• 한국지능시스템학회논문지
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• 제20권1호
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• pp.76-80
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• 2010

급변하는 정보의 홍수 속에서 정보의 보안과 이를 가공하고 전송하는 것이 중요한 과제로 떠오르고 있다. 초기 정보보호이론과 암호화 전송단계에서는 간단한 치환과 수학적 계산 알고리즘을 적용한 암 복호화 과정을 이용하였다. 완벽한 정보보호는 One-time pad를 이용하는 것이나 이를 적용하기에는 하드웨어와 금전적 손실이 너무 크기에 실난수가 아닌 난수성을 만족하는 의사난수를 사용하고 있다. 본고에서 제안하는 변형 시리얼 테스트는 의사난수성을 입증하는 테스트 중 시리얼테스트에서 변형된 것으로 연산속도와 효율성 면에서 보다 더 강력한 난수성임을 입증하고 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1255-1258
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• 2003

Today with the sheep of information which is produced the variety is increasing geometrical progression. To recently the internet being supplied quickly, will reach and the computer users whom it uses increase and the documents which have become digital anger are increasing. From the dissertation which it sees directness it extracts a weight with possibility work and it uses it summarizes a statistics algorithm technique and a sentence. The summary literature course which the summary and the person due to a statistics algorithm summarize an agreement ratio it compares and it compares. And being more accurate like this statistical base summary method more little more, the good hit rate is high and it proposes the document summary algorithm method which is good.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• 한국지능시스템학회논문지
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• 제20권6호
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• pp.806-813
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• 2010

표정은 사람의 감정을 표현하는 대표적인 수단이다. 이러한 이유로 표정은 사람의 의도를 컴퓨터에 전하는데 효과적인 방법으로 사용될 수 있다. 본 논문에서는 2D 영상에서 사람의 표정을 보다 빠르고 정확하게 인식하기 위해 Discrete Adaboost 알고리즘과 신경망 알고리즘을 통합하는 방법을 제안한다. 1차로 Adaboost 알고리즘으로 영상에서 얼굴의 위치와 크기를 찾고, 2차로 표정별로 학습된 Adaboost 강분류기를 이용하여 표정별 출력 값을 얻으며, 이를 마지막으로 Adaboost 강분류기 값으로 학습된 신경망 알고리즘의 입력으로 이용하여 최종 표정을 인식한다. 제안하는 방법은 실시간이 보장된 Adaboost 알고리즘의 특성과 정확성을 개선하는 신경망 기반 인식기의 신뢰성을 적절히 활용함으로서 전체 인식기의 실시간성을 확보하면서도 정확성을 향상시킨다. 본 논문에서 구현된 알고리즘은 평온, 행복, 슬픔, 화남, 놀람의 5가지 표정에 대해 평균 86~95%의 정확도로 실시간 인식이 가능하다.

• 한국방사선학회논문지
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• 제12권5호
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• pp.637-643
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• 2018

블록형 섬광체와 픽셀형 섬광체를 이용한 반응 깊이 측정 검출기를 설계하였으며, 층 구분 능력을 DETECT2000을 사용하여 측정하였다. 블록형 섬광체를 사용하여 민감도를 향상했으며, 반응 깊이를 측정함으로써 공간분해능을 향상했다. 위층은 블록형으로 아래층은 픽셀형 섬광체를 위치시켜 감마선과 반응한 섬광체에서 발생한 빛의 분포를 변화시켰으며, 변화된 빛의 분포의 채널별 신호 특성 분석을 통해 반응 깊이를 측정하였다. 아래층을 픽셀형 섬광체로 구성하여 평면 영상 획득 시 위층의 블록형 섬광체에서도 픽셀형 섬광체의 위치와 비슷한 곳에서 영상을 획득할 수 있었다. 앵거 알고리듬을 사용하여 16채널의 신호를 4개의 채널로 감소시켜, 신호 특성 분석을 용이하게 하였으며, 층 구분은 간단한 알고리듬을 사용하여 측정하였고 층별 약 84%의 측정 정확도를 보였다. 본 검출기를 전임상용 PET에서 사용할 경우 반응 깊이 측정을 통해 검출 시야 외곽에서의 공간분해능을 향상할 수 있을 것이다.

• 의료커뮤니케이션
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• 제12권1호
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• pp.51-72
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• 2017

Purpose: Adolescents use emoticons to express their emotions in an online environment. Hence, medical experts can understand the emotions of adolescents by emoticons. The goal of this study was to investigate the relationship between various emotions and emoticons among the Korean adolescents. Methods: The questionnaire survey was conducted between September 1 and 30, 2014, involving 3,272 students in elementary schools, middle schools, and high schools affiliated in the Department of Education of the metropolitan city of Busan. A total of 1,717 students responded to the survey. The participants consisted of 806 males (46.9%), and 911 females (53.1%). Among these, there were 557 elementary school students (32.4%), 617 middle school students (35.9%), and 543 high school students (31.6%). A social networking analysis was conducted using NodeXL. Results: The frequency of emoticon use among adolescents runs in the order of joy, sadness, fear, surprise, anger, disgust, and then depression. Elementary school females mainly use emoticons to express joy; middle school females use emoticons to express sadness, surprise, anger, disgust, and depression; and high school females use emoticons to express fear. Age- and gender-specific emoticon networks were visualized by using the Haren-Korel fast multiscale algorithm. Commonly used emoticons by age and gender were expressed in the networks. Results of age- and gender-specific emoticon networks visualization show similar results of centrality of seven emoticons. Conclusion: In the digital communication environment, emoticons could be used to catch the emotions of adolescents in Korea.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1175-1186
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• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.

• 로봇학회논문지
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• 제8권4호
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• pp.266-272
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• 2013

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

• 제어로봇시스템학회논문지
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• 제15권1호
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• pp.94-98
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• 2009

In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2036-2038
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• 2001

In this paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using vision system. In the proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Also, we use R,G,B(red, green, blue) color image data and the gray image data to get the highly trust rate of feature point extraction. For this, we propose an algorithm to extract four feature points (eyebrow, eye, nose, mouth) from the face image acquired by the color CCD camera and find some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector(position and distance among the feature points). Finally, we show the practical application possibility of the proposed method.