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ieee International symposium on Biomedical Imaging

作者： Keyi Han Anqi Xiao Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The Transformer architecture achieves excellent global context understanding of sequential data through the self-attention mechanism, but it faces computational challenges when modeling long-range dependencies. Mamba, based on the state space model (SSM), has emerged as a notable approach for effectively addressing these long-range dependencies, significantly improving computational and storage efficiency. Therefore, we propose MamUNet, a new method that combines the powerful modeling capabilities of the Mamba model with the strengths of the UNet architecture for complex vessel segmentation tasks in NIR-II fluorescence images. The encoder employs the classic network ResNet to learn local low-level features, followed by Mamba for global feature learning to capture more contextual information. We conducted experiments on a dataset of human brain NIR-II fluorescence vessel images and three publicly available retinal vessel datasets. The results demonstrate that our method achieves more accurate vessel segmentation with lower computational cost, outperforming other comparison methods.

关键词： Representation learning Image segmentation Accuracy computational modeling Computer architecture Fluorescence Transformers Retinal vessels computational efficiency Faces

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Generic control Loop Model for Fluctuation Analysis in Production Systems 15

Generic Control Loop Model for Fluctuation Analysis in Produ...

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ieee 15th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Telatko, Rocky Ihlenfeldt, Steffen Reichelt, Dirk Univ Appl Sci Informat Math Dresden Germany Tech Univ Dresden Machine Tools Dev & Adapt Controls Dresden Germany

ISBN: (纸本)9781728195438

Production systems are stochastic systems. They are subject to continuous fluctuations. Fluctuations in production are provoked internally as well as externally. They are the cause of planned and unplanned events. The result of fluctuations are deviations between planned and actual data. In addition, fluctuations in production reduce system performance. In order to be able to control fluctuations, information about them must be available. Currently, such information is insufficient or missing. One reason may be the limited availability of data to date. New technologies in the age of digital transformation are opening up opportunities to solve this problem. The technologies provide tools that reveal enormous potential for the collection, processing and provision of data. The goal of the research project presented here is to demonstrate an approach for automated identification, measurement and quantification of variability in production systems. The first step of the implementation is the creation of a theoretical model for generic application.

关键词： data analytics data science digital transformation intelligent manufacturing systems process analysis signal processing

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Mechanical Brush Commutation Motor Drive Solution for Higher Education a New Electronic Motor control Board for University Laboratories to Support Educating the Embeeded Brushed Motor Drive Solutions

Mechanical Brush Commutation Motor Drive Solution for Higher...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mark Wendler József Kopják Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary

This paper outlines the challenges and outcomes in developing an embedded brushed DC motor drive tailored for higher educational laboratory teaching. It covers requirement definition, component design, integration, as... 详细信息

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

关键词： Motor drives Pulse measurements Education Manuals Pulse width modulation DC motors Time measurement

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A Constant Force Tracking Strategy for Complex Surface Robots Fused with NURBS Speed Planning and Teaching/Learning Mode 5

A Constant Force Tracking Strategy for Complex Surface Robot...

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5th World Robot Conference symposium on Advanced Robotics and automation, WRC SARA 2023

作者： Jinjun, Duan Kunkun, Cui An, Guo Lingyu, Wang Yiming, Bin Minhong, Wan Qiulan, Huang Nanjing University of Aeronautics and Astronautics School of Mechatronics China Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering China Zhejiang Lab Hangzhou China Zhejiang University Control Theory and Control Egineering China

ISBN: (纸本)9798350307320

Compliance control technology is particularly im-portant in contact operations such as grinding and polishing, surface tracking, and medical diagnosis. The core technology achieves contact control of a constant force for complex heterogeneous surfaces. However, due to the unknown environmental stiffness/position and dynamic changes, there are some problems, such as poor position/force tracking accuracy and a low degree of complex surface fit. To solve the above problems, a piecewise trajectory planning algorithm based on speed controllable NURBS and a teaching and learning mode constant force tracking strategy based on position/force coordination planning are proposed in this paper. First, a NURBS segmented trajectory planning algorithm with controllable speed is proposed by combining the NURBS trajectory planning algorithm with the T-type speed interpolation algorithm, and constant force tracking control based on the FCPressNURBS instruction set is realized by combining the adaptive admittance control algorithm. Second, based on the concept of teaching and learning modes, the position/force tracking parameters are obtained by the compliant control method, and the learning ideas is used to optimize the constant force tracking trajectory of the robot. Finally, the algorithm is tested on the ROKAE xMate robot, and the results show that the proposed algorithm improves the constant force tracking accuracy of the robot to the complex contact surface in the heterogeneous environment and can realize the adaptive constant force tracking effect of the robot to the unknown environment. © 2023 ieee.

