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共 35128 条记录，以下是4511-4520 订阅

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Enhancement of Power Quality in PM-BLDC Motor Drive using Bridgeless Landsman PFC Converter for Low Power Home Appliances 1

Enhancement of Power Quality in PM-BLDC Motor Drive using Br...

引用

1st international conference on intelligent Technologies for Sustainable Electric and Communications systems, iTech SECOM 2023

作者： Navaneethakrishnan, S.M. Arunkumar, S. Mugil Arasu, M. Ruthra Moorthy, P. Kumaraguru College of Technology Department of Eee Coimbatore India

ISBN: (纸本)9798350371185

This paper focuses on developing and deploying the Landsman converter with a Bridgeless (BL) arrangement to control a Brushless DC Motor (BLDCM) drive, which consists of an LC filter connected in series with the BL-Landsman converter which is then linked with VSI to drive the BLDCM. This arrangement eliminates the need for Diode Bridge Rectifiers (DBR) and the losses that show up with them. The converter is made to operate in a Discontinuous Inductor Current Mode (DCIM) in order to maneuver the input DC voltage fed to the Voltage Source Inverter (VSI) thereby controlling the speed of the BLDCM henceforth the control switches of VSI are operated by the signals of low frequency in this way the switching losses are minimized. This reduces the output ripples at the load side and improves the input side Power factor. The MATLAB -Simulink platform is utilized to evaluate the performance of the overall system. © 2023 ieee.

关键词： Brushless DC motors

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A Survey on Cooperative control of Multi-Agent systems 2

A Survey on Cooperative Control of Multi-Agent Systems

引用

2nd international conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Yang, Zecheng Ma, Nan Yao, Yongqiang Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Beijing University of Technology Faculty of Information and Technology Beijing100124 China Beijing Shuncheng High-tech Co. Ltd Beijing China

ISBN: (纸本)9798350360363

Cooperative control of multi-agent systems involves the coordination and collaboration of multiple intelligent agents to achieve common goals or tasks. This field finds extensive applications in robotics, intelligent transportation systems, the Internet of Things, and unmanned systems, garnering substantial attention from both academia and industry. Moreover, it serves as a fundamental requirement for intelligent interaction and human-robot collaboration, offering crucial support for machines in perceiving their external environments. Over the last decade, notable strides have been made in this domain. Consequently, this paper conducts a comprehensive review of recent advancements. Initially, this paper delineate the backdrop of cooperative control in multi-agent systems, followed by an exploration of the research's progression. Subsequently, various methodologies for formation and collaboration are introduced, with an in-depth discussion of advanced cooperative control algorithms for multi-agent systems. Lastly, this paper provides a prospective outlook on the future direction of multi-agent cooperative control. © 2023 ieee.

关键词： Multi agent systems

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Performance Evaluation of AI Algorithms on Heterogeneous Edge Devices for Manufacturing 18

Performance Evaluation of AI Algorithms on Heterogeneous Edg...

引用

ieee 18th international conference on automation Science and Engineering (ieee CASE)

作者： Rupprecht, Bernhard Hujo, Dominik Vogel-Heuser, Birgit Tech Univ Munich TUM Sch Engn & Design Inst Automat & Informat Syst Munich Germany

