scholarly journals Cable Temperature Alarm Threshold Setting Method Based on Convolutional Neural Network

2022 ◽  
Vol 2160 (1) ◽  
pp. 012076
Author(s):  
Lei Wang ◽  
Lin Niu ◽  
Xingwang He ◽  
Meng Guan ◽  
Hongbo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Power Grid ◽  
Power Cable ◽  
Power Network ◽  
Alarm Threshold ◽  
Normal Production ◽  
Threshold Setting ◽  
The City ◽  
Safety And Stability

Abstract Power cable is used more and more in the power network, and its significance to the safety and stability of the power network is increasingly prominent. Especially in the urban power grid, the high voltage cable is related to the normal production and life of the city. Because of the particularity of the laying environment, it is very difficult to find and eliminate the fault points once the cable faults occur, which seriously affects the reliability of the power grid. Currently, 25% of cable faults are caused by elevated cable temperature, so it is important to set the cable temperature alarm threshold accurately. In this paper, a method of setting temperature alarm threshold using convolutional neural network is proposed. Experiments show that this method is 60% more accurate than other methods.

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 Dog recognition in public places based on convolutional neural network

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771982967
Author(s):  
Jianquan Ouyang ◽  
Hao He ◽  
Yi He ◽  
Huanrong Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Public Places ◽  
Invariant Feature ◽  
Channel Information ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The City

With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%.

scholarly journals Research on Power Network Data Management Based on Convolutional Neural Network

2021 ◽  
Vol 1748 ◽  
pp. 032061
Author(s):  
Ruifeng Zhao ◽  
Bo Li ◽  
Wenxin Guo ◽  
Jiangang Lu ◽  
Shiming Li
Keyword(s):  
Neural Network ◽  
Data Management ◽  
Convolutional Neural Network ◽  
Network Data ◽  
Power Network

scholarly journals DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

2018 ◽  
Vol XLII-2 ◽  
pp. 893-900 ◽  
Author(s):  
R. Pierdicca ◽  
E. S. Malinverni ◽  
F. Piccinini ◽  
M. Paolanti ◽  
A. Felicetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Photovoltaic Cells ◽  
Automatic Detection ◽  
Thermal Infrared ◽  
The Novel ◽  
Infrared Sensor ◽  
Novel Approach ◽  
Safety And Stability ◽  
Degradation Problem

The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

scholarly journals Shop Weatherly – A Weather based Smart E-Commerce System Using CNN

2021 ◽  
Vol 11 (4) ◽  
pp. 2785-2800
Author(s):  
Jawaria Sallar ◽  
Sallar Khan ◽  
Shariq Ahmed ◽  
Parshan Kumar ◽  
Hasham Faridy ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Customer Satisfaction ◽  
Convolutional Neural Network ◽  
Online Shopping ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Free Environment ◽  
The City

In this current era of modern online shopping, people want to spend as little time as possible when it comes to buying products, therefore they prefer online shopping. People go shopping when the weather gets changed. For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather. Even when people want to gift clothes to someone living in another country there is no such platform that gives recommendation of clothes according to that city's weather. They usually face problems when they want to buy weather-based products from various E-commerce platforms where they see mixed clothes of all types of weather which is very time-consuming, they become so confused most of the time that they think about whether they should buy or not. In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according to the city weather which provides hassle-free environment eventually saves customer's time thereby increasing customer satisfaction.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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