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Optimal Productivity in Solar Power Plants Based on Machine Learning and Engineering Management [chapter]

Álvaro Huerta Herraiz, Alberto Pliego Marugán, Fausto Pedro García Márquez
<span title="2018-06-26">2018</span> <i title="Springer International Publishing"> Proceedings of the Twelfth International Conference on Management Science and Engineering Management </i> &nbsp;
This paper proposes a recurrent convolutional neural network algorithm for detecting failures, reducing the costs and the time of the inspections.  ...  The complexity of solar power plants is constantly increasing.  ...  Acknowledgements The work reported herewith has been financially supported by the Spanish Ministerio de Economía y Competitividad, under the Research Grant RTC-2016-5694-3.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-93351-1_77">doi:10.1007/978-3-319-93351-1_77</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tov3xbwfgjfg7gqduofnpinx3e">fatcat:tov3xbwfgjfg7gqduofnpinx3e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200309065203/https://ruidera.uclm.es/xmlui/bitstream/handle/10578/18943/hecho_201805c.pdf;jsessionid=29D1F0A8AC3302C12D4AA2840F4E8862?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/79/0a/790a40c83dd8d6ba08926667a1f8359a599db8ec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-93351-1_77"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A real-time video smoke detection algorithm based on Kalman filter and CNN

Alessio Gagliardi, Francesco de Gioia, Sergio Saponara
<span title="2021-03-19">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ihxuyfsqpjeoxn5hsmj4p7gmpy" style="color: black;">Journal of Real-Time Image Processing</a> </i> &nbsp;
In the final step the convolutional neural network verifies the actual presence of smoke in the proposed regions of interest.  ...  The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network.  ...  We can compare our neural network with the network designed in [18] on the same image dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11554-021-01094-y">doi:10.1007/s11554-021-01094-y</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/of73dd7hynfabj6er5sv2hschy">fatcat:of73dd7hynfabj6er5sv2hschy</a> </span>
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Paper titles

<span title="2020-09-28">2020</span> <i title="IEEE"> 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) </i> &nbsp;
in Images by Fully Convolutional Network with Bounding Text Labeling Long-distance FBG Sensor System for Remote Sensing and Internet of Things (IoT) applications Low-Complexity Neural Network Design  ...  Engagement Detection and Customer Attribute Estimation with Edge Device Reconfigurable MAC Systolic Array Architecture Design for Three-Dimensional Convolution Neural Network Reduce of convolutional neural  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icce-taiwan49838.2020.9258179">doi:10.1109/icce-taiwan49838.2020.9258179</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2eheaztzhncixhbvp7nrbzml4m">fatcat:2eheaztzhncixhbvp7nrbzml4m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428234020/https://ieeexplore.ieee.org/ielx7/9257985/9257988/09258179.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f8/b0/f8b057e92c869e804f28adb7fed378fee868e1b5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icce-taiwan49838.2020.9258179"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network

Usha Mittal, Sonal Srivastava, Priyanka Chawla
<span title="2019-07-01">2019</span> <i title="Science Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wake4w3hqzd4ndiy2zowpciv64" style="color: black;">Journal of Computer Science</a> </i> &nbsp;
In this paper, a comparative analysis has been done by applying Faster region based convolutional neural network on thermal images and visual spectrum images.  ...  In this paper, thermal images are used as thermal cameras capture the image by using the heat generated by the objects.  ...  The various deep learning approaches for object detection are: Region proposal Convolutional neural network , fast Region proposal Convolutional neural network (Girshick, 2015) , Faster Region proposal  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3844/jcssp.2019.961.971">doi:10.3844/jcssp.2019.961.971</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3bh47xh2y5e5hntadbc3km2eia">fatcat:3bh47xh2y5e5hntadbc3km2eia</a> </span>
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Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

Sergio Bemposta Rosende, Javier Sánchez-Soriano, Carlos Quiterio Gómez Muñoz, Javier Fernández Andrés
<span title="2020-11-01">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a2yvk5xhdnhpxjnk6yd33uudqq" style="color: black;">Energies</a> </i> &nbsp;
This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking.  ...  Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants.  ...  Acknowledgments: We thank Luis Pessini Pinilla for helping to develop system software and hardware. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/en13215712">doi:10.3390/en13215712</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6oytaqjc4rdldgjnrqq5evqpvu">fatcat:6oytaqjc4rdldgjnrqq5evqpvu</a> </span>
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ICIIS 2020 Table of Contents

<span title="2020-11-26">2020</span> <i title="IEEE"> 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) </i> &nbsp;
Detection and Severity Estimation Radhika Wadhawan Mayyank Garg Dr Ashish Kumar Sahani 455-459 83 83_ID1570670350 Empirical Study on Multi Convolutional Layer-based Convolutional Neural Network  ...  Classifier for Plant Leaf Disease Detection Sunil C K Jaidhar C D Nagamma Patil 460-465 84 84_ID1570674974 Handling of Class Imbalance for Plant Disease Classification with Variants of GANs  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iciis51140.2020.9342668">doi:10.1109/iciis51140.2020.9342668</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/46hhq64kabfwrn7itctd77ze2i">fatcat:46hhq64kabfwrn7itctd77ze2i</a> </span>
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Special issue on video and imaging systems for critical engineering applications [SI 1096]

