检索结果-内蒙古大学图书馆

Recent progress and perspectives in additive manufacturing of magnesium alloys

Journal of Magnesium and Alloys 2022年第6期10卷 1511-1541页

作者： Zhuoran Zeng Mojtaba Salehi Alexander Kopp Shiwei Xu Marco Esmaily Nick Birbilis College of Engineering and Computer Science Australian National UniversityACT.2601 Australia Additive Manufacturing Group Singapore Institute of Manufacturing Technology(SIMTech)Agency forScienceTechnology and Research(A∗STAR)73 Nanyang Drive637662 Singapore Meotec GmbH Philipsstr.8AachenGermany State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsh1410082 China Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139United States of America Volvo Materials Technology Laboratory Volvo GroupLundbyGothenburg 41715Sweden

Magnesium alloys remain critical in the context of light-weighting and advanced devices. The increased utilisation of magnesium(Mg)each year reveals growing demand for its Mg-based alloys. Additive manufacturing(AM) provides the possibility to directly manufacture components in net-shape, providing new possibilities and applications for the use of Mg-alloys, and new prospects in the utilisation of novel physical structures made possible from ‘3D printing’. The review herein seeks to holistically explore the additive manufacturing of Mg-alloys to date, including a synopsis of processes used and properties measured(with a comparison to conventionally prepared Mg-alloys). The challenges and possibilities of AM Mg-alloys are critically elaborated for the field of mechanical metallurgy.

关键词： Magnesium Additive manufacturing Microstructure 3D printing Mechanical properties Electrochemical properties

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques for Analysis of Mars Weather Data 15

Machine Learning Techniques for Analysis of Mars Weather Dat...

引用

15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Kemat, Baharu Bin Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Romania

ISBN: (纸本)9798350321388

The exploration of Mars has provided vast amounts of weather data that present unique challenges for analysis and prediction. To address these challenges, this research paper focuses on the application of machine learning techniques for the analysis of Mars weather data. The objective is to develop models that can effectively extract patterns, uncover hidden relationships, and enable accurate predictions in the Martian weather system. The research begins with a comprehensive review of the available Mars weather data. The dataset, consisting of historical records, serves as the foundation for training and evaluating machine learning models. The analysis of the Mars weather dataset reveals the planet's icy and harsh climate, with average maximum temperatures of around -21°C and average minimum temperatures of -80°C. The temperature variations show that the minimum temperature fluctuates within a narrower range of 20-30°C over the course of Mars sols from 0 to 2000, while the maximum temperature experiences larger variations of about 40-50°C. During this time, the atmospheric pressure on Mars fluctuates between 720 Pa and 950 Pa. In addition, using the elbow method revealed that 3 clusters were the ideal number for identifying distinct patterns in the weather data. The linear regression model also attained an accuracy of 85%, demonstrating its efficacy in forecasting weather patterns on Mars. © 2023 IEEE.

关键词： Sols

来源：评论

学校读者我要写书评

暂无评论

Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition

Patch-NetVLAD+: Learned patch descriptor and weighted matchi...

引用

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2022

作者： Cai, Yingfeng Zhao, Junqiao Cui, Jiafeng Zhang, Fenglin Feng, Tiantian Ye, Chen Tongji University School of Electronics and Information Engineering Department of Computer Science and Technology Shanghai201804 China Tongji University Institute of Intelligent Vehicle Shanghai201804 China Tongji University School of Surveying and Geo-Informatics Shanghai200092 China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific region (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding the LSRs that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 9.3% performance improvement than existing patch-based methods. © 2022 IEEE.

关键词： Location awareness Visualization Aggregates Semantics Intelligent systems IEEE Regions

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

引用

Sensors 2024年第8期24卷 2531页

作者： Kristiani, Endah Wang, Lu-Yan Liu, Jung-Chun Huang, Cheng-Kai Wei, Shih-Jie Yang, Chao-Tung Department of Computer Science Tunghai University Taichung City407224 Taiwan Department of Informatics Krida Wacana Christian University Jakarta11470 Indonesia Industrial Technology Research Institute Hsinchu310401 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung City411030 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City407224 Taiwan

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. © 2024 by the authors.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Fact-Checking via Recursively Verifying Presuppositions

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2189-2199页

作者： Xiucheng Lyu Runcong Zhao Jiazheng Li Bin Liang Min Yang Lin Gui Ruifeng Xu School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China Department of Informatics King’s College London London U.K. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong SAR China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China

Presuppositions are implicit assumptions that interlocutors take for granted. Verifying presuppositions is crucial for reasoning-based fact-checking that aims to reason about True or False, but existing pipelines have overlooked it. We first raise this critical issue with the community and propose a system that addresses the generation, verification, and reasoning of presuppositions through a recursive question-answering process. Importantly, our system automatically determines which knowledge entails which presuppositions, making it an unsupervised system for evidence retrieval, fact-checking, and justification production. We conduct a comprehensive experiment on the FEVER, Vitamin C, and FEVEROUS-S datasets. Our system outperforms competitive baselines, including LLM, and achieves state-of-the-art unsupervised performance. We highlight this critical issue to encourage further research on reasoning-based fact-checking.

关键词： Cognition Training Speech processing Pipelines Electronic mail Production Semantics Tree traversal Security Reliability

来源：评论

学校读者我要写书评

暂无评论

IoT based Novel Face Detection Scheme using Machine Learning Scheme

IoT based Novel Face Detection Scheme using Machine Learning...

