• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• 대한간호학회지
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• 제48권3호
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• pp.335-348
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• 2018

Purpose: The purpose of this study was to identify parental coping strategies in the face of early infant and toddler injury, and to provide basic data for a parental education program and the most desirable directions it should take. Methods: A Q-methodology to analyze the subjectivity of each item was used. Thirty-four Q-statements were derived from a literature review and interviews. Forty-seven parents were classified into a shape of normal distribution using a 9-point scale. Collected data were analyzed by the pc-QUANL program. Results: Five types of parental coping in early infant and toddler injury were identified. Type I was "hospital treatment focused", type II was "Improving the safety of the child's environment", type III was "expression of negative emotion", type IV was "taking the lead in problem solving", and type V was "Interrogating the person in charge of the situation in which the injury occurred". Conclusion: The results of this study indicate that different approaches to educational programs can be used for parents in early childhood injury.

• 복식문화연구
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• 제18권3호
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• pp.514-525
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• 2010

In the rapidly changed 21st century when the flow speed of information is so fast and the quantity of information is too much increasing, personal appearance and characteristic image-formation is highly evaluated. Nowadays personal appearance is very essential to competition and may be considered the non-linguistic expression of self-image. The beauty action of the youth is shown to follow the object which youth want to rely on or long for and it is affected by the influence of the sociocultural elements. The purpose of this study is to identify the corelation between appearance and beauty action according to the sociocultural elements of the juveniles concerning appearance and to make an inquiry into how they pursue beauty in the restrictive bound compared with the adults. As their restrictive beauty action, the practical reflection percentage of hair-style, management of face, hand and nail control will be investigated and analyzed. Furthermore this basic material of this survey was used for the development of beauty article, cosmetics and education program. With the findings of the beauty articles in which the growing generation have a deep interest, this study provided the basic materials to the customer analysis and also the reference materials to the beauty marketing.

• 제어로봇시스템학회논문지
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• 제16권10호
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• 한국통신학회논문지
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• 제35권9C호
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• pp.756-760
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• 2010

본 논문에서는 운전자 피로 감지를 위한 얼굴 동작을 효과적으로 인식하는 방법을 제안하고자 한다. 얼굴 동작은 얼굴 표정, 얼굴 자세, 시선, 주름 같은 얼굴 특징으로 나타난다. 그러나 얼굴 특징으로 하나의 동작 상태를 뚜렷이 구분한다는 것은 대단히 어려운 문제이다. 왜냐하면 사람의 동작은 복합적이며 그 동작을 표현하는 얼굴은 충분한 정보를 제공하기에는 모호성을 갖기 때문이다. 제안된 얼굴 동작 인식 시스템은 먼저 적외선 카메라로 눈 검출, 머리 방향 추정, 머리 움직임 추정, 얼굴 추적과 주름 검출과 같은 얼굴 특징 등을 감지하고 획득한 특징을 FACS의 AU로 나타낸다. 획득한 AU를 근간으로 동적 베이지안 네트워크를 통하여 각 상태가 일어날 확률을 추론한다.

• 감성과학
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• 제1권1호
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• pp.33-40
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• 1998

