• 제목/요약/키워드: waste classification

검색결과 162건 처리시간 0.025초

ICT기반 폐플라스틱 관리 전주기 기술 동향 (ICT-based Waste Plastic Management Life Cycle Technology)

  • 문영백;정훈;허태욱
    • 전자통신동향분석
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    • 제37권4호
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    • pp.28-35
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    • 2022
  • To solve the challenge of waste plastics, this study investigated the related technologies and company trends along the plastic life cycle, and primarily describes ICT technologies to improve efficiency in the process of sorting and sorting waste plastics. Waste plastic discharge caused by the explosive increase in parcel traffic because of COVID-19 is also growing exponentially. Hence, waste treatment is emerging as a social challenge. Most of the domestic waste classification depends on the manual process according to the waste pollution level. The plastic material classification approach using the spectroscopy approach reveals a high error in the contaminated waste plastic classification, but if the Artificial Intelligence-based image classification technology is employed together, the classification precision can be enhanced because of the type of waste plastic product and the contaminated part can be differentiated.

건설현장에서 발생하는 폐기물 인식 모델 개발 (Development of a waste recognition model at construction sites)

  • 나승욱;허석재
    • 한국건축시공학회:학술대회논문집
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    • 한국건축시공학회 2021년도 가을 학술논문 발표대회
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    • pp.219-220
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    • 2021
  • It is considered that the construction industry is one of the pivotal players in the national economy in terms of Gross Domestic Production (GDP) and employment. Behind the positive role of this industrial sector to the national economy, the construction industry generates approximately 50 % of the total waste generation from all the industrial sectors. There are several measures to mitigate the adverse impacts of the construction waste such as reduce, reuse and recycle. Recycling would be one of the effective strategies for waste minimisation, which would be able to reduce the demand upon new resources as well as enhance reusing the construction materials on sites. The automated construction waste classification system would make it possible not only to reduce the amount of labour input but also mitigate the possibility of errors during the manual classification process. In this study, we proposed an automated waste segmentation and classification system for recycling the construction and demolition waste in the real construction site context. Since the practical application to the real-world construction sites was one of the significant factors to develop the system, a YOLACT (You Only Look At CoefficienTs) algorithm was chosen to conduct the study. In this study, it is expected that the proposed system would make it possible to enhance the productivity as well as the cost efficiency by reducing the manpower for the construction and demolition waste management at the construction site.

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방사성폐기물 신분류기준을 고려한 중저준위 방사성폐기물 처분시설의 핵종재고량 예측 (Prediction of Radionuclide Inventory for the Low- and Intermediate-Level Radioactive Waste Disposal Facility by the Radioactive Waste Classification)

  • 정강일;정노겸;문영표;정미선;박진백
    • 방사성폐기물학회지
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    • 제14권1호
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    • pp.63-78
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    • 2016
  • To meet nuclear regulatory requirements, more than 95% individual radionuclides in the low- and intermediate-level radioactive waste inventory have to be identified. In this study, the radionuclide inventory has been estimated by taking the long-term radioactive waste generation, the development plan of disposal facility, and the new radioactive waste classification into account. The state of radioactive waste cumulated from 2014 was analyzed for various radioactive sources and future prospects for predicting the long-term radioactive waste generation. The predicted radionuclide inventory results are expected to contribute to secure the development of waste disposal facility and to deploy the safety case for its long-term safety assessment.

Sorting for Plastic Bottles Recycling using Machine Vision Methods

  • SanaSadat Mirahsani;Sasan Ghasemipour;AmirAbbas Motamedi
    • International Journal of Computer Science & Network Security
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    • 제24권6호
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    • pp.89-98
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    • 2024
  • Due to the increase in population and consequently the increase in the production of plastic waste, recovery of this part of the waste is an undeniable necessity. On the other hand, the recycling of plastic waste, if it is placed in a systematic process and controlled, can be effective in creating jobs and maintaining environmental health. Waste collection in many large cities has become a major problem due to lack of proper planning with increasing waste from population accumulation and changing consumption patterns. Today, waste management is no longer limited to waste collection, but waste collection is one of the important areas of its management, i.e. training, segregation, collection, recycling and processing. In this study, a systematic method based on machine vision for sorting plastic bottles in different colors for recycling purposes will be proposed. In this method, image classification and segmentation techniques were presented to improve the performance of plastic bottle classification. Evaluation of the proposed method and comparison with previous works showed the proper performance of this method.

