The triple histogram method for waste classification

2020 ◽  
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek
Keyword(s):  
Histogram Method ◽  
Waste Classification

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.

Investigation on the Noise Separation in Watt Balance Experiments

2008 ◽  
Vol 381-382 ◽  
pp. 619-622
Author(s):  
W. Zeng ◽  
Xiang Qian Jiang ◽  
P. Scott ◽  
L. Blunt
Keyword(s):  
Background Noise ◽  
International System ◽  
Histogram Method ◽  
Stationary Noise ◽  
Vibration Data ◽  
System Of Units ◽  
Environmental Vibration ◽  
Transient Events ◽  
Watt Balance ◽  
Definition Of

The detection of stationary and non-stationary noise in environmental vibration data is an important issue when considering the precision of the Watt balance, an electromechanical apparatus for the new definition of the kilogram in the international system of Units (SI). In this paper, the authors propose a frequency histogram method to find the structure of the stationary noise from large amount of datasets. For the non-stationary noise, the authors propose a wavelet based denoising methods to distinguish the transient events from the background “noise”, to find their duration and content and to identify their location in time.

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