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Novel Artificial Intelligence (AI) approaches try to process an excessive amount of field-level data. However, challenges arise as network bandwidth is limited, and thus this data cannot be entirely transferred to the cloud for further processing. Edge computing tries to overcome that limitation by bringing the computational resources closer to the data generating sources. However, edge devices are also constraint by both CPU power and memory, and strict real-time requirements of the manufacturing domain have to be met. Thus, the selection of suitable devices for specific AI algorithms poses a severe challenge. Currently, the choice is often made by a trial-and-error approach or by selecting more powerful devices than needed. This paper tries to address those challenges by showing relevant aspects for algorithm benchmarking in the manufacturing domain. Selected algorithms, namely Grubbs Test, Butterworth Filter, DBSCAN, Random Forest, Support Vector Machine, Matrix Multiplication, and Matrix Inversion, are examined. Analysis of their theoretical time and space complexity sheds some light on the behaviour of the algorithms with respect to their input data points. In addition, relevant metrics for the manufacturing domain, such as execution time, memory consumption, and energy consumption, are identified. This paper furthermore examines the algorithm behaviour on various heterogeneous hardware devices, such as PLCs, an MCU, IPCs, a single-board computer, and a dedicated edge device. Altogether, this paper can guide selecting suitable algorithms and hardware to equip cyber-Physical Production systems (CPPS) with novel data processing solutions thoughtfully. Moreover, the presented metrics can support the creation of novel ML benchmarks for smart manufacturing (SM).

关键词： Support vector machines Performance evaluation Production systems Memory management Benchmark testing Hardware Real-time systems

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A Comprehensive Review of Different Frameworks for Ensuring Data Privacy and Security for IoT Networks in Smart City 3

A Comprehensive Review of Different Frameworks for Ensuring ...

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kotian, Abhijith L. Abhishek, B. Allapur, Abhishek R. Gowda, Abhishek Gowda, Amith Alva’s Institute of Engineering and Technology Moodbidri India

ISBN: (纸本)9798331527549

Smart cities can be described as the integration of advanced technologies mainly comprising the Internet of Things (IoT) technology in an effort to change urban environments by increasing efficiency, sustainability, and quality of life for its citizens. Through IoT networks in smart cities, people can gather data, process data in real time, and analyze different industries, for example, transportation, energy management, healthcare, waste management, and public safety. However, the deployment of such interdependent devices raises numerous concerns with regard to data privacy and security. The vast quantities of data produced and exchanged among IoT networks comprise sensitive personal and operational information. This places these systems at high risk from unauthorized access, data breaches, and cyberattacks. In light of this, this paper reviews the state-of-the-art in smart city IoT network protection, analyzing methodology approaches to safeguarding data throughout its lifecycle. Some critical areas of focus revolve around encryption protocols to ensure the safety of the data itself during its transmission and storage, privacy preserving techniques that reduce the publicity of individual information, and network defence mechanisms that can discover and break cyber threats. We discuss such frameworks as encryption algorithms, access control mechanisms, intrusion detection systems, blockchain-based security approaches, and privacy-preserving methods such as differential privacy and homomorphic encryption. The paper outlines these approaches and comparatively evaluates their strengths, weaknesses, and applicability to diverse contexts of IoT networks within the context of smart cities. Comparison between approaches shows significant trade-offs regarding efficiency, scalability, and effectiveness in terms of privacy;thus, customized solutions will depend on the unique demands of each application area. Additional to the discussion above, we discuss the way ahead for sm

关键词： Network intrusion

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Binary Code Modularization Method Based on Graph Embedding 4

Binary Code Modularization Method Based on Graph Embedding

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4th ieee international conference on Software Engineering and Artificial Intelligence (SEAI)

作者： Yuan, Shubin Liu, Chenyu Shi, Jianheng Han, Yu Pu, Wei Zhao, Siwei Yang, Liqun CNOOC Informat Technol Co Ltd Beijing Branch Beijing Peoples R China CNOOC Informat Technol Co Ltd Shenzhen Guangdong Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350374353;9798350374346

In the field of cybersecurity, software reverse engineering serves as a crucial foundational technology, particularly in the analysis of software vulnerabilities and malicious code. A key challenge within this domain is the partitioning of binary code into functional modules, which aids analysts in swiftly and accurately comprehending software structure and functionality, thereby enhancing analysis efficiency. This paper presents a novel approach for binary code module partitioning based on graph embedding, aiming to address limitations inherent in traditional methodologies employed in software reverse engineering. By abstracting software systems into attribute graphs and utilizing graph embedding-based clustering techniques to embed and cluster function nodes, this method adequately considers both node attributes and similarities, thus improving the accuracy and robustness of module partitioning. Notably, the approach employs a graph embedding clustering method based on multi-head attention mechanisms, effectively facilitating module partitioning of binary files. Experimental results demonstrate the significant effectiveness and performance advantages of the proposed method on binary files.

