• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.18 no.3
• /
• pp.347-354
• /
• 2020

Site characterization for decommissioning Kori Unit 1 is ongoing in South Korea after 40 years of successful operation. Kori Unit 1's containment building is assumed to be mostly radioactively contaminated, and therefore radiation exposure management and detailed contamination investigation are required for decommissioning and dismantling it safely. In this study, site-specific Derived Concentration Guideline Levels (DCGLs) were derived using the residual radioactivity risk evaluation tool, RESRAD-BUILD code. A conceptual model of containment building for Kori Unit 1 was set up and limited occupational worker building inspection scenario was applied. Depending on the source location, the maximum contribution source and exposure pathway of each radionuclide were analyzed. The contribution of radionuclides to dose and exposure pathways, by source location, is expected to serve as basic data in the assessment criteria of survey areas and classification of impact areas during further decommissioning and decontamination of sites.

• Proceedings of the Korean Environmental Health Society Conference
• /
• 2001.11a
• /
• pp.64-69
• /
• 2001

The present work provides a result from the preliminary experiment for hydrocyclone technology. In this work, local lake sediments and waste coal fly ash were used as test samples, prior to the application of hydrocyclone technology to the waste sludge thickening. A few cyclones based on the Rietema standard geometry were prepared. Chemical analysis of the sediments showed that more organic contaminants were in smaller particles. The experimental tests further showed that physical characteristics of particles, configuration of the cyclone and operation condition would affect the separation efficiency. The current results showed that small size cyclones might improve the separation and concentration of the lake sediments, and higher inlet velocity would increase the concentration rate of under flow and absolute concentration of sediment particles.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.99-101
• /
• 2021

쓰레기 대란, 환경파괴의 상황 속 실제 재활용 쓰레기 가운데 절반 정도만이 재활용되고 있다. 재활용률을 높이기 위해, 올바른 분리배출 방법을 쉽고 편하게 찾을 수 있는 방식이 필요하다. 본 논문에서는 올바른 분리수거를 통해 재활용률을 증진하기 위한 분리수거 분류 서비스를 제안한다. 본 논문은 ResNet-34 모델을 통해 안드로이드 카메라로 촬영한 이미지의 분리배출 클래스를 예측하고 그에 따른 분리배출 가이드를 제공하는 시스템을 설계하였다. 향후 연구에서는 모델의 정확도 향상을 위해 온디바이스와 서버 모델을 분리하고 모델의 개인 맞춤화를 진행할 예정이다.

• Proceedings of the IEEK Conference
• /
• 2001.10a
• /
• pp.693-698
• /
• 2001

This paper establishes a coding method system including the liberation and size distribution of recycling materials in the shredder operation. Every particle in the shredded product becomes a code number using the liberation model and size distribution equation transforming of weight percentage into particles number percentage. One set of database can be obtained after all particles have been coded. This database is suitable for the size reduction operation in the process simulation of waste recycling. Coupling with the developed air classification, sizing and separating operations, the whole process simulation will be completely established for diversified application. A typical simulation for the rolling cutting shredder product of waste TV had been demonstrated under this coding system. The breakage size distribution of Gaudin and Schumann equation were selected for the shredding operation simulation. The Gaudin's liberation model was suitable fur the liberation simulation. Both of these equations were transformed weight percentage into particles distribution for the necessary of particle coding method. A better recycling operation for this shredded solid waste can be concluded from the comparison of simulation results with their sorted grade, recovery or economic of materials in different processes.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.6
• /
• pp.51-56
• /
• 2022

With the development of industrialization, the amount of food and waste is rapidly increasing. Accordingly, the government is aware of the seriousness and is making efforts in various ways to reduce it. As a part of that, the volume-based food system was introduced, and although there were several trials and errors at the beginning of the introduction, it shows a reduction effect of 20 to 30%. These results suggest that the volume-based food system is being established. However, the waste is caused by foreign substances in the process of recycling resources by collecting them from the 1st collection to the 2nd collection process. Therefore, in this study, to solve these problems fundamentally, artificial intelligence is applied to classify foreign substances and improve them. Due to the nature of food waste, there is a limit to obtaining many images, so we compare several models based on CNNs and classify them as abnormal data, that is, CNN-based models are trained on various types of foreign substances, and then models with high accuracy are selected. We intend to prepare improvement measures for maintenance, such as manpower input to protect equipment and classify foreign substances by applying it.

