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Incentive Mechanism for Collaborative Sensing in intelligent Transportation systems with Mixed Traffic: A Stackelberg Game Approach 5

Incentive Mechanism for Collaborative Sensing in Intelligent...

引用

5th international conference on Computer Engineering and intelligent control, ICCEIC 2024

作者： Huang, Xumin Zhong, Weifeng Lu, Binbin Wu, Yuan Xie, Shengli Kuzin, Victor School of Automation Guangdong University of Technology Guangzhou China University of Macau State Key Lab of IoT for Smart City Macau China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou China The Russian Academy of Engineering Moscow Russia

ISBN: (纸本)9798331507992

intelligent Transportation systems (ITS) simultaneously collects driving data of both the intelligent Connected Ve-hicles (ICVs) and non-ICVs for traffic management in the mixed traffic environment. To this end, a set of ICVs can be scheduled to cooperatively sense a target non-ICV and collect the traffic data to a roadside unit (RSU). An incentive mechanism is necessitated to provide proper incentives for the selfish ICVs. In this paper, we resolve the incentive problem of collaborative sensing in the ITS with the mixed traffic, according to a Stackelberg game approach. We investigate the strategic interactions of the RSU and ICVs in which there exist leader-follower relationships among them. After that, we analyze the Stackelberg equilibrium, and derive the close-form solution of the Stackelberg game. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of the proposed Stackelberg game. © 2024 ieee.

关键词： Information management

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Towards Sustainable cyber-Physical Manufacturing systems 22

Towards Sustainable Cyber-Physical Manufacturing Systems

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22nd IFAC conference on technology, Culture and international Stability (TECIS)

作者： Brennan, Robert W. Lyu, Guolin Univ Calgary Calgary AB T2N 1N4 Canada

We propose a self-managing services architecture for automated manufacturing systems to enable sustainable manufacturing operations. The architecture utilises both software agents and IEC 61499 function blocks to realise manufacturing systems that quickly respond to change while maintaining stable system operation and efficient use of available resources. To explore the system's ability to adapt to disturbances, a stochastic agent-based simulation of the automated manufacturing system with random equipment failures was developed. The results of the simulation demonstrate that the proposed self-managing services architecture is capable of rapidly reconfiguring part routings in order to reduce work in progress and maintain stable system operation. Copyright (C) 2024 The Authors.

关键词： Artificial Intelligence and Applications intelligent systems and Applications Sustainable Design and control

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Permanent Magnet Synchronous Motor control and Implementation of this control into the Microprocessor and its Realization

Permanent Magnet Synchronous Motor Control and Implementatio...

引用

2024 international conference on intelligent systems for cybersecurity, ISCS 2024

作者： Rathnakar, G. Negi, Harendra Singh Shashibhushan, G. Nandankar, Praful V. Vekariya, Daxa Maranan, Ramya Jss Science and Technology University Department of Mechanical Engineering Karnataka Mysuru570006 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sir M Visvesvaraya Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru562 157 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

Traditional methods of motor control, such as scalar control or field-oriented control (FOC), may struggle to provide the same level of precision and responsiveness as vector control. This work explores the implementation of vector control in the STM32F302R8 microprocessor for precise regulation of speed and torque in electric motors, commonly utilized in industrial automation and electric vehicles. Prior to vector control implementation, the microprocessor interfaces with essential components: the inverter for DC to AC power conversion, the control module housing control algorithms, and the resolver for rotor position feedback. The process involves programming and testing to accurately evaluate the rotor's rotation angle, crucial for effective vector control by ensuring correct voltage application to motor windings. Upon successful implementation and testing, the vector control system demonstrates functionality in regulating motor speed responsively to desired speed changes. © 2024 ieee.

关键词： Vectors

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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2024 ieee international conference on Service Operations and Logistics, and Informatics, SOLI 2024

作者： Tian, Yonglin Lin, Fei Zhang, Xinyuan Ge, Jingwei Wang, Yutong Dai, Xingyuan Lv, Yisheng Wang, Fei-Yue University of Chinese Academy of Sciences Beijing100049 China Tsinghua University Department of Automation Beijing100084 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Department of Engineering Science 999078 China

intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with intelligent systems for Transport automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly. © 2024 ieee.

