检索结果-内蒙古大学图书馆

Smart electronics and Communication (ICOSEC), International Conference on

作者： Sathiyakeerthi Madasamy B. Prabhu Shankar Rakesh Kumar Yadav Jayalakshmi K P Solution Architect Pilvi Systems Inc Lewisville TX USA Department of Computer Science and Engineering Faculty of Engineering and Technology Alliance University Karnataka India Department of Computer Science and Engineering SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Electronics and Communication Engineering St Joseph Engineering College Mangaluru Karnataka India

Predictive maintenance using machine learning is a powerful technique for industries seeking to enhance their operations with minimize downtime. In an IoT-enabled Industry 4.0 environment, this approach can be taken to a new level by leveraging the vast amounts of data generated by connected devices. To implement a machine learning methodology to projecting conservation in an Industry 4.0 environment, several key steps need to be taken. First, data from IoT devices across the industrial ecosystem should be collected and centralized in a data lake or similar storage system. This data should include information on equipment health, sensor readings, and other relevant metrics. Next, the data should be preprocessed and transformed to ensure its quality and consistency. This may involve cleaning, normalization, and feature engineering to create relevant variables for use in machine learning models. Once the data has been preprocessed, a range of machine learning models can be trained on it to predict equipment failures or other maintenance issues. This may involve ongoing tuning and optimization of model hyperparameters or retraining the models on new data as it becomes available. Finally, the predictions generated by the machine learning models should be integrated into a broader maintenance management system to enable timely action. This may include triggering maintenance requests, generating work orders, or even automating maintenance tasks through the use of robots or other industrial automation technologies. By implementing a machine learning method to projecting preservation in an IoT-enabled Industry 4.0 environment, industries can optimize their operations, minimize downtime, and improve overall equipment effectiveness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Traffic Accident Severity Prediction with Ensemble Learning Methods

SSRN

引用

SSRN 2023年

作者： Çeven, Süleyman Albayrak, Ahmetahmet Department of Electronics and Automation Technology Düzce Vocational School Düzce University Düzce Turkey Department of Computer Engineering Faculty of Technology Düzce University Düzce Turkey

In this study, decision tree-based models are proposed for classification of traffic accident severity. Traffic accident severity is classified into three categories. The data set used in the study belongs to the province of Kayseri, Turkey. The data consists of urban traffic accident reports (23074 accidents) between 2013 and 2021. There are 39 variables in the data set. As a result of data preprocessing, 15 variables that are meaningful and can be used for the model in the data set were determined. Since the input variables of the model mainly contain categorical data, they were coded with pseudo-coding and a total of 93 input variables were obtained. In the studies, ensemble learning methods such as Random Forest, AdaBoost and MLP methods were used. F1 scores of these methods were found to be 91.72%, 91.27% and 88.95%, respectively. Feature importance levels were calculated for 15 variables used in the model. Gini index and decision trees were used while calculating the importance of the features. Driver fault (0.64) was found to have the most effect on traffic accident severity. This study focuses especially on urban traffic accidents. Urban traffic is crowded in terms of both vehicles and pedestrians. As a result of this, according to the findings obtained in this study, traffic accidents occurred mostly at the intersections with crowded urban areas. © 2023, The Authors. All rights reserved.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Intelligent Emergency Communications Using Efficient Clustering Based Routing Strategy in Unmanned Aerial Vehicle Networks

Intelligent Emergency Communications Using Efficient Cluster...

引用

Frontier Technologies and Solutions (ICFTS), International Conference on

作者： K. Nagamani V. Sujatha M Praveena Abla Suliman Hussein R Adakane Suresh D Department of MBA QIS College of Engineering and Technology Ongole AP Department of Master of Computer Applications S.A. Engineering College Chennai Tamilnadu India Department of CSE QIS College of Engineering and Technology Ongole Andhrapradesh Dept of Software Engineering Faculty of Information Technology Zarqa University Jordan University of Business and Technology Jeddah SA Department of ME Yeshwantrao Chavan College of Engineering (YCCE) Nagpur India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The IECECR model is designed to use a network of Unmanned Aerial Vehicles (UAVs) to provide emergency communications in disaster areas where existing infrastructure is not accessible. UAVs are deployed in clusters with cluster heads (CHs) selected based on trust and mobility metrics to ensure stable and efficient data transmission to ground stations. It also allows intra- and inter-cluster for short and long-distance communication. Changes in environmental conditions through dynamic clustering led to improved network reliability and longevity. The analysis outlines the system motivation, considering infrastructure loss scenarios, and provides a system model that includes mini-drones as well as medium-sized UAVs with 3D navigation capabilities. The proposed cluster protocol integrates location-aware technologies and wireless communication functions, which shows its applicability in emergencies and disaster management. The implementation is performed in NS3 and the parameters which are considered for result calculations are packet delivery ratio, energy efficiency, data loss, routing overhead, and average delay are used to compare CMAC, TCDC, and VSNC with the IECECR model. It shows our proposed model enhances overhead and efficiency.

