Proposal of a Monitoring System to Determine the Possibility of Contact with Confirmed Infectious Diseases Using K-means Clustering Algorithm and Deep Learning Based Crowd Counting

2020 ◽  
Vol 9 (3) ◽  
pp. 122-129
Author(s):  
Dongsu Lee ◽  
ASHIQUZZAMAN A K M ◽  
Yeonggwang Kim ◽  
혜주 신 ◽  
Jinsul Kim
Keyword(s):  
Deep Learning ◽  
Infectious Diseases ◽  
Monitoring System ◽  
Clustering Algorithm ◽  
Crowd Counting

scholarly journals Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1161
Author(s):  
Kuo-Hao Fanchiang ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Monitoring System ◽  
Absolute Difference ◽  
Generative Adversarial Networks ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Full Time ◽  
Noise Interference ◽  
In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

scholarly journals The Global Infectious Diseases Epidemic Information Monitoring System—a tool for travel health management (Preprint)

2020 ◽  
Author(s):  
Ruiling Dong ◽  
Jianan He ◽  
Jie Sun ◽  
Xin Shi ◽  
Ying Ye ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Monitoring System ◽  
Language Processing ◽  
Health Management ◽  
Health Staff ◽  
Learning Technology ◽  
Web Crawler ◽  
Travel Health ◽  
Information Monitoring

BACKGROUND Obtaining comprehensive epidemic information for the targeted global infection disease is crucial for travel health. However, different infectious disease information websites may have various purposes which may lead misunderstanding for travelers and travel health staff for the accurate epidemic control and managmement. OBJECTIVE Developed A Global Infectious Diseases Epidemic Information Monitoring System (GIDEIMS)，in order to get comprehensive and timely global epidemic information. METHODS Distributed web crawler and cloud agent acceleration technology are used to automatically collect epidemic information for more than 200 infectious diseases from 26 established epidemic websites and Baidu news. Natural language processing and in-depth learning technology have been developed to intelligently process epidemic information collected in 28 languages. Currently, the GIDEIMS presents world epidemic information using a geographical map, including date, disease name and reported cases of different countries , epidemic situations in China, etc. RESULTS In order to make a practical assessment of the GIDEIMS, on July 16, 2019, We checked infectious disease data collected from GIDEIMS and other websites. Compared with the Global Incident Map and Outbreak News Today, GIDEIMS provided more comprehensive information on human infectious diseases. GIDEIMS is currently used in the Health Quarantine Department of Shenzhen Customs District (Shenzhen, China), and is recommended to the Health Quarantine Administrative Department of the General Administration of Customs (China) and travel health-related departments. CONCLUSIONS GIDEIMS provides a helpful tool for travelers and travel health management staff with travel health management.

scholarly journals A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data

2019 ◽  
Author(s):  
Suhas Srinivasan ◽  
Nathan T. Johnson ◽  
Dmitry Korkin
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Feature Learning ◽  
Specific Cell ◽  
Clustering Methods ◽  
Model Based Clustering ◽  
Clustering And Classification ◽  
Living Organisms

AbstractSingle-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. The method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

scholarly journals Segmentation of Affected Crops using Deep Learning

2019 ◽  
Vol 8 (5S3) ◽  
pp. 232-234
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Target Area ◽  
Learning Technology ◽  
Machine Learning Algorithm ◽  
Normal Human ◽  
Human Monitoring ◽  
Disease Attack

Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

Crowd Counting Using Deep Learning in Edge Devices

2021 ◽  
Author(s):  
Zuo Huang ◽  
Richard Sinnott ◽  
Qiuhong Ke
Keyword(s):  
Deep Learning ◽  
Crowd Counting

A Vision-Based Hygiene Monitoring System: Using Deep Learning to Assess Handwashing Procedure in Real Time

2019 ◽  
Author(s):  
Maria Galkin ◽  
Kashmala Rehman ◽  
Benjamin Schornstein ◽  
Warren Sunada-Wong ◽  
Harvey Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Monitoring System
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