• Electronics and Telecommunications Trends
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• v.37 no.4
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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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.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.14 no.1
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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.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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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.

• 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.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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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.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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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.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.13 no.1
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• pp.45-53
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• 2015

Since the commercial operation of Kori Unit #1 nuclear power plant(NPP) started in 1978, 23 units at present are operating in Korea. Radioactive wastes will be steadily generated from these units and accumulated. In addition, the life-extension of NPPs, construction of new NPPs and decontamination and decommissioning research facilities will cause radioactive wastes to increase. Recently, Korea has revised the new classification criteria as was proposed by IAEA. According to the revised classification criteria, low-level, very-low-level and exempt waste are estimated to about 98% of total disposal amount. In this paper, current status of overseas cases and disposal method with new classification criteria are analyzed to propose the most reasonable method for estimating the amount of decommissioning waste when applying the new criteria.

• Journal of Environmental Policy
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• v.3 no.2
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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.

• Nuclear Engineering and Technology
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• v.43 no.6
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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.