关键词： Compliance control

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Adaptive Optimal control of Continuous-Time Linear Systems via Hybrid Iteration

Adaptive Optimal Control of Continuous-Time Linear Systems v...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Qasem, Omar Gao, Weinan Bian, Tao Florida Inst Technol Mech & Civil Engn Melbourne FL 32901 USA WorldQuant LLC Old Greenwich CT 06870 USA

ISBN: (纸本)9781728190488

In this paper, we propose a novel dynamic programming (DP) algorithm, under the name of hybrid iteration (HI), for continuous-time linear systems. The proposed HI approach combines the advantages of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of an initial stabilizing control policy required in PI, and at the same time it maintains a faster convergence rate compared with VI. Based on the proposed HI algorithm, a data-driven adaptive optimal controller design is also proposed. Simulation results for randomly generated continuous-time linear systems with different system orders demonstrate that the proposed HI approach can save CPU time up to 73% and reduce the number of iterations to converge up to 98% comparing with the VI approach.

关键词： Hybrid iteration adaptive dynamic programming (ADP) adaptive optimal control reinforcement learning

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An Efficient Lightweight Privacy-Preserving Mechanism for Industry 4.0 Based on Elliptic Curve Cryptography

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第9期18卷 6494-6502页

作者： Velliangiri, S. Manoharn, Rajesh Ramachandran, Sitharthan Venkatesan, Krishnasamy Rajasekar, Vani Karthikeyan, P. Kumar, Pardeep Kumar, Abhishek Dhanabalan, Shanmuga Sundar BV Raju Inst Technol Dept Comp Sci Engn Narsapur 502313 India Sanjivani Coll Engn Dept Comp Sci Kopargaon 423603 India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Erode 638060 India Jain Deemed Univ Dept Comp Sci & Engn Bangalore 560069 Karnataka India Anurag Univ Dept Artificial Intelligence Hyderabad 500088 India Jain Deemed Univ Sch Comp Sci & IT Bengaluru 560069 India RMIT Univ Funct Mat & Microsyst Res Grp Melbourne Vic 3001 Australia

The present trend of automation anddata interchange in industrial technology is known as Industry 4.0. Industry 4.0 is altering its next generation of distribution networks by making them more responsive and efficient. There are various issues when implementing Internet of Thing (IoT) devices in an Industry 4.0 scenario, mainly due to reduced IoT nodes or devices with insufficient infrastructure to operate security solutions. As a result, securing the environment will require a lightweight and effective security solution. The authors of this study offer a lightweight and flexible authentication method as part of an access control strategy for secure data exchange in-vehicle networks. Lightweight operations, hash functions, XOR operations, and concatenation are all used in the proposed protocol. The security of this approach is also evaluated using AVISPA. The authentication process between IoT networks and resilience to various security threats will be demonstrated. The experimental analysis shows that the computational cost attained is 7.96 s, and the communication cost attained is 834 bits. Similarly, the resource utilization for this computation is 11%, and the average verification time is 7 ms. The experimental analysis demonstrates that the proposed method is efficient with lower computational cost, communication costs, verification cost, and resource utilization than the existing state-of-the-art approaches

关键词： Fourth Industrial Revolution Authentication Security Elliptic curves Elliptic curve cryptography Blockchains Cryptography Elliptic curve cryptography (ECC) Industry 4 0 Internet of Things (IoTs) mutual authentication

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A Closed-Loop AR-based BCI for Real-World System control

A Closed-Loop AR-based BCI for Real-World System Control

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Gorman, Campbell Wang, Yu-Kai Univ Technol Sydney Sch Comp Sci Sydney NSW Australia Univ Technol Sydney Australia AI Inst Sydney NSW Australia

ISBN: (纸本)9781728190488

Both Augmented Reality (AR) and Brain-Computer Interfaces (BCI) have drawn a lot of attention in recent applications. These two new technologies will significantly impact and develop interactions between human and intelligent agents. While there are several studies already conducted in the control of devices using AR based, steady state visually evoked potentials (SSVEP) control systems in a lab environment, this study seeks to implement a portable, closed-loop, AR-based BCI to assess the feasibility of controlling a physical device through SSVEP. This portable, closed-loop AR-based BCI provides users with the unique opportunity to simultaneously interact with the surrounding environment and control autonomous agents with an 88% accuracy. The potential benefits of this application include reduced restrictions on handicapped individuals or concurrent control of multiple devices through a single AR interface. Ultimately, we hope this outcome can bridge the BCI field with further real-world, practical applications.

关键词： Portable Brain-Computer Interface Augmented Reality Steady State Visually Evoked Potential Real-World

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SIGNRL: A Population-Based Reinforcement Learning Method for Continuous control

SIGNRL: A Population-Based Reinforcement Learning Method for...