• Journal of Radiation Industry
• /
• v.17 no.1
• /
• pp.19-32
• /
• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2022.04a
• /
• pp.225-226
• /
• 2022

The construction industry, which has a labour-intensive and conservative nature, is exclusive to adopt new technologies. However, the construction industry is viably introducing the 4th Industrial Revolution technologies represented by artificial intelligence, Internet of Things, robotics and unmanned transportation to promote change into a smart industry. An image-based artificial intelligence technology is a field of computer vision technology that refers to machines mimicking human visual recognition of objects from pictures or videos. The purpose of this article is to explore image-based artificial intelligence technologies which would be able to apply to the construction sites. In this study, we show two examples which is one for a construction waste classification model and another for cast in-situ anchor bolts defection detection model. Image-based intelligence technologies would be used for various measurement, classification, and detection works that occur in the construction projects.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.17 no.4
• /
• pp.437-445
• /
• 2019

After the permanent shutdown of K1 in 2017, decommissioning processes have attracted great attention. According to the current decommissioning roadmap, the dismantling of the activated components of K1 may start in 2026, following the removal of its spent fuel. Since the reactor vessel (RV) and reactor vessel internal (RVI) of K1 contain massive components and are relatively highly activated, their decommissioning process should be conducted carefully in terms of radiological and industrial safety. For achieving maximum efficiency of nuclear waste management processes for K1, we present activation analysis of the segmentation process and waste classification of the RV and RVI components of K1. For RVI, the active fuel regions and some parts of the upper and lower active regions are classified as intermediate-level waste (ILW), while other components are classified as low-level waste (LLW). Due to the RVI's complex structure and high activation, we suggest various underwater segmentation techniques which are expected to reduce radiation exposure and generate approximately nine ILW and nineteen very low level waste (VLLW)/LLW packages. For RV, the active fuel region and other components are classified as LLW, VLLW, and clearance waste (CW). In this case, we suggest in-situ remote segmentation in air, which is expected to generate approximately forty-two VLLW/LLW packages.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.8 no.1
• /
• pp.56-63
• /
• 2020

The usage of OPC-free alkali activated ground granulated blast furnace slag(GGBS) mortar has been widely studied on the previous years, due to its advantages on sustainability, durability and workability. This paper brings a new view, aiming to classify the best application in situ for each mortar, according to the type and activator content. By this practical implication, more efficiency is achieved on the construction site and consequently less waste of materials. In order to compare the different activators, the following experiments were performed: analysis of compressive strength at 28 days, setting time measured by needles penetration resistance, analysis of total pore volume performed by MIP and permeability assessment by RCPT test. In general, activated GGBS had acceptable performance in all cases compared to OPC, and remarkable improved durability. Following the experimental results, it was confirmed that each activator and different concentrations impose distinct outcome performance to the mortar which allows the classification. It was observed that the activator Ca(OH)2 is the most versatile among the others, even though it has limited compressive strength, being suitable for laying mortar, coating/plaster, adhesive and grouting mortar. Samples activated with NaOH, in turn, presented in general the most similar results compared to OPC.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.305-308
• /
• 2022

코로나 19 장기화로 비대면, 원격 수업, 재택 근무 등 생활 형태가 변하면서 일회용품 쓰레기도 증가했다. 분리배출 표시제도가 자주 변경되어 가정에서 판단시 어려움을 느낄 수 있다. 이에 본 연구에서는 재활용 가능 여부를 알려주고, 생활폐기물의 수거 기준에 맞는 처리방법을 알 수 있도록 돕는 분류 시스템을 개발하고 평가한다.