检索结果-内蒙古大学图书馆

2nd International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

来源：评论

学校读者我要写书评

暂无评论

Design an Optimal Augmented PID controller for Electric Vehicle Speed control 23th

Design an Optimal Augmented PID Controller for Electric Vehi...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Abood, Layla H. Ali, Inaam I. Abood, May H. Control and System Engineering Department University of Technology Baghdad Iraq Electrical Engineering Department University of Technology Baghdad Iraq Computer Engineering Department University of Al Iraqia Baghdad Iraq

ISBN: (纸本)9783031648465

The prominence growth of the Electric Vehicle (EV) industry requests from researcher to adopts effective controlling techniques for marinating different issues. In this paper an augmented PID controller is suggested for controlling speed in EV based using sigmoid function relation and the smart and unique Gray wolf Optimization (GWO) algorithm to achieve a satisfied and robust system desired value, a comparison is utilized with PID controllers (Optimal PID, Normal PID) to evaluate its behavior then a robustness analysis is applied by varying system desired speed in multiple periods, the controller track the desired value perfectly and force the system to an efficient response with robust system behavior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electric vehicles

来源：评论

学校读者我要写书评

暂无评论

Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

引用

computer Modeling in engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

来源：评论

学校读者我要写书评

暂无评论

Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

引用

Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

来源：评论

学校读者我要写书评

暂无评论

Practical Realization of Only Active Floating Lossless Inductance Simulator 10

Practical Realization of Only Active Floating Lossless Induc...

引用

10th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2024

作者： Roongmuanpha, Natchanai Unhavanich, Sumalee Pukkalanun, Tattaya Tangsrirat, Worapong School Of Engineering Department Of Computer Engineering Bangkok10520 Thailand Faculty Of Digital Technology Bangkok10300 Thailand School Of Engineering Department Of Instrumentation And Control Engineering Bangkok10520 Thailand

ISBN: (纸本)9798350385946

This paper describes an electronically controllable floating lossless inductance simulator circuit implemented with only off-the-shelf available integrated circuits, namely LT1228 and operational amplifier (OA). The proposed design consists solely of two LT1228s and a single OA. By using an active element named LT1228, the equivalent inductance can be tuned electronically with depended on the externally supplied current applied to LT1228. The proposed simulator imposed just one component matching restriction that required the equality of two transconductances. It is easily attainable with a simple current mirror. The worthiness of the proposed floating inductance simulator has been proven and applied to the construction of the 2nd-order RLC highpass/bandpass filter as an application. PSPICE simulations are used to investigate the characteristics of the proposed simulator and its filter realization. © 2024 IEEE.

关键词： Operational amplifiers

来源：评论

学校读者我要写书评

暂无评论

DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

引用

Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

来源：评论

学校读者我要写书评

暂无评论

YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

引用

Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Satellite Integration into 5G:Deep Reinforcement Learning for Network Selection

引用

Machine Intelligence Research 2022年第2期19卷 127-137页

作者： Emanuele De Santis Alessandro Giuseppi Antonio Pietrabissa Michael Capponi Francesco Delli Priscoli Department of Computer Control and Management Engineering“Antonio Ruberti”University of Rome La SapienzaRome 00185Italy

This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load *** particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

关键词： Network selection HetNet deep reinforcement learning deep-Q-network(DQN) 5G communications

来源：评论

学校读者我要写书评

暂无评论

Accelerated Quantum Selection Optimization of a Microwave Filter 1

Accelerated Quantum Selection Optimization of a Microwave Fi...

引用

1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Martinez, Gabriel F.E. Niccolai, Alessandro Zich, Eleonora L. Zich, Riccardo E. Politecnico di Milano Department of Energy Milan Italy Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798331527587

Optimization techniques have long been employed in designing electromagnetic structures. With the emergence of quantum computing, hybrid quantum-classical optimization algorithms are appearing as promising solutions. This paper presents a new approach that integrates quantum algorithms with classical optimization to profit from the advantages of both quantum and classical computing. © 2024 IEEE.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Annotated survey and perspectives on rail transport energy system RAMS evaluation technology

引用

Green Energy and Intelligent Transportation 2024年第3期3卷 75-90页

作者： Ruifeng Shi Lingzhi Zhang Feng Lin Jin Ning Limin Jia Kwang Y.Lee School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China China Institute of Energy and Transportation Integrated Development Beijing 102206China State Key Lab of Rail Traffic Control&Safety Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering Baylor UniversityWacoTX 76798USA

The rail transit system plays a crucial role in modern *** the increasing demand for clean and green energy in the transport sector,its energy system is expected to achieve low-carbon and highly efficient energy utilization in rail ***,the gradual development of the rail transport energy system has led to an increase in its complexity,and the rising difficulty of system assessment has faced the limitations of traditional assessment ***,it is essential to develop effective assessment *** paper begins by providing a systematic review of the development status of Reliability,Availability,Maintainability and Safety(RAMS)assessment and analyzing the shortcomings of traditional RAMS assessment technology in the context of rail transit energy ***,based on the four fundamental properties of RAMS,it summarizes the current state of key assessment technologies in the field of rail ***,the paper delves into the challenges and potential solutions concerning the implementation of RAMS assessment technology for rail transit energy ***,the paper offers an outlook on the future development of RAMS assessment for rail transport energy *** comprehensively analyzing these aspects,the paper aims to contribute valuable insights into optimizing the rail transit energy system,promoting its sustainable and efficient operation in the context of clean and green energy utilization.

关键词： Integration of energy and transportation RAMS evaluation Rail transit energy system

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：