• 대한가정학회지
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• 제46권6호
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• pp.49-59
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• 2008

The purpose of this study was to examine the role of consumers' self-image and pursued-image of clothes on the clothing purchase decision making according to the location. Data were obtained from a questionnaire filled out by 575 women living in Seoul and Jechon. For data comparative analysis, paired t-test, t-test, factor analysis and multiple regression analysis were used. The results of this study are as follows: 1. There were significant differences in self-image and pursued-image in terms of clothing purchases between women who live in Seoul and Jechon residents. 2. Demographic variables influenced to the self-image and pursued-image of clothes factor. Among them, size of the city was the most important factor which influence to the clothing purchase behavior. 3. Self-image, pursued-image of clothes, problem recognition and evaluative criteria factors significantly differed between Seoul and Jechon residents. In two cities, problem recognition factor which was arisen by external stimulus and all of the evaluative criteria factors showed significant differences. 4. When the cities were partitioned by size(large and small city), the influence of self-image and pursued-image of clothes on the clothing purchase behavior showed different phases. Generally, self image and pursued-image of clothes were more important to various problem recognition and evaluative criteria factors in large city(i.e. Seoul) than in small city(i.e. Jechon). However economic rational factor was the exception.

• 대한두개안면성형외과학회지
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• 제14권2호
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• pp.89-95
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• 2013

Background: If patients have a better understanding about their problem and treatment, compliance and satisfaction with treatment will increase. For this purpose, simple repeated explanations regarding a patients' problem and treatment are essential. Critical pathway (CP) has a very wide range in medicine with the exception of the plastic surgery field. The authors developed a CP for facial bone fractures and implemented it clinically. The aim of this study was to evaluate the effectiveness of the CP on the degree of recognition of the problem along with patient satisfaction with the treatment process. Methods: From May 2011 to October 2011, a total of 82 patients suffering from facial bone fractures were studied. The CP for facial bone fractures was developed by plastic surgeons, residents and nurses. Subsequently, the authors investigated the degree of recognition of the disease and patient satisfaction with the treatment through the use of a questionnaire. The authors compared the score of the questionnaires before and after implementation of the clinical pathway. Results: The degree of the recognition of the problem changed from 3.1 to 4.2 (p<0.001). Further, the degree of satisfaction with the treatment process changed from 3.6 to 4.3 (p<0.05). Overall, there was a two point increase in improvement. Conclusion: Implementation of the CP for facial bone fractures was effective in improving the degree of recognition and satisfaction. The authors expect that hereafter, the CP for facial bone fractures will be implemented actively in the plastic surgery field.

• 한국항행학회논문지
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• 제16권5호
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• pp.864-870
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• 2012

언어 학습 콘텐츠에서 공통적으로 가질 수 있는 문제점들을 분석하고 문제점에 대하여 음성 패턴인식기술을 적용하여 기존의 문제점을 해결하였다. 언어 학습 콘텐츠의 첫 번째 문제점은 온라인 학습 자세이다. 수업 진행은 되었지만 다른 웹 페이지를 열어 게임을 하는 등 학생들의 집중력은 떨어졌다. 두 번 째 문제점은 Speaking 학습 과정을 만들었지만 실제로 따라 읽는지 판단할 수가 없었다. 세 번 째 문제점은 학습 관리 시스템에 의한 기계적 진행이 아니라 선생님들의 평가에 의해 잘하는 학생들과 못하는 학생간의 학습 진행에 차이를 둘 필요가 생겼다. 마지막으로 가장 큰 문제는 기존에 만들어 놓은 콘텐츠들은 그대로 유지되면서 위의 문제들을 해결할 수 있어야 했다. 이러한 배경 하에 음성 패턴인식기술은 말하기 학습 전용 학습 프로그램으로 학습 진행을 위한 음성인식은 물론 학습 자체를 위한 음성인식 기능들을 모두 가지고 있으며 인식 절차에 사용된 학습자의 발화 데이터를 원하는 형태의 오디오 파일로 변경하여 서버의 특정 위치로 전송하거나 SQL서버에 등록할 수도 있으며, 또한 컴포넌트이기 때문에 그 어떠한 시스템이나 프로그램이라도 모두 적용 가능하고 이미 만들어진 콘텐츠 전체를 손상시키지 않고 쉽게 삽입하여 새로운 기능들을 사용할 수 있었다. 본 논문으로 교육 방식을 보다 인터렉티브하게 바꾸어 적극적인 수업참여가 되도록 기여하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1319-1323
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• 2004

