• 제목/요약/키워드: emotion control

검색결과 595건 처리시간 0.196초

제어 기반 감성 모델 (Emotion-Based Control Model)

  • Ko, Sung-Bum;Lim, Gi-Young
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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    • pp.199-202
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    • 2001
  • We, Human beings, use both powers of reason and emotion simultaneously, which surely help us to obtain flexible adaptability against the dynamic environment. We assert that this principle can be applied into the general system. That is, it would be possible to improve the adaptability by covering a digital oriented information processing system with an analog oriented emotion layer. In this paper, we proposed a vertical slicing model with an emotion layer in it. And we showed that the emotion-based control allows us to improve the adaptability of a system at least under some conditions.

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제어 기반 감성 (Emotion - Based Control)

  • Ko, Sung-Bum;Lim, Gi-Young
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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    • pp.227-230
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    • 2001
  • We, Human beings, use both powers of reason and emotion simultaneously, which surely help us to obtain flexible adaptability against the dynamic environment. We assert that this principle can be applied into the general system. That is, it would be possible to improve the adaptability by covering a digital oriented information processing system with an analog oriented emotion layer. In this paper. we proposed a vertical slicing model with an emotion layer in it. And we showed that the emotion-based control allows us to improve the adaptability of a system at least under some conditions.

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Sliding Factor Development on Mechanical Emotion in Mobile Phone of Slide Type

  • Lee, Jaein;Byun, Jungwoong;Jeong, Jaehwa;Lim, C.J.
    • 대한인간공학회지
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    • 제31권6호
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    • pp.757-764
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    • 2012
  • Objective: The aim of this study is to find the optimal values of sliding factors which influence the mechanical emotion of users when they use sliding type mobile phones. Background: There are various researches that study the emotion of using mobile phones. They focus the correlation between emotion words and design factors and use the commercial products by the subjects in the experiment. However, it has a limit in finding the optimal point of emotional factors because we can search the restricted values in the mass production of the products. Therefore, we will find the optimal points by realizing the full range of the user's mechanical emotion. Method: First, we need to get the detailed factors which can describe the mechanical emotion in sliding up and down the mobile phone. Next, we find the control factors by considering the correlation between the factors of the sliding emotion and the possibility of quantitative design. To find the optimal points on the control factors, we make a sliding evaluation system which can help users feel the sliding mechanical emotion by realizing control factors. Finally, we find the optimal points by doing the experiment the system being used. Results: The critical values of the factors which are the main variables of this study are Open Max Force and Dead point Ratio. The optimal point of the Open Max Force is 200~250g/f, and the Dead point Ratio is 40~50%. Conclusion: In this study we develop the sliding evaluation system to realize the control factors of the sliding type phone and find the optimal values of the critical factors. Application: The results can be used as the criteria for designing sliding type phone.

음성의 감성요소 추출을 통한 감성 인식 시스템 (The Emotion Recognition System through The Extraction of Emotional Components from Speech)

  • 박창현;심귀보
    • 제어로봇시스템학회논문지
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    • 제10권9호
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    • pp.763-770
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    • 2004
  • The important issue of emotion recognition from speech is a feature extracting and pattern classification. Features should involve essential information for classifying the emotions. Feature selection is needed to decompose the components of speech and analyze the relation between features and emotions. Specially, a pitch of speech components includes much information for emotion. Accordingly, this paper searches the relation of emotion to features such as the sound loudness, pitch, etc. and classifies the emotions by using the statistic of the collecting data. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

LED조명 시스템을 이용한 음악 감성 시각화에 대한 연구 (Emotion-based music visualization using LED lighting control system)

  • 응웬반로이;김동림;임영환
    • 한국게임학회 논문지
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    • 제17권3호
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    • pp.45-52
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    • 2017
  • 본 논문은 음악의 감성을 시각화하는 새로운 방법을 제안하는 논문으로 시각화를 위하여 감성 LED조명 제어 시스템을 고안하였으며 이 시스템은 청중들의 음악 경험을 더욱 풍요롭게 하기 위함에 목적이 있다. 이 시스템은 기존 연구의 음악 감성 분석 알고리즘을 이용하였는데 하나의 음악에서 하나의 감성을 추출하는 기존 연구의 한계를 보완하고자 음악의 1초당 세그먼트에서 감성을 추출 하여 감성 변화를 감지하는 방법을 연구하였다. 또한 LED조명의 감성 컬러는 IRI컬러 모델과 Thayer의 감성 모델을 기초로 새로운 감성 컬러 36가지를 정의 하여 각 감성에 맞는 색으로 연출 할 수 있도록 구성하고 애니메이션과 더불어 감성 LED 조명으로 표현 할 수 있도록 하였다. 이 시스템의 검증을 위한 실험 결과, 듣는이의 감성과 감성 LED 조명 시스템의 감성 일치율이 60% 이상으로 향후 음악 감성 기반 서비스의 중요한 밑거름이 될 것으로 보인다.

