• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.147-149
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• 2022

This paper proposes a state machine design methodology in which an interactive robot that mimics discrete trial training (DTT protocol) can support social interaction training for children with autism. The robot applied to social interaction training uses the response to the provided training stimulus as a quantitative indicator by processing the data received from the sensors measuring the behavioral response of the child. In this process, the state machine is used as information that classifies the state of the acquired data and provides the subsequent stimulus for DTT protocol. Through the joint attentional training, it can be used as evidence-based treatment information by quantitatively classifying the data on the number of sustainable and DTT protocol and the child's response, as well as the current reaction status of the child to the observer performing remote monitoring. At the same time, it was confirmed that it is possible to properly respond to misrecognition situations.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.117-118
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• 2007

A robot needs a human detection algorithm for interaction with a human. This paper proposes a method that finds people using a SVM (support vector machine) classifier and a stereo camera. Feature vectors of SVM are extracted by HoG (histogram of gradient) within images. After training extracted vectors from the clustered images, the SVM algorithm creates a classifier for human detection. Each candidate for a human in the image is generated by clustering of depth information from a stereo camera and the candidate is evaluated by the classifier. When compared with the existing method of creating candidates for a human, clustering reduces computational time. The experimental results demonstrate that the proposed approach can be executed in real time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1496-1505
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• 2018

The technology of emotion recognition is necessary for human computer interaction communication. There are many cases where one cannot communicate without considering one's emotion. As such, emotional recognition technology is an essential element in the field of communication. n this regard, it is highly utilized in various fields. Various bio-sensor sensors are used for human emotional recognition and can be used to measure emotions. This paper proposes a system for recognizing human emotions using two physiological sensors. For emotional classification, two-dimensional Russell's emotional model was used, and a method of classification based on personality was proposed by extracting sensor-specific characteristics. In addition, the emotional model was divided into four emotions using the Support Vector Machine classification algorithm. Finally, the proposed emotional recognition system was evaluated through a practical experiment.

• Korean Journal of Child Studies
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• v.32 no.4
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• pp.1-14
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• 2011

The purpose of this study was to investigate young children's perceptions of a teacher assistant robot, IrobiQ. in a kindergarten classroom. The subjects of this study were 23 6-year-olds attending to G kindergarten located in E city, Korea, where the teacher assistant robot had been in operation since Oct. 2008. Each child responded to questions assessing the child's perceptions of IrobiQ's identity regarding four domains : it's biological, intellectual, emotional and social identity. Some questions asked the child to affirm or deny some characteristics pertaining to the robot and the other questions asked the reasons for the answer given. The results indicated that while majority of children considered an IrobiQ not as a biological entity, but as a machine, they thought it could have an emotion and be their playmate. The implications of these results are two folds : firstly, they force us to reconsider the traditional ontological categories regarding intelligent service robots to understand human-robot interaction and secondly, they open up an ecological perspective on the design of teacher assistant robots for use with young children in early childhood education settings.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.70-75
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• 2016

Nowadays, there are many research for recognizing face of people using the machine vision. the machine vision is classification and analysis technology using machine that has sight such as human eyes. In this paper, we propose algorithm for classifying human face using this machine vision system. This algorithm consist of Convolutional Neural Network and cascade face detector. And using this algorithm, we classified the face of subjects. For training the face classification algorithm, 2,000, 3,000, and 4,000 images of each subject are used. Training iteration of Convolutional Neural Network had 10 and 20. Then we classified the images. In this paper, about 6,000 images was classified for effectiveness. And we implement the system that can classify the face of subjects in realtime using USB camera.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.357-363
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• 1994

In many industrial situations the human acts as the primary scheduler since there often exist various constraints and considerations that may not be mathematically or quantitatively defined. For proper design of interactive scheduling systems, how human strategy and performance are affected by the fashion of human-computer interaction at various levels of task complexity should be investigated. In this study, two scheduling experiments were conducted. The first one showed that human schedulers could perform better than simple heuristic rules with each of typical performance measures such as average machine utilization, average tardiness, and maximum tardiness. In experiment 2, the effect of providing computer-generated initial solution was investigated. The results was that in complex problems the subjects performed significantly better when the initial solutions were generated by themselves, evidencing the importance of the continuity of strategic search through the problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.854-861
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• 2005

The functional behavior of a portable consumer electronic (PCE) product is nearly all expressed with human-machine interaction (HMI) tasks. Although physical prototyping and computer aided design (CAD) software can show the appearance of the product, they cannot properly reflect its functional behavior. In this paper, we propose a virtual prototyping (VP) system that incorporates virtual reality and HMI functional simulation in order to enables users to capture not only the realistic look of a PCE product but also its functional behavior. We obtain geometric part models of the product and their assembly and kinematics information with the help of CAD and reverse engineering tools, and visualize them with various display tools. We adopt state transition methodology to capture the HMI functional behavior of the product into a state transition chart, which is later used to construct a finite state machine (FSM) for the functional simulation of the product. The FSM plays an important role to control the transition between states of the product. The proposed VP system receives input events such as mouse clicks on buttons and switches of the virtual prototype model, and it reacts to the events based on the FSM by activating associated activities. The VP system provides the realistic visualization of the product and the vivid simulation of its functional behavior. It can easily allow users to perform functional evaluation and usability testing. Moreover, it can greatly reduce communication errors occurring in a typical product development process. A case study about VP of an MP3 player is given to show the usefulness of the proposed VP system.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.201-211
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• 2017

Social scientists have studied interaction between human beings, while computer scientists have expanded the research domain from human-human to human-machine, human-agent, or machine-machine. The reason why an adoption of Smart Work is failed is an anxiety about ICT usage which middle managers have. It is important to explore the concept both to reduce an anxiety on an application and to increase continuance to use it. Therefore this study takes "empathy" as a key factor to play a leading role both to relieve the anxiety about the application and to improve the intention to use it. The data is gathered from a survey of undergraduate who have experience to use MS-Access. The findings show that application empathy decrease the application anxiety, but the empathy increase the continuance mediated by cognitive and affective attitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1090-1100
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• 2024

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.3-42
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• 2002

Fuzzy Neural Network (FNN) is a compulsory element in a voice-controlled machine due to its inherent capability of interpreting imprecise natural language commands. To control such a machine, user's perception of imprecise words is very important because the words' meaning is highly subjective. This paper presents a voice based controller centered on an adaptable FNN to capture the user's perception of imprecise words. Conversational interface of the machine facilitates the learning through interaction. The system consists of a dialog manager (DM), the conversational interface, a Knowledge base, which absorbs user's perception and acts as a replica of human understanding of imprecise words,...