scholarly journals Analysis on the Effectiveness of the Input in Household Waste Classification of Residents—Taking S City in China as an Example

2021 ◽  
Vol 13 (21) ◽  
pp. 11632
Author(s):  
Yangyang Zhang ◽  
Wenfang Huang
Keyword(s):  
Classification System ◽  
Classification Model ◽  
Household Waste ◽  
Environmental Effectiveness ◽  
Waste Classification ◽  
Classification Pattern ◽  
The Government ◽  
Questionnaire Surveys ◽  
Marginal Effectiveness

S city in China has implemented a waste classification system and constructed a waste classification model with government-led market and public participation. In order to explore the effectiveness of waste classification input in S city, this paper conducts analyses from the points of view of the classification facility’s construction, environmental effectiveness, social acceptability and operation sustainability, based on interviews with and questionnaire surveys completed by related parties. The results show that the current waste classification facility system in S city is basically completed; high rates of both properties and residents comply with the waste classification system. S city has established a government-led waste classification pattern that depends on social participation. This pattern has been recognized and accepted by residents and is economically sustainable. At the same time, it is pointed out that the current marginal effectiveness of the waste classification input is showing a declining trend. Future investment should shift from investment in facilities and equipment to incentives for autonomous management by residents, and the corresponding evaluation of investment and effectiveness should also change accordingly. This requires the government to guide the refined management system.

Some Legal Considerations on the Mandatory Obligation of Household Waste Classification: A Case Study of Xiamen City

2012 ◽  
Vol 524-527 ◽  
pp. 2646-2649
Author(s):  
Xiao Qin Zhu ◽  
Jin Long He ◽  
Wei Lin Li
Keyword(s):  
Household Waste ◽  
Local Rule ◽  
Waste Forms ◽  
Xiamen City ◽  
Waste Classification ◽  
Recycle And Reuse

The mandatory classification of household waste forms the foundation and prerequisite for the waste recycle and reuse. This paper takes Xiamen’s local rule on the municipal household waste as a positivistic case, analyzes the results of the provision stipulating the mandatory obligation of household waste classification and the causes, and proposes relevant advices to improve the situation.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals CNN-Based Acoustic Scene Classification System

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 371
Author(s):  
Yerin Lee ◽  
Soyoung Lim ◽  
Il-Youp Kwak
Keyword(s):  
Low Complexity ◽  
Classification Model ◽  
Weight Class ◽  
Scene Classification ◽  
Audio File ◽  
General Classification ◽  
Team Task ◽  
Real World Applications ◽  
Activation Mapping

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.

scholarly journals Perceived Environmental Responsibilities and Green Buying Behavior: The Mediating Effect of Attitude

2020 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Guang-Wen Zheng ◽  
Abu Bakkar Siddik ◽  
Mohammad Masukujjaman ◽  
Syed Shah Alam ◽  
Alvina Akter
Keyword(s):  
Structural Equation ◽  
Environmental Concern ◽  
Mediating Effect ◽  
Primary Data ◽  
Equation Modeling ◽  
Buying Behavior ◽  
Exploratory Testing ◽  
Green Business ◽  
The Government ◽  
Questionnaire Surveys

The unsustainable operations of producers account for significant carbon emission and subsequent adverse impacts on nature. This study aims to identify the factors that influence consumers’ green buying behavior. The research focuses on the exploratory testing of theories using standardized questionnaires and interviews. Using a convenience selection approach, questionnaire surveys were used to gather primary data from a sample size of 305. The sample demographic reflects people who often make purchases; data were also obtained from shopping centers and elsewhere. The hypothesis testing of variables measured via five-point Likert scale questions was performed using structural equation modeling. We applied closed-ended questions relating to green buying behavior for the convenience of respondents. The empirical result established the effects of attitude, perceived severity of environmental problems, environmental concern, and subjective norms on Bangladeshi consumers’ green buying behavior. Additionally, it was discovered that attitude mediates the association between the perceived environmental responsibility and green buying behavior. Therefore, the government should play a constructive role in educating the public and promoting green business initiatives through improved coordination and legislative intervention.

scholarly journals UV Spectral Classification of B Stars

1985 ◽  
Vol 111 ◽  
pp. 411-413
Author(s):  
Janet Rountree ◽  
George Sonneborn ◽  
Robert J. Panek
Keyword(s):  
Classification System ◽  
Classification Criteria ◽  
High Dispersion ◽  
Spectral Classification ◽  
Early Type ◽  
B Stars ◽  
Dimensional Classification ◽  
Early Type Stars ◽  
Comprehensive Classification

Previous studies of ultraviolet spectral classification have been insufficient to establish a comprehensive classification system for ultraviolet spectra of early-type stars because of inadequate spectral resolution. We have initiated a new study of ultraviolet spectral classification of B stars using high-dispersion IUE archival data. High-dispersion SWP spectra of MK standards and other B stars are retrieved from the IUE archives and numerically degraded to a uniform resolution of 0.25 or 0.50 Å. The spectra (in the form of plots or photowrites) are then visually examined with the aim of setting up a two-dimensional classification matrix. We follow the method used to create the MK classification system for visual spectra. The purpose of this work is to examine the applicability of the MK system (and in particular, the set of standard stars) in the ultraviolet, and to establish classification criteria in this spectral region.

System for Cross-Classification of Small Group Studies

1968 ◽  
Vol 23 (3_suppl) ◽  
pp. 1295-1304
Author(s):  
Richard Heslin ◽  
Dexter Dunphy
Keyword(s):  
Small Group ◽  
Classification System ◽  
Sufficient Information ◽  
Coding System ◽  
Similar System ◽  
Cross Classification

The article describes a method for placing information into a classification system that maximizes the flexibility in retrieval at a later time. It uses (1) a stack of edge-punched cards containing information of interest that has been punched according to (2) a coding system developed by the users. The authors have developed a classification system for the small-group field which is depicted and described in detail. It allows for coding a study on about 50 dimensions to (1) locate an article, (2) give an over-all description of the article, and (3) indicate the variables measured or discussed. Examples of uses and discussion of special features are provided to give the reader sufficient information to establish and use the system or a similar system for his own purposes.

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