• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.307-310
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• 2022

4차 산업혁명의 기술이 발전함에 따라 세계는 눈부시게 발전하고 있다. 그러나 그와 동시에 어르신들의 치매로 인한 사회적 비용의 증가로 발생하는 문제점 또한 무시하지 못할 수준에 이르렀다. 이에 조기에 치매를 선별하여 예방하는 것이 중요시되었으며 다양한 연구기관에서는 빠르고 쉽게 치매의 전 단계인 경도인지장애를 선별하고자 여러 선별 검사지를 제작하여 경도인지장애 선별을 통한 치매를 예방하는 데 노력하는 중이다. 하지만 다양한 상황으로 각각의 효율성이 경도인지장애 선별에 대한 기대에 미치지 못하고 있다. 그리하여 본 연구는 기존에 개발된 경도인지장애 선별지 들을 비교 분석 및 코로나 상황에 적합하게 비대면 방식으로 경도인지장애 선별이 가능하도록 보다 효율적으로 개선하였으며 이를 IoT 기기에 접목하였다. 또한 IoT 기기는 어르신들의 경도인지장애 선별검사에 대한 부담감을 줄이고자 친근한 인형 형태로 개발하였으며 상호작용을 위한 기능 제작을 통하여 마음 돌봄 서비스를 구현하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.758-760
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• 2022

본 연구는 머신러닝의 한 종류인 YOLOv5를 이용하여 기존 육안 선별작업을 자동화 하는 기계를 설계하는 것이다. 본 연구에서는 영상촬영과 선별작업을 진행하는 컨베이어 기구와 선별 프로그램을 제작하고, 모든 표면을 검사해 사과의 품질을 3단계로 구별하는 작업을 진행하였다. 결과적으로 투입된 사과의 품질을 성공적으로 분류 하였다.

• Resources Recycling
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• v.26 no.1
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• pp.69-77
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• 2017

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small e-waste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.

• Environmental engineer
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• s.132
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• pp.8-13
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• 1997

• Proceedings of the Korean Institute of Resources Recycling Conference
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• 2006.09a
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• pp.85-98
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• 2006

• Bulletin of Food Technology
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• v.8 no.1
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• pp.34-46
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• 1995

• Resources Recycling
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• v.30 no.4
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• pp.3-10
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• 2021

The field of recycling for waste electronic components, which is the typical example of an urban mine, requires the development of useful sorting techniques. In this study, a sorter based on image identification by deep learning was developed to select electronic components into four groups. They were recovered from waste printed circuit boards and should be separated to depend on the difference after treatment. The sorter consists of a workstation with GPU, camera, belt conveyor, air compressor. A small piece (less than 3.5 cm) of electronic components on the belt conveyor (belt speed: 6 cm/s) was taken and learned as teaching data. The accuracy of the image identification was 96% as kinds and 99% as groups. The optimum condition of sorting was determined by evaluating accuracies of image identification and recovery rates by blowdown when changing the operating condition such as belt speed and blowdown time of compressed air. Under the optimum condition, the accuracy of image classification in groups was 98.7%. The sorting rate was more than 70%.

• Resources Recycling
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• v.26 no.6
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• pp.73-83
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• 2017

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laser-induced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.

• Environmental engineer
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• s.264
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• pp.56-59
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• 2008

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.1-11
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• 2015

In this study, an online seed potato sorting system using a visible and near infrared (40 1100 nm) transmittance spectral technique and statistical model was evaluated for the nondestructive determination of infected and sound seed potatoes. Seed potatoes that had been artificially infected with Pectobacterium atrosepticum, which is known to cause a soil borne disease infection, were prepared for the experiments. After acquiring transmittance spectra from sound and infected seed potatoes, a determination algorithm for detecting infected seed potatoes was developed using the partial least square discriminant analysis method. The coefficient of determination($R^2_p$) of the prediction model was 0.943, and the classification accuracy was above 99% (n = 80) for discriminating diseased seed potatoes from sound ones. This online sorting system has good potential for developing a technique to detect agricultural products that are infected and contaminated by pathogens.