关键词： binary code modularization graph embedding clustering attention mechanisms

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Design and Implementation of an intelligent Crawling Robot System 4

Design and Implementation of an Intelligent Crawling Robot S...

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4th ieee international conference on Electronic technology, Communication and Information, ICETCI 2024

作者： Chen, Guangyi Li, Danfeng Meng, Huishan Chen, Yuanji Ye, Jihua Qin, Yong Qin, Manping Wu, Chaopeng Hechi University School of Artificial Intelligence and Smart Manufacturing Hechi China Shenzhen Grandsun Electronic Co. Ltd. Shenzhen China

ISBN: (纸本)9798350361643

To improve the efficiency and safety of rescue and transportation of materials on unstructured terrain, this method reduces manufacturing costs, and adapts to complex environments. A simple intelligent crawling robot is designed to achieve high maneuverability in various terrains, landforms, and geological environments. Compared with traditional crawling robot systems, this intelligent crawling robot is designed using low-cost embedded chips, which have the advantages of small size, low power consumption, and high performance. It is designed using embedded chips and is easy to integrate into the system. This project utilizes the GD32F103C8T6 as the main control chip, along with a Bluetooth module, infrared module, ultrasonic module, voltage reduction module, servo module, and other peripheral configuration modules. Through the collaboration of various modules, the robot completes the corresponding transportation and rescue functions. Through testing and verification, this simple crawling robot can carry materials on unstructured terrain. Moreover, compared with traditional crawling robots, it significantly reduces manufacturing costs. Finally, the paper provides a summary and outlook on the future development trends of crawling robots. © 2024 ieee.

关键词： intelligent robots

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ML-IBotD: Machine Learning based intelligent Botnet Detection 3

ML-IBotD: Machine Learning based Intelligent Botnet Detectio...

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3rd ieee international conference on Artificial Intelligence, ICAI 2023

作者： Arshad, Sobia Zanib, Rida Akram, Adeel Haider, Ali Saeed, Talha Raza, Muhammad Shaheem NCCS DPI Lab UET Taxila Pakistan

ISBN: (纸本)9798350322125

With the advancements in communication technologies, an abundance of smart devices and internet-based applications in every walk of human life has resulted in the production of a huge number of data transmissions over the internet. In line with this emergence, the number of cybersecurity attacks is also rising. Among notable network attacks like mal ware, phishing, etc., we focused on botnet attacks which can cause huge damage on a large scale because botnet works in network form which appears as an adverse risk for the internet. In the botnet, there are many compromised systems known as bots controlled by the botmaster. On the other hand, Machine Learning (ML) is playing an important role in the detection of such network attacks with notable accuracy. In this paper, we select a dataset of CIC-IDS2017 due to its real interpretation of botnets. Then flows are extracted and then relevant four features are selected from the flows. In this paper, we apply four classifiers of SVM, KNN, DT, and Ensemble classifier on a real dataset of CIC-IDS2017. The highest achieved testing accuracy is 99.56% with the Ensemble classifier. © 2023 ieee.

关键词： Botnet

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Gradient and self-attention enabled convolutional neural network for crack detection in smart cities 29

Gradient and self-attention enabled convolutional neural net...