In this paper, a problem of Thai sign language recognition using a neural network is considered. The paper addresses the problem in classifying certain signs conveying quantitative meaning, e.g., large or small. By treating those signs corresponding to different quantities as derived from different classes, the recognition error rate of the standard multi-layer Perceptron increases if the precision in recognizing different quantities is increased. This is due the fact that, to increase the quantitative recognition precision of those signs, the number of (increasingly similar) classes must also be increased. This leads to an increase in false classification. The problem is due to misinterpreting the amount of quantity the quantitative signs convey. In this paper, instead of treating those signs conveying quantitative attribute of the same quantity type (such as 'size' or 'amount') as derived from different classes, here they are considered instances of the same class. Those signs of the same quantity type are then further divided into different subclasses according to the level of quantity each sign is associated with. By using this two-level classification, false classification among main gesture classes is made independent to the level of precision needed in recognizing different quantitative levels. Moreover, precision of quantitative level classification can be made higher during the recognition phase, as compared to that used in the training phase. A standard multi-layer Perceptron with a back propagation learning algorithm was adapted in the study to implement this two-level classification of quantitative gesture signs. Experimental results obtained using an electronic glove measurement of hand postures are included.

• 대한전자공학회논문지SP
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• 제42권4호
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• pp.59-68
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• 2005

본 논문에서는 측면 포즈 정규화를 통해 얻어진 부분영역을 이용해 대상의 포즈 변화에 강인한 얼굴인식 방법을 제안한다. 포즈변화에 강인한 얼굴인식을 위해 일반적으로 사용되는 방법인 포즈 정규화 방법은 포즈정규화과정 중에 가려져 보이지 않는 영역에 대한 정보를 가지고 있지 않기 때문에 문제가 발생하게 된다 일반적으로는 보상을 통해 문제를 해결 하고 있지만, 보상에 의해 영상이 왜곡이 되거나 특징정보를 잃는 경우가 많다. 이런 문제를 해결하기 위해 깊이찬가 큰 영역에서 주로 발생하는 왜곡을 줄이도록 정면이 아닌 측면으로의 정규화를 시도한다 또한 정규화후 왜곡이 발생한 영역은 제거하고 왜곡이 발생하지 않은 영역만을 이용해 인식과정을 수행한다 포즈가 좌우변화만 존재하는 경우와 상하변화도 존재하는 경우 두 가지 경우로 나누어 다루었으며 각각의 경우에 대해 실험을 통해 인식 성능의 향상을 확인하였다

• ETRI Journal
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• 제18권1호
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• pp.29-41
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• 1996

This paper describes a method to select a suitable feature for speech recognition using information theoretic measure. Conventional speech recognition systems heuristically choose a portion of frequency components, cepstrum, mel-cepstrum, energy, and their time differences of speech waveforms as their speech features. However, these systems never have good performance if the selected features are not suitable for speech recognition. Since the recognition rate is the only performance measure of speech recognition system, it is hard to judge how suitable the selected feature is. To solve this problem, it is essential to analyze the feature itself, and measure how good the feature itself is. Good speech features should contain all of the class-related information and as small amount of the class-irrelevant variation as possible. In this paper, we suggest a method to measure the class-related information and the amount of the class-irrelevant variation based on the Shannon's information theory. Using this method, we compare the mel-scaled FFT, cepstrum, mel-cepstrum, and wavelet features of the TIMIT speech data. The result shows that, among these features, the mel-scaled FFT is the best feature for speech recognition based on the proposed measure.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.977-985
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• 2020

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.208-223
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• 2015

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.27-32
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• 2002

In this paper, we would study the applicability of neural networks to the recognition process of Korean stenographic character image, applying the classification function, which is the greatest merit of those of neural networks applied to the various parts so far, to the stenographic character recognition, relatively simple classification work. Korean stenographic recognition algorithms, which recognize the characters by using some methods, have a quantitative problem that despite the simplicity of the structure, a lot of basic characters are impossible to classify into a type. They also have qualitative one that It Is not easy to classify characters fur the delicacy of the character farms. Even though this is the result of experiment under the limited environment of the basic characters, this shows the possibility that the stenographic characters can be recolonized effectively by neural network system. In this system, we got 90.86% recognition rate as an average.

• Journal of information and communication convergence engineering
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• 제2권3호
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.