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ieee symposium Series on computational intelligence (SSCI)

作者： Daniel F. Zambrano-Gutierrez Alberto C. Molina-Porras Emmanuel Ovalle-Magallanes Iván Amaya José C. Ortiz-Bayliss Juan G. Avina-Cervantes Jorge M. Cruz-Duarte Tecnologico de Monterrey Monterrey Mexico University of Guanajuato Salamanca Mexico

In engineering processes that require continuous control, it is common to face significant challenges. Addressing these challenges through explicit modeling can take much work and effort. For this reason, Reinforcement Learning (RL) has gained popularity as a feasible strategy for solving this problem. In this context, various value-based methodologies, policies, or combinations have been employed to obtain an optimal learning policy. However, problems such as convergence to local maxima and high variance in training persist. In addition, computational time and cost increase in complex environments, so more robust RL methodologies are required. This paper proposes a Swarm intelligence Guided Neural Reinforcement Learning (SIGNRL) algorithm, which uses Particle Swarm Optimization as a multi-agent parameter explorer to find the optimal policy. Numerical results obtained in the OpenAI Gym Cart-Pole environment show that SIGNRL, with its gradient-free learning, exhibits good convergence and lower variance in continuous control tasks.

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A novel fault diagnosis in sensors of quadrotor unmanned aerial vehicle

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Journal of Ambient intelligence and Humanized Computing 2023年第10期14卷 14081-14099页

作者： Taimoor, Muhammad Lu, Xiao Maqsood, Hamid Sheng, Chunyang School of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China Department of Electrical Technology Government Polytechnical Institute Karak27200 Pakistan

Rapid faults diagnosis and isolation in flight control systems is vital to evade undesirable effects on the environment, humans as well as on the system itself. In this research, a new strategy based on an online dynamical system is proposed for sensors fault diagnosis and isolation of quadrotor unmanned aerial vehicle (UAV). In the presented strategy, the multi-layer neural network (MLNN) is adopted as an observer for sensor faults diagnosis of quadrotor unmanned aerial vehicle (UAV). For the improvement of faults detection accuracy and effectiveness, two new adaptive weight-updating approaches are used in this study. Lyapunov function theory-based approaches are proposed for updating the learning rate parameters of a multi-layer neural network (MLNN). The key purpose of utilizing these approaches is to attain the global minima for nonlinear functions without enhancement of computational power to enhance the accuracy of faults diagnosis and isolation. The proposed fault detection (FD) approaches are applied to quadrotor unmanned aerial vehicle (UAV), the simulated results demonstrate that the proposed approaches are having the capability of rapid faults diagnosis and isolation of sensors compared to the conventional neural network and the techniques used in the literature (Chen et al. in ieee Trans Syst Man Cybern Syst Hum 46:260–270, 2016a, b, c, d;Payam et al. in J Intell Robot Syst 90:473–484, 2018). © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fault detection

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Special Session: Exploring the Potential of Versal ACAP: Advancing Onboard Edge AI for Spacecraft 37

Special Session: Exploring the Potential of Versal ACAP: Adv...

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37th ieee International symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2024

作者： Ciancarelli, Carlo Di Ienno, Davide Trois, Renato Scandelli, Luca De Biase, Catriel Serri, Paolo Leboffe, Antonio Pascucci, Dario Steenari, David Furano, Gianluca Thales Alenia Space Italia S.p.A. Rome Italy Thales Alenia Space Italia S.p.A. Gorgonzola Italy Thales Alenia Space Italia S.p.A. L'Aquila Italy European Space Agency Noordwijk Netherlands

ISBN: (纸本)9798350366884

In the space domain, the necessity for Artificial intelligence (AI) technologies and computationally demanding applications has become increasingly evident. This need arises from the growing complexity of space missions and the corresponding objective to enhance spacecraft decision-making capabilities. Traditional approaches have faced limitations in terms of flexibility as well as computational constraints, demanding a new perspective. With a focus on addressing these challenges, this investigation delves into the potential of the Versal Adaptive Compute Acceleration Platform (ACAP) developed by Xilinx. This hardware solution promises to enable efficient data processing and real-time inference for Edge AI applications on board spacecraft. The final objective of this ongoing study is to explore Versal ACAP capabilities on a range of use cases, including AI-driven telemetry anomaly detection and Synthetic Aperture Radar (SAR) data processing. AI-based telemetry anomaly detection occupies a critical space in spacecraft systems, as it promises to improve Fault Detection, Isolation, and Recovery (FDIR) functionalities. Traditionally, SAR data processing has been executed on the ground, whereas the Versal ACAP presents the opportunity to perform preliminary SAR data analysis onboard, enabling rapid extraction of information and facilitating more agile mission planning and decision-making. While the Versal ACAP promises to advance Edge AI applications in space, it also presents relevant challenges: power consumption and thermal dissipation pose a critical concern as spacecraft systems operate within strict power budgets and limited dissipation capabilities. Striking a delicate balance between the computational requirements of AI algorithms and spacecraft design constraints becomes a crucial consideration for successful deployment. This work presents an initial investigation into the Versal ACAP through the development of a prototype board, auxiliary to traditional On Boar

关键词： Budget control

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