• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.6-96
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• 2002

In this study an image processing system is designed and developed, which can detect and assort some defects on the surface of an object moving with high speed. For real time surface detection of high speed moving object, the fast processing should be managed and the image information including some surface features should be captured. It is difficult to acquire the noise free image due to various light sources and high speed moving materials under the environment of the general industrial site. In general, because pre-processing methods are employed for getting a noise free feature, the image processing speed has some limitation and the expensive image processing devices are required. This...

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.167-172
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• 2015

In this paper, we propose a new approach to design and control a smart gripper of robot system. A control method for flexible grasping a object in partially unknown environment was proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases. The first step is scanning process which two first joints were moved to mid-position of the detected range by a state-variable feedback position controller, after the scanning was finished. The contact force of fingertip was then controlled using the detection sensor of the servo controller for finger joint control. The proposed grasping planning was tested on rectangular bar.

• Journal of the Korean Magnetics Society
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• v.5 no.5
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• pp.579-582
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• 1995

The use of amorphous magnetic alloys as tags or targets in electronic article surveillance systems such as antishoplifting desvices is briefly reviewed. Improved tags became possible with the discovery in 1988 of asymmetric magnetization reversal (AMR) in certain amorphous alloys annealed in applied field approximately equal to the earth's field. These asymmetric hysteresis loops are highly unusual, if not unique, and so greatly diminish the probability of false alarms in a detection system. furthermore, the jump field Hj, which is the coercive field in negative applied fields, can be controlled over a useful range by controlling the field applied to the sample during annealing. By applying several tags to an object, each with a different jump field, it is possible to identify the object with a numeric code that can be remotely read by nonoptical means.

• Annual Conference of KIPS
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• 2020.05a
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• pp.536-539
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• 2020

길이 좁고 차도와 인도의 구분이 없는 골목길의 특성상 사각지대가 많고 보행자의 동선을 예측하기 힘들어 교통사고가 많이 발생하고 있다. 따라서 본 논문에서는 AI 를 활용, 영상 내 사물을 추적하여 골목길에서의 사고를 예방하는 시스템을 제안한다. 해당 시스템은 Object - Detection & Tracking 을 사용하여 보행자 및 차량을 식별·추적하여 두 개 이상의 사물이 동시에 교차로에 접근 시 사고 예방 알람을 발생시킨다. 이 시스템을 전국에 설치되어 있는 CCTV 에 활용하면 추가적인 비용과 설치 시간에 제한받지 않고 전국적으로 응용할 수 있을 것으로 기대된다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.716-717
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• 2023

최근 자율주행 기술이 큰 주목을 받고 있지만 고가의 센서를 필요로 하기 때문에 연구 및 상용화에 큰 어려움을 겪고 있다. 따라서 본 논문은 쉽게 사용 가능한 딥러닝 2D 객체 인식 모델과 범용 태블릿에 탑재된 저비용 LiDAR 센서를 이용하여 실시간 3D 객체 탐지가 가능한 시스템을 개발한다. 개발된 시스템을 실제 1/10 크기의 차량 모델에 적용하여 테스트해본 결과 개발 용이성과 정확도 측면에서 자율주행을 위한 저비용 센서로 충분히 활용될 가능성이 있음을 확인하였다.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.211-217
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• 2012

Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1087-1094
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• 2004

Normally, formalization and standardization are followed while doing any system analysis and design. But, in actual system development, products can't be written using the automated tool for all products due to their own characteristics. That results the inconsistency among development products and various kinds of problem can occur in the products. In this paper, we present a technique that supports consistency among products of analysis and design phase developed according to object oriented method. And, this paper also presents the implementation tool.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.373-376
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• 2007

The construction industry is an inevitable part of modem development. Now-a-days, the construction industry experiencing several issues related with the maintenance of productivity, quality and labor. Hence there is an immediate requirement for the development of technologies enabling the automated construction equipment. The new technologies should also assure the sufficient safety and efficiency. In the present investigation, an attempts have been made for the development of object sensing algorithm and safety control system for intelligent excavating system. we have analyzed some elemental technologies for sensing objects and also proposed a technology for safety control system as well. The proposed technology will highly influence the safe working performance of construction industry in the positive sense.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.243-250
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• 2019

In this paper, we propose a system for processing and analyzing drone image information which can be applied variously in disasters-security situation. The proposed system stores the images acquired from the drones in the server, and performs image processing and analysis according to various scenarios. According to each mission, deep-learning method is used to construct an image analysis system in the images acquired by the drone. Experiments confirm that it can be applied to traffic volume measurement, suspect and vehicle tracking, survivor identification and maritime missions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.382-384
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• 2021

In this study, we implement a system that automatically classifies types of plastics using YOLO (You Only Look Once), a real-time object recognition algorithm. The system consists of Nvidia jetson nano, a small computer for deep learning and computer vision, with model trained to recognize plastic separation emission marks using YOLO. Using a webcam, recycling marks of plastic waste were recognized as PET, HDPE, and PP, and motors were adjusted to be classified according to the type. By implementing this automatic classifier, it is convenient in that it can reduce the labor of separating and discharging plastic separation marks by humans and increase the efficiency of recycling through accurate recycling.