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8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Sathyaseelan, K. Vyas, Tushar Madala, Ravikiran Chamundeeswari, Vijaya Rai Goyal, Himanshu Jayaraman, Raja Department of Information Technology Mallasamudram India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India University of Cumberlands Ph.D Information Technology DurhamNC United States Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India St. Martin's Engineering College Department of Computer Science and Engineering Secunderabad India

ISBN: (纸本)9798350347791

In the New Year, Internet of Things (IoT) is industrializing in a few certifiable applications, for example, clever transportation, brilliant city to make human existence reliable. Extreme amounts of detecting information are being delivered from various sensors devices in the Modern IoT as a result of the growing industrialization of IoT. Understanding the constraints of flow innovations and the potential for future exploration techniques is necessary to the establishment of a secure and dependable information environment. A dishonest individual can connect with a trusted person through the blockchain, a decentralized digital ledger that tracks the conversation from start to finish. This report urges the medical care industry to deploy blockchain-coordinated digital protection in order to preserve clinically relevant things in light of the use of artificial intelligence in clinical settings. Applications based on blockchain may be able to precisely pinpoint the most serious, potentially dangerous mistakes in the medical sector. Blockchain-based decentralized information insurance safeguards patient health records from data theft. © 2023 IEEE.

关键词： Blockchain

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Seamless Low-Voltage Ride-Through Control for Grid-Forming Inverter Based on Virtual Synchronous Generator (CICED 2024) 11

Seamless Low-Voltage Ride-Through Control for Grid-Forming I...

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11th China International Conference on Electricity Distribution, CICED 2024

作者： Xiao, Zhaoxia Sun, Puhang Gao, Jian Liu, Wenlong Fang, Hongwei Micallef, Alexander Xiong, Junjie Li, Guangdi School of Electrical Engineering Tiangong University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China University of Malta Department of Electrical Engineering Msida Malta Electric Power Research Institute State Grid Jiangxi Nanchang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350368345

The grid-forming converter should have low-voltage ride-through (LVRT) capability. A seamless low-voltage ride-through control method for inverters based on the grid-forming virtual synchronous generator (VSG) is proposed in this paper. The method includes a synchronous compensation controller to ensure the inverter output voltage is always synchronized with the grid voltage. When the grid voltage dips below the threshold, the VSG control of the grid-connected inverter seamlessly switches to the low voltage ride through control, with the synchronous compensation controller enabled. When the grid voltage recovers, the control reverts to VSG control and deactivates the synchronous compensation controller after the transient state ends. This method ensures that the output voltage of the grid-connected inverter is always synchronized with the grid voltage during control mode changes. The presented simulation results validate the correctness and effectiveness of the proposed method. © 2024 IEEE.

关键词： Synchronous generators

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Design of High Fault-Tolerant Adaptive Control System for Fixed-wing UAVs Based on Transient Characteristics 6

Design of High Fault-Tolerant Adaptive Control System for Fi...

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2023 IEEE 6th International Conference on information Systems and computer Aided Education, ICISCAE 2023

作者： Cui, Weixuan Jiang, Teng Shandong University of Science and Technology Department of Electrical and Information Engineering Jinan250000 China

ISBN: (纸本)9798350313444

Aiming at the longitudinal trajectory tracking control problem of fixed-wing UAVs, a basic controller is designed based on feedback linearization and model following control to achieve the stability of the closed-loop system and trajectory asymptotic tracking control under fault-free conditions. Considering that the actuators of fixed-wing UAVs may have different types and sizes of faults, corresponding compensation controllers are designed for various fault modes. In order to comprehensively solve the multiple uncertainties of actuator failure, a comprehensive controller structure is designed using a combination of weighted fusion and adaptive control, and the controller parameters related to the failure are estimated. Finally, the H ∞ compensation term is added to the basic controller design to suppress the adverse impact of the fault compensation controller parameter estimation error on the output tracking performance of the closed-loop system. Theoretical analysis and simulation results show that the proposed control scheme can not only ensure the stability of the closed-loop system, but also enable the fixed-wing UAV attitude control system output to asymptotically track the given reference in the event of an uncertain actuator failure. Model output, the transient performance in the fault compensation control process can be improved by designing appropriate H ∞ compensator parameters. © 2023 IEEE.

