• 한국전자통신학회논문지
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• 제8권8호
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• pp.1187-1193
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• 2013

본 논문은 사실적인 3D 얼굴 모델링과 얼굴 표정 생성 시스템을 제안한다. 사실적인 3D 얼굴 모델링 기법에서 개별적인 3D 얼굴 모양과 텍스쳐 맵을 만들기 위해 Generic Model Fitting 기법을 적용하였다. Generic Model Fitting에서 Deformation Function을 계산하기 위해 개별적인 얼굴과 Generic Model 사이의 대응점을 결정하였다. 그 후, Calibrated Stereo Camera로부터 캡쳐 된 영상들로부터 특징점을 3D로 복원하였다. 텍스쳐 매핑을 위해 Fitted된 Generic Model을 영상으로 Projection하였고 사전에 정의된 Triangle Mesh에서 텍스쳐를 Generic Model에 매핑 하였다. 잘못된 텍스쳐 매핑을 방지하기 위해, Modified Interpolation Function을 사용한 간단한 방법을 제안하였다. 3D 얼굴 표정을 생성하기 위해 Vector Muscle기반 알고리즘을 사용하고, 보다 사실적인 표정 생성을 위해 Deformation 과 vector muscle 기반의 턱 rotation을 적용하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.76-81
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• Journal of Mechanical Science and Technology
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• 제18권8호
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• pp.1388-1400
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• 한국정밀공학회지
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• 제26권4호
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• pp.82-90
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• 2009

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

• 센서학회지
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• 제20권2호
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.