• 전자공학회논문지
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• 제53권1호
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• pp.129-136
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• 2016

초전형 적외선(pyroelectric infrared : PIR) 센서를 이용하여 정지 및 이동인체 감지 시스템을 개발하였다. 감지 시스템은 전원부, 초전형 적외선 센서 인터페이스 회로, 극소형 스템핑 모터, 그리고 디지털 제어부로 구성된다. 정지된 인체를 감지하는 원리는 PIR 센서와 프레넬 렌즈(Fresnel lens)를 부착한 스템핑 모터를 180도로 6초 간격으로 회전하여 인체의 유무를 검출하고 일정기간 동안 인체 감지 신호가 없으면 모터를 정지시키는 것이다. 제안한 감지 시스템에 필요한 제어 알고리즘을 개발하였다. 실험을 통해 감지 시스템은 $37^{\circ}C$의 정지 및 이동하는 인체에 대하여 6m의 감지거리와 30도의 감지 각도를 갖고 있다는 것을 확인하였다.

• Journal of Multimedia Information System
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• 제4권1호
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• pp.33-38
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• 2017

This article suggests a new algorithm for detecting two-wheelers on the road that have various shapes according to viewpoints. Because of complicated shapes, it is more difficult than detecting a human. In general, the Histograms of Oriented Gradients(HOG) feature is well known as a useful method of detecting a standing human. We propose a method of detecting a human on a two-wheelers using the spatial relocation of HOG (Histogram of Oriented Gradients) features. And this paper adapted the contrast method which is generally using in the image process to improve the detection rate. Our experimental results show that a two-wheelers detection system based on proposed approach leads to higher detection accuracy, less computation, and similar detection time than traditional features.

• 드라이브 ㆍ 컨트롤
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• 제19권2호
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• pp.27-35
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• 2022

This study presents one man-portable, hazardous gas detecting and capturing robot. The robot can be fit in the trunk of a sedan car. Its weight is less than 20 kg. A dedicated gas intake mechanism is proposed for the robot. The robot can detect and capture gases at a height of 2 m above the ground, although the height of the robot is about 0.2 m. The performance of the gas intake mechanism is verified through computational fluid dynamics (CFD) analysis and experiments. Its gas detecting signals were acquired by serial communication and processed in Robot Operating System (ROS) based control software. The proposed robot can successfully move on rough terrains such as stairs, sand roads, and rock roads.

• 제어로봇시스템학회논문지
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• 제20권9호
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• pp.957-963
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• 2014

This paper proposes a predictive control for an efficient human following robot using Kinect sensor. Especially, this research is focused on detecting of foot-end-point and foot-vector instead of human body which can be occluded easily by the obstacles. Recognition of the foot-end-point by the Kinect sensor is reliable since the two feet images can be utilized, which increases the detection possibility of the human motion. Depth image features and a decision tree have been utilized to estimate the foot end-point precisely. A tracking point average algorithm is also adopted in this research to estimate the location of foot accurately. Using the continuous locations of foot, the human motion trajectory is estimated to guide the mobile robot along a smooth path to the human. It is verified through the experiments that detecting foot-end-point is more reliable and efficient than detecting the human body. Finally, the tracking performance of the mobile robot is demonstrated with a human motion along an 'L' shape course.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2005년도 생물공학의 동향(XVI)
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• pp.474-477
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• 2005

Surface Plasmon Resonance(SPR) sensor system can be applicable for detecting of many biospecific interactions. In this study, the feasibility of the experimental $SPREETA^{TM}$ evaluation kit to analyze human IgG, Pseudomonas aeruginosa, was investigated. The sensor prepared for detecting of anti-human IgG has response on $0.1{\mu}{\ell}$ of the anti-human IgG solution. SPREETA was able to detect P. aeruginosa solution in the range above $10^8\;CFU/mL$ with the chitosan/ alginate multilayers.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.825-831
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• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• 한국정밀공학회지
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• 제31권6호
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• pp.527-534
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• 2014

The control system of human torso model and driving system of stethoscope for physical assessment trainer are developed. The detecting characteristics of circular pickup coil which is driven by square wave voltage signal with resonance frequency of LC circuits are investigated and it is confirmed that the pickup coil can detect the existence of chestpiece near the coil region. The control system of human torso model is composed of 8 channel pickup coils, Mp3 and Bluetooth module. The driving system of stethoscope is composed of chestpiece with contact switch and Bluetooth headset. The chestpiece detecting algorithm check the contact of chestpiece with human body model first and excite the pickup coil sequentially to find the location. The proposed system can be applied the physical assessment trainer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• 제어로봇시스템학회논문지
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• 제14권8호
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• pp.809-817
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• 2008

This paper describes an obstacle detecting system of an unmanned ground vehicle (UGV). The unmanned ground vehicle is consists of several systems such as vehicle control system, navigation system, obstacle detecting system and integration system. Among these systems, the obstacle detecting system is a driving assistance system of UGV. Through the UGV is driving, the system detects obstacles such as cars, human, tree, curb and hills and then send information of obstacles position to integration system for safety driving. In this research, the obstacle detecting system is composed of 5 laser scanners and develop algorithms of detecting obstacles, curb, uphill and downhill road.

• 융합신호처리학회논문지
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• 제3권4호
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• pp.74-78
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• 2002

인체감지용 초전형 적외선 센서 시스템은 최소 2개 이상의 센서를 사용한다. 상부전극과 바닥전극의 디자인을 새로이 하여 센서 2개가 인체감지를 가능하게 하는 센서 시스템을 센서 1개로 구성한 변환기를 설계하였다. 초전성 효과를 나타내는 재료로는 P(VDF/TrFE) 필름을 이용하였다 제작된 적외선 센서는 기존의 2개 이상의 센서를 사용하던 시스템에 비해 거의 같은 결과인 적외선원 13 $\times$ 10$10^-6$ 에 대하여 9.62 $\times$ 10$10^5$ V/W 의 높은 전압감도를 나타내었으며 NEP (noise equivalent power) 는 3.95 $\times$ $10^-7$/ W, 그리고 specific detectivity D$^*$는 5.06 $\times$ $10^5$ cm/W나타내었다.