检索结果-内蒙古大学图书馆

2022 IEEE International Conference on Advances in Computing, Communication and Applied informatics, ACCAI 2022

作者： Ahamed, Saahirabanu Bhatt, Pankaj Sultanuddin, S.J. Walia, Ranjan Haque, M. Akiful Inayathahamed, S.B. Jazan University Department of Computer Science Saudi Arabia KIET Group of Institutions KIET School of Pharmacy Uttar Pradesh India Measi Institute of Information Technology Department of MCA Chennai India Model Institute of Engineering and Technology Department of Electrical Engineering Jammu India Anurag University Dept. of Pharmacy Medchal India Kalasalingam Business School Kalasalingam Academy of Research and Education Deemed to Be University Tamil Nadu 626126 India

ISBN: (纸本)9781665495295

Over the last decade, health care enabled services and accompanying advanced technology have been the subject of much research. Medical equipment, sensors, and healthcare professionals may all be linked through the Internet of Things (IoT) to deliver high-quality medical services everywhere. Patient safety has improved as a consequence, health care costs have decreased, access to health care has grown, and the sector as a whole has become more efficient and productive. According to recent study, Internet of Things (IoT)-based technologies offer enormous promise for enhancing health care. The most effective data analysis methods now use machine learning-Assisted techniques. Due to the conductivity of the top three layers, intelligent applications and services such as sickness detection, behavioural recognition, and intelligent support may be supplied. This research examined medical IoT improvements in terms of enabling technology, health care services, and implementations to address a variety of health care challenges. Additionally, potential issues and concerns about the Health Care IoT system are explored. As a consequence, future researchers interested in investigating and discovering new applications for health care IoT will have a credible resource for learning about the technology's many applications. © 2022 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems 12

Visual Cryptography and Blockchain for Protecting Against Ph...

引用

12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Malik, Shilpa Neagu, Bogdan Constantin Raboaca, Maria Simona Verma, Chaman Sandip University School of Computer Science and Engineering Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India Central Institute of Educational Technology Delhi India 'Gheorghe Asachi' Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

A recent poll suggests that a lot of people don't vote because they feel like they already have too much to do. This is because of many things, like the fact that everyone has to go to a polling place, wait in a long line, and be mentally and physically tired before they can vote. We have pushed for people to be able to vote online, but there are a growing number of problems and risks that could come with this. When websites are attacked by things like phishing, it might be hard to log in. With the help of Visual Cryptography (VC), We proposed Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems we made a voting system that lets people discuss and decide on important business ideas without giving away any information. © 2022 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Cyber-Physical System for Industrial Automation Using Quantum Deep Learning 11

Cyber-Physical System for Industrial Automation Using Quantu...

引用

11th International Conference on System Modeling and Advancement in research Trends, SMART 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eotvos Lorand Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

Automation industries are using Industry 4.0 solutions for several reasons. Still, the most important is to make it easier for their employees to get secure information and to work better with people in other departments. The goal of this effort is to improve productivity and efficiency by making it easier to make good decisions at the right time. This will make it possible to make these kinds of improvements. Constraint-based security solutions are becoming increasingly popular as the digital and physical worlds become more connected. Based on the processing speed, the amount of memory that can be used, and the amount of backup power that can be used. The algorithms and security frameworks used in earlier research need many resources. Several researchers have given an overview of the many security problems with the CPS and their studies and recommendations. But constraint-based applications need light security systems that can work with hardware with limited capabilities. We proposed a safety mechanism that can be used with the already-made constraint-based CPS program. To propose Cyber-Physical System for Industrial Automation Using Quantum Deep Learning, We proposed Cyber-Physical System for Industrial Automation that uses Quantum Learning to find attacking patterns. © 2022 IEEE.

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

Programmable large-scale simulation of lattices with photonic synthetic frequency dimensions

Programmable large-scale simulation of lattices with photoni...

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Senanian, Alen Wright, Logan G. Wade, Peter F. Doyle, Hannah K. McMahon, Peter L. Department of Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

We present a programmable analog simulator with up to 105 lattice sites capable of simulating 2D and 3D lattices, as well as lattices with non-planar connectivity. We also demonstrate the injection of arbitrary lattic...

ISBN: (纸本)9781957171258

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits 30th

Reconfigurable Virtual Accelerator (ReVA) for Large-Scale A...

