• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.557-564
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• 2015

There are many prior studies for more natural Human-Computer Interaction. Until now, the efforts is continued in order to recognize motions in various directions. In this paper, we suggest a user-specific recognition by hit detection method using Kinect camera and human proportion. This algorithm extracts the user-specific valid recognition rage after recognizing the user's body initially. And it corrects the difference in horizontal position between the user and Kinect, so that we can estimate a action of user by matching cursor to target using only one frame. Ensure that efficient hand recognition in the game to take advantage of this method of suggestion.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.38-43
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• 2006

유비쿼터스 컴퓨팅 환경이 발달하면서 홈네트워크 환경의 사용자 상황을 쉽게 인식하고 사용자의 상황 정보에 따라 좀 더 지능적인 서비스를 제공 할 수 있게 되었다. 지능적 서비스를 효과적으로 구현하기 위해서는 상황 정보를 객관적으로 표현할 수 있어야 한다. 상황 정보는 개체의 상황을 특성화 하는데 사용 될 수 있는 정보를 의미한다. 본 논문에서는 홈네트워크 서비스를 효과적으로 제공하기 위하여 상태 정보를 User context, Device context, Proximity(유저와 장치간의 거리) context로 분류하고, 그 상황 정보를 효과적으로 표현할 수 있는 XML기반의 HIML(Human Interaction Markup Language)을 설계하였다. 또한 설계된 HIML 문서를 통해 다양한 플랫폼에서 작동하고, 여러 가전기기와 센서장비들의 상호작용이 가능하게 하는 미들웨어를 설계하고 그 기능을 실험하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.429-432
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• 2011

Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from a single view and ignores the issue of view invariance. In this paper, we present an independent Gabor features (IGFs) method comes from the derivation of independent Gabor features in the feature extraction stage. The Gabor transformed human image exhibit strong characteristics of spatial locality, scale and orientation selectivity.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2690-2692
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• 2002

This paper describes a technique for human sensibility evaluation using 10-channel EEG(electroencephalogram). The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as, relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance using the proposed method is about 72.6%. This will be promising performance in the human sensibility evaluation.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.304-308
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• 1998

Technical advances in computer networks and the Internet bring a new communication era and provide effective solutions for cooperative works and research. These technological advances introduced the concept of cyberspace that many people involve reseach and a project at different locations at the same time. In this paper, we present a fast and effective interaction protocol that is aeapted to the COLAB(COIIaborative LABoratory) Systems which use a high-speed ATM Network. The CCOLAB systems is developed for researchers those who are doing a large project on the collaborative research environment. The interaction protocol that we developed supports multi-session and multi-channel on the TCP/IP Network and provides more flexible solution to control multimedia data on the network.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.350-357
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• 2000

본 논문은 근 몇 년간 꾸준히 진행되어진 중한 기계번역시스템에 대한 연구의 기초 위에서, 뉴스 타이틀 번역이라는 특정 도메인에 초점을 맞추어 이의 언어적 특성을 살펴보고, 중한 언어적 유사성에 기반 한 뉴스 타이틀 번역을 위한 중한 기계번역시스템에 대하여 설명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.637-640
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• 2023

This paper presents multimodal image fusion with human pose for detecting abnormal human behaviors in low illumination conditions. Detecting human behaviors in low illumination conditions is challenging due to its limited visibility of the objects of interest in the scene. Multimodal image fusion simultaneously combines visual information in the visible spectrum and thermal radiation information in the long-wave infrared spectrum. We propose an abnormal event detection scheme based on the multimodal fused image and the human poses using the keypoints to characterize the action of the human body. Our method assumes that human behaviors are well correlated to body keypoints such as shoulders, elbows, wrists, hips. In detail, we extracted the human keypoint coordinates from human targets in multimodal fused videos. The coordinate values are used as inputs to train a multilayer perceptron network to classify human behaviors as normal or abnormal. Our experiment demonstrates a significant result on multimodal imaging dataset. The proposed model can capture the complex distribution pattern for both normal and abnormal behaviors.

• Management & Information Systems Review
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• v.2
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• pp.267-294
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• 1998

Recently, self-efficacy is one of the critical constructs that have been found to influence human decisions about behavior selection and the performance associated with the selected behavior. The construct has been widely adopted and tested In the fields of social psychology and/or other behavioral sciences. In information systems field, however, it has been hardly studied, although computer self-efficacy could have been an important factor explaining and predicting human computer usage behaviors. From this perspective, main purposes of the study is to understand causal relation among the factors influencing computer self- efficacy, computer usage behavior and computer self-efficacy. The research reported in this study have several objectives; 1) to develop a measure of computer self-efficacy, 2) to Identify the factors influencing self-efficacy, and 3) to reveal the relationship between self-efficacy and computer usage behavior and then 4) to explain the causal model of computer self-efficacy. By reviewing the literature, past experience, others' use, encouragement by others, and anxiety are selected as the factors influencing computer self-efficacy. Four hypotheses concerning the relationship between each of the variables and computer self-efficacy are tested by LISREL. One more hypothesis about the relationship between computer self-efficacy and computer usage is also tested. The results show that computer self-efficacy is significantly influence by computer anxiety, encouragement by others, and computer experience, and that it is closely correlated with computer usage behavior.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.943-946
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• 2008

Many works have been done to enable computer, as brain of robot, to learn color categorization, most of them rely on modeling of human color perception and mathematical complexities. This paper aims at developing the innate ability of the computer to learn the human-like color categorization.