• 전자공학회논문지
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• 제50권6호
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• pp.212-220
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• 2013

최근 사용자의 생체 신호 정보를 기반으로 사용자 인지향상을 위하여, 상황에 적합한 서비스를 제공하기 위한 인간-컴퓨터/기계 상호작용 (Human computer/machine interaction: HCI/HMI) 시스템이 급격하게 증가하고 있는 추세이다. 이와 같이 인간-컴퓨터/기계 상호작용 기반의 효과적인 사용자 인지향상 시스템을 개발하기 위해서는 사용자의 명시적 의도 파악과 더불어 사용자의 묵시적 의도 파악이 중요하다. 사람의 시각 운동 이론에 따르면, 사람의 안구운동 정보와 동공 반응은 사람의 의도와 행동에 대하여 많은 량의 정보를 제공한다. 이에 본 논문에서는 사용자의 묵시적 의도를 판별하기 위하여, 피험자에게 제공되는 자극영상의 관심(흥미) 영역 (area of interest: AOI) 내에서의 안구운동 패턴인 응시 시간/횟수, 동공 응답 패턴의 동공크기와 동공의 크기변화인 기울기 정보를 분석하는 새로운 접근 방법을 제안한다. 제안하는 모델은 항행적 의도 발생, 정보적 의도발생, 정보적 의도 소멸과 같은 세 가지 유형으로 인간의 묵시적 의도를 식별한다. 여기서 항행적 의도란 주어진 자극영상 내에서 무언가 흥미로운 것을 찾는 행위를 말하며, 이에 반해 정보적 의도는 특정 위치에서 특정 객체는 찾는 행위를 의미한다. 본 연구에서는 사용자 안구운동 패턴과 동공분석 정보 기반으로 서로 다른 묵시적 의도인 항행적 의도, 정보적 의도 발생, 그리고 정보적 의도 소멸 사이에서 그 천이를 감지할 수 있는 계층적 SVM (hierarchical support vector machine: H-SVM)을 이용하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1960-1963
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• 2003

For applications such as video surveillance and human computer interface, we propose an efficiently integrated method to detect and track faces. Various visual cues are combined to the algorithm: motion, skin color, global appearance and facial pattern detection. The ICA (Independent Component Analysis)-SVM (Support Vector Machine based pattern detection is performed on the candidate region extracted by motion, color and global appearance information. Simultaneous execution of detection and short-term tracking also increases the rate and accuracy of detection. Experimental results show that our detection rate is 91％ with very few false alarms running at about 4 frames per second for 640 by 480 pixel images on a Pentium IV 1㎓.

• 한국정밀공학회지
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• 제30권8호
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• pp.870-877
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• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.7-12
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• 2008

사람과 로봇의 인터랙션에 있어 정서의 역할은 중요하다. 그러므로 사람과 유사한 정서 메커니즘이 로봇에게 필요하다. 본 논문에서는 사람의 정서에 대한 심리적 연구를 바탕으로 사람의 내적 요소에 기반한 새로운 인공 정서 표현 시스템을 제안한다. 제안된 시스템은 external stimulus, emotion, mood, personality, tendency, machine rhythm 의 6가지 정보 요소를 활용하며, 각 요소들은 그들의 특성에 따라 결과적으로 emotion 표현 패턴의 변화에 영향을 준다. 그 결과 통일한 외부 자극들은 내적 상태에 따라 emotion의 변화를 만들어 낸다. 제안된 시스템은 사람과 로봇의 자연스러운 인터랙션을 유지하고, 친밀한 관계를 형성할 수 있도록 도움을 줄 것이다.

