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      Waste level detection and HMM based collection scheduling of multiple bins


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          In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM’s previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.

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          Networks and vehicle routing for municipal waste collection

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            Municipal solid waste management in Pudong New Area, China.

            The increase in population, the rapid economic growth and the rise in community living standards accelerate municipal solid waste (MSW) generation in developing cities. This problem is especially serious in Pudong New Area, Shanghai, China. The daily amount of MSW generated in Pudong was about 1.11 kg per person in 2006. According to the current population growth trend, the solid waste quantity generated will continue to increase with the city's development. In this paper, we describe a waste generation and composition analysis and provide a comprehensive review of municipal solid waste management (MSWM) in Pudong. Some of the important aspects of waste management, such as the current status of waste collection, transport and disposal in Pudong, will be illustrated. Also, the current situation will be evaluated, and its problems will be identified.
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              Development drivers for waste management.

              This paper identifies six broad groups of drivers for development in waste management. Public health led to the emergence of formalized waste collection systems in the nineteenth century, and remains a key driver in developing countries. Environmental protection came to the forefront in the 1970s, with an initial focus on eliminating uncontrolled disposal, followed by the systematic increasing of technical standards. Today, developing countries seem still to be struggling with these first steps; while climate change is also emerging as a key driver. The resource value of waste, which allows people to make a living from discarded materials, was an important driver historically, and remains so in developing countries today. A current trend in developed countries is closing the loop, moving from the concept of 'end-of-pipe' waste management towards a more holistic resource management. Two underpinning groups of drivers are institutional and responsibility issues, and public awareness. There is no, one single driver for development in waste management: the balance between these six groups of drivers has varied over time, and will vary between countries depending on local circumstances, and between stakeholders depending on their perspective. The next appropriate steps towards developing a sustainable, integrated waste management system will also vary in each local situation.

                Author and article information

                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
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                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                29 August 2018
                : 13
                : 8
                : e0202092
                [001] Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
                Nanjing University of Information Science and Technology, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ These authors also contributed equally to this work.


                Current address: School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, Australia

                Author information
                © 2018 Aziz et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                : 5 September 2017
                : 27 July 2018
                Page count
                Figures: 6, Tables: 4, Pages: 14
                Funded by: funder-id http://dx.doi.org/10.13039/501100003093, Ministry of Higher Education, Malaysia;
                Award ID: D000016-16001
                Award Recipient :
                This research is fully funded by High Impact Research Grant Scheme (D000016-16001) under the Ministry of Higher Education, Malaysia.
                Research Article
                Physical sciences
                Probability theory
                Markov models
                Hidden Markov models
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Support Vector Machines
                Engineering and Technology
                Optical Equipment
                Physical Sciences
                Operator Theory
                Kernel Functions
                Research and Analysis Methods
                Imaging Techniques
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Mathematical Functions
                Physical Sciences
                Applied Mathematics
                Research and Analysis Methods
                Simulation and Modeling
                Engineering and Technology
                Sanitary Engineering
                Solid Waste Management
                Custom metadata
                All data files are available from the https://figshare.com/articles/Solid_waste_bin_images_with_3_bin_per_node/6269042 database.



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