호텔기업 종업원의 감성지능이 고객지향성과 CRM성과에 미치는 영향 (The Effects of Emotional Intelligence on the Customer Orientation and Customer Relationship Management Performance of Hotel Employees)

  • 전타식;남택영
    • 유통과학연구
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    • 제10권10호
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    • pp.17-24
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    • 2012
  • Purpose - This study aimed to (a) investigate the effects of emotional intelligence on customer orientation, (b) examine the impact of customer orientation on customer relationship management (CRM) performance (including CRM-related variables such as 'relationship commitment,' 'image of corporation,' and 'customer loyalty'), and (c) identify the conceptual framework of emotional intelligence. Research design, data, and methodology - The data were collected using a questionnaire given to a sample of employees of luxury hotels in the metropolitan area. To test the hypotheses, AMOS were conducted for the 271 respondents of the sample using the SPSS Win 17.0 software. The concept of emotional intelligence (EI) has been on the radar of many leaders and managers over the past few decades. Emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thoughts, and interactions with others. Emotional intelligence consists of four factors: understanding the self's emotion, understanding other people's emotions, emotion utilization, and emotion control. Understanding the self's emotion means to understand of my own emotions. Understanding other people's emotions is to understand of the emotions of the people around me and to know how my friends feel based on their behavior. The concept of emotion utilization means to set goals for myself and then try to achieve them, encouraging myself to do my best. The concept of emotion control means I can control my temper, handle difficult situations rationally, and calm down quickly when I am very angry. Results - As a result of the analysis, three factors (understanding the self's emotion, understanding of other people's emotions, and emotion utilization) were shown to have a significant effect on customer orientation. Emotion control had an insignificant effect on customer orientation. Only emotion control makes it difficult to solve customers' problems because it is a passive behavior. In order to solve the customers' problems, hotel employees have to show a positive attitude. Second, customer orientation had a significant effect on customer relationship management performance (customer relationship commitment, corporate image, and customer loyalty). In other words, customer orientation increases commitment to customer relationships. For example, employees who have a customer-orientated perspective provide good service to their customers, while employees who don't have a customer-orientated perspective can't satisfy their customers. Customer orientation can also generate a good image among customers, because they evaluate the image of a hotel through the behavior of hotel employees. So it is very important for employees to show excellent customer orientation. Conclusions - It is very important for hotel CEOs to manage their employees' emotional intelligence. In order to increase their employees' emotional intelligence abilities, CEOs have to manage the overall corporate culture and reward programs to achieve what they want. This is because the system can lead to a customer-orientated mind-set and CRM performance among employees. As a result, the hotel CEO has to pay attention to the emotional intelligence of employees to achieve strong CRM performance. The sentence as originally written was a bit unclear. If this edit does not retain your intended meaning please consider: "Only emotion control does not have a significant impact on customer orientation, and therefore on the ability of an employee to solve customer problems, because it is a passive behavior." Please use the version of the sentence that best captures your original meaning.

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2D 얼굴 영상을 이용한 로봇의 감정인식 및 표현시스템 (Emotion Recognition and Expression System of Robot Based on 2D Facial Image)

  • 이동훈;심귀보
    • 제어로봇시스템학회논문지
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    • 제13권4호
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    • pp.371-376
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    • 2007
  • This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

음성신호를 이용한 감성인식에서의 패턴인식 방법 (The Pattern Recognition Methods for Emotion Recognition with Speech Signal)

  • 박창현;심귀보
    • 제어로봇시스템학회논문지
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    • 제12권3호
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    • pp.284-288
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

감성 인식을 위한 강화학습 기반 상호작용에 의한 특징선택 방법 개발 (Reinforcement Learning Method Based Interactive Feature Selection(IFS) Method for Emotion Recognition)

  • 박창현;심귀보
    • 제어로봇시스템학회논문지
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    • 제12권7호
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    • pp.666-670
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    • 2006
  • This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.