关键词： Drones

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OmniRace: 6D Hand Pose Estimation for Intuitive Guidance of Racing Drone

OmniRace: 6D Hand Pose Estimation for Intuitive Guidance of ...

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2024 international conference on intelligent Robots and systems

作者： Serpiva, Valerii Fedoseev, Aleksey Karaf, Sausar Abdulkarim, Ali Alridha Tsetserukou, Dzmitry Skolkovo Inst Sci & Technol Moscow Intelligent Space Robot Lab Bolshoy Blvd 30Bld 1 Moscow 121205 Russia

ISBN: (纸本)9798350377712;9798350377705

This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our knowledge, this is the first technology enabling low-level control of high-speed drones through gestures. OmniRace employs a gesture interface based on computer vision and a deep neural network to estimate 6-DoF hand pose. The advanced machine learning algorithm robustly interprets human gestures, allowing users to control drone motion intuitively. Real-time control tests validate the system's effectiveness and its potential to revolutionize drone racing and other applications. Experimental results conducted in simulation environment revealed that OmniRace allows the users to complite the UAV race track significantly (by 25.1%) faster and to decrease the length of the test drone path (from 102.9 to 83.7 m). Users preferred the gesture interface for attractiveness (1.57 UEQ score), hedonic quality (1.56 UEQ score), and lower perceived temporal demand (32.0 score in NASA-TLX), while noting the high efficiency (0.75 UEQ score) and low physical demand (19.0 score in NASA-TLX) of the baseline remote controller. The deep neural network attains an average accuracy of 99.75% when applied to both normalized datasets and raw datasets. OmniRace can potentially change the way humans interact with and navigate racing drones in dynamic and complex environments. The source code is available at https://***/SerValera/***.

关键词： Drones

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Blockchain-Integrated Deep Learning for Secure Health Data Sharing and Consent Management 2

Blockchain-Integrated Deep Learning for Secure Health Data S...

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2nd international conference on intelligent cyber Physical systems and Internet of Things (ICoICI)

作者： K Deepthika Shobana, G. Reddy, Kumbam Venkat Srimathi, S. Kumar, Binod Upadhyay, Shrikant Dayananda Sagar Univ CSE Cyber Secur Bangalore Karnataka India Takshashila Univ Sch Comp Sci Ongur India Vignana Bharati Inst Technol Dept IT Ghatkesar Medchal India SIMATS Saveetha Sch Engn Dept Bio Technol Chennai Tamil Nadu India Brainware Univ CSE Barasat WB India MLR Inst Technol Elect & Commun Engn Hyderabad India

ISBN: (纸本)9798331540661;9798331540678

The collection of medical data encompasses a variety of patient records that hold significant value for ongoing treatment and future research works. To ensure the privacy of the data, it is imperative to store and share it securely. Utilizing blockchain technology in managing healthcare data is prevalent due to its decentralized nature and ability to provide tamper-proof security measures. In healthcare's dynamic and ever-changing realm, the importance of securely sharing sensitive health data and managing consent effectively cannot be overstated. These factors are crucial in safeguarding patient privacy and promoting collaborative research efforts. This study aims to investigate the potential integration of blockchain technology and Long Short-Term Memory (LSTM) deep learning models to develop a comprehensive framework that ensures secure health data sharing and effective consent management. Integrating blockchain technology's decentralized and immutable ledger with the sequential learning capabilities of LSTM presents a promising approach to tackle the various obstacles related to data integrity, provenance, and patient-centric consent within healthcare ecosystems. The proposed system aims to improve the security and transparency of health data transactions while enabling dynamic consent management. This empowers individuals to have more control over their data. In this study, aims to assess the effectiveness of the blockchain-integrated LSTM model in ensuring health data security. Additionally, the investigated model facilitates smooth and privacy-preserving collaboration among various healthcare stakeholders. The experiments involved utilizing two publicly available data sources, CICIDS-2017 and NSL-KDD. These experiments evaluated the proposed model's performance compared to existing state-of-the-art approaches within non-blockchain and blockchain settings. The results demonstrated that the proposed model exhibited superior performance across both datasets, ach

关键词： Blockchain Deep Learning LSTM Health Data Sharing Consent Management Privacy Security

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Construction of intelligent Building automation control System Based on Fuzzy Clustering Algorithm

Construction of Intelligent Building Automation Control Syst...

