• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.157-164
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• 2011

Emotion science is one of the rapidly expanding engineering/scientific disciplines which has a major impact on human society. Such growing interests in emotion science and engineering owe the recent trend that various academic fields are being merged. In this paper we propose the potential importance of the biochip technology in which the human emotion can be precisely measured in real time using body fluids such as blood, saliva and sweat. We firstly and newly name such a biochip an Emotion-On-a-Chip (EOC). EOC consists of biological markers to measure the emotion, electrode to acquire the signal, transducer to transfer the signal and display to show the result. In particular, microfabrication techniques made it possible to construct nano/micron scale sensing parts/chips to accommodate the biological molecules to capture the emotional bio-markers and gave us a new opportunities to investigate the emotion precisely. Future developments in the EOC techniques will be able to help combine the social sciences and natural sciences, and consequently expand the scope of studies.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.983-991
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• 2019

This study aims to analyze movies which contain various stories according to the size of their shots. To achieve this, it is needed to classify dataset according to the shot size, such as extreme close-up shots, close-up shots, medium shots, full shots, and long shots. However, a typical video storytelling is mainly composed of close-up shots, medium shots, full shots, and long shots, it is not an easy task to construct an appropriate dataset for extreme close-up shots. To solve this, we propose an image cropping method based on the region of interest (ROI) detection. In this paper, we use the face detection and saliency detection to estimate the ROI. By cropping the ROI of close-up images, we generate extreme close-up images. The dataset which is enriched by proposed method is utilized to construct a model for classifying shots based on its size. The study can help to analyze the emotional changes of characters in video stories and to predict how the composition of the story changes over time. If AI is used more actively in the future in entertainment fields, it is expected to affect the automatic adjustment and creation of characters, dialogue, and image editing.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.103-116
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• 2017

We studied the actual condition of work environment and work morale according to the type of employment of service workers by using the raw data of the Fourth Work Environment Survey (2014) conducted by the Institute of Occupational Safety and Health. In this study, the condition of work environment were composed of work posture, emotional labor, and work autonomy. Also, dimensions related to work morale were composed of competence, job satisfaction, social support, and job stress. In addition, the employment was classified into three types of regular workers, temporary workers, and daily workers. The results showed that temporary and daily workers were more likely to work in a less favorable environment than regular workers, and there was a close correlation between work environment and work morale. Based on this study, it is possible to know about the actual situation and problems of the service workers, and it is hoped that company can search for measures to increase the working environment and work morale of the workers in order to provide better service.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.207-217
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• 2014

Many a number of policies have been tried to reduce auto accidents so far, but it is obvious that further studies are still needed to find a more fundamental and multi-dimensional preventive measure with effect. The National Mental Health Statistics shows that the most profound forms of negative emotions, that is, depression and anxiety, have been increasing, but studies on such a topic are scarce to find. Therefore, we conducted a structural analysis between the negative emotions, including depression and anxiety, of drivers and their driving behaviors using a Structural Equation Modeling(SEM) technique. The review of past literature and studies indicated that not all of human emotions manifest themselves as the ultimate behaviors because they go through emotion regulation Strategies. For this reason, the purpose of this study was set to analyze the structural model developed in this study reflecting the emotion regulation strategies. The result of our analysis showed that the driver's negative emotion had a more significant influence on dangerous driving behaviors than safe ones, and especially, the expressive suppression strategy was found to be the highest factor. Also, the total effect analysis with the negative emotional factors showed that expressive suppression had more significant influence compared to that of cognitive reappraisal. The implication of this study might provide a better understanding on driving behaviors of the drivers and could be used as a fundamental study for future policy development to reduce traffic accidents.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.65-87
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• 2017

We propose a method to analyze the user reviews and ratings of the products in the online shopping mall and automatically extracts the features of the products to determine the characteristics of a product. By judging whether a rating is given by a specific feature of a product, our method distributes the score to each feature. Conventional methods force users to wastes time reading overflowing number of reviews and ratings to decide whether to buy the product or not. Moreover, it is difficult to grasp the merits and demerits of the product, because of the way reviews and ratings are provided. It is structured in a way that it is impossible to decide which rating is given to the which characteristics of the product. Therefore, in this paper, to resolve this problem, we propose a method to automatically extract the feature of the product from the user review and distribute the score to appropriate characteristics of the product by calculating the rating of each feature from the overall rating. proposed method collects product reviews and ratings, conducts morphological analysis, and extracts features and emotional words of the products. In addition, a method for determining the polarity of a sentence in which the feature appears is given a weight value for each feature. results of the experiment and the questionnaires comparing the existing methods show the usefulness of the proposed method. We also validates the results by comparing the analysis conducted by the product review experts.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.15-28
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• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• Journal of Life Science
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• v.30 no.11
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• pp.1021-1032
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• 2020

Numbers of companion animals and people rearing them are increasing in developed countries. As a result, businesses related to companion animals are becoming more advanced and specialized. Dogs have been cohabiting with humans as companions (pets) for thousands of years and, as a result, eat carbohydrate-rich foods similar to humans and maintain lives similar to their owners. Tight bonds between dogs and their owners are formed by sharing similar lifestyles, including a dwelling and food. Owners are responsible for their pets and treat them with emotional stability. Pets depend on their owners, although the food situation can cause stress. Since pet dogs are carnivorous in nature, providing pet dogs with a nutritionally balanced diet and functional materials is important for a healthy gut microbiome. Recently, the gut microbiota has become a research focus because it is associated with protection from harmful pathogens and immune regulation while maintaining physiological homeostasis. An abnormal gut microbiota is related to pathogenic processes and various gut, metabolic, mental, and neurological diseases. Additionally, pet dogs at risk of disease affect the health of their owners. Therefore, this review discusses the composition and diversity of the gut microbiota of dogs and the relationships between the gut microbiota and diseases.

• Journal of the Korea Institute of Building Construction
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• v.19 no.1
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• pp.9-18
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• 2019

Concrete, which is the most widely used building material in modern times, has been improved not only in strength but also in structural performance such as increase in toughness and ductility, weight reduction, and improvement in quality of human life. Due to the surge in demand for the building, there is a tendency to be used variously from architectural panel and architecture to interior accessories. In Korea, a light-transmitting concrete, LEFC(Light Emotion Friendly Concrete), that insert plastic rods to stimulate emotional sensation through the combination of light and concrete has developed. In previous research, it was confirmed that the use of a synthetic foam agent rather than an animal foam agent did not cause a fogging phenomenon. In this study, lightweight by applying foaming agent to LEFC and two types of fiber (Nylon Fiber, Polyvinyl Alcohol) were compared to achieve to investigate the fiber to be applied in future. An equation that can predict the loss and adhesion reduction of the concrete section according to the diameter of the rod (5mm, 10mm) and the interval (10mm, 15mm, 20mm) was proposed.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.