• 한국정밀공학회지
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• 제21권11호
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• pp.209-218
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• 2004

The purpose of this paper is to suggest a practical and simple method for the identification of the joint mechanical properties and to apply it to human knee joints. The passive moment at a joint was modeled by three mechanical parts, that is, a gravity term, a linear damper term and a nonlinear spring term. Passive pendulum tests were performed in 5 fat and 5 thin men. The data of pendulum test were used to identify the mechanical properties of joints through sequential quadratic programming (SQP) with random initial values. The identification was successful where the normalized root-mean-squared (RMS) errors between the simulated and experimental joint angle trajectories were less than 10％. The parameter values of mechanical properties obtained in this study agreed with literature. The inertia, gravity and the damping constant were greater at fat men, which indicates more resistance to body movement and more energy consumption fer fat men. The suggested method is noninvasive and requires simple setup and short measurement time. It is expected to be useful in the evaluation of joint pathologies.

• Smart Structures and Systems
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• 제29권3호
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.1-10
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• 2020

Safety can be defined as being maintained or reduced to a level below which the possibility of human or physical harm can be tolerated through continuous identification of risks and safety risk management. FAA, EASA, IATA and Boeing, major organizations that conduct research and analysis for aviation safety around the world, report that about 70 percent of aviation accidents are caused by human factors, which have led to a surge in interest in human factors-induced accident prevention activities around the world. As part of this purpose, the FAA in the U.S. is raising awareness among aviation workers by publicizing the 12 human errors (Boeing, 2016), which account for the largest part of aviation accidents under the theme of Dirty Dozen, to prevent aviation accidents. Therefore, based on the domestic helicopter accidents reported to the Air Railroad Accident Investigation Committee from 2007 until recently, this study aims to use HFACS to extract human factors for the six recent helicopter accidents in Korea, analyze the extracted human factors in conjunction with the Dirty Dozen concept, and then present measures to prevent accidents by item.

• 한국안전학회지
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• 제20권4호
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• pp.114-121
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• 2005

Railroad shunting movements connecting and disconnecting train sets are very susceptible to human errors since they depend on human decision-making and action procedure that are variable to situation to situation. Nevertheless, in the investigation of railroad accidents, all the accident causes related with human factors have merely been categorized as 'careless treatment' of the workers without any systematic approach of behavioral sciences or the analysis of human errors. In this research, therefore, 137 accident cases occurred during railroad shunting movements and 435 accident cases occurred during driving were analyzed with a special interest of human errors. According to results, the traditional accident investigation scheme used for last several decades did not seem to be appropriate for catching up true accident causes with respect to human errors. In addition, both signal men and locomotive drivers made many mistakes in judgement/action stage while the former mainly commit judgement tasks where as the latter mainly commit cognition tasks. Ant those tasks such as 'confirmation of signal and route', 'location check-up of connected train sets', and 'route identification for a shift of track' ranked highly for accident susceptibility.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.153.6-153
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• 2001

3-Dimentional 3D digitization of the human is a technology that is still relatively new. There are present uses such as radiotherapy, identification systems and commercial uses and potential future applications. In this paper, we analyzed and experimented to determine the easiest and most efficient method, which would give us the most accurate results. We also constructed a database of realistic expressions and high quality human heads. We scanned people´s heads and facial expressions in 3D using a Minolta Vivid 700 scanner, then edited the models obtained on a Silicon Graphics workstation. Research was done into the present and potential uses of the 3D digitized models of the human head and we develop ideas for ...

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권5호
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• pp.354-362
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• 2010

본 연구는 동적 베이스 망을 이용하여, 사람의 보행 동작을 보행 방향과 보행 자세로 분리하여 계층적으로 분석하는 방법을 제안한다. DBN의 일종인 FHMM을 기본 바탕으로 하여, 걸음걸이 동작 특성을 고려하여 순환 고리형 상태 공간 구조로 '보행 동작 디코더'(Gait Motion Decoder, GMD)를 설계한다. 기존 연구에는 보행자의 식별에만 치중을 하고 보행 방향의 변화, 관찰 각도에 제한적이거나 보행 동작에 대한 분석이 없었다. 반면에 본 연구에서는 동작과 자세를 적극적으로 표현하여 임의 방향의 보행, 방향의 변화, 보행 자세까지 인식할 수 있도록 하였다. 실험 결과 동작과 자세의 관점에서 걸음걸이 방향을 분석한 결과 96.5%의 방향 인식률을 기록하였다. 본 연구는 보행 동작을 방향과 보행 자세로 계층적으로 분석하는 최초의 방법 및 시도이며 향후 상황별 휴먼 동작 분석에 크게 활용할 수 있을 것이다.

• 한국환경보건학회지
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• 제36권4호
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• pp.294-305
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• 2010

This study estimated the health risk of heavy metals in particulate matter $(PM)_{2.5}$ in a Gwangyang industrial complex. The $PM_{2.5}$ containing heavy metal was collected from January to November, 2008 using a denuder air sampler and by IC (Ion Chromatograph). The risk assessment was performed in a four-step process; hazard identification, exposure assessment, dose-response assessment and risk characterization. In the hazard identification process, $Cr^{6+}$, Ni, As, and Pb were categorized as human carcinogens and probable human carcinogens, while Ti, Mn, Se, P, $Cr^{3+}$, Cu, and Zn were not classified as human carcinogens. It was found that the excess cancer risk by Central Tendency Exposure (CTE) of $Cr^{6+}$ and As in $PM_{2.5}$ was > $10^{-6}$, and the total excess cancer risk posed by carcinogen heavy metals in $PM_{2.5}$ was > $10^{-6}$. It was also determined that the total hazard index by CTE of non-carcinogen heavy metals in $PM_{2.5}$ was <1. Taken together, these results indicate a high cancer risk associated whit inhalation of heavy metal-containing$PM_{2.5}$ in industrial areas.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2018년도 공동국제학술발표회
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• pp.68-68
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• 2018

• 한국동물학회:학술대회논문집
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• 한국동물학회 1998년도 한국생물과학협회 학술발표대회
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• pp.114-114
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• 1998

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