检索结果-内蒙古大学图书馆

IoT-Enabled Predictive Maintenance and Analytic Hierarchy Process Based Prioritization of Real-Time Parameters in a Diesel Generator: An Industry 4.0 Case Study

引用

SN computer Science 2024年第1期5卷 145页

作者： Mohapatra, Ambarish Gajendra Mohanty, Anita Tripathy, Pradyumna Kumar Department of Electronics and Instrumentation Engineering Silicon Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

Diesel generator (DG) is a secondary power source that is becoming increasingly common for supplying continuous power to business and residential buildings. These hybrid machines provide the necessary electrical energy using both a diesel engine and an electric generator. Monitoring crucial machine characteristics on a regular basis is crucial for increasing their efficiency. condition monitoring systems (CMS) powered by the Internet of Things (IoT) have taken the role of Remote Monitoring (RM). Moreover, a key component of Industry 4.0 is predictive maintenance, which may be accomplished by implementing IoT-enabled RM. This paper portrays two aspects of the DG unit. Firstly, the performance analysis of the DG unit using a 4G-enabled IoT node installed on the motherboard. This research work develops and tests a framework for monitoring and analyzing key metrics, including engine speed, voltage and current generated, power factor, coolant, fuel, and battery health status. Secondly, the Analytic Hierarchy Process (AHP) can be used to rank the performance indicators that are most important for the efficient monitoring of the DG system in the context of IoT-enabled DG monitoring. The AHP technique enables decision-makers to objectively assess the relative weights of several criteria and to prioritize them. Using AHP, decision-makers can weigh each of these criteria based on how important they are in relation to one another. The performance of the DG system may then be assessed using these weights, and potential areas for improvement can be found. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AHP CMS Condition monitoring systems Diesel generator Industry 4.0 IoT Predictive maintenance Remote monitoring

来源：评论

学校读者我要写书评

暂无评论

SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

引用

Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Recent works in Sign Language Recognition using deep learning approach - A Survey 21

Recent works in Sign Language Recognition using deep learnin...

引用

21st OITS International Conference on Information technology, OCIT 2023

作者： Sreemathy, R. Jagdale, Jayashree Sayed, Aagaaz Ali Ramteke, Smeet H. Naqvi, Syed Farhan Kangune, Aditya Society for Computer Technology and Research's Pune Institute of Computer Technology Department of Electronics and Telecommunication Pune India Society for Computer Technology and Research's Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350358230

Across the globe, different countries have their own sign language used by hearing and speech-impaired people. The American, Arabic, British, Brazilian, and Indian Sign Languages have gained popularity and piqued researchers and engineers to develop solutions that will help in the task of Sign Language Recognition and Machine translation. Just like spoken languages, sign languages also have dialects, and hence multiple gestures can exist for one word. The task of Sign Language Recognition is complex and has been carried out for the last three decades. In this paper, we survey, compare, and summarize the proposed techniques of researchers in sign language and provide a proposed plan for our imminent work. An extensive survey of CNN networks, Transformer models, Sequence models, Keypoint-based methods, and Hardware-assisted methods applied on various sign language open datasets is presented in the paper which can form a basis of the literature survey for the research. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

引用

Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

来源：评论

学校读者我要写书评

暂无评论

Grid-Interactive Novel Resilient Control of Solar PVWind-Battery Storage Microgrid Under Distorted and Unbalanced Grid Voltages

引用

IEEE Transactions on Consumer electronics 2025年第1期71卷 1744-1757页

作者： Gali, Vijayakumar Jamwal, Prashant Kumar Gupta, Nitin Babu, B. Chitti Ahmadi, Mohammad Jawid Morey, Meghraj Sudhakar University of Hertfordshire School of Physics Engineering and Computer Science Department of Electrical and Electronics Engineering HatfieldAL109AB United Kingdom Nazarbayev University Department of Electrical and Computer Engineering Astana Kazakhstan Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Communication Kancheepuram India

Integrating solar photovoltaic (PV), wind, and battery storage (BS) systems into the grid introduces significant power quality (PQ) challenges. In particular, the intermittent nature of solar PV and wind energy systems (WES), combined with nonlinear loads, can lead to grid instability. As a result, maintaining a reliable and high-quality power supply to consumers becomes a substantial challenge. This article presents two novel solutions to address these challenges: the anti-windup mixed-order generalized integrator (AWMOGI) and the adaptive delay operation period filter (ADOPF). The AWMOGI effectively extracts fundamental components from distorted utility grid voltages, minimizing DC offset and reducing steady-state oscillations. Meanwhile, the ADOPF swiftly captures positive sequence voltage components by introducing a two-sampling-interval delay in the d-q coordinate system. The hierarchical-loop controller parameters are optimized using a customized Harris Hawks Optimization (HHO) algorithm, which enhances error reduction and facilitates rapid establishment of reference values for DC-link voltage loop control. An adaptive dynamic power management scheme also enables the microgrid (MG) to manage surplus power from MG sources, reducing stress on the BS and ensuring DC-link voltage stability. The experimental results demonstrate significant improvements in handling utility grid abnormalities, dynamic loads, and intermittent microgrid source operation. The experimental results show a zero DC-off set and steady-state errors, a fast computational time of 35e-5s, and the THD of grid currents of 3.16%, which complies with the IEEE 519 standards. © 1975-2011 IEEE.

