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Minas Dasygenis - One of the best experts on this subject based on the ideXlab platform.

  • A cloud based smart recycling bin for in-house Waste Classification
    2020 International Conference on Electrical Communication and Computer Engineering (ICECCE), 2020
    Co-Authors: Nikolaos Baras, Dimitris Ziouzios, Minas Dasygenis, Constantinos Tsanaktsidis
    Abstract:

    Due to the Earth's population rapid growth along with the modern lifestyle the urban Waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of Waste materials, which is a time consuming procedure. Most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with Waste Classification for personal in-house usage. A centralized Information System (IS) collects measurements from smart bins that can be deployed virtually anywhere and classifies the Waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of Waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low compared to other implementations.

  • MOCAST - A cloud based smart recycling bin for Waste Classification
    2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2020
    Co-Authors: Nikolaos Baras, Dimitris Ziouzios, Minas Dasygenis, Constantinos Tsanaktsidis
    Abstract:

    Due to the Earth’s population rapid growth along with the modern lifestyle the urban Waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of Waste materials, which is a time consuming procedure. However, most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with Waste Classification. A centralized Information System (IS) collects measurements from smart bins that are deployed all around the city and classifies the Waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of Waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low.

  • A Smart Recycling Bin for Waste Classification
    2019 Panhellenic Conference on Electronics & Telecommunications (PACET), 2019
    Co-Authors: Dimitris Ziouzios, Minas Dasygenis
    Abstract:

    As there is an obvious and increasing need to preserve valuable resources and reduce Waste and pollution, several researches are focusing into this area. However, the solutions provided are neither budget-friendly nor effective to be practical in a real-world application. In this paper, we present a Smart Recycling Bin using modern approaches for Waste Classification. The design of the system permits the low-cost manufacturing of the final product, while uses state of the art technologies such as neural networks and the LoRaWAN protocol. We implemented a low-cost Smart Bin prototype able to classify different types of Waste with an accuracy of 92.1%. The system also remotely transmits valuable data to the corresponding authorities, which can increase their effectiveness in Waste management. Index Terms-Smartbin, Recycling, LoRa, Raspberry Pi, embedded system

Dimitris Ziouzios - One of the best experts on this subject based on the ideXlab platform.

  • A cloud based smart recycling bin for in-house Waste Classification
    2020 International Conference on Electrical Communication and Computer Engineering (ICECCE), 2020
    Co-Authors: Nikolaos Baras, Dimitris Ziouzios, Minas Dasygenis, Constantinos Tsanaktsidis
    Abstract:

    Due to the Earth's population rapid growth along with the modern lifestyle the urban Waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of Waste materials, which is a time consuming procedure. Most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with Waste Classification for personal in-house usage. A centralized Information System (IS) collects measurements from smart bins that can be deployed virtually anywhere and classifies the Waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of Waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low compared to other implementations.

  • MOCAST - A cloud based smart recycling bin for Waste Classification
    2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2020
    Co-Authors: Nikolaos Baras, Dimitris Ziouzios, Minas Dasygenis, Constantinos Tsanaktsidis
    Abstract:

    Due to the Earth’s population rapid growth along with the modern lifestyle the urban Waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of Waste materials, which is a time consuming procedure. However, most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with Waste Classification. A centralized Information System (IS) collects measurements from smart bins that are deployed all around the city and classifies the Waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of Waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low.

  • A Smart Recycling Bin for Waste Classification
    2019 Panhellenic Conference on Electronics & Telecommunications (PACET), 2019
    Co-Authors: Dimitris Ziouzios, Minas Dasygenis
    Abstract:

    As there is an obvious and increasing need to preserve valuable resources and reduce Waste and pollution, several researches are focusing into this area. However, the solutions provided are neither budget-friendly nor effective to be practical in a real-world application. In this paper, we present a Smart Recycling Bin using modern approaches for Waste Classification. The design of the system permits the low-cost manufacturing of the final product, while uses state of the art technologies such as neural networks and the LoRaWAN protocol. We implemented a low-cost Smart Bin prototype able to classify different types of Waste with an accuracy of 92.1%. The system also remotely transmits valuable data to the corresponding authorities, which can increase their effectiveness in Waste management. Index Terms-Smartbin, Recycling, LoRa, Raspberry Pi, embedded system

Constantinos Tsanaktsidis - One of the best experts on this subject based on the ideXlab platform.

  • MOCAST - A cloud based smart recycling bin for Waste Classification
    2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2020
    Co-Authors: Nikolaos Baras, Dimitris Ziouzios, Minas Dasygenis, Constantinos Tsanaktsidis
    Abstract:

    Due to the Earth’s population rapid growth along with the modern lifestyle the urban Waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of Waste materials, which is a time consuming procedure. However, most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with Waste Classification. A centralized Information System (IS) collects measurements from smart bins that are deployed all around the city and classifies the Waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of Waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low.

  • A cloud based smart recycling bin for in-house Waste Classification
    2020 International Conference on Electrical Communication and Computer Engineering (ICECCE), 2020
    Co-Authors: Nikolaos Baras, Dimitris Ziouzios, Minas Dasygenis, Constantinos Tsanaktsidis
    Abstract:

    Due to the Earth's population rapid growth along with the modern lifestyle the urban Waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of Waste materials, which is a time consuming procedure. Most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with Waste Classification for personal in-house usage. A centralized Information System (IS) collects measurements from smart bins that can be deployed virtually anywhere and classifies the Waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of Waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low compared to other implementations.

