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The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout, which allows measurement of signals from each spot where electrodes are attached based on EEG characteristics. The measured data were added in a database. In the intelligent neuro-fuzzy model, wave transform was used for extracting brain wave characteristics according to user intentions and to eliminate noise from the signals in an effort to increase reliability. Moreover, to construct the option rules of the neuro-fuzzy system, FCM technique and optimal cluster evaluation method were used. Furthermore, the asymmetric Gaussian membership function was used to improve performance, whereas SD and WF divided into left and right sides were used to express the chromosomes. Optimal EEG electrode locations were found, and comparative analysis was performed on the differences based on membership function, number of clusters, and number of learning generations, learning algorithm, and wavelet settings. The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user's willingness to participate in rehabilitation and his or her quality of life.
Tae-Yeun Kim; Sung-Hwan Kim; Hoon Ko. Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System. ACM Transactions on Internet Technology 2021, 21, 1 -17.
AMA StyleTae-Yeun Kim, Sung-Hwan Kim, Hoon Ko. Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System. ACM Transactions on Internet Technology. 2021; 21 (3):1-17.
Chicago/Turabian StyleTae-Yeun Kim; Sung-Hwan Kim; Hoon Ko. 2021. "Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System." ACM Transactions on Internet Technology 21, no. 3: 1-17.
Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.
Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim; Ho-Da Kim. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors 2021, 21, 1997 .
AMA StyleTae-Yeun Kim, Hoon Ko, Sung-Hwan Kim, Ho-Da Kim. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors. 2021; 21 (6):1997.
Chicago/Turabian StyleTae-Yeun Kim; Hoon Ko; Sung-Hwan Kim; Ho-Da Kim. 2021. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6: 1997.
As the importance of providing personalized services increases, various studies on personalized recommendation systems are actively being conducted. Among the many methods used for recommendation systems, the most widely used is collaborative filtering. However, this method has lower accuracy because recommendations are limited to using quantitative information, such as user ratings or amount of use. To address this issue, many studies have been conducted to improve the accuracy of the recommendation system by using other types of information, in addition to quantitative information. Although conducting sentiment analysis using reviews is popular, previous studies show the limitation that results of sentiment analysis cannot be directly reflected in recommendation systems. Therefore, this study aims to quantify the sentiments presented in the reviews and reflect the results to the ratings; that is, this study proposes a new algorithm that quantifies the sentiments of user-written reviews and converts them into quantitative information, which can be directly reflected in recommendation systems. To achieve this, the user reviews, which are qualitative information, must first be quantified. Thus, in this study, sentiment scores are calculated through sentiment analysis by using a text mining technique. The data used herein are from movie reviews. A domain-specific sentiment dictionary was constructed, and then based on the dictionary, sentiment scores of the reviews were calculated. The collaborative filtering of this study, which reflected the sentiment scores of user reviews, was verified to demonstrate its higher accuracy than the collaborative filtering using the traditional method, which reflects only user rating data. To overcome the limitations of the previous studies that examined the sentiments of users based only on user rating data, the method proposed in this study successfully enhanced the accuracy of the recommendation system by precisely reflecting user opinions through quantified user reviews. Based on the findings of this study, the recommendation system accuracy is expected to improve further if additional analysis can be performed.
Tae-Yeun Kim; Sung Bum Pan; Sung-Hwan Kim. Sentiment Digitization Modeling for Recommendation System. Sustainability 2020, 12, 5191 .
AMA StyleTae-Yeun Kim, Sung Bum Pan, Sung-Hwan Kim. Sentiment Digitization Modeling for Recommendation System. Sustainability. 2020; 12 (12):5191.
Chicago/Turabian StyleTae-Yeun Kim; Sung Bum Pan; Sung-Hwan Kim. 2020. "Sentiment Digitization Modeling for Recommendation System." Sustainability 12, no. 12: 5191.
All persons in self-driving vehicle would like to receive each service. To do it, the system has to know the person’s state from emotion or stress, and to know the person’s state, it has to catch by analyzing the person’s bio-information. In this paper, we propose a system for inferring emotion using EEG, pulse, blood pressure (systolic and diastolic blood pressure) of user, and recommending color and music according to emotional state of user for a user service in self-driving vehicle. The proposed system is designed to classify the four emotional information (stability, relaxation, tension, and excitement) by using EEG data to infer and classify emotional state according to user’s stress. SVM algorithm was used to classify bio information according to stress index using brain wave data of the fuzzy control system, pulse, and blood pressure data. When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. The bio-information classification system based on the stress index proposed in this paper will help to study the interaction between human and computer (HCI) in the 4th Industrial Revolution by classifying emotional color and emotional sound according to the emotion of the user it is expected.
Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim. Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles. Journal of Advanced Transportation 2020, 2020, 1 -11.
AMA StyleTae-Yeun Kim, Hoon Ko, Sung-Hwan Kim. Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles. Journal of Advanced Transportation. 2020; 2020 ():1-11.
Chicago/Turabian StyleTae-Yeun Kim; Hoon Ko; Sung-Hwan Kim. 2020. "Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles." Journal of Advanced Transportation 2020, no. : 1-11.
