• Smart Structures and Systems
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• 제4권6호
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Smart Structures and Systems
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• 제5권4호
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• pp.427-442
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• 2009

In this paper, the acceptability of system identification results for health monitoring of instrumented bridges is addressed. This is conducted by comparing the confidence intervals of identified modal parameters for a bridge in California, namely Truckee I80/Truckee river bridge, with the change of these parameters caused by several damage scenarios. A challenge to the accuracy of the identified modal parameters involves consequences regarding the damage detection and health monitoring, as some of the identified modal information is essentially not useable for acquiring a reliable damage diagnosis of the bridge system. Use of strong motion data has limitations that should not be ignored. The results and conclusions underline these limitations while presenting the opportunities offered by system identification using strong motion data for better understanding and monitoring the health of bridge systems.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.321-323
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• 2015

Monitoring crane motion in real time is the first step to identifying and mitigating crane-related hazards on construction sites. However, no accurate and reliable crane motion capturing technique is available to serve this purpose. The objective of this research is to explore a method for real-time crane motion capturing and investigate an approach for assisting hazard detection. To achieve this goal, this research employed various techniques including: 1) a sensor-based method that accurately, reliably, and comprehensively captures crane motions in real-time; 2) computationally efficient algorithms for fusing and processing sensing data (e.g., distance, angle, acceleration) from different types of sensors; 3) an approach that integrates crane motion data with known as-is environment data to detect hazards associated with lifting tasks; and 4) a strategy that effectively presents crane operator with crane motion information and warn them with potential hazards. A prototype system was developed and tested on a real crane in a field environment. The results show that the system is able to continuously and accurately monitor crane motion in real-time.

• 융합신호처리학회논문지
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• 제8권1호
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• pp.1-5
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• 2007

본 논문에서는 24시간 감시가 필요한 코마 환자나 움직이기 힘든 중환자들에게 사용될 수 있는 움직임 검출과 음향 감지를 이용한 무인 환자감시시스템을 구현하였다. 이 시스템의 구성은 노트북, 아날로그 CCTV 카메라(또는 PC 카메라), A/D 컨버터, 마이크로폰 그리고 감시 프로그램으로 구성되었다. 감시 프로그램은 프레임 차이법과 음향 레벨측정을 기본으로 사용하며, 움직임이 검출될 때의 자료수집과 저장과 같은 기능을 가진다. 모든 시스템은 코마 상태의 환자를 가정한 실험을 통해 검증하였다. 실험 결과 본 논문에서 제안한 무인 환자감시 시스템은 응급 상황과 코마 환자의 관리에서 효율적으로 사용될 수 있을 것이라 예상된다.

• 대한임베디드공학회논문지
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• 제5권2호
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• pp.46-56
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• 2010

This study proposes a monitoring system by cooperating the image information and the sensor information in a sensor network system. The monitoring system proposed in this study is divided into internal spaces, such as offices and laboratories, and external spaces including other various spaces. In the internal spaces, motions in objects are detected through cameras while some peripherals like lights are controlled by analyzing some temperature, humidity, and illuminance data detected by sensor nodes. In the external spaces, it is to watch certain intruders to the internal spaces through the interested region for exceptional time by installing cameras, motion detectors, and body detectors in such interested regions. In the results of the test that was applied to a practically limited environment by implementing some interfaces for the proposed system, it was considered that it is possible to watch surroundings effectively using the image information obtained from cameras and sensor information acquisited from sensor nodes.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.113-116
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• 2002

Windows provides Directshow for efficient multimedia streaming processings such as multimedia capture, storage, display and etc. Presently, many motion picture codecs and audio codecs are made to be used in Directshow framework and Windows also supports many codecs (MPEG4, H,263, WMV, WMA, ASF, etc.) in addition to a lot of useful tools for multimedia streaming processing. Therefore, Directshow can be effectively utilized for developing windows-based multimedia streaming applications such as visual monitoring systems which needs to store real-time video data for later retrieval. In this paper, we present our efforts for developing a Directshow Filter System supporting storage of motion pictures in various motion picture codecs. Our Directshow Filter system also provides an additional functionality of motion detection.

• Journal of Multimedia Information System
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• 제3권2호
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• pp.9-12
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• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.

• 전자공학회논문지CI
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• 제40권1호
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• pp.30-38
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• 2003

본 논문은 비디오 영상에서 움직이는 물체를 분할하는 방법을 제안한다. 물체들의 크기가 작거나 서로 겹쳐있을 경우(occlusion), 또는 잡음이 많은 경우에도 안정적인 이 방법은 움직임 검출(motion detection)과 움직임 분할(motion segmentation) 두 단계로 구성되어 있다. 움직임 검출을 하기 위하여 인접 영상간의 차영상(difference image) 분석을 통해 움직임이 있는 부분을 추출하며, 이때 적응적 임계치 방법을 이용하여 빛의 변화나 노이즈가 포함된 환경에서도 안정적으로 추출한다. 움직임 분할 단계에서는 움직임이 검출된 부분을 초기영역으로 분할 한 뒤, 이 영역들의 모션정보에 따라 이웃 한 영역들을 병합함으로써 독립적으로 움직이는 물체를 분할한다. 이러한 방법은 검출된 영역에 대해서만 움직임 분할을 함으로 많은 계산효과를 얻을 수 있으며 실제 도로영상에서 제안된 방법을 실험해본 결과 비디오 감시시스템에 적합함을 알 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제9권5호
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• pp.1780-1787
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• 2014

Arterial oxygen saturation ($SpO_2$) monitoring for newborns requires special attention in neonatal intensive care units (NICUs). Newborns have very low photo-plethysmogram (PPG) amplitudes and their body movements are difficult to contain. Hardware design and its associated signal processing algorithms should be robust enough so that faulty measurements can be avoided. In this study, improved designs were implemented to deal with low perfusion, motion artifact, and the influence of ambient light. Dynamic range was increased by using different LED intensities and a feedback system. To minimize the effects of motion artifact and to discard other unqualified data, four additional algorithms were used, which were based on dual-trace detection, continuity of DC level, morphology of PPG, and simultaneity check of $SpO_2$. Our $SpO_2$ system was tested with newborns with normal respiration in the NICU. Our system provided fast, real-time responses and 100% artifact detection was accomplished under 84% of $SpO_2$.

• 한국멀티미디어학회논문지
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• 제21권3호
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• pp.348-357
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• 2018

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.