생활 폐기물 다중 객체 검출과 분류를 위한 i-YOLOX 구조에 관한 연구 (A Study on the i-YOLOX Architecture for Multiple Object Detection and Classification of Household Waste)

  • 왕웨이광;정경권;이태원
    • 융합보안논문지
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    • 제23권5호
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    • pp.135-142
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    • 2023
  • 생활 폐기물 쓰레기는 기후 변화, 자원 부족, 환경 오염을 불러오는 대표적인 문제로서, 이러한 문제를 해결하기 위해 지능적으로 쓰레기를 분류하는 방식을 연구하였고, 전통적인 분류 알고리즘부터 기계학습, 신경망에 이르기까지 많은 연구가 진행되고 있다. 그러나, 다양한 환경과 조건에서 쓰레기를 분류하기에는 여전히 데이터셋이 부족하고, 신경망 네트워크 구성 복잡도가 증가하며, 성능 측면에서도 실생활에 적용하기에 아직 미흡하다. 따라서 본 논문에서는 신속한 분류와 정확도 향상을 위해 i-YOLOX를 제안하고, 네트워크 매개변수, 검출속도, 정확도 등을 평가한다. 이를 위해 17개의 폐기물 범주를 포함하는 10,000개의 가정용 쓰레기 대상 샘플로 데이터 세트를 구성하고, YOLOX 구조에 Involution 채널 컨볼루션 연산자와 CBAM(Convolution Branch Attention Module)을 도입하여 i-YOLOX를 구성하고, 기존의 YOLO 구조와 성능을 비교한다. 실험 결과 복잡한 장면에서 쓰레기 객체 검출 속도와 정확도가 기존의 신경망에 비해 향상되어, 제안한 i-YOLOX 구조가 생활 폐기물 다중 객체 검출과 분류에 효과적임을 확인하였다.

Municipal waste classification system design based on Faster-RCNN and YoloV4 mixed model

  • Liu, Gan;Lee, Sang-Hyun
    • International Journal of Advanced Culture Technology
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    • 제9권3호
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    • pp.305-314
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    • 2021
  • Currently, due to COVID-19, household waste has a lot of impact on the environment due to packaging of food delivery. In this paper, we design and implement Faster-RCNN, SSD, and YOLOv4 models for municipal waste detection and classification. The data set explores two types of plastics, which account for a large proportion of household waste, and the types of aluminum cans. To classify the plastic type and the aluminum can type, 1,083 aluminum can types and 1,003 plastic types were studied. In addition, in order to increase the accuracy, we compare and evaluate the loss value and the accuracy value for the detection of municipal waste classification using Faster-RCNN, SDD, and YoloV4 three models. As a final result of this paper, the average precision value of the SSD model is 99.99%, the average precision value of plastics is 97.65%, and the mAP value is 99.78%, which is the best result.

Waste Classification by Fine-Tuning Pre-trained CNN and GAN

  • Alsabei, Amani;Alsayed, Ashwaq;Alzahrani, Manar;Al-Shareef, Sarah
    • International Journal of Computer Science & Network Security
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    • 제21권8호
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    • pp.65-70
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    • 2021
  • Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

신 분류기준을 적용하기 위한 원전 해체폐기물량 및 처분 비용 산정에 대한 사전 연구 (A Pre-Study on the Estimation of NPP Decommissioning Radioactive Waste and Disposal costs for Applying New Classification Criteria)

  • 송종순;김영국;이상헌
    • 방사성폐기물학회지
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    • 제13권1호
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    • pp.45-53
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    • 2015
  • 1978년 고리 1호기의 상업 운전을 시작으로 현재 우리나라에서는 총 23기의 원전이 운영 중에 있다. 운영 중인 원전으로부터 방사성폐기물이 계속 발생되고 누적되어 갈 것이다. 또한 원전의 수명 연장과 신규 원전의 추가 건설 이외에도 제염해체 연구시설 등 각종 원자력 시설에서 발생하는 방사성폐기물은 꾸준히 증가하고 있다. 우리나라는 최근 IAEA에서 권고하는 신 분류기준을 적용한 신분류기준에 대해 원자력안전위원회 고시를 개정하였다. 중·저준위폐기물을 IAEA 신 분류기준을 적용하여 세분화한다면, 약 98%를 차지하는 저준위 및 극저준위 방사성폐기물과 규제면제폐기물을 효과적으로 처분 할 수 있게 된다. 본 논문에서는 신 분류기준을 적용한 해외 적용 사례와 처분 방안 현황을 분석하여 국내에 적용 가능한 최적의 합리적인 적용 방안 및 해체 방사성폐기물량을 산정해 보고자 한다.