引用

2023 international conference on Internet of Things, Robotics and Distributed Computing, ICIRDC 2023

作者： Liu, Jing He, Yu Harbin Huade University Heilongjiang Harbin150025 China

ISBN: (纸本)9798350375275

With the vigorous development of the construction industry, the market demand for automatic control systems is increasing, and it has attracted domestic and foreign automation system manufacturers to continuously improve the software for computer control management, emphasizing the humanized use of interfaces, and ensuring the orderly use of building machinery and fire protection, security, and parking management. In this regard, this article constructs an intelligent building automation control system based on fuzzy clustering algorithm, which can effectively improve the intelligence and prevention level of buildings. The intelligent building equipment automation control system utilizes modern scientific technologies such as communication technology, computer network technology, and intelligent monitoring technology. It uses hierarchical clustering to obtain the initial number of clusters from offline data, and then performs fuzzy clustering on incremental data. However, there is a problem of needing to set parameters to identify outlier samples. Comparative experiments have been conducted to verify the time for receiving control instructions between the traditional system and the system in this paper. The average receiving time of the traditional system is 18.95 seconds, while the average receiving time of the system in this paper is 12.75 seconds, which shortens the receiving time of control instructions by 6.2 seconds compared to the traditional system. The above experimental results once again validate the effectiveness of the system in this paper. © 2023 ieee.

关键词： intelligent buildings

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th international conference on cyber technology in automation, control, and intelligent systems, cyber 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 ieee.

关键词： Motion estimation

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The Analysis of AI Based Algorithms in Personalized and Adaptive E-learning Experiences 2

The Analysis of AI Based Algorithms in Personalized and Adap...

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2nd ieee international conference on IoT, Communication and automation technology, ICICAT 2024

作者： Kumar, Ajay Srivastava, Shashank Buddha Institute of Technology Department of Computer Science & Engineering UP Gorakhpur273209 India

ISBN: (纸本)9798350368109

The integration of Artificial Intelligence (AI) into e-learning systems has heralded a new era of personalized and adaptive learning experiences. This comprehensive review delves into the latest advancements and implications of AI in e-learning. By harnessing machine learning algorithms, natural language processing techniques, and predictive analytics, AI-powered e- learning systems provide personalized content delivery, adaptive assessments, and intelligent tutoring capabilities. Additionally, this review examines how AI enhances learner engagement, improves learning outcomes, and tackles the challenges of scalability and accessibility in education. It also addresses ethical considerations, challenges, and future directions for AI-based e-learning, underscoring the transformative potential of AI in shaping the future of education. © 2024 ieee.

关键词： Adversarial machine learning

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Advancing Industrial cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks 5

Advancing Industrial Cybersecurity: Machine Learning-Based D...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Kumar, S Gouri Kiran Prasad, Raavi Satya College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Dhanekula Institute of Engineering & Technology Department of Computer Science & Engineering A.P. Vijayawada India

ISBN: (纸本)9798331518097

A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, lost confidential data, and fines from the government, cyberattacks on IIoT systems can cause significant financial losses. Rapid reaction and recovery are made possible by efficient detection systems, which can lessen these losses. Many researchers have applied machine learning (ML) approaches to predict IIoT attacks in advance. Industrial networks can be highly effectively and dynamically protected against various cyber threats by utilizing MLthrough IIoT attack distinguishing. This ML methodology is a vital component of today's cybersecurity toolkit for IIoT contexts owing to its capacity for handling massive info, adjusting for novel threats, & delivering insights in real-time. Industrial networks can be highly effectively and dynamically protected against cyber threats by utilizing ML to IIoT attack identification. ML is a vital component of today's cybersecurity toolkit for IIoT contexts for its capacity to handle massive quantities of info, adjust to emerging risks, & deliver insights in real time. IIoT data can contain noise, missing values, or irrelevant information, negatively affecting model performance. Identifying and extracting relevant features from IIoT data was highly complex and time-consuming. The methodology's effectiveness is heavily related to features with high quality utilized. This paper discusses the various techniques, their limitations, and the gaps studied during the literature survey. Among the machine learning approaches, the model should adapt to the new infrastructure attacks, which is effectively possible only through meta-heuristic learning algorithms. © 2024 ieee.

关键词： cyber attacks

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