To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the SVM classification algorithm, the system’s performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the SVM classification algorithm. According to the SVM classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of SVM and other algorithms, the SVM classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user’s characteristics or requirements.
Tae-Yeun Kim; Sang-Hyun Bae; Young-Eun An. Design of Smart Home Implementation Within IoT Natural Language Interface. IEEE Access 2020, 8, 84929 -84949.
AMA StyleTae-Yeun Kim, Sang-Hyun Bae, Young-Eun An. Design of Smart Home Implementation Within IoT Natural Language Interface. IEEE Access. 2020; 8 (99):84929-84949.
Chicago/Turabian StyleTae-Yeun Kim; Sang-Hyun Bae; Young-Eun An. 2020. "Design of Smart Home Implementation Within IoT Natural Language Interface." IEEE Access 8, no. 99: 84929-84949.
본 논문에서는 사용자의 음성 감성 정보를 고려하여 매칭 된 콘텐츠를 추천하기 위해 감성을 6가지의 상황(보통, 기쁨, 슬픔, 화남, 놀람, 지루함)으로 정의하였으며 정규화 된 음성을 음성 감성 정보로 분류하기 위해 GAFS 알고리즘과 SVM 알고리즘을 사용하였다. 또한 콘텐츠(이미지, 음악) 정보를 요인분석, 대응 일치 분석, 유클리디안 거리를 이용하여 콘텐츠 감성 정보로 분류하였다. 마지막으로 감성에 따라 분류된 음성 정보와 감성 협업 필터링을 이용하여 감성 정보 값에 따라 감성 선호도를 예측함으로써 사용자 감성에 맞는 콘텐츠를 모바일 애플리케이션에 추천하도록 설계하였다. 성능 평가를 위해 본 논문에서는 MAE 알고리즘을 통해 검증을 수행하였다. 성능 평가 결과 사용자의 감성에 따른 콘텐츠를 추천함으로써 사용자의 특성 및 만족도를 고려할 수 있을 것으로 기대한다.
Tae-Yeun Kim; Kyung-Soo Lee; Young-Eun An. A Study on the Recommendation of Contents using Speech Emotion Information and Emotion Collaborative Filtering. Journal of Digital Contents Society 2018, 19, 2247 -2256.
AMA StyleTae-Yeun Kim, Kyung-Soo Lee, Young-Eun An. A Study on the Recommendation of Contents using Speech Emotion Information and Emotion Collaborative Filtering. Journal of Digital Contents Society. 2018; 19 (12):2247-2256.
Chicago/Turabian StyleTae-Yeun Kim; Kyung-Soo Lee; Young-Eun An. 2018. "A Study on the Recommendation of Contents using Speech Emotion Information and Emotion Collaborative Filtering." Journal of Digital Contents Society 19, no. 12: 2247-2256.
Tae Yeun Kim; Sanghyun Bae. Designing an Intelligent Rehabilitation Wheelchair Vehicle System Using Neural Network-based Torque Control Algorithm. KSII Transactions on Internet and Information Systems 2017, 11, 5878 -5904.
AMA StyleTae Yeun Kim, Sanghyun Bae. Designing an Intelligent Rehabilitation Wheelchair Vehicle System Using Neural Network-based Torque Control Algorithm. KSII Transactions on Internet and Information Systems. 2017; 11 (12):5878-5904.
Chicago/Turabian StyleTae Yeun Kim; Sanghyun Bae. 2017. "Designing an Intelligent Rehabilitation Wheelchair Vehicle System Using Neural Network-based Torque Control Algorithm." KSII Transactions on Internet and Information Systems 11, no. 12: 5878-5904.
Implementation of Intelligent Home Network and u-Healthcare System based on Smart-Grid Intelligent Home Network;Smart-Grid;SVM classification Algorithm;u-Healthcare;ZIGBEE; In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.
Tae Yeun Kim; Sang Hyun Bae. Implementation of Intelligent Home Network and u-Healthcare System based on Smart-Grid. Journal of the Chosun Natural Science 2016, 9, 199 -205.
AMA StyleTae Yeun Kim, Sang Hyun Bae. Implementation of Intelligent Home Network and u-Healthcare System based on Smart-Grid. Journal of the Chosun Natural Science. 2016; 9 (3):199-205.
Chicago/Turabian StyleTae Yeun Kim; Sang Hyun Bae. 2016. "Implementation of Intelligent Home Network and u-Healthcare System based on Smart-Grid." Journal of the Chosun Natural Science 9, no. 3: 199-205.
ByoungHo Song -; KyoungWoo Park -; Tae Yeun Kim. U-health Expert System with Statistical Neural Network. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 2011, 3, 54 -61.
AMA StyleByoungHo Song -, KyoungWoo Park -, Tae Yeun Kim. U-health Expert System with Statistical Neural Network. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences. 2011; 3 (1):54-61.
Chicago/Turabian StyleByoungHo Song -; KyoungWoo Park -; Tae Yeun Kim. 2011. "U-health Expert System with Statistical Neural Network." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 1: 54-61.