关键词： Wireless communication Disasters Disaster management Autonomous aerial vehicles Routing Energy efficiency Data models Delays Reliability Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

Internet of Things (IoT) from the Viewpoint of Energy Efficiency and Security: A Review

Internet of Things (IoT) from the Viewpoint of Energy Effici...

引用

BIT Conference (BITCON), International

作者： Binod Kumar P Somasundari Shrikant Upadhyay Anindita Chakraborty Ankita Lakra Manoj Kumar Department of CSE & IT Jharkhand Rai University Ranchi Jharkhand India Department of Computer Science and Engineeing Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering-AIML Brainware University Kolkata India Faculty of Computing and IT Usha Martin University Ranchi Jharkahnd India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The availability of repetitively sprouting smart devices used in many different technical areas has caused the internet of things energy absorption to grow quickly in current smart devices. Evidence and message technology has cutting-edge intensely. This results in creation of smart gadgets, communication technologies, and various commercial and noncommercial tasks. The main objective of this survey paper is to identify the energy efficiency and security in smart devices or IoT devices. A methodical IoT ecology is therefore proposed, encompassing the diverse IoT components that lack compatibility and energy efficiency. In this review bids an immediate of numerous strategies for dealing with the problem of energy efficiency in IoT. Initially, a feasible review is done to recognize the crucial role of energy efficiency, confidentiality, and security in smart devices and IoT system. A methodical IoT ecology is therefore proposed, encompassing the diverse IoT components that lack compatibility and energy efficiency. In this review submissions an immediate of numerous strategies for dealing with the problem of energy efficiency, confidentiality, and security in IoT system. In its discussion, the study focuses on various factor and smart component IoT devices that consume battery very fast. A qualified analysis and survey summary are also provided that, research has not yet fully clarified the fundamental factors that contribute to heterogeneity and power absorption at the industrial and technical tiers. Based on this, found issues with technical combination. Important parameters have been considered which includes power consumption, scalability, fog data, and focus on to diminishes the smashes, retransmission upstairs, and vitality absorption in an IoT forthcoming net has been covered in our in-depth study.

关键词： Surveys Industries Reviews Absorption Energy efficiency Internet of Things Security Smart devices Interoperability Guidelines

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic Approach for Optimizing Neural Networks Parameters in Battery State of Charge Estimation

Metaheuristic Approach for Optimizing Neural Networks Parame...

引用

International Conference on computer Applications and Industrial electronics (ICCAIE)

作者： Zuriani Mustaffa Mohd Herwan Sulaiman Azlan Abdul Aziz Faculty of Computing Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Computer Science and Mathematics Universiti Teknologi MARA Arau Perlis Malaysia

To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy Management for PV-Powered EV Charging With Grid Integration and Battery Energy Storage System using Dung Beetle Optimizer

Energy Management for PV-Powered EV Charging With Grid Integ...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Neelashetty K Ruchira Rawat Doradla Raja Ramesh Gordhan Jethava Praful V. Nandanka Natrayan L Department of Electrical and Electronics Engineering Guru Nanak Dev Engg College Bidar Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Mechanical Engineering Sri Vasavi Institute of Engineering and Technology Nandamuru Andhra Pradesh India Information Technology Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat India Department of Electrical Engineering Government College of Engineering Nagpur Maharashtra India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

An Energy Management (EM) System for Photovoltaic (PV)-powered Electric Vehicle (EV) charging with grid integration and Battery Energy Storage System (BESS) optimizes energy consumption by integrating renewable energy, storage, and grid capabilities. However, drawbacks include the high initial costs associated with installation and infrastructure, as well as on-going operational costs for managing and maintaining both the storage and charging systems. Furthermore, the intermittency of solar energy can lead to inefficiencies in utilizing Renewable Energy Sources (RESs), requiring additional storage capacity to ensure a reliable power supply, while emissions from battery production, operation, and disposal may counteract some of the system’s environmental benefits. To overcome these drawbacks, this manuscript proposes an approach for EM for PV powered EV charging with BESS. The suggested method is Dung Beetle Optimizer (DBO). The major objective of this work is to reduce operational costs, improve energy efficiency, and lower carbon emissions by improving the use of renewable energy, minimizing grid reliance, and optimizing battery storage operations. The DBO optimizes the scheduling of EV charging, the operation of battery storage (charging and discharging cycles), and the interaction with the grid. Artificial Neural Network (ANN), Genetic Algorithm (GA), Adaptive Interaction Artificial Neural Network (AI-ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are some of the existing methods that are compared with the suggested method once it is implemented in MATLAB. By achieving the lowest operational cost of 0.121M$/year and reducing carbon emissions to 505.73ktons, the suggested DBO method outperforms traditional approaches while maintaining high energy efficiency and environmental sustainability in PV-powered EV charging with BESS.