• 센서학회지
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• 제21권1호
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• pp.28-33
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• 2012

The visual sensing of human hands plays an important part in many man-machine interaction/interface systems. Most existing visionbased hand detection techniques depend on the color cues of human skin. The RGB color image from a vision sensor is often transformed to another color space as a preprocessing of hand detection because the color space transformation is assumed to increase the detection accuracy. However, the actual effect of color space transformation has not been well investigated in literature. This paper discusses a comparative evaluation of the pixel classification performance of hand skin detection in four widely used color spaces; RGB, YIQ, HSV, and normalized rgb. The experimental results indicate that using the normalized red-green color values is the most reliable under different backgrounds, lighting conditions, individuals, and hand postures. The nonlinear classification of pixel colors by the use of a multilayer neural network is also proposed to improve the detection accuracy.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.129-138
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• 2022

With the increasing reliance of computing systems in our everyday life, there is always a constant need to improve the ways users can interact with such systems in a more natural, effective, and convenient way. In the initial computing revolution, the interaction between the humans and machines have been limited. The machines were not necessarily meant to be intelligent. This begged for the need to develop systems that could automatically identify and interpret our actions. Automatic gesture recognition is one of the popular methods users can control systems with their gestures. This includes various kinds of tracking including the whole body, hands, head, face, etc. We also touch upon a different line of work including Brain-Computer Interface (BCI), Electromyography (EMG) as potential additions to the gesture recognition regime. In this work, we present an overview of several applications of automated gesture recognition systems and a brief look at the popular methods employed.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3460-3463
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• 2021

Present computing power and enhanced technology is progressing at a dramatic rate. These systems can unravel complex issues, assess and control processes, learn, and-in many cases-fully automate production. There is no doubt that technological advancement is improving many aspects of life, changing the landscape of virtually all industries and enhancing production beyond what was thought possible. However, the human is still a part of these systems. Consequently, as the advancement of systems transpires, the role of humans within those systems will unavoidably continue to adapt as well. Due to the human tendency for error, this technological advancement should compel a persistent emphasis on human error reduction as part of maximizing system efficiency and safety-especially in the context of the nuclear industry. Within this context, as new systems are designed and the role of the human is transformed, human error should be targeted for a significant decrease relative to predecessor systems and an equivalent increase in system stability and safety. This article contends that optimizing the roles of humans and machines in the design and implementation of new types of automation in nuclear facility systems should involve human error reduction without ignoring the essential importance of human interaction within those systems.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1995년도 제2회 단조심포지엄 단조기술의 진보
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• pp.55-63
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• 1995

Press accidents are one of the most popular industrial accidents. Two factors are considered to lead to press accidents, one is human, the other press machine itself. Accident itself is analyzed and concluded to be due to incomplete interaction between press and human. A press design considering safety as a methodology is introduced to reduce the industrial accidents by press machine, that is micro-inching locking system.

• 한국정밀공학회지
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• 제28권6호
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• pp.663-669
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• 2011

This paper examines methods to estimate torque and angle in shoulder flexion from surface electromyography(sEMG) signals for intuitive and delicate control of robotic assistance device. Five muscles on the upper arm, three for shoulder flexion and two for shoulder extension, were used to offer favorable sEMG recording conditions in the estimation. The methods tested were the mean absolute value (MAV) with linear regression and the artificial neural network (ANN) method. An optimal condition was sought by varying combination of muscles used and the parameters in each method. The estimation performance was evaluated using the correlation values and normalized root mean square error values. In addition, we discussed their possible use as an estimation of motion intent of a user or as a command input in a physical human-machine interaction system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2676-2681
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• 2003

The Haptic Exploration Laboratory at The Johns Hopkins University is currently exploring many problems related to haptics (force and tactile information) in human-machine systems. We divide our work into two main areas: virtual environments and robot-assisted manipulation systems. Our interest in virtual environments focuses on reality-based modeling, in which measurements of the static and dynamic properties of actual objects are taken in order to produce realistic virtual environments. Thus, we must develop methods for acquiring data from real objects and populating pre-defined models. We also seek to create systems that can provide active manipulation assistance to the operator through haptic, visual, and audio cues. These systems may be teleoperated systems, which allow human users to operate in environments that would normally be inaccessible due to hazards, distance, or scale. Alternatively, cooperative manipulation systems allow a user and a robot to share a tool, allowing the user to guide or override the robot directly if necessary. Haptics in human-machine systems can have many applications, such as undersea and space operations, training for pilots and surgeons, and manufacturing. We focus much of our work on medical applications.