신호가 직교되는 않는 문제점이 있음을 보였다. 이러한 문제점에 대한 해결책으로 훈련신호 중앙에 보호구간을 삽입하는 새로운 훈련신호 발생 방법을 제안하였다. 제안한 방법은 훈련신호들이 서로 직교하기 때문에 수신기에서 Walsh decoding sum기법을 통하여 시간영역에서 채널응답을 추정할 수 있음을 보였다.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1995년도 추계학술대회
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• pp.193-196
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• 1995

An algorithm using topological mapping has been developed for a real-time detection of the QRS complexes of ECG signals. As a measurement of QRS complex energy, we used topological mapping from one dimensional sampled ECG signals to two dimensional vectors. These vectors are reconstructed with the sampled ECG signals and the delayed ones. In this method, the detection rates of CRS complex vary with the parameters such as R-R interval average and peak detection threshold coefficient. We use mean, median, and iterative method to determint R-R interval average and peak estimation. We experiment on various value of search back coefficient and peak detection threshold coefficient to find optimal rule.

• 한국전자통신학회논문지
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• 제17권3호
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• pp.409-416
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• 2022

본 논문은 시간지연 컨볼루션 혼합신호의 암묵잡음분리방법에 관한 것이다. 폐쇄된 공간에서 음향신호의 혼합모델은 다채널이기 때문에 convolutive 암묵신호분리방법을 적용하며 두 마이크 입력신호의 시간지연된 데이터 샘플들을 사용한다. 이 신호분리방법은 분리계수를 직접 계산하는 것이 아니라 역방향 모델을 이용하여 혼합계수를 산출하며, 계수의 갱신이 2차 통계적 성질에 기반한 반복적인 계산에 의해 이루어진다. 제안한 암묵신호분리의 성능을 검증하기 위해 많은 시뮬레이션을 수행하였다. 모의실험 결과, 이 방법을 사용한 잡음분리는 컨볼루션혼합에 상관없이 안전하게 동작하고, 일반적인 적응 FIR(Finite Impulse Response) 필터구조에 비해 PESQ(Perceptual Evaluation of Speech Quality)가 0.3점 개선되는 것으로 나타났다.

• Transactions on Control, Automation and Systems Engineering
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• 제1권2호
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• pp.99-105
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• 1999

This paper presents a fuzzy H$\infty$ filtering problem for a class of uncertain nonlinear systems with time-varying delayed states and unknown inital state on the basis of Takagi-Sugeno(T-S) fuzzy model. The nonlinear systems are represented by T-S fuzzy models, and the fuzzy control systems utilize the concept of the so-called parallel distributed compensation. Using a single quadraic Lyapunov function, the stability and L2 gain performance from the noise signals to the estimation error are discussed. Sufficient conditions for the existence of fuzzy H$\infty$ filters are given in terms of linear matrix inequalities (LMIs). The filtering gains can also be directly obtained from the solutions of LMIs.