• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.551-560
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• 2023

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers.

• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• ETRI Journal
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• v.46 no.1
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• pp.118-126
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• 2024

In human conversations, listeners often utilize brief backchannels such as "uh-huh" or "yeah." Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human-machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single-task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single-task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.9-20
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• 1999

A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.728-731
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• 1997

This paper discusses approximation modelling of discrete-time linear time-varying system(LTVS). The wavelet transform is considered as a tool for representing and approximating a LTVS. The joint time-frequency properties of wave analysis are appropriate for describing the LTVS. Simulation results is included to illustrate the potential application of the technique.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1168-1171
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• 2003

The visuo-motor map is based on the Kohonen's self-organizing map. The map is learned the relation of the end effecter coordinates and the joint angles. In this paper, a 3 d-o-fmanipulator which moves in the 2D space is targeted. A CCD camera is set beside the manipulator, and the end effecter coordinates are given from the image of a manipulator. As a result of learning, the end effecter can be moved to the destination without exact teaching.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.758-763
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• 2001

웹 기반 프로젝트학습은 학생들 스스로 웹에서 자신들의 제작물을 만들면서 자신의 기능과 지식을 실제로 적용하는 과정과 학습경험을 모두 의미한다. 그러므로 학습자들의 다양하고 폭넓은 학습활동이 기대된다. 본 연구에서 설계한 프로젝트 학습 시스템은 수시로 교사와 전문가의 도움도 받고 다른 팀의 동료 학생들로부터 평가를 받으면서 아동의 관심과 흥미, 주제 중심, 활동 중심, 문제 중심의 프로젝트 활동을 할 수 있도록 한다. 그리고 이들 웹 상에서 구현함으로써 학습자 중심의 공동 프로젝트 학습이 되도록 한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.33-42
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• 1992

최근 제조공정상의 학습효과를 고려한 통합로트량 결정모형이 개발되었다. 본 논문은 기존의 단일 구매자에서 복수구매자의 모델로 확장된 가격할인 모형을 다룬다. 이 모형에서는 복수구매자의 발주간격은 가장 짧은 구매자의 발주간격에 정수배라는 가정하에서 모형이 개발되었다. 구매자의 계수변화로 인한 민감도 분석이 되었다. 소개된 모형의 효과를 보이기 위해 수치예재를 이용하였다.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1121-1232
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• 1990

A hierarchical neural network structure is described for robot PTP trajectory planning. In the first level, the multi-layered Perceptron neural network is used for the inverse kinematics with the back-propagation learning procedure. In the second level, a saccade generation model based joint trajectory planning model in proposed and analyzed with several features. Various simulations are performed to investigate the characteristics of the proposed neural networks.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2143-2145
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• 2002

This paper proposes the control problem of an inverted pendulum system based on adaptive fuzzy sliding mode. The universal approximating capability, learning ability, adaptation capability and disturbance rejection are collected in one control strategy. The proposed scheme does not require an accurate dynamic model and the joint acceleration measurement, yet it guarantees asymptotic trajectory tracking. Experimental results perform with an inverted pendulum to show the effectiveness of the approach.