• Journal of Biomedical Engineering Research
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• 제20권4호
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• pp.485-493
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• 1999

Human can generate various posture and motion with nearly 350 muscle pairs. From the viewpoint of mechanisms, the human skeleton mechanism represents great kinematic and dynamical complexity. Physical and behavioral fidelity of human motion requires dynamically accurate modeling and controling. This paper describes a mathematical modeling, and dynamic simulation of human body. The human dynamic model is simplified as a rigid body consisting of 18 actuated degrees of freedom for the real time computation. Complex kinematic chain of human body is partitioned as 6 serial kinematic chains that is, left arm, right arm, support leg, free leg, body, and head. Modeling is developed based on Newton-Euler formulation. The validity of proposed dynamic model, which represents mathematically high order differential equation, is verified through the dynamic simulation.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.685-689
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• 2009

Evolutionary Computation is not only widely used in optimization and machine learning, but also being applied in creating novel structures and entities. This paper proposes evolutionary topic maps that can suggest new and creative knowledge not easily producible by humans. Interactive evolutionary computation method is applied into topic maps in order to accept human evaluation on feasibility of intermediate topic maps. Evolutionary topic maps are creativity support tools, helping users to encounter new and creative knowledge. Further work can greatly improve the system by providing more operations, preventing over-convergence, and overcoming user fatigue problem by providing more intuitive user interface, better visualization, and interpolation mechanisms.

• ETRI Journal
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• 제35권6호
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• pp.949-959
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• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.

• The Transactions of the Korean Institute of Electrical Engineers A
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• 제53권8호
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• pp.461-465
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• 2004

This paper analysed the induced current density by electric field of human body under the 765 kV transmission line considering permittivity and conductivity. As permittivity of human body is very high as $10^6$ at 60 Hz, special numerical computation technique in Surface Charge Method(SCM) for composite media with extremely different properties is applied to reduce calculation error of induced current density and electric field inside the human body. Calculation results show that the average of the induced current density inside human body is about 3mA/$m^2$, which is less than ICNIRP criterion (10mA/$m^2$).

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (3)
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• pp.765-767
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• 1998

Many application in human computer interaction(HCI) require tacking a human face and facial features. In this paper we propose efficient parallel processing system for face tracking under heterogeneous networked. To track a face in the video image we use the skin color information and connected components. In terms of parallelism we choose the master-slave model which has thread for each processes, master and slaves, The threads are responsible for real computation in each process. By placing queues between the threads we give flexibility of data flowing

• Proceedings of the IEEK Conference
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.3-6
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• 2000

In this paper, ‘gesture’ is discussed as a means of human-friendly communication between man and machine. We classify various gestures into two Categories: ‘contact based’ and ‘non-contact based’ Each method is reviewed and some real applications are introduced. Also, key design issues of the method are addressed and some contributions of soft-computing techniques, such as fuzzy logic, artificial neural networks (ANN), rough set theory and evolutionary computation, are discussed.

• Proceedings of the KSPS conference
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• 대한음성학회 2006년도 춘계 학술대회 발표논문집
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• pp.135-138
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• 2006

The communication method between human and robot is one of the important parts for a human-robot interaction. And speech is easy and intuitive communication method for human-being. By using speech as a communication method for robot, we can use robot as familiar way. In this paper, we developed TTS for human-robot interaction. Synthesis algorithms were modified for an efficient utilization of restricted resource in robot. And synthesis database were reconstructed for an efficiency. As a result, we could reduce the computation time with slight degradation of the speech quality.

• Journal of IKEEE
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• 제24권3호
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• pp.783-790
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• 2020

In this paper, the human detecting radar system for indoor security applications is proposed, and its FPGA-based implementation results are presented. In order to minimize the complexity and memory requirements of the computation, the top half of the spectrogram was used to extract features, excluding the feature extraction techniques that require complex computation, feature extraction techniques were proposed considering classification performance and complexity. In addition, memory requirements were minimized by designing a pipeline structure without storing the entire spectrogram. Experiments on human, dog and robot cleaners were conducted for classification, and 96.2% accuracy performance was confirmed. The proposed system was implemented using Verilog-HDL, and we confirmed that a low-area design using 1140 logics and 6.5 Kb of memory was possible.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• 제12권3호
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• pp.409-426
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• 2001

Absorbed power of the human head radiated from a 900 MHz portable phone and temperature rise are computed using FDTD(Finite-Difference Time-Domain) method. For this computation the 5 layered media for the human head modeling and the monopole antenna attached to metallic box for the portable phone are used. To reflect the real circumstances typical sizes of human heads and portable phones are considered in the calculation. The length of monopole antenna is 8.15 cm, and the output power of a phone is 600 mW. Under the predetermined model the distribution of 1 g, 10 g averaged SAR and temperature rise rate over the human head are calculated, from which it was found that the position of maximum SAR is near at the head skin surface, not deep places far into the head. The position of the highest temperature is located far from the head skin more than that of the maximum SAR occured. The averaged SAR and temperature along the distance between the head and phone are calculated according to seperation distance between the head and phone.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.