폐기물의 개념 및 분류체계에 관한 연구 (A Study on Definition and Classification System of Wastes)

  • 홍동희
    • 환경정책연구
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    • 제3권2호
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    • pp.113-137
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    • 2004
  • The objective of this study is to introduce the definitions and classification methods of wastes in international agreements and legislations, examine the concept of wastes and their classification systems in Korea, and finally analyze and compare the concept of wastes in different countries for finding better solutions and suggestions. The study summarizes the concept of wastes as introduced in the Basel Convention, OECD, EU, US, and UK. First, each of the member countries adapt to the same concepts of wastes as defined in their international agreements; second, the intention of the wastes holder and the conditions of the wastes are considered at the same time when defining the concepts. Upon close examination of the classification of wastes systems as introduced in the Basel Convention, OECD, EU, US, and UK, the wastes are classified into toxic and non-toxic wastes according to the existence of poisonous substances. Therefore, it is classified as a toxic waste when any toxic substance on its list is included in the waste, while others are considered as a non-toxic waste if they don't contain poisonous substances. Secondly, in the UK, the matter of toxic or non-toxic wastes are classified, not according to the existence of the poisonous substances, but based on the generation of sources. In Korea, the concepts of wastes are divided into the two categories - a concept as defined in actual legislations and a concept in its translation. The Korean classification of the wastes include Wastes Management Act, amended in 1995, which stipulates that toxic substances should be managed in a special way as the designated wastes. It appears that the Act utilizes the classification method that classifies the wastes according to the existence of poisonous substance. Korea's concepts of wastes should be changed after recognition of the concepts in international agreement (Basel Convention, EU) and other foreign laws(US, UK) that consider subjective and objective standards at the same time when they define the concepts. Also, the development of technology in recycling and reuse of the wastes can remove the current absolute notion of the wastes so that it also should not be passed over. Also, because a classification structure of wastes has a close relationship with a disposal structure, its classification system should be fixed gradually to come up with the development of wastes disposal technology and its policy.

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RADIOLOGICAL CHARACTERISTICS OF DECOMMISSIONING WASTE FROM A CANDU REACTOR

  • Cho, Dong-Keun;Choi, Heui-Joo;Ahmed, Rizwan;Heo, Gyun-Young
    • Nuclear Engineering and Technology
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    • 제43권6호
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    • pp.583-592
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    • 2011
  • The radiological characteristics for waste classification were assessed for neutron-activated decommissioning wastes from a CANDU reactor. The MCNP/ORIGEN2 code system was used for the source term analysis. The neutron flux and activation cross-section library for each structural component generated by MCNP simulation were used in the radionuclide buildup calculation in ORIGEN2. The specific activities of the relevant radionuclides in the activated metal waste were compared with the specified limits of the specific activities listed in the Korean standard and 10 CFR 61. The time-average full-core model of Wolsong Unit 1 was used as the neutron source for activation of in-core and ex-core structural components. The approximated levels of the neutron flux and cross-section, irradiated fuel composition, and a geometry simplification revealing good reliability in a previous study were used in the source term calculation as well. The results revealed the radioactivity, decay heat, hazard index, mass, and solid volume for the activated decommissioning waste to be $1.04{\times}10^{16}$ Bq, $2.09{\times}10^3$ W, $5.31{\times}10^{14}\;m^3$-water, $4.69{\times}10^5$ kg, and $7.38{\times}10^1\;m^3$, respectively. According to both Korean and US standards, the activated waste of the pressure tubes, calandria tubes, reactivity devices, and reactivity device supporters was greater than Class C, which should be disposed of in a deep geological disposal repository, whereas the side structural components were classified as low- and intermediate-level waste, which can be disposed of in a land disposal repository. Finally, this study confirmed that, regardless of the cooling time of the waste, 15% of the decommissioning waste cannot be disposed of in a land disposal repository. It is expected that the source terms and waste classification evaluated through this study can be widely used to establish a decommissioning/disposal strategy and fuel cycle analysis for CANDU reactors.