• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.57-63
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• 2015

Compared to other emotion recognition technology, facial expression recognition technology has the merit of non-contact, non-enforceable and convenience. In order to apply to a psychological robot, vision technology must be able to quickly and accurately extract the face region in the previous step of facial expression recognition. In this paper, we remove the background from any image using the YCbCr skin color technology, and use Haar-like Feature technology for robust face detection. We got the result of improved processing speed and robust face detection by removing the background from the input image.

• Perspectives in Nursing Science
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• v.17 no.2
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• pp.80-89
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• 2020

Purpose: The aim of this study is to review literature on anger management intervention programs for parents published over the last 10 years and to extract the key elements of the interventions through an integrative review. Methods: This research was carried out in stages following Whittemore and Knafl's integrative literature methodology. Key words in Korean and English were used to search the PubMed, MEDLINE, EMbase, CINAHL, RISS, KISS and National Assembly Library databases. Several intervention factors were extracted from the selected papers on the basis of the framework which was helpful to identify the intervention patterns and were classified into meaningful themes. Results: The extracted intervention factors from the final nine studies classified into four themes: 1) Modifying irrational beliefs through cognitive approaches, 2) Empowering parenting competencies through learning a parent's role, 3) Utilizing emotion management skills, and 4) Parent-child relationship improvement training based on self-reflection. Conclusion: Four main themes were drawn from the key components of the various interventions. These findings should be considered in practice, and further intervention development studies for parents using these findings should be conducted.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.3
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• pp.381-386
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• 2015

In recent, many researches have been studying sensitivity and psychology of human being on color and the necessity of psychology therapy by color. Among them, a picture of children can be a tool to represent their emotion. Information of colors and direction on a child's picture often represent his internal psychological states unconsciously and is different from the brightness of a color. In this paper, we propose a method to extract domain colors by color classification and subdivision the classes of brightness using fuzzy inference. In addition, it is shown that our method is used for analysing the psychology status of children through their pictures.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.131-138
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• 2014

A close-copy stylization of intonation curve was used for an acoustical analysis of emotional speech. For the analysis, 408 utterances of five emotions (happiness, anger, fear, neutral and sadness) were processed to extract acoustical feature values. The results show that certain pitch point features (pitch point movement time and pitch point distance within a sentence) and sentence level features (pitch range of a final pitch point, pitch range of a sentence and pitch slope of a sentence) are affected by emotions. Pitch point movement time, pitch point distance within a sentence and pitch slope of a sentence show no significant difference between male and female participants. The emotions with high arousal (happiness and anger) are consistently distinguished from the emotion with low arousal (sadness) in terms of these acoustical features. Emotions with higher arousal show steeper pitch slope of a sentence. They have steeper pitch slope at the end of a sentence. They also show wider pitch range of a sentence. The acoustical analysis in this study implies the possibility that the measurement of these acoustical features can be used to cluster and identify emotions of speech.

• Korean Institute of Interior Design Journal
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• no.41
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• pp.155-162
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• 2003

The factors to limit interior circumstance and the expression of the space start In the point how to provide the order in the interior space according to the intention of design. The controlling of the natural and artificial surroundings should be focused by synthesizing and analyzing the condition of circumstance, function, and emotion to provide the comfortable room to a customer in a fitness center. A fitness center aiming at the future requires various function according to it's role depending on the change of the social values. In addition, the fitness center reflecting the various regional culture according to the change of values requires the expansion of the cultural and life space. The characteristics of fitness space like this affects the mental effect of a customer with the color and quality of the materials suitable for the characteristics of grouping, continuity, symbolism, and generation as well as the unification in the total design and the simplicity of maintenance and management. The paper aims to extract factors related to the circumstance plan of the interior and to suggest it's application in the interior design. The design applicable to the circumstance plan of the interior will keep changing according to the social change and the change of the understanding along with the factors. The continuous research on the interior circumstance planning of a fitness center could be approach to develop in a systematic method and the paper will be expected to be a little step toward it.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1637-1642
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• 2015

Next-generation applications using technology IT fused to biological signals from the emotional state to extract a lot of research has been, and the sensitivity of the human sensory functions influences the physiological condition known to be the fact that. In this paper, Propose an Emotional-lighting control algorithm using bio-signals. LED lighting for Emotion light is environmentally friendly and has a high efficiency and long life. In particular, LED lights are different colors represent the possible single light sphere advantages. And, Human sensitivity for determining a more accurate biological signals using EEG was collected using EEG equipment sensitivity was determined to analyze the EEG.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.27-32
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• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.563-569
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• 2012

The studies of opinion mining or sentiment analysis have been the focus with social web proliferation. Sentiment analysis requires sentiment resources to decide its polarity. In the existing sentiment analysis, they have been built resources designed with intensity of sentiment polarity and decided polarity of opinion using the ones. In this paper, I will present sentiment categories for not only polarity of opinion but also the basis of positive/negative opinion. I will define psychological emotions to primary sentiments for the reasonable classification. And I will extract the informations of sentiment from social web texts for the actual distribution of sentiments in social web. Re-classifying primary sentiments based on extracted sentiment information, I will organize sentiment categories for the social web. In this paper, I will present 23 categories of sentiment by using proposed method.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.133-141
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• 2016

Recent web services provide tagging function to users, and let them express the topic of the contents of their articles. Moreover, we can extract context information like emotion of the writer efficiently by using tags attached to the articles or images. And we are able to better understand article than traditional algorithm. (eg. TF-IDF) Therefore, if we use tags in recommendation system, we can recommend high quality resources to the users. This study proposes a recommendation method that provide web resources (articles, users) through simple algorithm based on related tag set extracted from the article. Through the experiments, we show that the result was satisfactory, and we measure the satisfaction of users.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.