• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.717-724
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• 2020

Due to the important role played by emotion in human interaction, affective computing is dedicated in trying to understand and regulate emotion through human-aware artificial intelligence. By understanding, emotion mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction will be better managed as they are all associated with emotion. Various studies for emotion recognition have been conducted to solve these problems. In applying machine learning for the emotion recognition, the efforts to reduce the complexity of the algorithm and improve the accuracy are required. In this paper, we investigate emotion Electroencephalogram (EEG) feature reduction and classification using Stack AutoEncoder (SAE) and Long-Short-Term-Memory/Recurrent Neural Networks (LSTM/RNN) classification respectively. The proposed method reduced the complexity of the model and significantly enhance the performance of the classifiers.

• Journal of Fashion Business
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• v.26 no.1
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• pp.1-15
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• 2022

Digital transformation has induced changes in human life patterns; consumption patterns are also changing to digitalization. Entering the era of industry 4.0 with the 4th industrial revolution, it is important to pay attention to a new paradigm in the fashion industry, the shift from developer-centered to user-centered in the era of the 3rd industrial revolution. The meaning of storing users' changing life and consumption patterns and analyzing stored big data are linked to consumer sentiment. It is more valuable to read emotions, then develop and distribute products based on them, rather than developer-centered processes that previously started in the fashion market. An AI(Artificial Intelligence) deep learning algorithm that analyzes user emotion big data from user experience(UX) to emotion and uses the analyzed data as a source has become possible. By combining AI technology, the fashion industry can develop various new products and technologies that meet the functional and emotional aspects required by consumers and expect a sustainable user experience structure. This study analyzes clear and useful user experience in the fashion industry to derive the characteristics of AI algorithms that combine emotions and technologies reflecting users' needs and proposes methods that can be used in the fashion industry. The purpose of the study is to utilize information analysis using big data and AI algorithms so that structures that can interact with users and developers can lead to a sustainable ecosystem. Ultimately, it is meaningful to identify the direction of the optimized fashion industry through user experienced emotional fashion technology algorithms.

• Management & Information Systems Review
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• v.29 no.4
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• pp.143-167
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• 2010

Diverse research topics relating to Social Network Services (SNS) such as, social affective factors in relationships among internet users, social capital value of SNS, comparing attributes why users are intending to participate in SNS, user's lifestyle and their preferences, and the exploratory seeking potential of SNS as a social capital need to be focused on. However, these researches that have been undertaken only consider facts at a particular period of the changing computing environment. In accordance with this indispensability, the integrated view on what technical, social and business characteristics and attributes need to be acknowledged. The purpose of this study is to analyze the evolving attributes and characteristics of SNS from Web 1.0 to Mobile web 2.0 through the Web 2.0 and Mobile 1.0 period. Based on the relevant literature, the attributes that drive the changing technological, social and business aspects of SNS have been developed and analyzed. This exploratory study analyzed major attributes and relationships between SNS and users by changing the paradigms which represented each period. It classified and chronicled each period by representing paradigms and deducted the attributes by considering three aspects such as technological, social and business administration. The major findings of this study are, firstly, the web based computing environment has been changed into the platform attribute for users in the aspect of technology. Users can only read, listen and view information through the web site in the early stages, but now it is possible that users can create, modify and distribute all kinds of information. Secondly, the few knowledge producers of web services have been changed into a collective intelligence by groups of people in the aspect of society. Information authority has been distributed and there is no limit to its spread. Many businesses recognized the potential of the SNS and they are considering how to utilize these advantages toward channel of promotion and marketing. Thirdly, the conventional marketing channel has been changed into oral transmission by using SNS. The market of innovative mobile technology such as smart phones, which provide convenience and access-ability toward customers, has been enlarged. New opportunities to build friendly relationship between business and customers as a new marketing chance have been created. Finally, the role of the consumer has been changed into the leading role of a prosumer. Users can create, modify and distribute information, and are performing the dual role of customer and producer.

• Science of Emotion and Sensibility
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• v.8 no.1
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• pp.47-54
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• 2005

This study is aimed to inspect how the different sensitivities in Behavioral activation system(BAS) and behavioral inhibition system(BIS) modulate on the properties of physiological responses stimulated by positive or negative affective sound. We measured the electroencephalogram(EEG) and electrocardiogram (ECG) of 32 students, consisted of four groups depending on the BAS and BIS sensitivities, during listening to meditation music or noise. The EEG was recorded at Fpl and Fp2 sites and Power spectral density(PSD) of HRV was derived from the ECG, and the power of HRV was calculated for 3 major frequency ranges(low frequency[LF], medium frequency and high frequency[HF]). After listening to music or noise, subjects reported the affect induced by the sound. For EEG, the power in the alpha band at Fp2, especially in the alpha-2 band(9.0-11.0 Hz) increased during the subjects listening to music, while the power at Fpl increased during noise. During listening to meditation music, there is a tendency that the left-sided activation in prefrontal cortex(PFC) is positively correlated with the difference of BAS(Z)-BIS(Z). During listening to noise, there is a tendency that the right-sided activation in PFC is dominant in case any of the sensitivity of BAS or BIS is high. For HRV, we found that the index of MF/(LF+HF), during listening to music, was higher significantly in the individuals with a low BIS but high BAS than in the individuals with a low sensitivity both BIS and BAS individuals. With high BIS, regardless of the BAS sensitivity, the difference of this index values was not significant. From these results we suggest that the physiological responses of different individuals in BAS and BIS react differently under the same emotionally provocative challenge.