引用

30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Yaguchi, Kazuki Maeda, Eriko Kawai, Shunya Teruya, Daichi Osana, Yasunori Miyoshi, Takefumi Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University 2-39-1 Kurokami Kumamoto Chuo-ku860-8555 Japan WasaLabo LLC. K-2 Buransyu 6-5-20 Minaminaruse Tokyo Machida-shi194-0045 Japan Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan

ISBN: (纸本)9783031856372

In recent years, hardware acceleration in large-scale computing fields such as Artificial Intelligence and High Performance Computing, faces hardware resource shortages. To overcome this problem, we propose Reconfigurable Virtual Accelerator (ReVA), which allows a large-scale acceleration circuit built using multiple FPGAs, processors and memory subsystems, to accelerate application programs. We have designed and implemented a prototype of Virtual Accelerator (VA) Generator for ReVA to investigate its performance. The VA Generator prototype employs an open-source HLS automated split compilation tool, RapidStream, to automatically generate placed-and-routed virtual accelerators that can be implemented on multiple FPGAs based on HLS dataflow designs. The VA Generator, which places VAs on the appropriate regions of FPGAs, allows large circuits to be performed like a single accelerator using several FPGAs. In addition, RapidStream’s parallel compilation technology allows the VA Generator to suppress the increasing compilation time according to the circuit size. Moreover, with our VA Generator prototype we have built and evaluated a VA of a large-scale circuit which cannot be fit in a single FPGA with multiple FPGAs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reconfigurable hardware

来源：评论

学校读者我要写书评

暂无评论

Experimental Analysis for Position Estimation using Trilateration and RSSI in Industry 4.0 11

Experimental Analysis for Position Estimation using Trilater...

引用

11th International Conference on System Modeling and Advancement in research Trends, SMART 2022

作者： Chouhan, Robin Singh Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map. © 2022 IEEE.

关键词： Indoor positioning systems

来源：评论

学校读者我要写书评

暂无评论

Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

引用

computers, Materials & Continua 2021年第6期67卷 3933-3948页

作者： Xiaorui Zhang Xuan Chen Wei Sun Xiaozheng He Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Civil and Environmental Engineering Rensselaer Polytechnic InstituteTroyNY12180USA

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

关键词： Vehicle re-identication densenet joint loss focal loss SE block

来源：评论

学校读者我要写书评

暂无评论

Regression models for predicting the effect of trash rack on flow properties at power intakes

引用

engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Karami, Hojat Salwana, Ely Rezaei, Alireza Shahmirzadi, Danyal Band, Shahab S. School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department Imam Muhammad Ibn Saud Islamic University Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Civil Engineering Department Semnan University Semnan Iran Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Malaysia Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliou Taiwan International Graduate School of AI National Yunlin University of Science and Technology Yunlin Taiwan

Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models–Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB)–and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3 m³ with various submergence depths and Froude numbers on nine trash racks with 63.7%–84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ((Formula presented.)) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for (Formula presented.) estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), (Formula presented.) (0.9971), WI (0.9727) for (Formula presented.) estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： experimental modelling Hydro power plant regression models vortex strength

来源：评论

学校读者我要写书评

暂无评论

Improving State of Charge Estimation for Lithium-Ion Batteries through Optimized CNN Models

Improving State of Charge Estimation for Lithium-Ion Batteri...

引用

Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Indradeep Kumar Madhavi Dasari Chaitanya Danamaraju Bindu K V V. Mohanavel Joshuva Arockia Dhanraj Department of Aeronautical Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computer Science Sapthagiri College of Engineering Bengaluru Karnataka India Department of Electrical and Electronics Engineering RNS Institute of Technology Bengaluru Karnataka India Department of Science And Humanities( General Engineering) R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Chennai Tamil Nadu India University Centre for Research & Development (UCRD) Chandigarh University Mohali Punjab India

Lithium-ion (Li-ion) batteries are the preferred choice for electric vehicles (EVs) because of their extended lifespans, low self-discharge rates, high voltage, and high energy density. A well-functioning Battery Management System (BMS) is critical to the efficient operation of an EV. The State of Charge (SoC) is an important statistic that reflects the remaining charge in the battery, and its exact assessment is essential for BMS and improving EV efficiency, which extends the battery's life and decreases the probability of catastrophic failure. However, SOC estimation is complicated and affected by numerous unknowns, such as battery age and external temperature. In this study, we estimated SOC using a Convolutional Neural Network (CNN) model. To improve the CNN architecture, this study has applied three different optimization algorithms: Particle Swarm Optimization (PSO), Elephant Search Algorithm (ESA), and Equilibrium Optimization (EO). Sensor data from lithium-ion batteries were carefully processed. The processed dataset was then supplied to the CNN and three optimized CNN models. These models were tested using error, R2, and time metrics to identify the optimal technique. CNN-ESA outperformed the other CNN models in SOC estimation, with the lowest error rates and the highest R2 value of 0.9987. This simulation result demonstrates the effectiveness of applying ESA to improve CNN architectures for better Li-ion battery SOC estimates. It enhances the efficiency and lifespan of EVs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multimodal Feature Extraction for Memes Sentiment Classification 2

Multimodal Feature Extraction for Memes Sentiment Classifica...

引用

2nd IEEE Conference on Information technology and Data Science, CITDS 2022

作者： Ouaari, Sofiane Tashu, Tsegaye Misikir Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022 IEEE.

关键词： computer vision

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：