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Design and Implementation of a Web-based Collaborative Manufacturing System Based on Knowledge Graph 4

Design and Implementation of a Web-based Collaborative Manuf...

引用

4th ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yuan, Meng Wu, Huaiyu Shen, Zhen Han, Yunjun Zhou, Jiehan Yue, Zhijian Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China University of Oulu Faculty of Information Technology and Electrical Engineering OuluTS352 Finland

ISBN: (纸本)9798350349252

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. This paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). The system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. The proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. The paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. The system's architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. The conclusion underscores the system's potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing. © 2024 ieee.

关键词： Smart manufacturing

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Research on the Design and Implementation of intelligent Tutoring System Based on AI Big Model

Research on the Design and Implementation of Intelligent Tut...

引用

2023 ieee international conference on Unmanned systems, ICUS 2023

作者： Shi, Wenjing Nie, Zhuming Shi, Yuhan Anhui Normal University School of Education Sciences Wuhu China

ISBN: (纸本)9798350316308

This research aims to design and implement an intelligent tutoring system based on artificial intelligence (AI) big models to provide personalized tutoring and guidance to help students better understand and apply their knowledge in various subjects. The system uses AI big model technology to simulate the role of a teacher and provide customized learning support for each student by analyzing their learning data and behavioral patterns. For the system design, we use the deep learning algorithm of AI big model to build a student learning model by analyzing and processing student data. The model can provide personalized learning paths and teaching resource recommendations for students based on their learning characteristics and progress. In addition, the system provides real-time learning feedback and assessment to help students understand their learning progress and provide corresponding suggestions for improvement. In terms of system implementation, we adopt advanced AI big model technology and computing platforms to ensure the efficiency and scalability of the system. We also develop a user-friendly interface so that students and teachers can easily use the system and get the required tutoring and guidance. In summary, the intelligent tutoring system based on the AI grand model has an important potential for application in the field of education. Through personalized tutoring and guidance, the system can help students better understand and apply their knowledge in various subjects and improve their learning effectiveness and motivation. Future research can further explore the optimization and improvement of the system to meet the learning needs and teaching goals of different students. © 2023 ieee.

关键词： Students

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Enhanced Unet3+ with Attention Mechanism and Its Purpose in intelligent Traffic Management

Enhanced Unet3+ with Attention Mechanism and Its Purpose in ...

引用

2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Saini, Anjali Singh, Shivam Aswal, Aman Singh Agrawal, Abha Chandigarh University Punjab India

ISBN: (纸本)9798331517953

The rapid expansion of urban areas demands advanced solutions for intelligent traffic management to enhance the efficiency and safety of transportation networks. This paper introduces an enhanced UNet3+ architecture augmented with an attention mechanism, tailored for high-resolution image segmentation in traffic environments. The proposed model leverages the attention mechanism to prioritize critical features within the input data, leading to more accurate segmentation of vehicles, road infrastructure, and other relevant elements in complex urban scenes. Experimental results on benchmark traffic datasets demonstrate that the enhanced UNet3+ model significantly outperforms conventional methods, delivering superior accuracy and robustness in diverse traffic scenarios. The integration of this advanced segmentation approach into intelligent traffic systems promises to improve real-time traffic monitoring, optimize flow management, and reduce congestion. The findings underscore the potential of deploying deep learning-based segmentation models in smart city initiatives, driving the development of more adaptive and resilient traffic management solutions. © 2024 ieee.

关键词： Highway administration

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ROStopbox: A Human-Centered Vehicle Environment data acquisition System

ROStopbox: A Human-Centered Vehicle Environment Data Acquisi...

