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Autonomous Inspection Method of UHV Substation Robot Based on deep learning in Cloud Computing Environment

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第5期33卷 2450088-2450088页

作者： Wang, Zhao-lei Meng, Rong Zhao, Zhi-long State Grid Hebei Extra High Voltage Co Shijiazhuang 050070 Heibei Peoples R China

Aiming at the problem that substation robots cannot automatically find and analyze the fault equipment and carry out patrol inspection, a method of autonomous patrol inspection for ultra-high voltage (UHV) substation robots based on deep learning in cloud computing environment is proposed. Firstly, based on the cloud computing environment, an autonomous patrol system is designed to upload the data obtained by robots to the cloud platform for processing, so as to complete data analysis quickly and with high quality. Then, the deep learning algorithm (DL) is used for fault diagnosis, and the FP-growth algorithm is combined to realize the association mining of fault data, so as to clarify the patrol order of fault-related nodes. Finally, the improved ant colony algorithm (IAC) is used to optimize the path of the robot to complete the reliable inspection of the substation in the shortest time. Based on the selected UHV substation, the experimental analysis of the proposed method shows that the fault diagnosis error rate and time are about 4.3% and 14.2s, respectively, and the patrol path is only 180.351m, and the patrol time is 19.708s, which can realize the optimal patrol of the robot.

关键词： Cloud computing environment UHV substation robot fault diagnosis association mining self-inspection improved ant colony algorithm deep learning algorithm

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Smart-Monitor: Patient Monitoring System for IoT-Based Healthcare System Using deep learning

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IETE JOURNAL OF RESEARCH 2022年第2期68卷 1435-1442页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Autonomous Dept Elect & Elect Engn Sivakasi 626005 India

Automated physiological signal monitoring to elderly sick patient is not only for fast access of data but also to get reliable service by accurate prediction by healthcare service provider. To address this challenge, this research focuses on novel Internet of Things (IoT) application-based physiological signal monitoring system to advance e-healthcare system. For the realization of the proposed system, deep Neural Network-based accurate Signal Prediction and estimation algorithm was employed. The proposed system is prototyped as an advanced electronics component by using an intelligent sensor for signal measurement, National Instrument myRIO for smart data acquisition. Smart-Monitor is designed with intelligent sensor as the consumer product. To validate the proposed Smart-Monitor system, four physiological signal prediction accuracies for two users were computed. In prototype experimental set-up, an average accuracy of 97.2% was obtained. This shows that the proposed automated system is reliable and accurate monitoring is possible. From the experimental result, we validate the proposed system can provide reliable assist and accurate signal prediction.

关键词： Internet of Things deep learning algorithm Elderly care Smart healthcare patient monitoring

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Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

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TEXTILE RESEARCH JOURNAL 2020年第9-10期90卷 971-980页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 Tamil Nadu India

This research paper focuses on the innovative detection of defects in fabric. This approach is based on the design and development of a computer-assisted system using the deep learning technique. The classification network is modeled using the ResNet512-based Convolutional Neural Network to learn the deep features in the presented fabric. Being an accurate method, this enables accurate localization of minute defects too. Our classification is based on three major steps;firstly, an image acquired by the NI Vision model and pre-processed for a standard pattern to Kullback Leibler Divergence calculation. Secondly, standard textile fabrics are presented to train the Convolutional Neural Network to classify the defective region and the defect-free region. Finally, the testing fabrics are examined by the trained deep Convolutional Neural Network algorithm. To verify the performance, multiple fabrics are presented and the classification accuracy is evaluated. For standard defects on defective fabrics, an average accuracy of 96.5% with 98.5% precision is obtained. Experimental results on the standard Textile Texture Database dataset confirmed that our method provides better results compared with similar recent classification methods, such as the Support Vector Machine and Bayesian classifier.

关键词： deep learning algorithm fabric inspection defect classification Convolution Neural Network

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A simple deep learning based image illumination correction method for paintings

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PATTERN RECOGNITION LETTERS 2020年 138卷 392-396页

作者： Goswami, Suranjan Singh, Satish Kumar IIIT Allahabad IT Allahabad 211015 Uttar Pradesh India

Image illumination correction has been a long standing topic for research in the Computer Vision problem. However, all previous literature on this topic has either been statistical in nature in the sense that a specified algorithm has been developed for approaching a particular case of illumination normalization, or involves extremely complex deep learning methods for illumination correction of either one of over illuminated or under illuminated images. We present here a very simple deep learning based image illumination correction architecture which works on color images of paintings irrespective of whether they are under or over illuminated. We have tested the results using a synthetic database as well as on real world painting images of diverse nature. (C) 2020 Elsevier B.V. All rights reserved.

关键词： deep learning algorithm Low space-time complexity Image enhancement RGB color image High range variation

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Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea

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WATER SUPPLY 2023年第2期23卷 934-947页

作者： Han, Heechan Kim, Donghyun Wang, Wonjoon Kim, Hung Soo Chosun Univ Dept Civil Engn Gwangju South Korea Inha Univ Inst Water Resource Syst Incheon South Korea Inha Univ Dept Civil Engn Incheon South Korea

Accurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indices, Atlantic multidecadal oscillation (AMO), El Nino-southern oscillations (ENSO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), Nino 3.4, and Southern Oscillation Index (SOI) for the period of 1981-2020, were used as input variables of the model. The proposed model was trained with 29 years of data (1981-2009) and tested with 12 years of data (2009-2020). We investigated 29 input data combinations to evaluate the predictive performance according to different input datasets. The model showed the average values of metrics ranged from 0.5 to 0.6 for CC and from 40 to 80 cm for root mean square error (RMSE) at three dams. The prediction results from the model showed lower performance as the lead time increased. Also, each dam showed different prediction results for different seasons. For example, Soyangriver/Daecheong dams have better accuracy in prediction for the wet season than the dry season, whereas the Andong dam has a high prediction ability during the dry season. These investigations can be used for better efficient dam management using a data-driven approach.

