Automated Municipal Solid Waste Sorting for Recycling Using a Mobile Manipulator

2016 ◽  
Author(s):  
Sathish Gundupalli Paulraj ◽  
Subrata Hait ◽  
Atul Thakur
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mobile Manipulator ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Identification ◽  
Classification Rate ◽  
Rapid Urbanization ◽  
Clustering Model ◽  
Thermographic Image

Municipal solid waste (MSW), generated at an unprecedented rate due to rapid urbanization and industrialization contains useful recyclable materials like metals, plastic, wood, etc. Recycling of useful materials from MSW in the developing countries is severely constrained by limited door-to-door collection and poor means of segregation. Recovery of recyclables is usually performed by waste pickers, which is highly risky and hazardous for their health. This paper reports the development of a robotic mobile manipulation system for automated sorting of useful recyclables from MSW. The developed robot is equipped with a thermal imaging camera, proximity sensor and a 5-DOF robotic arm. This paper presents an approach for sorting based on automated identification from thermographic images. The developed algorithm extracts keypoint features from the thermographic image and feeds into clustering model to map them into a bag-of-word vectors. Finally, Support Vector Machine (SVM) classifier is used for identifying the recyclable material. We used the developed algorithm to detect three categories of recyclables namely, aluminum can, plastic bottle and tetra pack from given thermographic images. We obtained classification rate of 94.3% in the tests. In future, we plan to extend the developed approach for classifying a wider range of recyclable objects as well as to incorporate motion planning algorithms to handle cluttered environments.

scholarly journals Assessment of Municipal Solid Waste Management Practices in Akure, Ondo State, Nigeria

2020 ◽  
pp. 1-10
Author(s):  
Oladapo Michael Ogungbade ◽  
Butu William Ali ◽  
Abdulganiyu Oriola Kilani ◽  
Gbenga John Oladehinde ◽  
Tolulope Joy Akeju
Keyword(s):  
Waste Management ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Management Practices ◽  
Solid Waste Management ◽  
Simple Random Sampling ◽  
Municipal Solid Waste Management ◽  
Rapid Urbanization ◽  
Ondo State ◽  
Waste Management Practices

Rapid urbanization and uncontrolled population growth in the city of Akure create a huge generation of municipal solid waste (MSW) and waste management authority has not been able to manage it properly. This has led to inefficient waste collection methods, insufficient coverage of the collection system and improper disposal of solid waste. This paper investigated municipal solid waste management practices in Akure, Ondo State, Nigeria. Simple random sampling was used in selecting 392 respondents within the eight districts in the study area. Information was collected on socio-economic characteristics of the respondents; nature and compositions of municipal solid waste and solid waste management practices in the study area. The study showed that female respondents (58.2%) were more than male (41.8%) in the study area. Also, a larger proportion of the respondents earned above N40,000 per month. More than one-third of the respondents (37.8%) created squander from vegetable and food remains, next to this were plastic (19.1%), paper (8.2%) and metal waste (6.6%). Further findings showed that the majority of the respondents (66%) do not segregate waste before disposal while the wastebasket was commonly preferred to collect waste due to its affordability. The study concluded that despite the establishment of waste management authority, most of the wastes collected was not sorted before disposal while the majority of the respondents preferred to burn their waste. The study recommended that government and non-governmental organizations should pay much attention to the issue of waste management in the area as waste was not properly managed.

scholarly journals Life Cycle Assessment of Municipal Solid Waste Management System in Kathmandu, Nepal

2016 ◽  
Vol 1 ◽  
pp. 97
Author(s):  
Ramesh Ranabhat
Keyword(s):  
Waste Management ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Management System ◽  
Solid Waste Management ◽  
Population Census ◽  
Municipal Solid Waste Management ◽  
Rapid Urbanization ◽  
Waste Management System ◽  
Solid Waste Management System

<p class="Default">Due to rapid urbanization, ever increasing population, limited resources and industrialization all-inclusive, the environmentally habitual management of municipal solid waste has become a global challenge.  According to report of the National Population Census 2011, growth rate of Nepalese Population is 1.4 percent per annum with population density estimated at 181 per sq. KMs. Solid waste management inNepal, the current practice of the illegal dumping of solid waste on the river banks has created a serious environmental and public health problem. The focus of this study was to carry out the magnitude of the present SWM problems by identifying the sources, types, quantities, dangers and opportunities they pose. It will be helpful to examine the adequacy of the existing institutional arrangements and implement a strategic and operational plan for SWM and to establish the EASEWASTE data base of municipal solid waste management system inKathmandu City,Nepal. </p><p class="Default"><em>Journal of Advanced College of Engineering and Management, Vol. 1, 2015,</em> pp. 97-106</p>

scholarly journals Sustainability Contested: Analysis of Stakeholders Participation in Municipal Solid Waste Management - A Case Study