引用

2024 international Automated Vehicle Validation conference

作者： Schneider, Daniel Nesensohn, Jonas Schick, Bernhard Watzenig, Daniel Kempten Univ Appl Sci Inst Adv Driver Assistance Syst & Connected Mobil Kempten Germany Graz Univ Technol Inst Comp Graph & Vis Graz Austria AVL Deutschland GmbH Adv Solut Lab Karlsruhe Germany

ISBN: (纸本)9798350354089;9798350354072

As the development towards fully automated driving continues, passenger trust in automation and human well-being when using advanced driver assistance systems will become increasingly important. To support the existing data-driven development process, systems for acquiring perceptual data such as from camera and LiDAR along with physiological and subjective information will be required in the future. The simultaneous recording of physiological signals alongside data from traditional perception sensors can therefore help to identify scenarios in which passengers are stressed, have an increased workload, or feel unsafe. By recording questionnaires in parallel, it is possible to also correlate the physiological signals with passengers' subjective perception. The combination of these signal sources makes it possible to identify causes of drivers' increased workload in the environmental data, describe development targets, and safeguard rules for future assistance systems and automated driving functions, keeping humans in the loop. Here, we present an approach for the simultaneous acquisition of sensor information from multiple domains such as physiological measurement data, environmental sensors, vehicle-internal communication, and subjective questionnaires, paying attention to an unobtrusive measurement setup. We demonstrate the benefits of our approach by identifying a relevant scenario in real traffic conditions in which we observe the impact on the driver's physiological parameters.

关键词： Automated Driving System Real World Testing Robot Operating System Physiology Human Factors

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Massive IRS-MIMO-RSMA with Polarization Multiplexing: An Enhanced SIC-Free Approach

Massive IRS-MIMO-RSMA with Polarization Multiplexing: An Enh...

引用

ieee international conference on Communications (ieee ICC)

作者： de Sena, Arthur S. da Costa, Daniel B. Nardelli, Pedro H. J. Bader, Faouzi Debbah, Merouane Technol Innovat Inst United Arab Emirates Abu Dhabi U Arab Emirates Lappeenranta Lahti Univ Technol Lappeenranta Finland

ISBN: (纸本)9781538674628

Dual-polarized rate-splitting multiple access (RSMA) with polarization multiplexing has appeared recently as an attractive downlink transmission technique for dual-polarized massive multiple-input multiple-output (MIMO) systems. Dual-polarized RSMA does not require successive interference cancellation (SIC), which avoids practical issues of imperfect SIC decoding. Nevertheless, depolarization phenomena can still degrade the performance of this featured technique. In this work, we exploit the advanced capabilities of a dual-polarized intelligent reflecting surface (IRS) for unleashing an enhanced RSMA polarization multiplexing. To optimize the IRSs, we develop a multi-objective Frank-Wolfe-based algorithm for jointly mitigating cross-polar interference and improving the reception of common and private data streams at multiple users. Representative simulation examples demonstrate that dual-polarized IRS-MIMO-RSMA can efficiently mitigate detrimental depolarization phenomena and outperform conventional systems.

关键词： Dual-polarization intelligent reflecting surfaces rate-splitting multiple access massive MIMO

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Extensions of Local-Mean K-Nearest Neighbor Classifier with Various Distance Metrics 5

Extensions of Local-Mean K-Nearest Neighbor Classifier with ...

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5th ieee international conference on Power, intelligent computing and systems, ICPICS 2023

作者： Lei, Xin Yang, Sichang Zhou, Longsheng School of Computer and Information Technology Xinyang Normal University Xinyang Henan China School of Internet of Things Engineering Jiangnan University Wuxi Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9798350333442

K-Nearest Neighbor (KNN) classification algorithm is one of the simplest methods in data mining classification technology. The LMKNN algorithm based on this method uses the local average vector of each category to classify the data. However, because distance measurement only uses Euclidean distance algorithm, it is not suitable for some cases. Therefore, this paper proposes a local mean k-nearest neighbor classification algorithm based on different distance algorithms. And compare the classification capabilities of the LMKNN model based on four distance measurement algorithms through experiments. The results show that the LMKNN algorithm based on Minkowski and City block distance metric schemes has more advantages than the LMKNN algorithm based on Euclidean distance metric schemes. © 2023 ieee.

关键词： data mining

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Performance Assessment of Multi-Controller in Software Defined Network 4

Performance Assessment of Multi-Controller in Software Defin...

