• Journal of the Korean Institute of Educational Facilities
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• v.27 no.2
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• pp.79-86
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• 2020

Since the introduction of the new Variation Type school, few studies have focused on students' adaptation to the changes in their learning environments at school. This paper is based on the Stage-Environment Fit theory, which asserts that a successful school life(in terms of motivation to learn) is ensured only when the school environment meets the social and emotional needs of students. Focusing on the third-grade student's adaptation to a new Variation Type school during their middle school period, the following conclusions were drawn. First, the transition to a new Variation Type school during middle school is much more difficult than adjusting to a new Variatio Type school upon admission to middle school. Second, this difficulty in adaptation is caused by socio-emotional dissatisfaction in adolescent students, for whom deconstruction of previous friendships can hinder motivation to learn. Third, third-grade students who experienced stress due to spatial changes tended to have a negative attitude towards the new Variation Type itself as they feel more tired from failing to use the space properly. Fourth, to transition successfully to a new Variation Type school, socio-emotional problems must be solved through the reduction of scale of the homebase, and the provision of various choices increasing the number of homebase.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.773-778
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• 2009

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, vocal tract normalization method is used to develop the robust speech recognition system for emotional variations. Experimental results from the isolated word recognition using HMM showed that the vocal tract normalization method reduced the error rate of the conventional recognition system by 41.9% when emotional test data was used.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.468-472
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• 1997

Visual Emotion is attempted to be evaluated by EEG(Electroencephalogram). Twenty university students were participated in this study. IAPS(International Affective Picture System) was used for the visual stimuli. Most positive and most negative emotional response were pairely compared. The results showed alpha increase, delta and beta decrease with postive emotional response, and alpha-delta inter-variation with emotional progress.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.683-687
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• 2010

This paper studied some methods which use frequency warping method that is the one of the speech transformation method to develope the robust speech recognition system for the emotional variation. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions and it is observed that speech spectrum is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, new training method that uses frequency warping in training process is presented to reduce the effect of emotional variation and the speech recognition system based on vocal tract length normalization method is developed to be compared with proposed system. Experimental results from the isolated word recognition using HMM showed that new training method reduced the error rate of the conventional recognition system using speech signal containing various emotions.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.528-533
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• 2010

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variation on the speech signal and the speech recognition system were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, a training method that cover the speech variations is proposed to develop the emotionally robust speech recognition system. Experimental results from the isolated word recognition using HMM showed that propose method reduced the error rate of the conventional recognition system by 28.4% when emotional test data was used.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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• pp.837-844
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• 2013

This study performs erection of a pendulum hanging at a free end of an arm by rotating the arm to the upright position. A mathematical model of a rotary inverted pendulum system (RIPS) is derived. A brain emotional learning based intelligent controller (BELBIC) is designed and used as a controller for swinging up and balancing the pendulum of the RIPS. In simulations performed in the study, a pendulum is initially inclined at $45^{\circ}$ with respect to the upright position. A simulation is also performed for evaluating the adaptiveness of the designed BELBIC in the case of system variation. In addition, a simulation is performed for evaluating the robustness of the designed BELBIC against a disturbance in the control input.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.281-288
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• 2009

Nowadays, many robots have evolved to imitate human social skills such that sociable interaction with humans is possible. Socially interactive robots require abilities different from that of conventional robots. For instance, human-robot interactions are accompanied by emotion similar to human-human interactions. Robot emotional expression is thus very important for humans. This is particularly true for facial expressions, which play an important role in communication amongst other non-verbal forms. In this paper, we introduce a method of creating lifelike facial expressions in robots using variation of affect values which consist of the robot's emotions based on emotional boundaries. The proposed method was examined by experiments of two facial robot simulators.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.586-591
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• 2008

This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal. Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.

• Korean Journal of Adult Nursing
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• v.15 no.1
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• pp.33-44
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• 2003

Purpose: The purpose of this study was to identify the patterns and related factors of fatigue in patients with breast cancer undergoing radiotherapy. Method: 31 women with breast cancer receiving radiotherapy were recruited from the out-patient radiologic clinic of the university hospital in Seoul, Korea over a period of 3 months. Data was collected prospectively concerning three points for $5\frac{1}{2}\;-\;6\frac{1}{2}$ weeks : before radiotherapy(T1), 2 weeks after starting radiotherapy(T2) and the completion of radiotherapy(T3). Data were analysed by repeated measure ANOVA, Pearson correlaton, and multiple regression. Result: 1. Score of fatigue increased significantly over the course of radiotherapy. 2. Score of symptom distress and emotional distress increased and functional status scores decreased significantly over time. 3. Fatigue was positively related with symptom distress and emotional distress and negatively related with functional status over the course of radiotherapy. 4. At T2, emotional distress explained 24.7% of the variation in fatigue. At T3, symptom distress(41.9%) and emotional distress(7.2%) explained the variance in fatigue. Conclusion: The results of this study provided evidence that fatigue increased over the course of radiotherapy and symptom distress and emotional distress were influencing factors of fatigue in this group. The results of this study suggest that comprehensive intervention strategy for fatigue should be developed to maintain quality of life during and following radiotherapy considering these factors.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.655-660
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• 2005

This paper studied the feature parameters less affected by the emotional variation for the development of the robust speech recognition technologies. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. In this study, LPC cepstral coefficient, met-cepstral coefficient, root-cepstral coefficient, PLP coefficient, RASTA met-cepstral coefficient were used as a feature parameters. And CMS and SBR method were used as a signal bias removal techniques. Experimental results showed that the HMM based speaker independent word recognizer using RASTA met-cepstral coefficient :md its derivatives and CMS as a signal bias removal showed the best performance of $7.05\%$ word error rate. This corresponds to about a $52\%$ word error reduction as compare to the performance of baseline system using met - cepstral coefficient.