• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3236-3238
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• 1999

This paper proposes a new face recognition technique in the dynamic link architecture which shows robustness against size variation and distortion. The face recognition technique in the dynamic link architecture so far was not appropriate for the recognition of various size of faces because of the fixed size of the graph and the fixed value of a of the Gabor filter not considering the size of the face. The proposed face recognition algorithm can represent the input facial image by a suitable size of labeled graph, and it can also adjust the dilation width and the height of the vibrating amplitude of the Gabor filter, thus face recognition in the dynamic link architecture is even applicable regardless of the size of the face.

• 한국산학기술학회논문지
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• 제16권5호
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• pp.3412-3420
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• 2015

스마트폰은 일상생활의 필수 요소가 되었으며, 스마트폰의 내장된 센서는 사용자의 상황인지를 위해 유용하게 활용될 수 있다. 하지만, 동적 상황 인지를 통해 안전 및 사고예방을 위한 연구가 미흡한 상황이다. 본 논문에서는 모바일 디바이스의 가속도 센서, 마이크, GPS 센서를 이용해 위험상황을 동적으로 인지할 수 있는 기법을 제안한다. 본 동적인지기법의 센싱 기법에 대한 아키텍쳐와 프로세스를 제안하며, 아키텍쳐의 적합성을 검증을 위해 모바일 어플리케이션을 개발한다.

• 한국산학기술학회논문지
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• 제15권7호
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• pp.4562-4572
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• 2014

최근 스마트폰의 위치 기반 서비스는 급속도로 성장하는 모바일 기술 중 하나이다. 본 논문에서는 모바일에서 위치 기반 서비스를 사용할 때 특정한 위치에 따라 위치 기반 서비스의 주기를 변경시키기 위한 기법을 제안한다. 이를 통해 위치 기반 서비스의 주기를 위치에 따라 커스터마이제이션 할 수 있으며, 결과적으로 일정한 주기로 위치 기반 서비스를 이용할 때보다 모바일 자원의 낭비를 줄일 수 있는 프로세스를 제안한다.

• The Journal of the Acoustical Society of Korea
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• 제16권3E호
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• pp.14-18
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• 1997

One fo the major problems in speech recognition is the mismatch between training and testing environments. Recently, SNR normalization technique, which normalizes the dynamic range of frequency channels in mel-scaled filterbank, was proposed[1]. While it showed improved robustness against additive noise, it requires a reliable speech detection mechanism and several adaptation parameters to be optimized. In this paper, we propose a modified SNR normalization technique. In this technique, we take simply the maximum of filterbank output and predetermined masking constant for each frequency band. According to the speaker-independent isolated word recognition in car noise environments, proposed modification yields better recognition performance that the original SNR normalization method, with rather reduced complexity.

• 대한음성학회지:말소리
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• 제56호
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• pp.207-223
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• 2005

The goal of language model adaptation is to improve the background language model with a relatively small adaptation corpus. This study presents a language model adaptation technique where additional text data for the adaptation do not exist. We propose the information retrieval (IR) technique with N-gram language modeling to collect the adaptation corpus from baseline text data. We also propose to use a dynamic language model interpolation coefficient to combine the background language model and the adapted language model. The interpolation coefficient is estimated from the word hypotheses obtained by segmenting the input speech data reserved for held-out validation data. This allows the final adapted model to improve the performance of the background model consistently The proposed approach reduces the word error rate by $13.6\%$ relative to baseline 4-gram for two-hour broadcast news speech recognition.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.1074-1079
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• 2007

We will introduce speech/speaker recognition algorithm for the isolated word. In general case of speaker verification, Gaussian Mixture Model (GMM) is used to model the feature vectors of reference speech signals. On the other hand, Dynamic Time Warping (DTW) based template matching technique was proposed for the isolated word recognition in several years ago. We combine these two different concepts in a single method and then implement in a real time speaker/speech recognition system. Using our proposed method, it is guaranteed that a small number of reference speeches (5 or 6 times training) are enough to make reference model to satisfy 90% of recognition performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.213-218
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• 2009

This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.

• 전자공학회논문지B
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• 제33B권8호
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• pp.105-113
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• 1996

In this paper, a new korean digita speech recognition technique was proposed using muktolayer perceptron (MLP). In spite of its weakness in dynamic signal recognition, MLP was adapted for this model, cecause korean syllable could give static features. It is so simle in its structure and fast in its computing that MLP was used to the suggested system. MLP's input vectors was transformed using karhunen-loeve transformation (KLT), which compress signal successfully without losin gits separateness, but its physical properties is changed. Because the suggested technique could extract static features while it is not affected from the changes of syllable lengths, it is effectively useful for korean numeric recognition system. Without decreasing classification rates, we can save the time and memory size for computation using KLT. The proposed feature extraction technique extracts same size of features form the tow same parts, front and end of a syllable. This technique makes frames, where features are extracted, using unique size of windows. It could be applied for continuous speech recognition that was not easy for the normal neural network recognition system.

• 센서학회지
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• 제30권2호
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1052-1057
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• 1994

Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.