2021 ◽  
Vol 16 (3) ◽  
pp. 974-988
Author(s):  
Vishnu J. Menon ◽  
Antony Palackal
Keyword(s):  
Waste Management ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Solid Waste Management ◽  
Human Life ◽  
Developed Countries ◽  
Municipal Solid Waste Management ◽  
Human Beings ◽  
Rapid Urbanization ◽  
The Government

Waste has always been a part of human life settlement and we have been either very careless with our waste by discarding it into the streets, the air, water, and in our backyards, or consciously dumping it close to those least powerful segments of the society at all times. Waste has been a problem for human beings and people have been least concerned about its eco-friendly disposal. Developed countries came up with many programmes, regulations and policies to address the municipal solid waste crisis, but still it is an unresolved problem. Municipal solid waste management is still a complex issue everywhere in the globalized and techno scientific world due to the carefree mindset, rapid urbanization process, unscientific development process and lack of social responsibility. In these circumstances, municipal solid waste managementcannot be addressed by mere technological innovations or adoptions. Moreover, the responsibility of municipal solid waste managementcannot be left to the Government alone. Instead, participation of various stakeholders needs to be ensured and coordinated for achieving sustainability. Taking Thiruvananthapuram Municipal Corporation in the state of Kerala, India as a case, this paper discusses the extent and ways in which various stakeholders engage in the two main approaches for municipal solid waste management, namely- centralized approach and decentralized approach. The research study was conducted during the period June, 2020 to December, 2020.

The recognition of plastic bottle using linear multi hierarchical SVM classifier

2021 ◽  
pp. 1-14
Author(s):  
LiHua Cai ◽  
Jin Cao ◽  
MingQiang Wang ◽  
Ta Zhou ◽  
HaiFeng Fang
Keyword(s):  
Loss Function ◽  
Deep Structure ◽  
Principal Component ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Input Matrix ◽  
Classification Rate ◽  
Pet Bottles ◽  
Combination Methods

Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.

scholarly journals Optimization of solid waste collection using RSM approach, and strategies delivering sustainable development goals (SDG’s) in Jeddah, Saudi Arabia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Neyara Radwan ◽  
Nadeem A. Khan ◽  
Rania Abdou Gaber Elmanfaloty
Keyword(s):  
Saudi Arabia ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Process Parameters ◽  
Urban Areas ◽  
Industrial Development ◽  
Waste Collection ◽  
Solid Waste Disposal ◽  
Rapid Urbanization ◽  
Solid Waste Collection

AbstractThe rapid industrial development, high population growth, and rapid urbanization of Saudi Arabia have led to increased pollution and waste levels. Every day, solid waste disposal for governments and local authorities becomes a significant challenge. Saudi Arabia produces over 15 million tonnes of solid waste annually, with a population of around 29 million. The waste production per person is estimated at between 1.5 and 1.8 kg per day per person. About 75% of the population in urban areas is concentrated, making it imperative that government steps are taken to boost the country's waste recycling and management scenario. The production of solid waste in Riyadh, Jeddah, and Dammam, three of the largest cities, exceeds seven million tonnes annually, which shows the enormity of the civic body problem. During this study, the design Expert software was involved in the optimization of process parameters during the collection of municipal solid waste (MSW) from Jeddah city. The use of design experiments and numerical optimization is quite effective in optimizing the different process parameters on the overall cost. Saudi Arabia has a critical need for a resilient waste system and agile waste management system to control its municipal solid waste quickly and environmentally friendly for achieve Saudi Vision 2030. For this study design of experiment, software was employed to optimize the cost per trip, thereby considering process parameters. It is therefore essential to examine the existing practices and future opportunities for solid waste collection, storage, and disposal. This study considered that MSW generated in Saudi Arabia which is having great potential to be converted into wealth. Hence, considering the current environment situation, energy prospective and future management strategies for MSW have also been reviewed.

scholarly journals A Peak Detection Algorithm for Localization and Classification of Heart Sounds in PCG Signals using K-means Clustering

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Detection Algorithm ◽  
Heart Sounds ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Classifier ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Clustering Model ◽  
Peak Detection Algorithm

Traditionally, the heart sound classification process is performed by first finding the elementary heart sounds of the phonocardiogram (PCG) signal. After detecting sounds S1 and S2, the features like envelograms, Mel frequency cepstral coefficients (MFCC), kurtosis, etc., of these sounds are extracted. These features are used for the classification of normal and abnormal heart sounds, which leads to an increase in computational complexity. In this paper, we have proposed a fully automated algorithm to localize heart sounds using K-means clustering. The K-means clustering model can differentiate between the primitive heart sounds like S1, S2, S3, S4 and the rest of the insignificant sounds like murmurs without requiring the excessive pre-processing of data. The peaks detected from the noisy data are validated by implementing five classification models with 30 fold cross-validation. These models have been implemented on a publicly available PhysioNet/Cinc challenge 2016 database. Lastly, to classify between normal and abnormal heart sounds, the localized labelled peaks from all the datasets were fed as an input to the various classifiers such as support vector machine (SVM), K-nearest neighbours (KNN), logistic regression, stochastic gradient descent (SGD) and multi-layer perceptron (MLP). To validate the superiority of the proposed work, we have compared our reported metrics with the latest state-of-the-art works. Simulation results show that the highest classification accuracy of 94.75% is achieved by the SVM classifier among all other classifiers.

