检索结果-内蒙古大学图书馆

2024 IEEE Global Communications Conference, GLObECOM 2024

作者： Wang, bingying Zheng, Jun Li, Cheng Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798350351255

This paper proposes an enhanced resource selection (eInP-Rs) scheme for efficient intra-platoon message delivery of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs). To achieve this goal, the eInP-Rs scheme allows each vehicle to transmit CAM and DENM packets on a C-v2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based sPs scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2;a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1;an intra-platoon cooperation mechanism is introduced to enable the PL of a platoon to know the resource and channel occupation information of the platoon's hidden nodes on CH1 and CH2;and a packet collision detection mechanism is used to enable a non-platoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. simulation results show that the proposed eInP-Rs scheme outperforms the standardized sensing-based sPs scheme in terms of the intra-platoon CAM/DENM delivery ratio and the average intra-platoon DENM delivery delay. © 2024 IEEE.

关键词： Magnetic levitation vehicles

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Design of Regulated voltage Flow Control Mechanism using DC-to-DC boost Converter 4

A Comprehensive Design of Regulated Voltage Flow Control Mec...

引用

4th IEEE International Conference on smart Technologies in Computing, Electrical and Electronics, ICsTCEE 2023

作者： Rajalakshmi, G. Maheswari, M. Uma senthilkumar, b. Princy, b. Anni Glory, E. Ganga, s. Sathyabama Institute Of Science And Technology Department Of Ece Tamil Nadu Chennai India R.M.D. Engineering College Department Of Eee Tamil Nadu Chennai India V.S.B. Engineering College Department Of Eee Tamil Nadu Karur India Panimalar Engineering College Department Of Cce Tamil Nadu Chennai India Simats Saveetha School Of Engineering Department Of Biosciences Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department Of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350384628

The outcomes of this research have led to the development of an innovative method for converters that enables the flexible distribution of load power across several inputs. In addition, the management of energy storage input sources like charging and discharging may be done with a high degree of precision. The primary objective of this project is to construct a DC-to-DC boost converter and test its performance in real-world conditions. The input and output voltages are managed by a microprocessor, which is also responsible for transmitting the necessary control signal to the switching circuit. The input and output voltages are shown on a liquid crystal display (LCD). In response to the information provided by the potentiometer, the microcontroller alters the proportion of time that the control signal is active. This modulates the output voltage. The off-line power supply of a computer, for example, takes the 120v or 240v ac energy supplied by the utility company and rectifies it into a dc value of around 170v or 340v. A constant supply of either 5v or 3.3v is often required by the processor ICs;hence, a dc-dc converter lowers down the voltage to one of those levels. This system's dynamic behavior is nonlinear because it functions in switch-mode, which operates in a nonstandard manner. Alterations in the load or the input line voltage are two examples of the kind of significant changes that might cause the system to vary from its regular settings and make it susceptible to failure. because the input is often derived from a solar array, the design and simulation that is presented in this work account for the whole spectrum of radiations and temperatures. During the course of this study, we investigate the boost converter equations and recommend the design components and simulation for a DC/DC converter. The study will be carried out in a solar system to establish the times of day during which the system generates the most amount of power. © 2023 IEEE.

关键词： Flow control

来源：评论

学校读者我要写书评

暂无评论

Investigation of Deep Learning Methodologies in satellite Image based ship Detection

Investigation of Deep Learning Methodologies in Satellite Im...

引用

2022 International Conference on sustainable Computing and Data Communication systems, ICsCDs 2022

作者： Joseph, I. Karunakaran, v. sujatha, T Edwin Rai, s benson velliangiri, s Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India Bengaluru India Sri Krishna College of Engineering and Technology Department of Ai Ds Coimbatore India Higher Colleges of Technology Department of Computer Information Sciences Fujairah United Arab Emirates B v Raju Institute of Technology Department of Computer Science and Engineering Telengana Hyderabad India

ISBN: (纸本)9781665478847

Maritime surveillance is one of the major phases of security concern of borders to track the illegal movement across the borders. Over the past decade, deep learning proves in majority of applications for an efficient object detection and classification. This research article gives the detailed overview of various deep learning based architecture for an efficient ship detection using satellite images. © 2022 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Predicting the Maximum Temperature at Two Meters Above Earth's surface Using CNN-LsTM Attention Mechanism

Predicting the Maximum Temperature at Two Meters Above Earth...