引用

4th ieee international conference on computing, Communication, and intelligent systems, ICCCIS 2023

作者： Jaiswal, Ankita Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan Barik, Lalbihari Pandey, Trilok Nath Mishra, Manoj Ranjan School of Computer Engineering KIlT Deemed to Be University Bhubaneswar India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350306118

In a network design, the control plane and the data plane are separated by the architectural concept of software defined networking (SDN). A centralised controller that serves as the only point of control for the whole network is introduced by the SDN architecture. The controller serves as a logically centralized entity in charge of communicating the appropriate rules to a switch's flow table. Using a single controller to handle a lot of flows becomes insufficient. A lot of large-scale networks have employed multi-controller SDN to meet their objectives. In this research, we use a multi-controller infinite buffer waiting queue model to analyze the system performance metrics. Both numerical and simulated assessments are used to illustrate the proposed model. © 2023 ieee.

关键词： Controllers

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Fast and accurate object recognition based on tactile data by using Dendrite Net

Fast and accurate object recognition based on tactile data b...

引用

ieee international Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Liang, Haonan Gao, Tianshi Gao, Tian Luo, Jiangtao Wang, Jiang Deng, Bin Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781665453837

Tactile perception plays an important role in a variety of applications such as object detection and robotic grasping of objects. However, there is a lack of an intelligent object recognition algorithm based on tactile data with small storage space, fast computing speed, and high accuracy. Inspired by the involvement of dendrites of biological neurons in the computation of neural networks, we introduce dendritic computation to the task of object recognition based on tactile pressure data. Firstly, we design a tactile acquisition glove with a flexible film sensor array based on force-sensitive-resistors to obtain the pressure of the object. We conduct experiments by using this haptic acquisition device and obtain 24,500 tactile pressure images. Then we determine the optimal parameters of the DD model on the tactile pressure data by adjusting the number of DD modules. By using the DD model, we get information about the logical relationships between the tactile pressure image features. Finally, we achieve object recognition by capturing the pressure signal of the object and converting it into a pressure image. We train the model to achieve 99.84% accuracy, while the model achieves a storage space of 1.72MB and a recognition speed of 0.75ms, which outperforms existing algorithms in terms of real-time predictability, model storage space, and accuracy. This paper has significant implications for the future development of haptic applications in robotics.

关键词： tactile pressure object recognition Dendrite Net robotic

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A Deep Q-Learning Approach to Intrusion Detection and Prevention systems: Enhancing Cybersecurity through intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 ieee.

关键词： Cybersecurity

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Utilizing Big data for Personalized Nutrition Interventions and Health Outcomes

Utilizing Big Data for Personalized Nutrition Interventions ...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Labhane, Suchitra Marwaha, Sanjana Sharma Shastri, Swati Raj, Nihal Tomar, Mayank Koshal, Avadhesh Kumar Datta Meghe Institute of Management Studies Department of Mba Atrey Layout Maharashtra Nagpur440022 India Amity University Department of Amity Institute of Social Sciences Uttar Pradesh Noida201313 India Motherhood University Department of Facultyof Science Uttarakhand Haridwar247661 India

ISBN: (纸本)9798331543617

The use of big data in the context of proven nutrition interventions and their effects on health issues is examined in this exploratory work. We want to tailor dietary recommendations to individual needs by leveraging big databases, which include health records, hereditary information, and healthy behaviors. Big data analytics integration makes it possible to find distinctive health connections and beneficial patterns, which in turn makes it possible to express personalized dietary recommendations that can improve health problems. Our research shows how big data may change conventional nutritional knowledge and lead to a more individualized strategy that takes specific nature and life elements into account. The results show notable improvements in the treatment of habitual conditions, the enhancement of salutary recommendations, and the promotion of general well-being. To enhance proven nutrition, this study highlights the value of interdisciplinary collaboration by fusing data wisdom, nutrition, and healthcare moxie. Refining data integration techniques and investigating the long-term benefits of supported nutrition programs on various populations are examples of potential exploration directions. © 2024 ieee.

关键词： Electronic health record

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