• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.419-426
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• 1999

A novel motion analysis system is presented in this paper. The proposed system is inspired by processing functions observed in the fly visual system, which detects changes in input light intensities, determines motion on both the local and the wide-field levels. The system has several differences from conventional motion analysis system. First, conventional systems usually focused on matching similar feature or optical flow, but neural network is applied in this system. Back propagation is used by learning method, and Tine Delay Neural Network (TDNN) is also used as analysis method. Second, while conventional systems usually limited on only two frames of sequence, the proposed system accept multiple frames of sequence. The experimental results showed a 94.7% correct rate with a speed of 71.47 milli seconds for real and synthetic images.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.778-787
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• 2003

Intrusion detection systems based on rules are not efficient for mutated attacks, because they need additional rules for the variations. In this paper, we propose an intrusion detection system using the time delay neural network. Packets on the network can be considered as gray images of which pixels represent bytes of them. Using this continuous packet images, we construct a neural network classifier that discriminates between normal and abnormal packet flows. The system deals well with various mutated attacks, as well as well known attacks.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.214-217
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• 1999

In this paper, genetic algorithm (GA) is implemented to search for the optimal structures (i.e. the kind of neural networks, the number of inputs and hidden neurons) of neural networks which are used approximating a given nonlinear function. Two kinds of neural networks, i.e. the multilayer feedforward [1] and time delay neural networks (TDNN) [2] are involved in this paper. The synapse weights of each neural network in each generation are obtained by associated training algorithms. The simulation results of nonlinear function approximation are given out and some improvements in the future are outlined.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.5-13
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• 1994

This paper presents a study on Neural Networks for Phoneme Recognition and performs the Phoneme Recognition using TDNN (Time Delay Neural Network). Also, this paper proposes training algorithm for speech recognition using neural nets that is a proper to large scale TDNN. Because Phoneme Recognition is indispensable for continuous speech recognition, this paper uses TDNN to get accurate recognition result of phonemes. And this paper proposes new training algorithm that can converge TDNN to an optimal state regardless of the number of phonemes to be recognized. The recognition experiment was performed with new training algorithm for TDNN that combines backpropagation and Cauchy algorithm using stochastic approach. The results of the recognition experiment for three phoneme classes for two speakers show the recognition rates of $98.1\%$. And this paper yielded that the proposed algorithm is an efficient method for higher performance recognition and more reduced convergence time than TDNN.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.405-407
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• 1994

In order to provide reliable power service and to prevent a potentail hazard and damage, it is important to detect high impedance fault in power distribution line. This paper presents a neural network based approach for the detection of high impedance faults. A time delay neural network has been selected and trained for the fault currents obtained from field experiments. Detection experiments have been performed with the data from four different high impedance surfaces. Experimental results indicated the feasibility of using TDNN for the detection of high impedance faults.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.414-422
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• 2001

This paper proposes a classifier of music genre using time delay neural network(TDNN) fur an audio data retrieval systems. The classifier considers eight kinds of genres such as Blues, Country, Hard Core, Hard Rock, Jazz, R&B(Soul), Techno and Trash Metal. The comparative unit to classify the genres is a melody between bars. The melody pattern is extracted based un snare drum sound which represents the periodicity of rhythm effectively. The classifier is constructed with the TDNN and uses fourier transformed feature vector of the melody as input pattern. We experimented the classifier on eighty training data from ten musics for each genres and forty test data from five musics for each genres, and obtained correct classification rates of 92.5% and 60%, respectively.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.406-410
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• 2007

성대마이크는 디바이스의 특성상 환경 잡음을 최소화하는 장점이 있다. 그러나 고주파정보의 손실과 부분적인 포먼트 정보의 손실 때문에, 성대마이크를 이용한 명령어 인식기는 표준마이크를 이용한 명령어 인식기보다 낮은 성능을 보인다. 본 논문은 한국어 음운자질의 특성을 적용한 특징추출 알고리즘과 최적화된 인식모델을 이용하여 높은 성능을 갖는 명령어 인식시스템을 제안한다. 성대 울림 특성이 한국어 내의 분포 분석하여 성대 울림 정보만으로 명령어 인식기 개발이 가능함을 보이고 음성인식에 높은 성능을 보이는 Time Delay Neural Network(TDNN)[1]을 성대신호 명령어 인식에 최적화한 구조를 제안한다. 실험을 통해 찾은 최적 TDNN 구조를 성대신호에 적용한 했을 때 약 87%의 높은 성능을 보였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.662-665
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• 2001

기존의 규칙기반 침입탐지 시스템은 사후처리시 규칙 추가로 인하여 새로운 변종의 공격을 탐지하지 못한다. 본 논문에서는 규칙기반 시스템의 한계점을 극복하기 위하여, 시간지연 신경망(Time Delay Neural Network; 이하 TDNN) 침입탐지 시스템을 제안한다. 네트워크강의 패킷은 바이트 단위를 하나의 픽셀로 하는 0에서 255사이 값으로 이루어진 그레이 이미지로 볼 수 있다. 이러한 연속된 패킷이미지를 시간지연 신경망의 학습패턴으로 사용한다. 정상적인 흐름과 비정상적인 흐름에 대한 패킷 이미지를 학습하여 두 가지 클래스에 대한 신경망 분류기를 구현한다. 개발하는 침입탐지 시스템은 알려진 다양한 침입유형뿐만 아니라, 새로운 변종에 대해서도 분류기의 유연한 반응을 통하여 효과적으로 탐지할 수 있다.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.91-98
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• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.483-489
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• 2020

This paper proposes acoustic model training using self-attention for low-resource speech recognition. In low-resource speech recognition, it is difficult for acoustic model to distinguish certain phones. For example, plosive /d/ and /t/, plosive /g/ and /k/ and affricate /z/ and /ch/. In acoustic model training, the self-attention generates attention weights from the deep neural network model. In this study, these weights handle the similar pronunciation error for low-resource speech recognition. When the proposed method was applied to Time Delay Neural Network-Output gate Projected Gated Recurrent Unit (TNDD-OPGRU)-based acoustic model, the proposed model showed a 5.98 % word error rate. It shows absolute improvement of 0.74 % compared with TDNN-OPGRU model.