关键词： actuator failure adaptive control feedback linearization fixed wing UAV transient performance

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Enhanced Predictive Model to Diagnose Coronary Heart Disease Using Decision Tree 2

Enhanced Predictive Model to Diagnose Coronary Heart Disease...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Joshi, Sujata Dixit, Ananya Nair, Mydhili K. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. Institute of Technology and Management Department of Information Science and Engineering Bangalore India School of Computer Science Rv University Bangalore India

ISBN: (纸本)9798350373110

As per WHO reports, Heart diseases or Cardiovascular Diseases (CVDs) rank as the leading cause of death worldwide. Approximately 17.7 million individuals succumbed to CVDs in 2015, accounting for 31% of all global fatalities. Among these fatalities, an estimated 7.4 million were attributed to coronary heart disease, one of the prevailing forms of heart disease.. According to the Global Burden of Disease study, nearly quarters (24.8%) of all deaths in India are due to CVD. Hence there is a need for careful and periodic methods of examining heart related diseases. The majority of cardiovascular diseases found in India are coronary heart diseases (CHD) The main contribution of this work is to develop efficient predictive models for Coronary heart diseases using decision trees. In this work, the predictive models are developed for diagnosis of heart disease using the cardiovascular disease dataset from UCI repositories with the objective of improving the accuracy. We have also obtained echocardiograph data obtained from M. S. Ramaiah hospital and used in this work. It is found that the data used in this work is heterogeneous in nature and the decision trees work well on heterogeneous data and also they are expressive and interpretative, we have opted to use decision trees. The predictive model namely Enhanced-Pred-Model CVD-DATA-DT is developed using both the datasets. It is found that the model shows marginal accuracy increase from 77% in baseline model to 80% by the proposed model. The proposed method has better performance in terms of accuracy as well as error rate. The models are also tested on the echocardiograph data for prediction of LVH event and has shown improvement in the performance. © 2024 IEEE.

关键词： Prediction models

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Deep Learning Based License Plate Number Recognition for Smart Cities

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computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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Providing an Intelligent Hybrid Routing Method in Wireless Sensor Networks 23th

Providing an Intelligent Hybrid Routing Method in Wireless S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Hajiabadi, Mahdi Rohani Abraham, Ajith Department of Computer Science University of Regina Regina Canada Department of Computer Engineering and IT Payame Noor University Tehran Iran Faculty of Computer and Information Technology Engineering Islamic Azad University Qazvin Qazvin Iran School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9783031647758

Optimal energy consumption in Wireless Sensor Networks (WSNs) is important. Previous research has shown that by organizing network nodes into a number of clusters, one can achieve greater energy efficiency leading to longer network life. The WSN is made up of a very large number of sensor nodes that communicate with each other to perform specific tasks. These nodes have limited energy, processing power, and memory. Because the lifetime of the network depends on the brightness of the nodes, the energy consumption of each node is very important. Therefore, we need to find solutions to reduce the energy consumption of the nodes. One way to reduce energy consumption is clustering, many algorithms have been introduced in this field. In this paper, we optimize Particle Swarm Optimization (PSO) algorithm based on fuzzy logic and PSO algorithm. The fitting criterion will be based on the minimum energy consumed by the nodes of the network during each operation period, It leads to a balance in the energy consumption of the headers and, as a consequence, a lengthening of the life of the network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy logic

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IoT-Enabled Solar-Powered Water Trash Collector with Conveyor Belt 8th

IoT-Enabled Solar-Powered Water Trash Collector with Conveyo...

