• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.279-282
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• 2002

자율신경계 반응은 인간의 각성을 측정하는 변수가 될 수 있다(황민철 외, 2001). 본 연구에서는 자율신경계 반응만으로 인간의 2차원 감성 분류를 할 수 있는지를 살펴보았다. 5명의 피험자에게 대중가요나 효과음 등과 같은 다양한 청각자극을 제시하여 감성을 유발한 후, 자율신경계의 반응을 3가지 생리신호(GSR, SKT, PPG)를 통해 측정하여 반응 패턴을 분석하였다. 결과적으로, 자율신경계 반응 패턴은 각성/이완뿐만 아니라 쾌/불쾌간 감성 구분의 가능성을 확인할 수 있었다.

• Journal of Science Education
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• v.34 no.2
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• pp.185-192
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• 2010

The purpose of this study was to develope the negative-emotion prediction model by hormonal changes during the scientific inquiry. For this study, biological classification task was developed that are suitable for comprehensive scientific inquiry. Forty-seven 2nd grade secondary school students (boy 18, girl 29) were participated in this study. The students are healthy for measure hormonal changes. The students performed the feathers classification task individually. Before and after the task, the strength of negative emotion was measured using adjective emotion check lists and they extracted their saliva sample for salivary hormone analysis. The results of this study, student's change of negative emotion during the feathers classification process was significant positive correlation(R=0.39, P<0.001) with student's salivary cortisol concentration. According to this results, we developed the negative emotion prediction model by salivary cortisol changes.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.57-66
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• 2023

In this study, we propose a novel research framework for the recommendation system that can estimate the user's emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.174-180
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• 2024

In this paper, we explore an emotion classification method through multimodal learning utilizing wav2vec 2.0 and KcELECTRA models. It is known that multimodal learning, which leverages both speech and text data, can significantly enhance emotion classification performance compared to methods that solely rely on speech data. Our study conducts a comparative analysis of BERT and its derivative models, known for their superior performance in the field of natural language processing, to select the optimal model for effective feature extraction from text data for use as the text processing model. The results confirm that the KcELECTRA model exhibits outstanding performance in emotion classification tasks. Furthermore, experiments using datasets made available by AI-Hub demonstrate that the inclusion of text data enables achieving superior performance with less data than when using speech data alone. The experiments show that the use of the KcELECTRA model achieved the highest accuracy of 96.57%. This indicates that multimodal learning can offer meaningful performance improvements in complex natural language processing tasks such as emotion classification.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.949-964
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• 2021

Accumulation of high-quality data is crucial for AI learning. The goal of using AI in fashion service is to propose of a creative, personalized solution that is close to the know-how of a human operator. These customized solutions require an understanding of fashion products and emotions. Therefore, it is necessary to accumulate data on the attributes of fashion products and fashion emotion. The first step for accumulating fashion data is to standardize the attribute with coherent system. The purpose of this study is to propose a fashion emotional classification system. For this, images of fashion products were collected, and metadata was obtained by allowing consumers to describe their emotions about fashion images freely. An emotional classification system with a hierarchical structure, was then constructed by performing frequency and CONCOR analyses on metadata. A final classification system was proposed by supplementing attribute values with reference to findings from previous studies and SNS data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.122-129
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• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.400-416
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• 2017

Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the $4^{th}$ order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.880-889
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• 2021

In everyday life, recognizing people's emotions from their frames is essential and is a popular research domain in the area of computer vision. Visual emotion has a severe class imbalance in which most of the data are distributed in specific categories. The existing methods do not consider class imbalance and used accuracy as the performance metric, which is not suitable for evaluating the performance of the imbalanced dataset. Therefore, we proposed a method for recognizing visual emotion using balanced data augmentation to address the class imbalance. The proposed method generates a balanced dataset by adopting the random over-sampling and image transformation methods. Also, the proposed method uses the Focal loss as a loss function, which can mitigate the class imbalance by down weighting the well-classified samples. EfficientNet, which is the state-of-the-art method for image classification is used to recognize visual emotion. We compare the performance of the proposed method with that of conventional methods by using a public dataset. The experimental results show that the proposed method increases the F1 score by 40% compared with the method without data augmentation, mitigating class imbalance without loss of classification accuracy.

• Phonetics and Speech Sciences
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• v.2 no.1
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• pp.77-85
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• 2010

This paper proposes an efficient feature vector processing technique to guard the Speech Emotion Recognition (SER) system against a variety of noises. In the proposed approach, emotional feature vectors are extracted from speech processed by comb filtering. Then, these extracts are used in a robust model construction based on feature vector classification. We modify conventional comb filtering by using speech presence probability to minimize drawbacks due to incorrect pitch estimation under background noise conditions. The modified comb filtering can correctly enhance the harmonics, which is an important factor used in SER. Feature vector classification technique categorizes feature vectors into either discriminative vectors or non-discriminative vectors based on a log-likelihood criterion. This method can successfully select the discriminative vectors while preserving correct emotional characteristics. Thus, robust emotion models can be constructed by only using such discriminative vectors. On SER experiment using an emotional speech corpus contaminated by various noises, our approach exhibited superior performance to the baseline system.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.