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29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Xie, Renping Chen, Mengyao Tao, Ming Ding, Kai Chen, HaoHan Dongguan University of Technology Dongguan China

ISBN: (纸本)9798350330717

intelligent transportation is an important guarantee for the safety and efficiency of urban transportation in smart cities, and regular road pavement inspection is the focus of road and bridge maintenance in intelligent transportation. Cracks in concrete pavement are the most common type of pavement damage and the earliest sign of pavement deterioration. However, existing crack detection algorithms suffer from incomplete crack detection and are easily disturbed by pseudo-cracks such as water spots and leaves. To address the above problems, this paper proposes a convolutional neural network (CNN) method that introduces a gradient module and an attention mechanism. The method adopts a CNN model based on the VGG-16 structure as the main body of the network structure, and optimally adjusts the network structure by incorporating a gradient layer and a self-attention mechanism, accelerating the convergence speed of network training and the global information learning ability. A negative sample dataset with pseudo-cracks, such as leaves, water spots and branches was constructed, and comparative experimental analysis was conducted in terms of both visual judgment and objective indicators. The experimental results show that after the introduction of the gradient layer and the self-attentive mechanism, not only the convergence speed of the network training is faster, but also the cracks in the concrete pavement images can be segmented more completely and accurately. © 2023 ieee.

关键词： convolution neural network crack detection image segmentation

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Ambient-Assisted Senior Living with Disabilities in an intelligent House using CNN Behavior Prediction 2

Ambient-Assisted Senior Living with Disabilities in an Intel...

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2nd ieee international conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Begum, S.Ariffa Subbulakshmi, N. Brintha, N.C. Chandru, R. Kumar, P.Somesh Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Sri Ramakrishna Engineering College Department of Eelctronics and Communication Engineering Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar District Tamilnadu Krishnan Koil India

ISBN: (纸本)9798350383287

This paper presents a novel strategy for enhancing the quality of life for elderly disabled people living in smart homes: Ambient-Assisted Living (AAL) systems. We anticipate and comprehend their everyday actions and behaviors by utilizing Convolutional Neural Networks (CNN) and Closed-Circuit Television (CCTV) technology. We gather information from carefully placed CCTV cameras, which we then analyze using a CNN model to identify particular behaviors like meal preparation, medication administration, and mobility patterns. In order to maintain accuracy over time, the CNN model is adjusted to match the particular requirements of every resident. Elderly residents' safety and security are improved by the system's ability to provide real-time alerts and interventions in response to behavioral anomalies or deviations from expected patterns. Caregivers and family members can access an intuitive interface that makes it possible for remote monitoring and providing essential support. This strategy represents a major breakthrough in AAL, providing individualized care and significantly raising the standard of living for elderly residents with disabilities while simultaneously tackling the problems brought on by an aging population. © 2024 ieee.

关键词： automation

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Video Warning Evaluation technology Based on Template Matching Algorithm for intelligent Vehicle-Infrastructure System 14

Video Warning Evaluation Technology Based on Template Matchi...

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14th international conference on Measuring technology and Mechatronics automation, ICMTMA 2022

作者： Fan, Qinglan Lu, Liyang Li, Zhenhua Li, Yiding Gao, Jian Research Institute of Highway Ministry of Transport Beijing100088 China Key Laboratory of Intelligent Transportation Systems Technologies Ministry of Communication Beijing100088 China

ISBN: (纸本)9781665499781

intelligent vehicle-infrastructure system (IVIS) uses advanced wireless communications and the new generation of Internet technology to enable comprehensive real-time dynamic information exchange between different vehicles and between vehicles and roads. It also provides active vehicle safety control and collaborative road management based on omnidirectional acquisition and fusion of traffic information, so as to fully realize effective coordination among people, vehicles and roads. This ensures traffic safety and improves traffic efficiency. In light of the urgent need to evaluate IVIS and the technology bottleneck in current testing, a flexible, reliable method for evaluating IVIS is implemented in this paper. First, an industrial camera with quick response and low delay is used to acquire video warning signals from on-board IVIS equipment in real time. The collected images are then accurately time-aligned through a synchronous acquisition scheme based on BeiDou Pulse Per Second (PPS). After that, different grayscale template matching algorithms are used to identify video warning signals. Then these algorithms are quantitatively evaluated and compared based on the accuracy and timeliness of recognition so as to select the optimal template matching algorithm. © 2022 ieee

关键词： intelligent vehicle highway systems

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