• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.421-426
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• 2020

다중 도메인 목적 지향 대화에서 기존 딥 러닝을 이용한 대화 상태 추적(Dialog state tracking)은 여러 턴 동안 누적된 사용자와 시스템 간 대화를 입력 받아 슬롯 밸류(Slot value)를 추출하는 모델들이 연구되었다. 하지만 이 모델들은 대화가 길어질수록 연산량이 증가한다. 이에 본 논문에서는 다중 도메인 대화에서 누적된 대화의 history 없이 슬롯 밸류를 추출하는 방법을 제안한다. 하지만, 단순하게 history를 제거하고 현재 턴의 발화만 입력 받는 방법은 문맥 정보의 손실로 이어진다. 따라서 본 논문에서는 도메인 상태(Domain state)를 도입하여 매 턴 마다 대화 상태와 함께 추적하는 모델을 제안한다. 도메인 상태를 같이 추적함으로써 현재 어떠한 도메인에 대하여 대화가 진행되고 있는지를 파악한다. 또한, 함축된 문맥 정보를 담고 있는 이전 턴의 대화 상태와 도메인 상태를 현재 턴의 발화와 같이 입력 받아 정보의 손실을 줄였다. 대표적인 데이터 셋인 MultiWOZ 2.0과 MultiWOZ 2.1에서 실험한 결과, 대화의 history를 사용하지 않고도 대화 상태 추적에 있어 좋은 성능을 보이는 것을 확인하였다. 또한, 시스템 응답과 과거 발화에 대한 의존성을 제거하여 end-to-end 대화 시스템으로의 확장이 좀 더 용이할 것으로 기대된다.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.101-107
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• 2009

In this paper, we describe a method of detecting emergency situation using images from surveillance cameras and propose a mobile robot tracking system for detailed examination of that situation. We are able to track a few persons and recognize their actions by an analyzing image sequences acquired from a fixed camera on all sides of buildings. When emergency situation is detected, a mobile robot moves and closely examines the place where the emergency is occurred. In order to recognize actions of a few persons using a sequence of images from surveillance cameras images, we need to track and manage a list of the regions which are regarded as human appearances. Interest regions are segmented from the background using MOG(Mixture of Gaussian) model and continuously tracked using appearance model in a single image. Then we construct a MHI(Motion History Image) for a tracked person using silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. And we also implement mobile robot tracking technology using the distance between the person and a mobile robot.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.1
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• pp.165-172
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• 2012

For practical education, many practices using various practical equipments have to be provided to students. In this study, the application of learning curve to represent student's learning process in a practical education using a equipment was studied. Learning curve model was originally developed in production management and based on human performance in human factors aspects. In this study, the application of learning curve model was studied on the eye tracking system, which is used to evaluate the usability of a product in design area. As a case study for its applicability, practical education for eye tracking system was provided to three students and then task completion times were measured for hardware system setup and gaze image recording. Learning curves were estimated for two tasks and then task completion times were predicted using the learning curves. Through ANOVA(analysis of variance) and correlation analysis, the applicability of learning curve to practical education was analysed. As the result, learning curve could be effectively applied to practical eduacation using equipment.

• Journal of the Korean Institute of Gas
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• v.23 no.2
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• pp.17-27
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• 2019

In recent years, dangerous materials and gas leak accidents have been frequently occurred. The hazardous materials storage facility accidents are not rapidly controlled when a leak is detected, unlike other chemical plants can be controled. Externally, the human has to approach and respond to the source of leaking directly. As a result, the human and material damage are likely to larger result in the process. The current approach has been passive response after ringing the alarm. In this study, the suggested tracking system of the leak resource is designed system to track the resource actively by utilizing the mobile sensor robot platform, which can be made easily through recent rapid development technology, is verified through prototype system. Thus, a suggested system should pave the way for minimizing the spread and damage of the accident based on the exact site situation of the initial leak and quick and early measures.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.942-956
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• 2014

This paper proposes a real-time hand pose tracking method using a monocular RGB camera. Hand tracking has high ambiguity since a hand has a number of degrees of freedom. Thus, to reduce the ambiguity the proposed method adopts the step-by-step estimation scheme: a palm pose estimation, a finger yaw motion estimation, and a finger pitch motion estimation, which are performed in consecutive order. Assuming a hand to be a plane, the proposed method utilizes a planar hand model, which facilitates a hand model regeneration. The hand model regeneration modifies the hand model to fit a current user's hand, and improves robustness and accuracy of the tracking results. The proposed method can work in real-time and does not require GPU-based processing. Thus, it can be applied to various platforms including mobile devices such as Google Glass. The effectiveness and performance of the proposed method will be verified through various experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.171-179
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• 2013

Recognition and image processing technology depends on illumination variation. One of the most important factors is the parameters of algorithms. When it comes to select these values, the system has different types of recognition accuracy. In this paper, we propose performance improvement of the eye tracking system that depends on some environments such as, people, location, and illumination. Optimized threshold parameter was decided by using reinforcement learning. When the system accuracy goes down, reinforcement learning used to train the value of parameters. According to the experimental results, the performance of eye tracking system can be improved from 3% to 14% by using reinforcement learning. The improved eye tracking system can be effectively used for human-computer interaction.

• Journal of Digital Contents Society
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• v.7 no.1
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• pp.67-74
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• 2006

In current video-conferencing systems. user's face movements are restricted by fixed camera, therefore it is inconvenient to users. To solve this problem, tracking of face movements is needed. Tracking using whole face needs much computing time and whole face is difficult to define as an one feature. Thus, using several feature points in face is more desirable to track face movements efficiently. This paper addresses an effective eye location algorithm which is essential process of automatic human face tracking system for natural video-conferencing. The location of eye is very important information for face tracking, as eye has most clear and simplest attribute in face. The proposed algorithm is applied to candidate face regions from the face region extraction. It is not sensitive to lighting conditions and has no restriction on face size and face with glasses. The proposed algorithm shows very encouraging results from experiments on video-conferencing environments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.1-10
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• 2010

As various interfacing devices for computational machines are being developed, a new HCI method using hand motion input is introduced. This interface method is a vision-based approach using a single camera for detecting and tracking hand movements. In the previous researches, only a skin color is used for detecting and tracking hand location. However, in our design, skin color and shape information are collectively considered. Consequently, detection ability of a hand increased. we proposed primary orientation edge descriptor for getting an edge information. This method uses only one hand model. Therefore, we do not need training processing time. This system consists of a detecting part and a tracking part for efficient processing. In tracking part, the system is quite robust on the orientation of the hand. The system is applied to recognize a hand written number in script style using DNAC algorithm. Performance of the proposed algorithm reaches 82% recognition ratio in detecting hand region and 90% in recognizing a written number in script style.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.159-167
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• 2012

Eye tracking technology tracks human eyes movements to understand user's intention. This technology has been improving slowly and should be used for a variety of occasions now. For example, it enables persons with disabilities to operate a computer with their eyes. This article will show a typical implementation of an eye tracking system for persons with disabilities, after introducing the design principles and specific implementation details of an eye tracking system. The article discussed the realization of self-adapting regulation algorithm in detail. The self-adapting algorithm is based on feedback signal controlling the lens movements to realize automatic focus, and to get a clear eyes image. This CCD camera automatic focusing method has self-adapting capacity for changes of light intensity on the external environment. It also avoids the trouble of manual adjustment and improves the accuracy of the adjustment.