• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (A)
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• pp.71-72
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• 2007

• 한국의류산업학회지
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• 제11권6호
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• pp.887-895
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• 2009

The purposes of this study were to provide informations to apparel companies and to contribute the education of clothing consumers through finding out the present status of clothes purchasing behaviors and the degree of the recognition and the application of care labels of the Chinese students in Daegu area. The data were collected from 166 Chinese students through the questionnaire and analyzed by the frequence, t-test, ANOVA, and Scheffe-test using SPSS 12.0. The results were as follows: 1. The main purchasing place was road shops of well-known brands, and the most decisive factor of purchasing was the display style of goods. They purchased 'any time when necessary' and impulsively. They payed mostly by cash and the most affecting factor of purchasing decision was the degree of fitting. 2. The recognition of the necessity of care label was found in 36.7% of respondent and their most rationale was 'for the management of clothes'. The recognition of care labels showed the highest in reliability and the lowest in application. There were significant differences in satisfaction of care label between male and female and in application according to purchasing places. In conclusion, the recognition of the necessity of care labels showed a little high level but relatively low in the understanding and the application.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1226-1232
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• 2010

A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• 한국인터넷방송통신학회논문지
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• 제22권3호
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• pp.69-74
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• 2022

뉴로모픽 아키텍처에서 동작하는 SNN (Spiking Neural Network) 은 인간의 신경망을 모방하여 만들어졌다. 뉴로모픽 아키텍처 기반의 뉴로모픽 컴퓨팅은 GPU를 이용한 딥러닝 기법보다 상대적으로 낮은 전력을 요구한다. 이와 같은 이유로 뉴로모픽 아키텍처를 이용하여 다양한 인공지능 모델을 지원하고자 하는 연구가 활발히 일어나고 있다. 본 논문에서는 음성 데이터 전처리 기법에 따른 뉴로모픽 아키텍처 기반의 음성 인식 모델의 성능 분석을 진행하였다. 실험 결과 푸리에 변환 기반 음성 데이터 전처리시 최대 84% 정도의 인식 정확도 성능을 보임을 확인하였다. 따라서 뉴로모픽 아키텍처 기반의 음성 인식 서비스가 효과적으로 활용될 수 있음을 확인하였다.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.986-1005
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• 2020

In this paper, we propose AnoVid, an automated video annotation tool based on deep neural networks, that automatically generates various meta data for each scene or shot in a long drama video containing rich elements. To this end, a novel meta data schema for drama video is designed. Based on this schema, the AnoVid video annotation tool has a total of six deep neural network models for object detection, place recognition, time zone recognition, person recognition, activity detection, and description generation. Using these models, the AnoVid can generate rich video annotation data. In addition, AnoVid provides not only the ability to automatically generate a JSON-type video annotation data file, but also provides various visualization facilities to check the video content analysis results. Through experiments using a real drama video, "Misaeing", we show the practical effectiveness and performance of the proposed video annotation tool, AnoVid.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1143-1149
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• 2010

Because the noise is the main cause for decreasing the performance at speech recognition, the place or environment is very important in speech recognition. To improve the speech recognition performance in the real situations where various extraneous noises are abundant, a novel combination of FIR and Wiener filters is proposed and experimented. The combination resulted in improved accuracy and reduced processing time, enabling fast analysis and response in emergency situations. Usually, there are many dangerous situations in our city life, so for the smarter city it is necessary to detect many types of sound in various environment. Therefore this paper is about how to detect many types of sound in real city, especially on CCTV. This paper is for implementing the smarter city by detecting many types of sounds and filtering one of the emergency sound in this sound stream. And then it can be possible to handle with the emergency or dangerous situation.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 10월 학술대회지
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• pp.43-49
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• 2003

This study reveals the perceptual role of stop release burst to Koreans' recognition of POA(place of articulation) and voicing in the English word-final stops. 10 Korean subjects participated in a perception experiment wherein the stimuli are prepared on the basis of the amount of acoustic information, which includes the release burst. The result shows that i) release burst plays an important role in the recognition of POA in the order of velar, alveolar, and bilabial stops, and ii) the release burst more enhances the correct recognition of voiceless stops than that of voiced stops. This result leads us to conclude that the role of stop release burst differs with respect to the POA and voicing of the stops, and it is possibly related to the different intensity of release in voicing and in each POA.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• 대한의용생체공학회:의공학회지
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• 제11권1호
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• pp.157-162
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• 1990

In this study, a peripheral auditory model is used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean place names and syllables. In the case of using the general learning rule, it is found that the weights are diverged for a long sequence because of the characteristics of the node function in the hidden and output layers. So, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation and to use long data. The recognition results are considerably good, even if time worping and endpoint detection are omitted and learning patterns and test patterns are made of average length of data. The recurrent network used in this study reflects well time information of temporal speech signal.