scholarly journals Solid waste classification using pyramid scene parsing network segmentation and combined features

2021 ◽  
Vol 19 (6) ◽  
pp. 1902
Author(s):  
Khadijah Khadijah ◽  
Sukmawati Nur Endah ◽  
Retno Kusumaningrum ◽  
Rismiyati Rismiyati ◽  
Priyo Sidik Sasongko ◽  
...  
Keyword(s):  
Solid Waste ◽  
Scene Parsing ◽  
Waste Classification ◽  
Combined Features

Investigation on decision-making mechanism of residents’ household solid waste classification and recycling behaviors

2019 ◽  
Vol 140 ◽  
pp. 224-234 ◽  
Author(s):  
Xiaoyan Meng ◽  
Xianchun Tan ◽  
Yi Wang ◽  
Zongguo Wen ◽  
Yuan Tao ◽  
...  
Keyword(s):  
Decision Making ◽  
Solid Waste ◽  
Household Solid Waste ◽  
Waste Classification

scholarly journals Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Leow Wei Qin ◽  
Muneer Ahmad ◽  
Ihsan Ali ◽  
Rafia Mumtaz ◽  
Syed Mohammad Hassan Zaidi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solid Waste ◽  
Precision Measurement ◽  
Mobile Applications ◽  
Industry 4.0 ◽  
Learning Model ◽  
Edge Computing ◽  
Support Vector ◽  
Waste Classification ◽  
Deep Learning Model

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

THE REALITY OF MEDICAL SOLID WASTE COLLECTION, TRANSPORTATION, STORAGE AND DISPOSAL AT THU DUC DISTRICT HOSPITAL, HO CHI MINH CITY IN 2019

2021 ◽  
Vol 62 (5) ◽  
Author(s):  
Phan Thi Thu Trang ◽  
Vo Van Hai ◽  
Le Thi Thanh Huong
Keyword(s):  
Waste Management ◽  
Solid Waste ◽  
Cross Section ◽  
Solid Waste Management ◽  
District Hospital ◽  
Ho Chi Minh City ◽  
Waste Collection ◽  
Solid Waste Collection ◽  
Descriptive Research ◽  
Waste Classification

There are 11 rooms and 38 departments with 1791 staffs in Thu Duc District Hospital. Moreover, there are above 6000 patients coming for examination and approximately over 900 in-patients at the hospital. Although the medical solid waste management was concentrated and focused on, there is still some insufficient points that need to be researched. Therefore, we are now proceeding in researchingour aim to describe the reality of medical solid waste management at Thu Duc District Hospital in 2019 and evaluate the factor that affects the waste management. The method for this research is cross-section descriptive research. We have observed all tools, equipments used for managing medical solid waste, and observed the medical solid waste classification in 36 departments/ clinical units, paraclinical, also have used designed inventory. The ratio of the department that meet thegoals in general of collecting, transporting, storage and waste disposal is not really high, only 75%, in which that the ratio for collecting by department only is lowest with 77.8%; next for collecting the waste by equipments with 80.6%; for waste classification by equipments is 83.3%.

scholarly journals Development of municipal solid waste classification in Korea based on fossil carbon fraction

2015 ◽  
Vol 65 (10) ◽  
pp. 1256-1260 ◽  
Author(s):  
Jeongwoo Lee ◽  
Seongmin Kang ◽  
Seungjin Kim ◽  
Ki-Hyun Kim ◽  
Eui-Chan Jeon
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Carbon Fraction ◽  
Fossil Carbon ◽  
Waste Classification

Application of Video Capture Theory in Municipal Solid Waste Classification

2014 ◽  
Vol 908 ◽  
pp. 408-411 ◽  
Author(s):  
Ping Cai ◽  
Kun Wang ◽  
Song Tao Kong ◽  
Jie Li
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Video Capture ◽  
Waste Classification ◽  
Capture Theory

According to disposal situation of municipal solid waste (MSW) in China, the method of video capture was used to identify MSW, and classify the recyclable waste and non-recyclable waste. Verifying experiments were carried on by Plastic bottles in MWS. The results showed that, proposed method can identify MSW accurately.

scholarly journals Determination of As and Hg in acetic acid extract by vapor generation coupled to atomic spectrometry for solid waste classification

2013 ◽  
Vol 106 ◽  
pp. 139-146 ◽  
Author(s):  
Tatiane de A. Maranhão ◽  
Jessee S.A. Silva ◽  
Rodolpho M. de Andrade ◽  
Vera L.A.F. Bascuñan ◽  
Fernando J.S. de Oliveira ◽  
...  
Keyword(s):  
Acetic Acid ◽  
Solid Waste ◽  
Atomic Spectrometry ◽  
Acid Extract ◽  
Vapor Generation ◽  
Waste Classification

scholarly journals Factors Influencing Household’s Solid Waste Classification Management

2020 ◽  
Vol 8 (5) ◽  
pp. 498-506
Author(s):  
Keneth Ibrahim Sanga ◽  
Dong Ying ◽  
Lu Huan
Keyword(s):  
Waste Management ◽  
Solid Waste ◽  
Solid Waste Management ◽  
Municipal Solid Waste Management ◽  
Environmental Attitude ◽  
Comprehensive Management ◽  
Household Solid Waste ◽  
Factors Influencing ◽  
Waste Classification ◽  
Classification Management

Households’ contribution in solid waste classification and recycling of municipal household solid waste is one of the essential factors influencing municipal solid waste management. We identify and investigate the factors influencing solid waste management classification as well as mechanism used by households to dispose waste in their communities. Based on the survey data of households in Hangzhou China in three different communities, factors that affect household disposal and their degree of influence have been analyzed, followed by discussion on decision-making mechanisms. The results show that household behavioral selection has been linked to several key factors which are Environmental attitude, knowledge and education, classification facilities and services, and policies. The research has also determined to reveal the response of the people to solid waste classification, and the combined effect of the factors is almost twice that of the former. In addition, environmentally friendly facilities and programs are most successful in encouraging the involvement of household in identification and recycling. Finally, the research put forward applicable policy suggestions for the comprehensive management of municipal Household Solid Waste classification and recycling.

Solid Waste Classification Using Deep Learning Techniques

2021 ◽  
Author(s):  
Abdulfattah E. Ba Alawi ◽  
Ahmed Y. A. Saeed ◽  
Fatima Almashhor ◽  
Reem Al-Shathely ◽  
Ahmed N. Hassan
Keyword(s):  
Deep Learning ◽  
Solid Waste ◽  
Learning Techniques ◽  
Waste Classification

Analysis of the Barrier Factors of Municipal Solid Waste Classification Recycling

2013 ◽  
Vol 726-731 ◽  
pp. 2618-2621 ◽  
Author(s):  
Yong Chun Wu ◽  
Lian Feng Xu
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Public Awareness ◽  
Waste Reduction ◽  
Scientific Management ◽  
Waste Classification ◽  
Theoretical Issues ◽  
Laws And Regulations ◽  
Recycling Facilities

Many cities in China are facing a "junk siege" dilemma, municipal solid waste classified recycling is one of the effective disposal methods of garbage scientific management, and is also the best way to waste reduction, recycling and harmless, However our municipal solid waste classification recycling remain stagnant, This paper argues that it is because of many obstacles that municipal solid waste classification recycling face makes municipal solid waste classification recycling have little effect, which include public awareness needs to be improved, the limitations of traditional garbage classification, lack of corresponding laws and regulations, garbage recycling facilities not complete and the corresponding theoretical issues remain to be explored etc.

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