Denia Djokic - One of the best experts on this subject based on the ideXlab platform.

  • A Characteristics-Based Approach to Radioactive Waste Classification in Advanced Nuclear Fuel Cycles
    2013
    Co-Authors: Denia Djokic
    Abstract:

    The radioactive Waste Classification system currently used in the United States primarily relies on a source-based framework. This has lead to numerous issues, such as Wastes that are not categorized by their intrinsic risk, or Wastes that do not fall under a category within the framework and therefore are without a legal imperative for responsible management. Furthermore, in the possible case that advanced fuel cycles were to be deployed in the United States, the shortcomings of the source-based Classification system would be exacerbated: advanced fuel cycles implement processes such as the separation of used nuclear fuel, which introduce new Waste streams of varying characteristics. To be able to manage and dispose of these potential new Wastes properly, development of a Classification system that would assign appropriate level of management to each type of Waste based on its physical properties is imperative. This dissertation explores how characteristics from Wastes generated from potential future nuclear fuel cycles could be coupled with a characteristics-based Classification framework. A static mass flow model developed under the Department of Energy's Fuel Cycle Research a Development program, called the Fuel-cycle Integration and Tradeoffs (FIT) model, was used to calculate the composition of Waste streams resulting from different nuclear fuel cycle choices: two modified open fuel cycle cases (recycle in MOX reactor) and two different continuous-recycle fast reactor recycle cases (oxide and metal fuel fast reactors). This analysis focuses on the impact of Waste heat load on Waste Classification practices, although future work could involve coupling Waste heat load with metrics of radiotoxicity and longevity. The value of separation of heat-generating fission products and actinides in different fuel cycles and how it could inform long- and short-term disposal management is discussed. It is shown that the benefits of reducing the short-term fission-product heat load of Waste destined for geologic disposal are neglected under the current source-based radioactive Waste Classification system, and that it is useful to classify Waste streams based on how favorable the impact of interim storage is on increasing repository capacity. The need for a more diverse set of Waste classes is discussed, and it is shown that the characteristics-based IAEA Classification guidelines could accommodate Wastes created from advanced fuel cycles more comprehensively than the U.S. Classification framework.

  • Waste Classification based on Waste form heat generation in advanced nuclear fuel cycles using the fuel cycle integration and tradeoffs fit model 13413
    2013
    Co-Authors: Denia Djokic, Steven J Piet, Layne F Pincock, Nick R Soelberg
    Abstract:

    This study explores the impact of Wastes generated from potential future fuel cycles and the issues presented by classifying these under current Classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based Classification framework based on new Waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of Waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of Waste form heat load on Waste Classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of Waste destined for geologic disposal are neglected under the current source-based radioactive Waste Classification system, and that it is useful to classify Waste streams based on how favorable the impact of interim storage is in increasing repository capacity.

  • The need for a characteristics-based approach to radioactive Waste Classification as informed by advanced nuclear fuel cycles using the fuel-cycle integration and tradeoffs (FIT) model
    2013
    Co-Authors: Denia Djokic, Steven J Piet, Layne F Pincock, Nick R Soelberg
    Abstract:

    This study explores the impact of Wastes generated from potential future fuel cycles and the issues presented by classifying these under current Classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based Classification framework based on new Waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of Waste streams resulting from different nuclear fuel cycle choices. Because heat generation is generally the most important factor limiting geological repository areal loading, this analysis focuses on the impact of Waste form heat load on Waste Classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. Waste streams generated in different fuel cycles and their possible Classification based on the current U.S. framework and international standards are discussed. It is shown that the effects of separating Waste streams are neglected under a source-based radioactive Waste Classification system. (authors)

Jun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Research on the Design of Smart Waste Classification and Collection Service System
    Advances in Ergonomics in Design, 2020
    Co-Authors: Yiting Zhao, Jun Zhang
    Abstract:

    This paper aims to explore the combination of the unmanned logistics system and the Waste Classification and collection in a prospective view, as well as the design strategy of the smart Waste collection system which is more efficient and pays more attention to the service experience. Through case study, the change of residents’ awareness, the update of smart community services, and the development opportunities of urban Waste Classification and collection in China are investigated. Based on the development of the terminal unmanned logistics, the correlation and the combination of the reverse unmanned logistics and the Waste collection are discussed. According to the thinking of product-service system design, the conceptual design of smart Waste Classification and collection system by constructing the intelligent two-way unmanned logistics vehicle system, combining the forward and reverse logistics, and perfecting the credit mechanism is put forward.

  • Research on User Experience of Garbage Tricycle Based on Logic of Behaviors and Operation Characteristics of Village Cleaners
    Advances in Ergonomics in Design, 2020
    Co-Authors: Danping Zhou, Jun Zhang
    Abstract:

    This paper selects Waste Classification and working process and behaviors of village cleaner in three villages around the Changsha City in south China as the research objects by using field survey method and task analysis method. This paper aims to clarify and visualize the local model of the Waste Classification system, and to explore the behavior of cleaner and find the pain points of it. Based on this research, a design process of garbage tricycle in village is established to guide the design and innovation process. Redefine participants, locate motivation of behaviors, plan processes of behaviors, seek new means, and create new scenarios and environments. It has certain significance to improve the work efficiency and promote the happiness and safety of village cleaner.