引用

bIT Conference (bITCON), International

作者： Ravi bhasker Nishant Kumar Ravi Kumar burman Abhishek Kumar Md. shoaib Alam Kamaldeep Kamaldeep Computer Science and Engineering Jharkhand University of Technology Ranchi India Electronics and Communication Engineering B.I.T. Sindri Dhanbad India Computer Science St.Xavier’s College Ranchi India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Climate monitoring, energy management, and agriculture are just a few of the sectors that depend on accurate temperature forecasts. by integrating a CNN and LsTM with an attention mechanism, this study seeks to develop a more precise temperature forecasting model. Using meteorological data gathered from NAsA's Prediction of Worldwide Energy Resources (POWER) platform, the suggested model is intended to forecast the maximum temperature at two meters above Earth's surface. benchmarks for comparison are established time series forecasting models like ARIMA and Fb Prophet. The attention mechanism helps the CNN-LsTM model better focus on significant time steps. CNN layers are used to capture spatial patterns in the data, and LsTM layers are used to characterize temporal dependencies. A number of metrics, including RMsE, MAE, RRsE, and R 2 , are used to assess the models' performance. As evidenced by the much reduced RMsE (1.61) and higher R 2 (0.92), the CNN-LsTM with attention mechanism performs better than traditional models in temperature prediction, according to the results. As compared to conventional methods, the study finds that deep learning models—especially CNN-LsTM with attention—offer a more reliable and accurate method for predicting temperature. This suggests new directions for future research in real-time forecasting, model optimization, and the incorporation of other environmental variables.

关键词： Meters Earth Deep learning Temperature distribution Attention mechanisms Time series analysis Weather forecasting Predictive models Forecasting Long short term memory

来源：评论

学校读者我要写书评

暂无评论

An Automatic ATM Card Fraud Detection Using Advanced security Model based on AOA-CNN-XGboost Approach 1

An Automatic ATM Card Fraud Detection Using Advanced Securit...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Porkodi, T. Rajkumar, R. sathiya, R. senthil Raja, M. B. S. Abdur Rahman Crescent Institute of Science & Technology Department of Commerce Tamil Nadu Chennai India Annamalai University Chidambaram Department of Commerce Tamil Nadu Cuddalore India St. Ann's College of Arts & Science Annamalai University Department of Computer Science Tamil Nadu Tindivanam India College of Engineering & Technology SRMIST Kattankulathur Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350371802

The implications of financial fraud are becoming more significant as the risk of it grows. For two main reasons, data mining for the purpose of detecting ATM card fraud is quite difficult. First, datasets about ATM card fraud frequently display significant bias. second, what constitutes legitimate and fraudulent behavior is defined differently every time. The variables chosen and the sample methods used have a direct bearing on how well ATM card fraud detection algorithms work. Card fraud at ATMs is a major threat to the security of online payment systems and banking. Model training, feature reduction, preprocessing, and procedure selection must all be part of a systematic approach. The importance of preprocessing in avoiding inaccurate model output and keeping data quality high cannot be overstated. As part of the preprocessing step known as feature reduction, correlation-based measures are used to examine feature correlation. After that, the model is trained using AOA-CNN-XGboost, which produces remarkable outcomes with a 97.13% accuracy rate. © 2024 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

secure De-Duplication Over Wireless sensing Data Using Convergent Encryption 3

Secure De-Duplication Over Wireless Sensing Data Using Conve...

引用

3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Yuvaraj, D. Kumar, v. Pradeep Anandaram, Harishchander samatha, badugu Krishnamoorthy, R. Thiyagarajan, R. Cihan University Department of Computer Science Duhok Iraq B v Raju Institute of Technology Dept of Cse Narsapur India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa India Vignan's Foundation for Science Technology and Research Department of Cse Guntur India Prathyusha Engineering College Department of Ece Chennai India Prathyusha Engineering College Department of It Chennai India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

The fast development of cloud computing technology has greatly accelerated the gathering and sharing of numerous types of data. Utilizing data deduplication technologies is crucial to decrease storage overhead and transmission costs. A significant quantity of processing and storage resources are made available for a variety of high-quality service applications using cloud computing, which is an online data center. In any case, cloud clients never again approach their information in a nearby information stockpiling foundation, making it challenging to really look at the respectability of information that has been re-appropriated. It has become crucial to assist the user in fully verifying the accuracy of the data that has been outsourced. This issue is settled utilizing safe encryption and de-duplication systems, and clients might use an outsider examiner to confirm the precision of information that has been re-appropriated and the encryption technique. Existing data deduplication systems still have efficiency and security issues. Information deduplication is achieved by the proposed procedures preceding the information being shipped off the distributed storage server, which brings down the expense of calculation and transmission. in particular, block-level deduplication with variable size. De-duplication strategies have been utilized to forestall information duplication by contrasting records in the client data set utilizing PHP;this further develops stock piling's memory proficiency and rates up handling. Clients encode information utilizing concurrent encryption techniques prior to re-appropriating it to the cloud to keep unapproved clients from getting to the information. © 2022 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games 2

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Abirami, P. Antony Joseph Rajan, D. vivekanandan, T. saillaja, v. Mohankumar, N. Rajmohan, M. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sakunthala Engineering College Vel Tech Multi Tech Dr Rangarajan Dr Department of Mathematics Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Kattankulathur Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371314

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual's performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system's influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Prediction and Optimization of Life Insurance Prediction based on FEA and ANN Hybrid Approach

Prediction and Optimization of Life Insurance Prediction Bas...

