• The Journal of the Society of Korean Medicine Diagnostics
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• v.14 no.2
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• pp.13-24
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• 2010

Background and purpose: Seven Emotions consist of Joy(喜), Anger(怒), Anxiety(憂), Thought(思), Sorrow(悲), Fear(恐), Fright(驚). If Seven Emotion is excessive, its extreme mental stimulation causes physical illness. There was no study of the Seven Emotion Disease in detail for now. Therefore the purpose of this study is to pigeonhole the Seven Emotion Disease. Methods: We extract the sentences about the Seven Emotion and related words in Donguibogam. We classify the sententences into Joy(喜), Anger(怒), Anxiety(憂), Thought(思), Sorrow(悲), Fear(恐), Fright(驚), Frustration, Mental Exhaustion, Character. We analysis pattern of Symptoms Derived from Seven Emotions. Results and Conclusions Seven Emotion give rise to various type of symptom. In special Anger cause more illness than other Seven Emotion.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1125-1133
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• 2014

Recently, there are many researches have been studying for analyzing user interests and emotions based on users profiles and diverse information from Social Network Services (SNSs) due to their popularities. However, most of traditional researches are focusing on their researches based on single resource such as text, image, hash tag, and more, in order to obtain what user emotions are. Hence, this paper propose a method for obtaining user emotional information by analyzing texts and images both from Instagram which is one of the well-known image based SNSs. In order to extract emotional information from given images, we firstly apply GRAB-CUT algorithm to retrieve objects from given images. These retrieved objects will be regenerated by their representative colors, and compared with emotional vocabulary table for extracting which vocabularies are the most appropriate for the given images. Afterward, we will extract emotional vocabularies from text information in the comments for the given images, based on frequencies of adjective words. Finally, we will measure WUP similarities between adjective words and emotional words which extracted from the previous step. We believe that it is possible to obtain more precise user emotional information if we analyzed images and texts both time.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.330-335
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• 2004

In this paper, we propose the technique for recognizing the human emotion by using the color image. To do so, we first extract the skin color region from the color image by using HSI model. Second, we extract the face region from the color image by using Eigenface technique. Third, we find the man's feature points(eyebrows, eye, nose, mouse) from the face image and make the fuzzy model for recognizing the human emotions (surprise, anger, happiness, sadness) from the structural correlation of man's feature points. And then, we infer the human emotion from the fuzzy model. Finally, we have proven the effectiveness of the proposed method through the experimentation.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.17-24
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• 1998

Noweays, The flow of international relations have to competition with product of each nation, a means of this competitoveness is developmennt of goods based on nation culture. That is in need to development of design have color of our country make sure of original design. Now we need a positive opposed to wants of consumers with expaneded market, so as to need of develogment of distinctive design. And then, this stuey have purpose analory of distinctive design as a ilustrate to case of traditional design with on a conjoint analysis, look into the modeling method and meaning of traditional design. At first, it setting up the base of design development, as a present into a investigate to extract course of shape's element for design distinction. And look around about the developing case of domestic company and modeling method, a pattern of traditional design development, As on of distinction's way has doing conjoint analysis for abstract to character of traditional form and have devdloping to product with a present expression elements and design concept for a basis.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.65-70
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• 2020

CNN's technologies that represent emotional detection include primitive CNN algorithms, deployment normalization, and drop-off. We present the methods and data of the three experiments in this paper. The training database and the test database are set up differently. The first experiment is to extract emotions using Batch Normalization, which complemented the shortcomings of distribution. The second experiment is to extract emotions using Dropout, which is used for rapid computation. The third experiment uses CNN using convolution and maxpooling. All three results show a low detection rate, To supplement these problems, We will develop a deep learning algorithm using feature extraction method specialized in image processing field.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.11-15
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• 1997

This study deals with the method for modeling relationship between human sensibility and design dldments of a product for applying human sensibility to product development, Inorder to extract sensibility characteeristics concerning a product, we figured out the relationship between xensibilith and design elements using corrdlation analysis and multiple regression analysis, and then modeled the realtionship between then through multiple objective linert programming. The results of this study can be effectively applied to develop a product based on human sensibility

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.499-517
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• 2008

In this paper, we propose an effective emotion feature extraction method for Korean and evaluate their availability in sentiment classification. Korean emotion features are expanded from several representative emotion words and they play an important role in building in an effective sentiment classification system. Firstly, synonym information of English word thesaurus is used to extract effective emotion features and then the extracted English emotion features are translated into Korean. To evaluate the extracted Korean emotion features, we represent each document using the extracted features and classify it using SVM(Support Vector Machine). In experimental results, the sentiment classification system using the extracted Korean emotion features obtained more improved performance(14.1%) than the system using content-words based features which have generally used in common text classification systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.122-126
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• 2002

In this study, we find features of 3 emotions (Happiness, Angry, Surprise) as the fundamental research of emotion recognition. Speech signal with emotion has several elements. That is, voice quality, pitch, formant, speech speed, etc. Until now, most researchers have used the change of pitch or Short-time average power envelope or Mel based speech power coefficients. Of course, pitch is very efficient and informative feature. Thus we used it in this study. As pitch is very sensitive to a delicate emotion, it changes easily whenever a man is at different emotional state. Therefore, we can find the pitch is changed steeply or changed with gentle slope or not changed. And, this paper extracts formant features from speech signal with emotion. Each vowels show that each formant has similar position without big difference. Based on this fact, in the pleasure case, we extract features of laughter. And, with that, we separate laughing for easy work. Also, we find those far the angry and surprise.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.99-108
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• 2008

This paper proposes an image retrieval system which integrates metadata, contents, and emotions in textile images. First, the proposed system searches images using metadata. Among searched images, the system retrieves similar images based on color histogram, color sketch, and emotion histogram. To extract emotion features, this paper uses emotion colors which was proposed on 160 emotion words by H. Nagumo. To enhance the user's convenience, the proposed textile image retrieval system provides additional functions as like enlarging an image, viewing color histogram, viewing color sketch, and viewing repeated patterns.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.364-371
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• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.