• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.93-97
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• 2009

현재 손가락 및 제스춰 인식 같은 영상처리를 이용하여 로봇의 움직임을 제어하는 방법이 많이 연구되고 있지만, 인식 속도가 느려서 빨리 제어하지 못하는 단점이 있다. 하지만 데이터 글러브와 같은 입력 장치를 이용하면 쉽고 빠르게 제어가 가능하다. 이에 본 논문에서는 로봇 키트를 이용하여 자동차 로봇을 구현하고, 이를 데이터 글러브(Data Glove)와 연동시켜 움직임을 제어하는 방법을 제안한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.596-605
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• 1994

An efficient measurement and evaluation system for hand tool tasks will provide a practical solution to the problem of designing and evaluating manual tool tasks in the workplace. Few studies on the biomechanical analysis of hand postures and tool handling tasks exist because of the lack of appropriate measurement techniques for hand force. A measurement system for the finger forces and joint angles for analysis of manual tool handling tasks was developed in this study. The measurement system consists of a force sensing glove made from twelve Force Sensitive Resistors and an angle-measuring glove (Cyberglove$\^$TM/, Virtual technologies) with eighteen joint angle sensors. A biomechanical model of the hand using the data from the measurement system was also developed. Systems of computerized procedures were implemented integrating the hand posture measurement system, biomechanical analysis system, and the task analysis system for manual tool handling tasks. The measurement system was useful in providing the hand force data needed for an existing task analysis system used in CTD risk evaluation. It is expected that the hand posture measurement developed in this study will provide an, efficient and cost-effective solution to task analysis of manual tool handling tasks. These tasks are becoming increasingly important areas of occupational health and safety of the country.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1633-1637
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• 2003

Existing input devices for desktop computers are not suitable for wearable computers because they are neither easy to carry nor convenient to use in a mobile working environment. Different input devices for wearable computers must be developed. In this paper, a wireless glove-based input device for wearable computers is proposed. The proposed input device consists of a pair of chording gloves. Its keys are mounted on the fingers and their chording methods are similar to those of a Braille keyboard. RF (Radio Frequency) and IrDA (Infrared Data Association) modules are used to make the proposed input device wireless. Since the Braille representation for numbers and characters is efficient and has been well established for many languages in the world, the proposed input device may be one of good input devices to computers. Furthermore, since the Braille has been used for visually impaired people, the proposed one can be easily used as an input device to computers for them.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.8
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• pp.1226-1232
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• 2008

Tight fitting glove pattern is necessary to convey oscillation to the skin from the sensors attached on the hands as found in the haptic device. However, it has been difficult to provide customized glove pattern for haptic device so far. The objective of the paper is to develop a 2D pattern that fit tightly to hands by adopting the recent 3D technology to the clothing science. In this study, the user graphic interface application software(2C-AN) for the semi-automatic garment pattern generation has been utilized to develop the methodology of construct tight-fitting glove pattern for the hand in natural position. A basic pattern was developed directly from the 3D images of hand and the verification of the proposed pattern was also provided.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.566-569
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• 2022

In this paper, the algorithm was developed to recognize hand golve gesture and implemented a system to remotely control the robot. The system consists of a camera and a controller that controls robot motion by hand position gesture. The camera recognizes the specific color of the glove and outputs the recognized range and position by including the color area of the glove. We recognize the velocity vector of robot motion and control the robot by the output data of the position and the detected rectangle. Through the several experiments, it was confirmed that the robot motion control was successfully performed.

• 한국해양학회지
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• v.25 no.4
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• pp.240-244
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• 1990

To solve two problems, temperature shock to the man and difficulty in exchanging the air in the glove box into N2 gas, during the subsampling of Suboxic and cold core sediments, a portable glove box with a refrigerating machine and a gas exchanging bag was constructed for subsampling of sediment cores on board. The refrigerator of the glove box can cool down 200 litter air at 30$^{\circ}C$ to 2$^{\circ}C$${\pm}$2$^{\circ}C$ within 5 minutes. The box was successfully operated during the second KORDI's deep sea research cruise of 1989 in the North equatorial Pacific. Pore water data obtained from the cruise show no evidence for artifacts caused by warming up or oxidation of sediments during subsampling.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.48-56
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• 2022

The importance of data analysis in baseball has been increasing after the success of MLB's Oakland which applied Billy Beane's money ball theory, and the 2020 KBO winner NC Dinos. Various studies using data in baseball has been conducted not only in the United States but also in Korea, In particular, the models using deep learning and machine learning has been suggested. However, in the previous studies using deep learning and machine learning, the focus is only on predicting the win or loss of the game, and there is a limitation in that it is difficult to interpret the results of which factors have an important influence on the game. In this paper, to investigate which factors is important by position, the prediction model for the Golden Glove award which is given for the best player by position is developed. To develop the prediction model, XGBoost which is one of boosting method is used, which also provide the feature importance which can be used to interpret the factors for prediction results. From the analysis, the important factors by position are identified.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.124-129
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• 1992

The goal of the proposed Intelligent Assisting System - IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degrees-of-freedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.341-346
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• 2009

The motion data glove is a representative human-computer interaction tool that inputs human hand gestures to computers by measuring their motions. The motion data glove is essential equipment used for new computer technologiesincluding home automation, virtual reality, biometrics, motion capture. For its popular usage, this paper attempts to develop an inexpensive visual.type motion data glove that can be used without any special equipment. The proposed approach has the special feature; it can be developed as a low-cost one becauseof not using high-cost motion-sensing fibers that were used in the conventional approaches. That makes its easy production and popular use possible. This approach adopts a visual method that is obtained by improving conventional optic motion capture technology, instead of mechanical method using motion-sensing fibers. Compared to conventional visual methods, the proposed method has the following advantages and originalities Firstly, conventional visual methods use many cameras and equipments to reconstruct 3D pose with eliminating occlusions But the proposed method adopts a mono vision approachthat makes simple and low cost equipments possible. Secondly, conventional mono vision methods have difficulty in reconstructing 3D pose of occluded parts in images because they have weak points about occlusions. But the proposed approach can reconstruct occluded parts in images by using originally designed thin-bar-shaped optic indicators. Thirdly, many cases of conventional methods use nonlinear numerical computation image analysis algorithm, so they have inconvenience about their initialization and computation times. But the proposed method improves these inconveniences by using a closed-form image analysis algorithm that is obtained from original formulation. Fourthly, many cases of conventional closed-form algorithms use approximations in their formulations processes, so they have disadvantages of low accuracy and confined applications due to singularities. But the proposed method improves these disadvantages by original formulation techniques where a closed-form algorithm is derived by using exponential-form twist coordinates, instead of using approximations or local parameterizations such as Euler angels.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.8
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• pp.46-53
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• 1999

We have developed glove based hand gesture recognition system for recognizing 3D gesture of operators in remote work environment. Polhemus sensor attached to the PinchGlove is employed to obtain the sequence of 3D positions of a hand trajectory. These 3D data are then encoded as the input to our recognition system. We propose the use of the strokes to be modeled by HMMs as basic units. The gesture models are constructed by concatenating stroke HMMs and thereby the HMMs for the newly defined gestures can be created without retraining their parameters. Thus, by using stroke models rather than gesture models, we can raise the system extensibility. The experiment results for 16 different gestures show that our stroke based composite HMM performs better than the conventional gesture based HMM.