Gwanggil Jeon, Awais Ahmad, Abdellah Chehri, Salvatore Cuomo
<span title="2020-01-24">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a> </i> &nbsp;
For instance, the complex nature of mobile and video imaging need radical version to the swift changes in our society, economy, environment, and technological revolution; there is an utmost need to address  ...  Moreover, artificial neural network when combined with pattern recognition techniques, such  ...  Acknowledgments We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-020-08672-5">doi:10.1007/s11042-020-08672-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dmusbepcancb5i6jqo7hhf6a2m">fatcat:dmusbepcancb5i6jqo7hhf6a2m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200510092914/https://link.springer.com/content/pdf/10.1007/s11042-020-08672-5.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/be/e0be0a1678e2e514c3280953bcc214696009ca79.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-020-08672-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis

Zbigniew Omiotek, Andrzej Kotyra
<span title="2021-01-12">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states.  ...  It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems.  ...  images of a gas burner DCNN 0.98 - [2] Detection of the stable, semi-stable and unstable combustion status in a thermal power plant DCAE+PCA+HMM 0.97 - [10] DCNN Conclusions A flame contour segmentation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21020500">doi:10.3390/s21020500</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33445635">pmid:33445635</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s25glaw2v5aa3czg3h47vkxcve">fatcat:s25glaw2v5aa3czg3h47vkxcve</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210116023110/https://res.mdpi.com/d_attachment/sensors/sensors-21-00500/article_deploy/sensors-21-00500-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ed/a3/eda3e048e5f95d243ab0677a2e5229b219f64b9e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21020500"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

2020 Index IEEE Transactions on Image Processing Vol. 29

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
., +, TIP 2020 5900-5911 Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC.  ...  ., +, TIP 2020 9678-9688 Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.3046056">doi:10.1109/tip.2020.3046056</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/24m6k2elprf2nfmucbjzhvzk3m">fatcat:24m6k2elprf2nfmucbjzhvzk3m</a> </span>
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Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

Parag Narkhede, Rahee Walambe, Shruti Mandaokar, Pulkit Chandel, Ketan Kotecha, George Ghinea
<span title="2021-01-09">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/erlv6evlfncqnbljl5rakvw4yq" style="color: black;">Applied System Innovation</a> </i> &nbsp;
In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques.  ...  We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model).  ...  Convolutional Neural Network Each Thermal image consists of non-linear features and are stored digitally in RGB format. Simple Neural Networks are not able to generalize complex patterns in images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/asi4010003">doi:10.3390/asi4010003</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/km7t36n5zrb77occvfke3kdpiy">fatcat:km7t36n5zrb77occvfke3kdpiy</a> </span>
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Artificial Intelligence-Based Detection System for Hazardous Liquid Metal Fire.pdf [article]

ganesh E N
<span title="2022-06-13">2022</span> <i title="figshare"> figshare.com </i> &nbsp;
In this paper, the conventional system is integrated with AI to identify indoor and open-air fire situations.  ...  Continuous monitoring and timely detection of risk reduce the impact caused by the leakage of liquid metal. At present, industries have sensors-based detection.  ...  For example, in the year 1995, at the Monju Nuclear Power Plant, Fig. 1 a Thermowell inside the pipe carrying sodium coolant broke due to the intense vibration.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.20061125.v1">doi:10.6084/m9.figshare.20061125.v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qup23itmrvbq3io3ewfyzjczza">fatcat:qup23itmrvbq3io3ewfyzjczza</a> </span>
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ICECA 2020 TOC

<span title="2020-11-05">2020</span> <i title="IEEE"> 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) </i> &nbsp;
Deepa 200 Convolutional Long Short Term Memory Model for Credit Card Detection G. K. Arun, K.  ...  Taki ElDin, Wassim Alexan, Ahmed El-Rafei 159 Automatic Classification of Human Gender using X-ray images with Fuzzy C means and Convolution Neural Network Kalyan Chakravarthy.G, P.Neelakanteswara  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iceca49313.2020.9297417">doi:10.1109/iceca49313.2020.9297417</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u35bireaknf5zljg2czdn2yaiu">fatcat:u35bireaknf5zljg2czdn2yaiu</a> </span>
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Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

Sergey Alekseevich Korchagin, Sergey Timurovich Gataullin, Aleksey Viktorovich Osipov, Mikhail Viktorovich Smirnov, Stanislav Vadimovich Suvorov, Denis Vladimirovich Serdechnyi, Konstantin Vladimirovich Bublikov
<span title="2021-09-30">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yws6xlpwzjf7llvhkn3wayuvfu" style="color: black;">Agronomy</a> </i> &nbsp;
Otherwise, Otsu's threshold binarization—a convolutional neural network (CNN) method is used.  ...  In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used.  ...  Acknowledgments: The authors are grateful to the Dean of the Faculty of Information Technologies and Big Data Analysis, Vladimir Soloviev, for a helpful discussion of this work and for comments on this  ... 
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Table of Contents

<span title="">2020</span> <i title="IEEE"> 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) </i> &nbsp;
text Formation Based on Knowledge Point Association Analysis and Fuzzy Clustering Yucheng Yang, Yibo Yue 385 65 Disease Detection in Plants using Convolutional Neural Network Sharath D M, S Arun  ...  Kishore 926 159 Pneumonia Detection Using Convolutional Neural Networks Shanay Shah, Heeket Mehta, Pankaj Sonawane 933 160 CNN based Hate-o-Meter: A Hate Speech Detecting Tool Ajinkya Chaudhari, Akshay  ... 
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A Deep Learning Approach Towards Railway Safety Risk Assessment

Hamad Alawad, Sakdirat Kaewunruen, Min An
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a  ...  INDEX TERMS Artificial intelligence, convolutional neural network, deep learning, railway station, risk management.  ...  In this study, we explore DL by utilising a convolutional neural network (CNN) to detect passenger falls.  ... 
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