关键词： deep learning algorithm large-scale climate variability monthly dam inflow prediction

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Computer-Assisted Real-Time Rice Variety learning Using deep learning Network

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Rice science 2022年第5期29卷 489-498页

作者： Pandia Rajan JEYARAJ Siva Prakash ASOKAN Edward Rajan SAMUEL NADAR Department of Electrical and Electronics Engineering Mepco Schlenk Engineering CollegeSivakasi 626005India

Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading *** existing grading system is manual,which introduces stress and strain to humans due to visual *** rice grading system development has been proposed as a promising research area in computer *** this study,an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and *** proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user *** firstly trained the network by a rice public dataset to extract rice discriminative ***,by using transfer learning,the pre-trained network was used to locate the region by extracting a feature *** proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning *** AlexNet architecture,we obtained an average accuracy of 98.2%with 97.6%sensitivity and 96.4%*** validate the real-time performance of proposed rice grading classification system,various performance indices were calculated and compared with the existing *** simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.

关键词： deep learning algorithm rice defect classification computer vision agriculture automated visual grading system

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A Hybrid Intrusion Detection System Based on Scalable K-Means plus Random Forest and deep learning

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IEEE ACCESS 2021年 9卷 75729-75740页

作者： Liu, Chao Gu, Zhaojun Wang, Jialiang Civil Aviat Univ China Coll Safety Sci & Engn Tianjin 300300 Peoples R China Civil Aviat Univ China Informat Secur Evaluat Ctr Tianjin 300300 Peoples R China Civil Aviat Univ China Coll Comp Sci & Technol Tianjin 300300 Peoples R China

Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an intrusion detection model that combines machine learning with deep learning is proposed in this paper. The model uses the k-means and the random forest (RF) algorithms for the binary classification, and distributed computing of these algorithms is implemented on the Spark platform to quickly classify normal events and attack events. Then, by using the convolutional neural network (CNN), long short-term memory (LSTM), and other deep learning algorithms, the events judged as abnormal are further classified into different attack types finally. At this stage, adaptive synthetic sampling (ADASYN) is adopted to solve the unbalanced dataset. The NSL-KDD and CIS-IDS2017 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed model has better TPR for most of attack events, faster data preprocessing speed, and potentially less training time. In particular, the accuracy of multi-target classification can reach as high as 85.24% in the NSL-KDD dataset and 99.91% in the CIC-IDS2017 dataset.

关键词： Machine learning algorithms Classification algorithms Intrusion detection deep learning Radio frequency Sparks Feature extraction Intrusion detection system machine learning algorithm k-means random forest deep learning algorithm

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deep learning approach to generate 3D civil infrastructure models using drone images

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SMART STRUCTURES AND SYSTEMS 2022年第5期30卷 501-511页

作者： Kwon, Ji-Hye Khudoyarov, Shekhroz Kim, Namgyu Heo, Jun-Haeng Yonsei Univ Sch Civil & Environm Engn 50 Yonsei Ro Seoul 03722 South Korea SISTech Co LTD 209 Neungdong Ro Seoul 05006 South Korea Korea Inst Civil Engn & Bldg Technol Res Strateg Planning Dept 283 Goyangdae Ro Goyang Si 10223 Gyeonggi Do South Korea

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced;the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

关键词： automatic model generation deep learning algorithm noise reduction point cloud semantic segmentation

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Sentiment Analysis and Sarcasm Detection using deep Multi-Task learning

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期129卷 2213-2237页

作者： Tan, Yik Yang Chow, Chee-Onn Kanesan, Jeevan Chuah, Joon Huang Lim, YongLiang Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis's overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.

关键词： Sentiment analysis Sarcasm detection deep learning algorithm Multi-task learning

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Leveraging deep learning for Designing Healthcare Analytics Heuristic for Diagnostics

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NEURAL PROCESSING LETTERS 2023年第1期55卷 53-79页

作者： Shafqat, Sarah Fayyaz, Maryyam Khattak, Hasan Ali Bilal, Muhammad Khan, Shahid Ishtiaq, Osama Abbasi, Almas Shafqat, Farzana Alnumay, Waleed S. Chatterjee, Pushpita Int Islamic Univ IIU Dept Basic & Appl Sci Islamabad Pakistan Smart E Hlth Islamabad 44000 Pakistan Allama Iqbal Mem Hosp Sialkot Pakistan Natl Univ Sci & Technol NUST Islamabad 44000 Pakistan Hankuk Univ Foreign Studies Dept Comp Engn Yongin 17035 Gyeonggi Do South Korea Shifa Int Hosp Islamabad Pakistan King Saud Univ Comp Sci Dept Riyadh Saudi Arabia Ton Duc Thang Univ Future Networking Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of;Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagn

关键词： learning healthcare system Medical diagnostics Healthcare analytics deep learning algorithm Neural nets Endocrine diseases Infectious diseases Big data

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