본 논문은 얼굴인식 분야에 있어서 필수 과정인 얼굴 및 얼굴의 주요소인 눈과 입의 추출에 관한 방법을 제시한다. 얼굴 영역 추출은 복잡한 배경하에서 움직임 정보나 색상정보를 사용하지 않고 통계적인 모델에 기반한 일종의 형찬정합 방법을 사용하였다. 통계적인 모델은 입력된 얼굴 영상들의 Hotelling변환 과정에서 생성되는 고유 얼굴로, 복잡한 얼굴 영상을 몇 개의 주성분 갑으로 나타낼 수 있게 한다. 얼굴의 크기, 영상의 명암, 얼굴의 위치에 무관하게 얼굴을 추출하기 위해서, 단계적인 크기를 가지는 탐색 윈도우를 이용하여 영상을 검색하고 영상 강화 기법을 적용한 후, 영상을 고유얼굴 공간으로 투영하고 복원하는 과정을 통해 얼굴을 추출한다. 얼굴 요소의 추출은 각 요소별 특성을 고려한 엣지 추출과 이진화에 따른 프로젝션 히스토그램 분석에 의하여 눈과 입의 경계영역을 추출한다. 얼굴 영상에 관련된 윤곽선 추출에 관한 기존의 연구에서 주로 기하학적인 모양을 갖는 눈과 입의 경우에는 주로 가변 템플릿(Deformable Template)방법을 사용하여 특징을 추출하고, 비교적 다양한 모양을 갖는 눈썹, 얼굴 윤곽선 추출에는 스네이크(Snakes: Active Contour Model)를 이용하는 연구들이 이루어지고 있는데, 본 논문에서는 이러한 기존의 연구와는 달리 스네이크를 이용하여 적절한 파라미터의 선택과 에너지함수를 정의하여 눈과 입의 윤곽선 추출을 실험하였다. 복잡한 배경하에서 얼굴 영역의 추출, 추출된 얼굴 영역에서 눈과 입의 영역 추출 및 윤곽선 추출이 비교적 좋은 결과를 보이고 있다.

• 한국의류학회지
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• 제11권2호
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• pp.69-89
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• 1987

The objective of this paper was to identify the general concept of subculture; to assess the symbolism of youth subculture style, such as Teds in the 40's, Mods in the 50's, Skinheads and Hippies in the 60's, and Punks in the 80's, giving an inspiration to many high fashion designers. The data were collected from eye-wittness reports, interviews, magazines such as Vogue, The Face, and The Sunday Times, postcards, and photos taken by authors. Youth subcultures were symbolized as 1) a social protest and disaffection against social class and racism, 2) an expression of shock value for nihilism, anarchism, and vandalism, 3) a meaningful sexual fetishism concerning gender confusion, and 4) an emotion of rage, fear, and alienation among working class youth. One cultural form in a subculture is its 'style.' Costumes, appearances, and accessories such as hair style, make-up, and jewellery were playing an important role in forming a subcultural style. The symbolism of youth subculture was well depicted on their clothing styles, which had influences on Zandra Rhodes's, Body Maps', and Hyper and Hyper's fashion design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2523-2538
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• 2017

In most of the existing literature, the definition of the class label has the following characteristics. First, the class label of the samples from the same object has an absolutely fixed value. Second, the difference between class labels of the samples from different objects should be maximized. However, the appearance of a face varies greatly due to the variations of the illumination, pose, and expression. Therefore, the previous definition of class label is not quite reasonable. Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper. In our algorithm, the maximization difference between the class labels of the samples from different objects should be satisfied. Meanwhile, the class label of the different samples from the same object is allowed to have small difference, which is consistent with the fact that the different samples from the same object have some differences. In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR). A large number of experiments show that our proposed algorithms can achieve very good performance.

• Journal of Microbiology and Biotechnology
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• 제5권5호
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• pp.245-249
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• 1995

Expression of the adjacent but divergently transcribed glpD and glpE gene is positively regulated by cAMP-CRP. In this study, we constructed several mutants in which a CRP-binding site is placed at different distances upstream of the glpD promoter. The effect of the spacer length on transcription activation by cAMP-CRP was tested in vivo by $\beta$-galactosidase. The cAMP-CRP complex activated transcription from glpD when bound at a number of positions, all of which lay on the same face of the DNA helix, although the degree of activation varied with the length of the spacer. By contrast, the insertion of spacer length with non-integral turns of the DNA helix extremely inhibited the activation of transcription. The observed transcription activation by cAMP of the glpD promoter was influenced by the distance between the CRP binding site and the transcription start point.

• ETRI Journal
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• 제37권6호
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.