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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Gael P. Atheupe Bhagyashri T. Gurjar Bruno Monsuez Renault Group and IP Paris Guyancourt FRANCE Department of Aerospace Engineering Intelligent Systems & Control Lab Indian Institute of Technology Bombay Mumbai India Computer science & systems engineering Lab (U2IS) of ENSTA Paris Institute Poly-technique de Paris (IP Paris) Palaiseau FRANCE

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper introduces a novel Sliding Mode-Based Observer tailored for a specific subset of nonlinear systems of order $\boldsymbol n$ featuring Lipschitz nonlinearities. The study establishes stability conditions that ensure convergence of the estimation error (s) in finite time until order $\boldsymbol n$ , thus, providing an accurate state (s) estimation without the necessity for disturbance matching conditions. Furthermore, the study presents an extension of the scope of application of the proposed method to tackle a unique scenario characterized by a time-varying and non-invertible function of the output dynamics of the system model. The effectiveness of the proposed observer is showcased through simulation examples.

关键词： Estimation error Automation Accuracy Observers Stability analysis Nonlinear dynamical systems Time-varying systems Robots Convergence

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A Self-Sovereign Identity Architecture Based on Blockchain and the Utilization of Customer's Banking Cards : The Case of Bank Scam Calls Prevention 15

A Self-Sovereign Identity Architecture Based on Blockchain a...

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15th International Conference on computer engineering and systems, ICCES 2020

作者： Ahmed, Khaled A. M. Saraya, Sabry F. Wanis, John F. Ali-Eldin, Amr M. T. Mansoura University Faculty of Engineering Computer Engineering and Control Systems Department Mansoura Egypt

ISBN: (纸本)9780738105598

The need to use a wide variety of online services is rising every day in today's digital age. In order to authenticate and authorize users to access services, identity management is essential. To solve several problems such as fragment identity repositories, aim for identity theft and data breaches and identity correlation, the evolution of identity models across siloes, centralized and presently self-sovereign identity based on distributed ledger is emerging. Self-sovereign identity returns the power to the proprietor of the identity and puts the user at the core of the model. In this paper, we propose a framework to identify scam bank service calls and avoid possible loss of personal accounts. Our framework is based on Hyperledger Indy, a public and permissioned Distributed Ledger Technology. The proposed framework strengthens the Know Your Customer process and enhances the trust of third party agencies delegated by banks to deliver many financial items. © 2020 IEEE.

关键词： Blockchain

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A Material Identification Approach Based on Wi-Fi Signal

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computers, Materials & Continua 2021年第12期69卷 3383-3397页

作者： Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace Security Peng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer Engineering University of ArkansasFayetteville72701USA College of Electrical Engineering Zhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

关键词： Internet of Things Wi-Fi signal channel state information material identification noise elimination

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On modal observers for beyond rigid body H∞ control in high-precision mechatronics

arXiv

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arXiv 2022年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Hungary

The ever increasing need for performance results in increasingly rigorous demands on throughput and positioning accuracy of high-precision motion systems, which often suffer from position dependent effects that originate from relative actuation and sensing of the moving-body. Due to the highly stiff mechanical design, such systems are typically controlled using rigid body control design approaches. Nonetheless, the presence of position dependent flexible dynamics severely limits attainable position tracking performance. This paper presents two extensions of the conventional rigid body control framework towards active control of position dependent flexible dynamics. Additionally, a novel control design approach is presented, which allows for shaping of the full closed-loop system by means of structured H∞ co-design. The effectiveness of the approach is validated through simulation using a high-fidelity model of a state-of-the-art moving-magnet planar *** Codes 37M15 © 2022, CC BY.

关键词： Closed loop systems

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Multi-view Unsupervised Representation Learning via Integration of Fuzzy Rules and Graph-Based Adaptive Regularization

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IEEE Transactions on Fuzzy systems 2025年

作者： Zhu, Yi Li, Dong Xi, Chao Pedrycz, Witold Macau University of Science and Technology School of Computer Science and Engineering China East China University of Technology School of Mechanical and Electronic Engineering Nanchang330013 China Gliwice Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach. Thecode is available at https://***/r/MvTSK-GAR-E3FB/***. © 1993-2012 IEEE.