关键词： Adaptive filters

来源：评论

学校读者我要写书评

暂无评论

Regional Forest Fire Detection and Control: An Integrated Approach Using Distributed Sensing and Automated Suppression Systems 5

Regional Forest Fire Detection and Control: An Integrated Ap...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Marathe, Abha Kadam, Manas Mohite, Shreya Narayanrao Lasurkar, Prachi Lanjewar, Riya Modhave, Pranav Vishwakarma Institute of Technology Electronics and Communication Engineering Pune India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331518097

Forest fires present a significant hazard to property, lives, and ecosystems globally, necessitating swift detection and containment measures. Historically, manual forest fire control systems relied on human observation and reporting, which often resulted in delayed responses and increased damage. The forest is divided into target areas, with each area equipped with intelligent software interfaces, automated response mechanisms, flame sensors, and sprinklers. The proposed system relies on advanced flame sensor technology for prompt and accurate fire outbreak detection. Upon detection, real-time fire data is transmitted to a cloud-based platform, ***, for comprehensive monitoring and analysis. This division allows for targeted intervention and efficient resource allocation based on the severity and location of the fire. The flame sensors used in this system have a range of up to 100 meters, allowing for extensive coverage of forest areas. Additionally, the system includes automated sprinklers that activate immediately upon fire detection, providing an immediate response to help contain and extinguish the fire. By leveraging *** for data transmission and analysis, stakeholders gain valuable insights into fire behavior, facilitating informed decision-making and long-term mitigation strategies. This integrated approach offers a comprehensive solution to combat forest fires, emphasizing early detection and efficient containment to minimize damage and protect both natural habitats and human communities. © 2024 IEEE.

关键词： Sprinkler systems (irrigation)

来源：评论

学校读者我要写书评

暂无评论

Train Collision Avoidance System by X-Band FMCW Radar and Long Range Camera Image Processing 12

Train Collision Avoidance System by X-Band FMCW Radar and Lo...

引用

12th International Electrical Engineering Congress, iEECON 2024

作者： Pongthavornkamol, Tiwat Klangnurak, Thanirat Manasummakij, Prateep Wisadsud, Sodsai Manatrainon, Supatra Kovavisaruch, La-Or Kaemarungsi, Kamol National Electronics And Computer Technology Center Pathumthani Thailand King Mongkut's Institute Of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798350383591

Sensor fusion between X-Band radar and long-range Pan-Tilt-Zoom (PTZ) camera for train collision avoidance system is proposed. The method of Constant False Alarm Rate (CF AR) is applied for the radar signal processing to improve the correct result of radar detection. The edge detection and object detection by digital image processing are applied to long range camera system for classification of identity and position of the object. Experiments of obstacle detection by radar and camera are performed. The proposed work will benefit the safety improvement of locomotive transportation application in which its details are presented and discussed in this paper. © 2024 IEEE.

关键词： Collision avoidance

来源：评论

学校读者我要写书评

暂无评论

Prediction of Listed Company Innovation Ability Based on Attention Mechanism

引用

Journal of the Operations Research Society of China 2023年第1期11卷 177-187页

作者： Huan-Yu Ma Yan Xu Yu-Long Liu Department of Information and Computer Science University of Science and Technology BeijingBeijing100083China No.15 Research Institute China Electronics Technology Group CorporationBeijing100083China

Innovation is the center of the enterprise development, which is the main driving force of enterprises’ competitiveness. To evaluate the enterprises’ innovation ability, we firstly establish an index system which aims at analyzing the enterprise innovation ability. We also retrieve the data of the listed companies in Wind database from 2015 to 2019 and label them using factor analysis method. Then, a new deep learning classificational framework with attention mechanism and LSTM is established. The results show that when attention mechanism and LSTM are added into the convolutional neural network(CNN), the model’s prediction performance is better improved, and the accuracy, recall, precision and F-score are 0.914, 0.914, 0.916 and 0.915, respectively. This indicates the strong generalization ability of our new model. Finally, we also find that patents and R &D expenditures are the most important factor affecting the corporate innovation ability through SHapley Additive exPlanations(SHAP). Companies with more patents and R &D expenditures are generally considered to be more innovative.

关键词： Corporate innovation ability convolutional neural network Attention mechanism long short-term memory

来源：评论

学校读者我要写书评

暂无评论

A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

引用

computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Integrating the Internet of Things to Protect Electric Vehicle Control Systems from Cyber Attacks

引用

IAENG International Journal of Applied Mathematics 2024年第3期54卷 433-440页

作者： Bhukya, Baddu Naik Venkataiah, V. Kuchibhatla, S. Mani Koteswari, S. Kumari, R.V.S. Lakshmi Raju, Yallapragada Ravi Department of Electrical and Electronics Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Computer Science Engineering CMR College of Engineering & Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering ACE Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Kakinada India Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam India Department of Computer Science and Technology Madanapalle Institute of Technology and Science Andhra Pradesh India

The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based on communication infrastructure, provides real-time cyber-physical device monitoring, control, and sensing, such as electric autos. Traditional communication infrastructure's vulnerability to cyberattacks hampered IoT's capacity to investigate these potential applications. This study proposes an algorithm for monitoring and controlling electric vehicles through the Internet of Things communication network to prevent fake data injection attacks. A state-space model depicts a fully autonomous electric car equipped with a vision system. Intelligent sensors and actuators from the Internet of Things are utilized to monitor and change system conditions, compensating for the large distance between the electric car and the control center. Sensing data is delivered from the vehicle to the central command center via an insecure, attack-prone communication path. The mean square error approach is used to derive states in the most effective state estimation system for comprehending and showing autos. A semi-definite programming approach is used to create an optimal control algorithm to manage the vehicle states. The simulation results show how precisely and successfully the proposed algorithms can foresee and regulate a vehicle's state. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：