• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.427-429
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• 2004

본 연구에서는 국내 방사선 작업 종사자의 연간 피폭량 중 상당부분(30%)를 차지하는 원자력 발전소 작업 종사자의 방사선 피폭량을 3차원 그래픽 시뮬레이션 기술 및 Java 프로그래밍과 수치해석 방법을 이용하여, 보다 안전한 작업 계획 수립에 필요한 작업 동선에 따른 방사선 피폭변화에 대하여 연구하였다. 원자력 발전소의 방사성 폐기물 처리 시설에 대해 3차원 그래픽으로 모델링 작업을 수행하고, 가상공간에서 선원과 작업자와의 거리 및 시간에 따른 방사선 피폭량을 수치 해석적으로 계산하였다. 선원의 종류에 따른 특정감마선($\tau$상수)을 입력하여 가상 작업 시뮬레이션 동안의 피폭선량을 평가하였으며, 시간에 따른 가상 작업자의 위치와 이동거리, 방사선 피폭량 등의 결과데이터 파일을 이용하여 작업 결과를 분석하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.473-474
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• 2023

잘못된 분리수거는 다른 재활용 폐기물의 재활용을 제한한다. 본 논문에서는 올바른 분리수거를 위해 페트병 라벨 유무 분류 모델을 구현했다. 초기 모델의 낮은 성능을 개선하기 위해 이미지 데이터의 노이즈를 줄이는 편집을 거치고 데이터 증강을 적용하였으며, 모델 개선 작업을 진행하여 과적합을 피하면서 더 나은 성능을 도출했다. 최종 모델은 초기 모델보다 비교적 우수한 성능을 보였으나, 실제 활용 면에서는 낮은 성능을 나타냈다. 이는 학습 데이터의 질과 데이터양의 부족에서 나타난 결과로 볼 수 있다.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.2
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• pp.105-112
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• 2004

A waste container, one of the key components of a multi-barrier system in a potential high level radioactive waste (HLW) repository in Korea ensures the mechanical stability against the lithostatic pressure of a deep geologic medium and the swelling pressure of the bentonite buffer. Also, it delays potential release of radionuclides for a certain period of time, before it is corroded by intruding impurities. Even though the material of a waste container is carefully chosen and its manufacturing processes are under quality assurance processes, there is a possibility of initial defects in a waste container during manufacturing. Also, during the deposition of a waste container in a repository, there is a chance of an incident affecting the integrity of a waste container. In this study, the appropriate Features, Events, and Processes(FEP's) to describe these incidents and the associated scenario on radionuclide release from a container to the biosphere are developed. Then the total system performance assessment on the Initial waste Container Failure (ICF) scenario was carried out by the MASCOT-K, one of the probabilistic safety assessment tools KAERI has developed. Results show that for the data set used in this paper, the annual individual dose for the ICF scenario meets the Korean regulation on the post closure radiological safety of a repository.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.305-305
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• 2004

핵물질을 취급하는 시설에서는 핵물질 안전조치 목적의 달성, 즉 핵물질의 군사적 전용 및 도난을 방지하기 위한 하나의 수단으로서 핵물질의 취급 및 이동을 감시하기 위한 감시시스템이 요구된다. 이 연구에서는 이러한 요구에 부응하기 위해 시설 내에서 핵물질이 이동 가능한 모든 경로에 중성자 모니터와 카메라 같은 감시 장비를 설치하고, 이들로부터 실시간으로 방사선 신호와 영상 데이터를 취득ㆍ분석하여 핵물질의 거동을 진단할 수 있는 핵물질 감시시스템을 개발하였다.(중략)

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.4 no.1
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• pp.65-75
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• 2006

Transparency on the Total System Performance Assessment (TSPA) is the key issue to enhance the public acceptance for a radioactive repository. To approve it, all performances on TSPA through Quality Assurance is necessary. The integrated Cyber R&D Platform is developed by KAERI using the T2R3 principles applicable for five major steps : planning, research work, documentation, and internal & external audits in R&D's. The proposed system is implemented in the web-based system so that all participants in TSPA are able to access the system. It is composed of three sub-systems; FEAS (FEp to Assessment through Scenario development) showing systematic approach from the FEPs to Assessment methods flow chart, PAID (Performance Assessment Input Databases) being designed to easily search and review field data for TSPA and QA system containing the administrative system for QA on five key steps in R&D's in addition to approval and disapproval processes, corrective actions, and permanent record keeping. All information being recorded in QA system through T2R3 principles is integrated into Cyber R&D Platform so that every data in the system can be checked whenever necessary. Throughout the next phase R&D, Cyber R&D Platform will be connected with the assessment tool for TSPA so that it will be expected to search the whole information in one unified system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.739-740
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• 2023

코로나19 의 여파로 생활 폐기물은 급속도로 늘어나는 반면 재활용 사업장의 여건은 개선되지 않고 있어 재활용 산업의 인력난 해결의 필요성이 떠오르고 있다. 이를 위해 본 논문에서는 딥러닝 모델을 활용하여 재활용 폐기물을 분류하는 방법을 제시한다. 딥러닝 모델은 최신 객체 탐지 모델인 YOLOv5를 사용하고, 객체 탐지 성능을 향상시키기 위해 실제 환경에서 수집된 학습용 데이터를 직접 라벨링하여 사용한다. 실험 결과 종류별 평균 0.69의 mAP50 스코어를 기록하였으며 이를 통해 딥러닝 모델을 활용하여 재활용 폐기물을 효율적으로 분류하는 것이 가능함을 확인하였다.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.329-334
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• 2004

The objective of this study regards the preceding condition of the construction disposal of waste which is appropriate, with occurrence quantity DB anger the occurrence quantity prediction which is accurate the regression model which it sees and with the method which is mote accurate prediction method of existing than to sleep it presents it does. This study acquires apartment results data of public construction and civil construction, and chose factor that exert biggest influence on the waste occurrence amount through question and interview memorial address by regression model variable. And presented regression mode] which uses statistics program named SPSS. Result of this study by regression model through constant results data DB anger existent error big experience than estimate method that corrector estimation is available show.

• Proceedings of the KSEEG Conference
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• 1999.04a
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• pp.212-213
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• 1999

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.332-332
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• 1999

• Convergence Security Journal
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• v.23 no.5
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• pp.135-142
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• 2023

In addressing the prominent issues of climate change, resource scarcity, and environmental pollution associated with household waste, extensive research has been conducted on intelligent waste classification methods. These efforts range from traditional classification algorithms to machine learning and neural networks. However, challenges persist in effectively classifying waste in diverse environments and conditions due to insufficient datasets, increased complexity in neural network architectures, and performance limitations for real-world applications. Therefore, this paper proposes i-YOLOX as a solution for rapid classification and improved accuracy. The proposed model is evaluated based on network parameters, detection speed, and accuracy. To achieve this, a dataset comprising 10,000 samples of household waste, spanning 17 waste categories, is created. The i-YOLOX architecture is constructed by introducing the Involution channel convolution operator and the Convolution Branch Attention Module (CBAM) into the YOLOX structure. A comparative analysis is conducted with the performance of the existing YOLO architecture. Experimental results demonstrate that i-YOLOX enhances the detection speed and accuracy of waste objects in complex scenes compared to conventional neural networks. This confirms the effectiveness of the proposed i-YOLOX architecture in the detection and classification of multiple household waste objects.