引用

Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Tata Hari Krishna sagar Chandu bandpatte K Mamatha bharanidharan R Deepak Asrani Ratan Rajan srivastava Department of Mechanical Engineering V R Siddhartha Engineering College Vijayawada India Department of Chemical Engineering Bharati Vidyapeeth Deemed to be University College of Engineering Pune Maharashtra India Department of Mathematics Vardaman College of Engineering (Autonomous) Shamshabad India Department of EEE Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering BN College of Engineering and Technology Lucknow India Department of Computer Science and Engineering B.N. College of Engineering and Technology Lucknow India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Underwriting, the process of accepting applicants into an insurance fund and determining their coverage terms, is a crucial aspect of obtaining life insurance. Everyone from the applicant to the underwriting team has a lot of work to do throughout the process. A long application form detailing the applicant's life is required to be filled out. After that, the application will be reviewed by the underwriting group, who will then provide the applicant with coverage options after ranking their risk. Training a model, extracting features, and preparing data all depend on proper order. In data preprocessing, we use data reduction, data cleansing, data normalization, and translation. Improve your algorithm's feature selection performance using CFs by removing noise and useless data. As an unsupervised linear feature extraction method, principal components analysis (PCA) can help minimize data size by identifying features with the most information. When training a FEA-ANN model, feature extraction is an essential step. When compared to ANN and FEA, two state-of-the-art algorithms, the proposed method performs better. According to the results, there was a notable improvement, as the accuracy rate was 96.51%.

关键词： Training Accuracy Computational modeling sociology Insurance Predictive models Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A New Method for scene Classification from the Remote sensing Images

引用

computers, Materials & Continua 2022年第7期72卷 1339-1355页

作者： Purnachand Kollapudi saleh Alghamdi Neenavath veeraiah Youseef Alotaibi sushma Thotakura Abdulmajeed Alsufyani Department of CSE B V Raju Institute of TechnologyNarsapurMedakTelanganaIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaVijayawadaA.P.India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of ECE P.V.P Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of *** recent years,a lot of interest has been generated in researching remote sensing image scene *** sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RsIsU)*** the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for Rs image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RsIsU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene *** benefits(POEP)include two *** multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RsIsU data ***,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order *** enablesCNNs to better cope with(RsIsU)issues by providing more representative feature *** data for this paper is stored in a UCI dataset with 21 types of *** the beginning,the picture was pre-processed,then the features were retrieved using REsNET-50,Alexnet,and vGG-16 integration of *** characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take *** utilize an ensemble classifier in our classification a

关键词： Remote sensing RsIsU DL REsNET-50 vGG-16

来源：评论

学校读者我要写书评

暂无评论

Waste Classification using Edge based Multilayer White shark Dense Net-spiking Neural Network from Urban Areas 3

Waste Classification using Edge based Multilayer White Shark...

引用

3rd International Conference on Automation, Computing and Renewable systems, ICACRs 2024

作者： Kiran, Chitra N. Rawat, Ruchira Madhavi, T. Pothinathan, s.K.M. Rabadiya, Dhaval Kumar, Karthik Department of Electronics and Communication Engineering Alliance University Karnataka Bangalore562106 India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun248 002 India R.B.V.R.R. Women's College Telangana Hyderabad India Department of Civil Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Faculty of Management studies Parul University P.O.Limda Ta.Waghodia Gujarat Vadodara391760 India School of Civil Engineering REVA University Karnataka Bengaluru560064 India

ISBN: (纸本)9798331532420

Effective waste management is crucial for human health and a clean environment. segregating waste based on types and classification is essential for recycling. Traditional methods, like manual sorting, are labor-intensive and error-prone, leading to environmental pollution and resource depletion and to classify the waste several deep learning algorithms used, but the accuracy is decreased, computational time, cost and complexity is increased. To overcome these issues, this work is proposed. This study proposes a Waste classification using Edge based Multilayer White shark Dense Net-spiking Neural Network (EbMW2sD-Net) from urban Areas. In this, for classifying the waste images urban areas are collected using stanford TrashNet dataset. These images are collected from the various IoT sensors, so that, these images are full of noises, so, that these data are pre-processed and its features are extracted using the Orthonormal s-Transforms with Multiple Discrete (OsTMD). Then these data are classified using the Edge based Multilayer skill Dense Net-spiking Neural Network method with seven categories (cardboard, glass, metal, organic, paper, plastic, and trash). Then, cardboard, organic, and paper class images are considered biodegradable waste, and other classes are considered non-biodegradable waste. The White shark Optimization Algorithm (WsOA) further improves prediction accuracy and reduces error rates, with the entire model implemented on the Python platform. From the analysis the EMHs-ParCNN model attains 99.9% accuracy, 99.8% recall and attains better results compared with the existing methods. © 2024 IEEE.

关键词： Cardboard

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：