scholarly journals Carbon credit estimation from composting of municipal solid waste in India

2007 ◽  
pp. 297-303
Author(s):  
Sunil Kumar
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Urban Areas ◽  
Complex Problem ◽  
Carbon Credit ◽  
Rapid Urbanization ◽  
Ground Water Pollution ◽  
Supreme Court Of India ◽  
Environmental Friendly ◽  
The Government

Management of municipal solid waste (MSW) is a complex problem for being faced all overthe world, Rapid urbanization and industrialization have led to scarcity of land and otherresources in urban areas and have increased the problem of disposal of the waste generated,The total MSW generation will continuously rise due to the outgoing urban growth,Uncontrolled haphazard dumping of MSW on the outskirts of towns and cities has createdoverflowing landfills, which are not only impossible to reclaim, but also have induced seriousenvironmental implications through contributions to ground water pollution and globalwarming, Successful composting of MSW is also practiced in a few pockets in some cities ofIndia but due to low compost quality, the market is very low. Moreover, the Government ofIndia is detennined to promote only composting technology for treatment of MSW as per therecommendation of Hon'ble Supreme Court of India based on the views from ExpertCommittee.MSW generally has a considerable fraction of biodegradable materials, even though theproportions vary from region to region. Biotreatment has, for many decades, been thepreferred method for effectively treating the biodegradable waste, Composting is an ancient,environmental friendly and globally recognized method of biotreatment and bioprocessing ofMSW, Composting generates a recycled organic product and minimizes the waste quantityleft for disposal, thereby reducing the demand for landfill sites, Different fractions of thebiodegradable organic components eventually mineralize to CO2 and H20 at different rates,The present paper describes the theoretical and experimental estimation of CO2 emission fromcomposting of MSW in India, which gives tentative estimation of carbon credit fromcomposting of MSW,

Two-Stage Automated Operational Modal Analysis Based on Power Spectrum Density Transmissibility and Support-Vector Machines

2021 ◽  
pp. 2150068
Author(s):  
Zhi-Wei Chen ◽  
Kui-Ming Liu ◽  
Wang-Ji Yan ◽  
Jian-Lin Zhang ◽  
Wei-Xin Ren
Keyword(s):  
Support Vector Machines ◽  
Power Spectrum ◽  
Modal Analysis ◽  
Power Spectrum Density ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Identification ◽  
Two Stage ◽  
Vector Machines ◽  
Spectrum Density

Power spectrum density transmissibility (PSDT) functions have attracted widespread attention in operational modal analysis (OMA) because of their robustness to excitations. However, the selection of the peaks and stability axes are still subjective and requires further investigation. To this end, this study took advantage of PSDT functions and support-vector machines (SVMs) to propose a two-stage automated modal identification method. In the first stage, the automated identification of peaks is achieved by introducing the peak slope (PS) as a critical index and determining its threshold using the SVM classifier. In the second stage, the automated identification of the stability axis is achieved by introducing the relative difference coefficients (RDCs) of the modal parameters as indicators and determining their thresholds using the SVM classifier. To verify its feasibility and accuracy, the proposed method was applied to an ASCE-benchmark structure in the laboratory and in a high-rise building installed with a structural health monitoring system (SHMS). The results showed that the automated identification method could effectively eliminate spurious modes and accurately identify the closely spaced modes. The proposed method can be automatically applied without manual intervention, and it is robust to noise. It is promising for application to the real-time condition evaluation of civil structures installed with SHMSs.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation

2021 ◽  
pp. 0734242X2110085
Author(s):  
Majeed S Jassim ◽  
Gulnur Coskuner ◽  
Metin Zontul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Support Vector Regression ◽  
Kernel Functions ◽  
Management Systems ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997–2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Robust multimodal biometric authentication algorithms using fingerprint, iris and voice features fusion

2021 ◽  
Vol 40 (1) ◽  
pp. 647-672
Author(s):  
Mohamed S. El_Tokhy
Keyword(s):  
Feature Fusion ◽  
Boundary Energy ◽  
Roc Curves ◽  
Point Of View ◽  
Support Vector ◽  
Svm Classifier ◽  
Biometric Authentication ◽  
Classification Rate ◽  
Computational Point ◽  
Practical Applications

Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications.

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