关键词： Photovoltaic systems Renewable energy sources Costs Battery energy storage system Carbon dioxide Artificial neural networks Electric vehicle charging Energy efficiency Batteries Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

引用

Optical and Quantum electronics 2022年第12期54卷 1-17页

作者： Aoudni, Yassine Kalra, Ashima Azhagumurugan, R. Ahmed, Mohammed Altaf Wanjari, Atul Krishnarao Singh, Bharat Bhardwaj, Arpit Department of Computers and Information Technology Faculty of Sciences and Arts Northern Border University Turaif Arar 91431 Saudi Arabia Department of Electronics and Communication Engineering Chandigarh Engineering College Punjab India Department of Electrical and Electronics Engineering Sri Sai Ram Engineering College West Tambaram Chennai 600044 India Department of Computer Engineering College of Computer Engineering & Sciences Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Sitaramji Chaudhari Science College Warud Dist-Amravati Maharashtra Warud India GLA University Mathura UP Mathura India BML Munjal University Kapriwas India

Optics means a general area of physics that covers a broad range of topics relevant to the study of light. Optics has subfields they are quantum optics, geometrical optics, and physical optics. Photonics refers to a subset of the optics discipline. The simplicity, high efficiency, reliability, transparency, and robust feature of PID controllers were few factors behind the reasons for its acceptance and more popularity for control in processing industries globally. All the conventional techniques for PID controller design and tuning offer early workable values for reaching favorable performances. The manual tuning of the PID controller variables becomes a difficult role that needs comprehensive knowledge and expertise in that field. Certain meta-heuristic techniques are explored to design PID controllers with the main aims of attaining minimum overshoot in lesser settling time and steady state response. For accomplishing enhanced robustness, minimization of disturbance elimination, operating, and physical restraints are treated as multi-optics functions. Therefore, this article presents a modified honey badger optimization (MoHBO) algorithm-based parameter tuning of the PID controller for optics and photonics. The tuning of the PID controller with constraints on the relative delay margin was considered an optimum disturbance rejection issue and summarized the tuning process. The presented MoHBO algorithm is derived by the inclusion of oppositional based learning (OBL) with standard HBO algorithm. In addition, the MoHBO algorithm is tested using the Magnetic Levitation System (MLS) and Phase Locked Loop system (PLL) to assure the enhanced robust nature of the PID controller. A wide ranging comparison study with other metaheuristic optimization algorithms demonstrates the better performance of the MoHBO algorithm over recent algorithms. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Metaheuristics Oppositional based learning Optics Parameter tuning Photonics PID controller

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment

An Enhanced Deep Super-Resolution Generative Adversarial Net...

引用

Electronic engineering (ICEEM), International Conference on

作者： Ibrahim Abd El-Fattah Taha E. Taha Walid El-Shafai Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department Electronics and Electrical Communications Engineering Alexandria Higher Institute of Engineering and Technology (AIET) Alexandria Egypt Computer Science Department Security Engineering Lab Prince Sultan University Riyadh Saudi Arabia

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated resource management system based on kubernetes technology 18

Automated resource management system based on kubernetes tec...

引用

18th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Sithiyopasakul, Jirayus Archevapanich, Tuanjai Purahong, Boonchana Sithiyopasakul, Paisan Benjangkaprasert, Chawalit King Mongkut's Institute of Technology Ladkrabang School of Engineering Bangkok Thailand Rajamagala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Communication Engineering Nonthaburi Thailand King Mongkut's Institute of Technology Ladkrabang School of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9780738111278

The purpose of this research is intended to study and analyze the Kubernetes technology using the processes of performance evaluation by inspecting how many requests and responses could the server handle. According to the ability of Kubernetes technology, there is no systematic measure of performance despite the Kubernetes is currently in use broadly. Therefore, this paper proposed to test out the system by measuring the effectiveness according to a structured process by studying such measurement variables including the number of requests per second, number of responses to requests, and resource extension period with Kubernetes technology. Which from the testing and analysis of all three variables as mentioned, it is possible to know the efficiency of the Kubernetes technology in such a similar environment. Moreover, the testing experiment could display a piece of information on the dashboard for visualization and analytic purposes. © 2021 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Telemedicine Platforms with Cloud-Based AI-Driven Diagnostics in Hospitals

Telemedicine Platforms with Cloud-Based AI-Driven Diagnostic...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Thangalakshmi, S. Mohankumar, N. Jayanthi, L.N. Blessy, Y.M. Meenakshi, R. Rajmohan, M. School of Marine Engineering and Technology Indian Maritime University Faculty - Electrical Tamil Nadu Chennai India Pune India Saveetha University Department of Commerce Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Hospitals may benefit greatly from telemedicine technologies, including cloud-based artificial intelligence (AI)-driven diagnostics. Regardless of location, instantaneous access to medical knowledge is made possible by these systems that allow for virtual consultations between patients and healthcare practitioners. Critical patient data is always available during consultations due to these systems' use of cloud computing to safely store and handle massive volumes of data. The use of AI-powered diagnostics improves the precision and effectiveness of diagnoses. To help healthcare providers make informed decisions, machine learning algorithms examine patient data, including medical imaging, test findings, and clinical notes. Faster and more accurate diagnoses are possible with the help of this technology since it can spot patterns and abnormalities that human practitioners may miss. Hospitals can streamline their workflow using telemedicine technologies that use AI for diagnoses. They improve the efficiency of patient triage, the prioritization of critical situations, and the allocation of resources. Better healthcare delivery, shorter wait times, and improved patient outcomes are the effects of this. © 2025 IEEE.

关键词： Patient treatment

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：