关键词： Unsupervised learning

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Tilted Least-Squares Parameter Estimation of Linear Regression Models in the Presence of Outliers

Tilted Least-Squares Parameter Estimation of Linear Regressi...

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IEEE Conference on Decision and control

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

The least squares estimator is the most popular identification method. In the absence of prior knowledge on the unknown noise, uniform weights on all samples are often as-sumed. In reality, potentially unknown contamination is always present and the uniform weights are not necessarily the best. Further, explicit information about the nature of contamination is usually absent. To this end, a relaxed-tilted least squares method is proposed here to assign unequal weights so that the effect of undesired noise contamination can be mitigated. The relaxed-tilted least squares method tilts the uniform prior on the samples so as to move the uniform distribution in a direction that enjoys the smallest estimation error in the neighborhood of the uniform distribution. Theoretical results are established including the ability of outlier removal and the guaranteed parameter convergence in the presence of outliers. Numerical algorithms are proposed and simulated, which support the theoretical derivations.

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument Maintenance support Engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

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Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation

arXiv

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arXiv 2024年

作者： Umar Niazi, M.B. Cho, Jung-Hoon Dahleh, Munther A. Dong, Roy Wu, Cathy Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This paper proposes incentive mechanisms that promote eco-driving in transportation networks with the overarching objective of minimizing emissions. The transportation system operator provides the drivers with energy-efficient driving guidance throughout their trips, and their eco-driving levels are measured by how closely they follow this guidance via vehicle telematics. Drivers choose their eco-driving levels to optimize a combination of their travel times and their emissions. To obtain optimal budget allocation and recommendations for the incentive mechanism, the system operator gathers drivers’ preferences, or types, to assess each driver’s trip urgency and natural willingness to eco-drive. In a setting where drivers truthfully report their types, we introduce the first-best incentive mechanism and show that the obedience condition holds (i.e., drivers find it optimal to comply with the system operator’s recommendations) when the recommended eco-driving profile constitutes a Nash equilibrium. Moreover, in a setting where drivers can strategically report their types, we introduce the second-best incentive mechanism and show that the proposed mechanism is incentive-compatible (i.e., drivers find it optimal to be truthful). Under this mechanism, we also show that all equilibrium outcomes are at least as good as the recommended eco-driving profile in terms of the system operator’s objective. Overall, this work offers a framework for designing eco-driving incentive mechanisms while considering both the strategic behavior of individual drivers and the network effects of collective decision-making. © 2024, CC BY.

关键词： Budget control

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Forward Kinematics Development of a 3-RRS Parallel Manipulator for Real-Time control

Forward Kinematics Development of a 3-RRS Parallel Manipulat...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： S. Ali A. Moosavian Omid Mahdizadeh Sina Hallajian Sara Ashrafi Mohammad Jamali Vahid Akbari Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab. K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781665454537

In control of parallel robots, forward kinematics calculation is mainly required. In some studies, obtained equations are solved numerically with massive calculations that is not acceptable for control implementations. This paper introduces a new method to extract forward kinematics for a 3- RRS robot. This method calculates the kinematics of passive joints with two separated equations, which can be solved to decrease the total computation demands. Also, a new weight compensation mechanism is proposed to improve the dynamic response by a linear pneumatic actuator placed between the ground and the end-effector. On the other hand, to use various control algorithms, the position of the rolling ball on the end- effector (moving platform) must be measured. This is performed using Convolutional Neural Networks (CNNs) as image processing algorithms. Since the system must work quickly for a high-frequency task, the proposed image processing algorithm can provide the desired fast feedback. Experimental results reveal the merits of the above-described proposed modules.

关键词： Pneumatic actuators Image processing Heuristic algorithms Kinematics Position measurement Pneumatic systems Mathematical models

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument maintenance support engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

关键词： Forecasting

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