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8th International Conference on information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Devapriya, S. Krishna, M. K. Nandana Gondi, Bhavika Vinuha, Chinta Pandi, V. Ravikumar Sathyan, Soumya Valsan, Vipina Suresh, Kavya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994854

This study presents a novel solution in the form of a solar-powered water trash collector, designed specifically for deployment in backwaters and smaller water bodies, to address the critical issues of water pollution and waste management. Focusing on the challenges faced by national rivers, including hazardous polymers, plastic waste, and decaying organic matter, the collector operates under IoT-enabled Bluetooth control using mobile application, incorporating a conveyor belt mechanism for efficient waste collection, particularly targeting prevalent plastic debris in freshwater environments. A notable feature of this solution is its integration of solar power, ensuring self-sustained operation and reduced environmental impact, aligning with the imperative for eco-friendly waste management technologies. Bluetooth control enhances adaptability for smaller water bodies, crucial for precise navigation. The core objective is to provide an effective solar-powered water trash collector tailored for backwaters, addressing the escalating waste production due to population growth. Additionally, a comprehensive waste management approach is proposed, facilitated by an Android application for seamless navigation of integrated trash bins. In conclusion, this study offers an innovative approach to tackle water pollution and waste management, introducing a solar-powered collector uniquely equipped to address backwater ecosystems’ specific challenges while promoting sustainability and environmental well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bluetooth

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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Airborne Multichannel UWB FMCW Radar for Snow Depth Measurements

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-18页

作者： Kolpuke, Shriniwas Abushakra, Feras Gogineni, S. Prasad Braaten, David Taylor, Drew Larson, Jordan D. Rapadas, Allain Luong, Tuan Remote Sensing Center Department of Aerospace Engineering and Mechanics University of Alabama TuscaloosaAL35487 United States Remote Sensing Center Department of Aerospace Engineering and Mechanics Department of Electrical and Computer Engineering University of Alabama TuscaloosaAL35487 United States Department of Geography and Atmospheric Science University of Kansas LawrenceKS66045 United States

We developed a high-sensitivity airborne multichannel ultrawideband (UWB) frequency-modulated continuous-wave (FMCW) radar for snow depth measurements. This low-transmit power eight-channel radar has a near-ideal point target response and provides a multilook ability. We developed a T-shape Mills-Cross antenna array to obtain a small, overlapped footprint. We integrated it with a 2 degrees of freedom (DOF) gimbal mechanism to correct pointing errors in the roll and pitch movements of the aircraft. We performed airborne UWB radar measurements in conjunction with in situ characterization and dielectric measurements with a monopole dielectric probe over snow in Grand Mesa, CO, USA, from 24th January to 14th February 2023. The radar mapped top and bottom snow interfaces and internal density changes of 1.2-2.6 m of snow with high signal-to-noise of about 45 dB with only 10 mW transmit power. The multichannel system with gimbaled antenna mount allowed us to capture quasi-specular returns even with aircraft roll deviations as large as 15°. The comparison between radar data and in situ measurements shows excellent agreement between the two in terms of snow depth and internal snow layers. © 2024 IEEE.

关键词： Snow

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Machine Learning-Based Lifespan Prediction Modelling for Electric Submersible Pumps in Oil Wells 2

Machine Learning-Based Lifespan Prediction Modelling for Ele...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rajakala, J. Gupta, Satyaveer Naidu, Pothala Mutyalu Basi Reddy, A. Revathi, S. Selvan, R. Senthamil Sai Spurthi Institute of Technology Department of Computer Science and Engineering Gangaram India Delhi Technical Campus Department of Electrical Engineering Greater Noida India Vignan Institute of Technology And Science Department of Cse Hyderabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

To maintain the output flow rate and prolong the life of oil wells, artificial lift is crucial to the petroleum industry. Electric Submersible Pumps (ESP) are a widely used artificial lift technique due to their ability to generate large flow rates in very deep wells. ESPs have a much shorter lifetime than anticipated, even though they are designed to function in harsh environments including corrosion, high temperatures, and high pressure. Because ESP intervention work is far more expensive than other artificial lift techniques, particularly for offshore wells, ESP failures result in production loss and raise replacement costs. Knowing when an ESP may fail is thus highly helpful in the oil production industry and affects how oil wells are designed, built, and managed. Using three machine learning algorithms - Decision Tree, Random Forest, and Gradient Boosting Machine - this study contributes to the construction of prediction models for ESP lifespan that take into account both dynamic and static ESP features. The model that produced the best results for ESP life cycle prediction was determined by comparing the output of various models. To predict the most influential characteristics on the length of ESP, this research also assessed the influence factor of different operational parameters. By knowing more about ESP behavior, early efforts to avert possible ESP failures may be realized thanks to the study's findings. © 2024 IEEE.

关键词： Submersible pumps

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