• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.71-81
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• 1995

In this paper, feature vector extraction methods and classification algorithms for the automatic classification of transient signals in underwater are discussed. A feature vector extraction method using wavelet transform, which shows good performance with small number of coefficients, is proposed and compared with the existing classical methods. For the automatic classification, artificial neural networks such as multilayer perceptron (MLP), radial basis function (RBF), and MLP-Class are utilized, where those neural networks as well as extracted feature vectors are combined to improve the performance and reliability of the proposed algorithm. It is confirmed by computer simulation with Traco's standard transient data set I and simulated data that the proposed feature vector extraction method and classification algorithm perform well, assuming that the energy of a given transient signal is sufficiently larger than that of a ambient noise, that there are the finite number of noise sources, and that there does not exist noise sources more than two simultaneously.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.773-774
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• 2016

배경잡음은 음성신호의 특징을 왜곡하기 때문에 음성인식 시스템의 인식율 향상의 방해요소가 된다. 따라서 본 논문에서는 배경잡음이 존재하는 환경에서의 음성인식을 실시하기 위해서, 신경회로망과 Mel 주파수 켑스트럼 계수를 사용하여 연속음성 식별 알고리즘을 제안한다. 본 논문의 실험에서는 본 알고리즘을 사용하여 배경잡음이 섞인 음성신호에 대하여 음성인식의 식별율 개선을 실현할 수 있도록 연구를 진행하며, 본 알고리즘이 유효하다는 것을 실험을 통하여 명백히 한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.3
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• pp.419-429
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• 1999

Identifying the boundary of the effective receiving power of waves is one of the most important factors for cell optimization. In this paper, we introduce a propagation loss prediction model which yields highly accurate prediction in very complex areas as Seoul where a mixture of many large buildings, small buildings, broad streets, narrow alleys, rivers and forests co-exist in an irregular arrangement. This prediction model is based on neural networks trained on field measurement data collected in the past. Using these data along with 3-D digital elevation maps and vector data for building structures, we extract the parameter values which mainly affect the amount of propagation loss. These parameter values are then used as the inputs to the neural network. Trained neural network becomes the approximated function of the propagation loss model which generalizes very well and can predict accurately in the regions not included in training the neural network. The experimental results show a superior performance over the other models in the cells operating in the city of Seoul.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.75-84
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• 1994

기계가공프로세스에서 절삭공구의 마멸과 채터진동은 공작기계의 가동율과 생산성을 크게 저해하는 요인이 되고 있다. 본 연구에서는 공구마멸과 채터현상이 혼재하는 상황에서, 이들 두 현상을 동시에 검출하는데, AE 및 가속도센서에서 검출된 신호와 AR계수 및 주파수대역 평균에너지를 특징입력으로 하는 인공신경회로망을 이용하였다. 그 결과, 공구마멸과 채터현상에 대응하는 서로 다른 신호특징의 차이를 동시에 식별하는 데 인공신경 회로망의 유용성을 입증하였으며, 시계열모델의 AR계수(70 .approx. 90%)보다는 주파수대역에너지법의 평균에너지 (80 .approx. 100%)를 신경회로망의 특징입력으로 하는 경우가 높은 성공률을 나타내었다.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.781-784
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• 2009

본 논문에서는 실제 수중 환경에서 선박 또는 잠수함으로부터 발생하는 인위적인 천이신호와 돌고래, 새우 등의 해양 생물로부터 발생하는 천이신호들을 식별하기 위한 특징벡터 추출 기법을 제안하였다. MFCC와 엔트로피 기반의 웨이블릿 패킷 기법을 이용하여 특징을 추출하고, 이 두 특징들을 동시에 적용하여 수중 천이신호를 식별하고자 한다. 기존의 방법인 MFCC와 웨이블릿 패킷 기법과 이 두 방법을 동시에 적용했을 때의 식별률을 비교하였고, 전방향 신경회로망(feed-forward neural network)을 그 특징벡터의 성능을 평가하기 위한 식별기로 사용하였다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.1-9
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• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.72-79
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• 2010

A EAARS(Electronic Attendance-Absence Recording System) is the important LSS(Learning Support System) for blending a on-line learning in the face-to-face classroom. However, the EAARS based on the smart card can not identify a real owner of the checked card. Therefore, we develop the CS(Client-Sever) system that manages the attendance-absence checking automatically, which is used the self-organizing neural network for the face recognition. A client system creates the ID file by extracting the face feature, a server system analyzes the ID file sent from client system, and performs a student identification by using the Recognized weight file saved in Database. As a result, The proposed CS EAARS shows the 92% efficiency in the CS environment that includes the various face image database of the real classroom.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.115-118
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• 2002

Neural networks have been known to have great discriminative power in pattern classification problems. In this paper, the multilayer perceptron neural networks are employed to automatically detect laryngeal pathology in speech. Also new feature parameters are introduced which can reflect the periodicity of speech and its perturbation. These parameters and cepstral coefficients are used as input of the multilayer perceptron neural networks. According to the experiment using Korean disordered speech database, incorporation of new parameters with cepstral coefficients outperforms the case with only cepstral coefficients.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.917-920
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• 1998

In this paper, we propose a modified elman neural network structure for nonlinear system identification. The proposed structure is that all of network output feed back into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the modified elman neural network structure in the nonlinear system identification.