引用

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

作者： Sumathi, K. Sakthi, D. Vishnu Nirmala, G Sellamuthu, P. Walia, Ranjan Usman, Mohd Sri Sairam Engineering College Department of ECE Chennai India R.M.D Engineering College kavaraipettai India Variyar Engineering College Vinayaka Mission's Research Foundation Department of Mechanical Engineering Salem India Model Institute of Engineering and Technology Department of Electrical Engineering Jammu India M D. College of Technology and Management Department of Computer Science

ISBN: (纸本)9781665495295

Face detection scheme is the most significant operation of the Digital Image Processing domain, in which it is supportive to many industries to resolve the security issues and the associated time savvy functionalities. At the moment, safety concerns about the Internet of Things (IoT) are widespread. Facial recognition in the presence of selective concealment has developed into a critical issue impacting social protection. This research proposes a unique paradigm for face recognition by using the Internet-of-Things security environment, in which it is utilized to identify certain criminal actions. Our developed framework incorporates color and contour information into a learning algorithm and its advantage in detecting faces with extreme occlusion has been proved to be persistent. These services are divided into three categories: initially to introduce a novel face identification approach depending on the efficiency feature;next, employ a Support Vector Machine (SVM) model to generate feature representations for occluded faces. Finally, an unique sparse classification framework with a deep learning approach is created to determine whether the observed face is covered. Statistical analysis demonstrates that this approach outperforms the advancements in terms of effectiveness and resilience. At a frame rate of ten frames-per-second, the created face recognition method achieves 98.5% accuracy despite the presence of various forms of extreme deformations in faces and the developed occlusion verification technique achieves 96.5% accuracy. © 2022 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

引用

Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

来源：评论

学校读者我要写书评

暂无评论

SLA-SCA: Secure and Lightweight Authentication and Key Agreement Mechanism for Smart City Applications

SLA-SCA: Secure and Lightweight Authentication and Key Agree...

引用

2023 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2023

作者： Pant, Bhairvi Obaidat, Mohammad S. Singh, Shreya Pant, Mudita Wazid, Mohammad Das, Ashok Kumar Hsiao, Kuei-Fang Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Ctr. for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500032 India University of Texas-Permian Basin Computer Science Department OdessaTX 79762 United States

ISBN: (纸本)9798350302561

The notion of a smart city is a modern concept characterized by the widespread integration of the Internet of Things (IoT) across various facets of urban life, including healthcare, traffic management, agriculture, and more. This establishes real-time communication and data exchange between all users, servers and devices through the Internet. As we begin to discuss communication between parties, one must also recognize the imperative need for this exchange to be secure and private, as such exchanges are extremely vulnerable to attacks such as man-in-the-middle and replay attacks and even situations like compromising of anonymity during exchanges. This paper focuses on finding an innovative solution to the same challenge. It proposes a mechanism to establish secure communication between the smart IoT devices and the connected cloud servers through a secure mutual authentication and key establishment process with a short name 'SLA-SCA'. The security analysis of the proposed SLA-SCA is done, which proves its resilience against the various potential attacks of such a communication. The proposed SLA-SCA outperformed the other existing schemes regarding important security and functionality features. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

AI-enabled Crop Health Monitoring and Nutrient Management in Smart Agriculture

AI-enabled Crop Health Monitoring and Nutrient Management in...

引用

International Conference on Contemporary Computing and informatics (IC3I)

作者： Suman Kumar Swarnkar Leelkanth Dewangan Omprakash Dewangan Tamanna Manishkumar Prajapati Fazle Rabbi Department of Computer Science and Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur India Department of CSE Datta Meghe Institute of Higher Education and Research (Deemed to be University) Faculty of Engineering & Technology (FEAT) Swangi Wardh Department of CS & IT Kalinga University Raipur Chhattisgarh India Department of Computer Science Shri D. N. Institute of Computer Applications Gujarat India Australian Computer Society Australia

The rapid advancement of artificial intelligence (AI) has paved the way for transformative applications in various fields, and agriculture is no exception. In this research paper, we explore the integration of AI technologies in smart agriculture to enhance crop health monitoring and nutrient management. By leveraging AI algorithms and techniques, we aim to improve the efficiency and sustainability of farming practices. The paper delves into the utilization of AI for early detection of crop diseases and pests, as well as the development of predictive models for precise nutrient management. Remote sensing and imaging technologies play a crucial role in real-time monitoring, allowing for informed decision-making. Through a series of case studies and evaluations, we demonstrate the effectiveness and potential benefits of AI-enabled crop health monitoring and nutrient management in modern agriculture. Despite the promising results, challenges and limitations are discussed, and future directions for research and application are suggested.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of an application for recognizing mathematical formulas using convolutional neural networks

Development of an application for recognizing mathematical f...

引用

Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Afshan Zareen Kuljeet Kaur Mayur V. Savsani Swapnil Parikh Harshal Patil Ramya Maranan Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Statistics Dr. Yagnik Road H & H. B. Kotak Institute of Science Rajkot Gujarat India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

In the ever-expanding landscape of digital content, the ability to recognize and understand mathematical formulas is crucial for various applications, including academic research, document processing, and educational technology. This project introduces a prototype system designed to recognize mathematical formulas using the YOLOv5 (You Only Look Once) object detection framework. YOLOv5’s real-time object detection capabilities make it a promising choice for identifying and extracting mathematical expressions seamlessly from digital documents. The proposed prototype system addresses a critical gap in the field of OCR by focusing on the specialized domain of mathematical notation recognition. The YOLOv5-based approach offers real-time processing capabilities, making it suitable for applications that require quick and accurate identification of mathematical content in various digital contexts.

关键词： YOLO Neural networks Optical character recognition Symbols Prototypes Training data Real-time systems

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：