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IEEE International Conference on Robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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Evaluation of the Performance of Wearables’ Inertial Sensors for the Diagnosis of Resting Tremor in Parkinson’s Disease

Evaluation of the Performance of Wearables’ Inertial Sensor...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Polvorinos-Fernández, Carlos Sigcha, Luis de Pablo, Laura Pereira Borzí, Luigi Cardoso, Paulo Costa, Nelson Costa, Susana López, Juan Manuel de Arcas, Guillermo Pavón, Ignacio Mechanical Engineering Department ETSI Industriales Universidad Politécnica de Madrid Spain Group University of Limerick LimerickV94 T9PX Ireland ALGORITMI Research Center School of Engineering University of Minho Guimarães Portugal PolitoBIOMed Lab–Biomedical Engineering Lab Politecnico di Torino Turin10129 Italy ANTHEA Lab–Data Analytics and Technologies for Health Lab Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy Physical Electronics Electrical Engineering and Applied Physics Department ETS de Ingeniería y Sistemas de Telecomunicación Universidad Politécnica de Madrid Spain

Currently, objective monitoring of resting tremor in Parkinson’s disease (PD) involves wearable devices and machine learning. Smartwatches may present an affordable method for remote and unintrusive tremor monitoring. However, the development of optimized systems is necessary to perform accurate monitoring in free-living settings. In this study, the potential of inertial sensors to detect resting tremors is evaluated. A smartwatch was placed on the wrist of six subjects with PD during the execution of MDS-UPDRS motor tasks. Data were collected over eight weeks from triaxial accelerometer and gyroscope simultaneously and used to implement machine learning algorithms to detect resting tremor. The best performance (accuracy 97.0% in tremor detection) was achieved using accelerometer data analysed with a Random Forest classifier, while the gyroscope showed lower performance (93.0%). The results indicates that the use of the accelerometer in commercial smartwatches can offer effective results for detecting resting tremors, while reducing computational workload. These results show opportunities for the development of robust free-living tremor monitoring systems using commodity devices and algorithms using a single sensor. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Gyroscopes

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ONLINE FEEDBACK OPTIMIZATION AND SINGULAR PERTURBATION VIA CONTRACTION THEORY∗

arXiv

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arXiv 2023年

作者： Cothren, Liliaokeawawa Bullo, Francesco Dall’Anese, Emiliano Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93101 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States

In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems, including those commonly encountered in Online Feedback Optimization (OFO). Our framework endows these systems with several robustness properties, enabling a more comprehensive characterization of their behaviors. The primary assumptions are the contractivity of the fast subsystem and the reduced model, along with an explicit upper bound on the time-scale parameter. For two-time scale systems subject to disturbances, we show that the distance between solutions of the nominal system and solutions of its reduced model is uniformly upper bounded by a function of contraction rates, Lipschitz constants, the time-scale parameter, and the variability of the disturbances over time. Applying these general results to the OFO context, we establish new individual tracking error bounds, showing that solutions converge to their time-varying optimizer, provided the plant and steady-state feedback controller exhibit contractivity and the controller gain is suitably bounded. Finally, we explore two special cases: for autonomous nonlinear systems, we derive sharper bounds than those in the general results, and for linear time-invariant systems, we present novel bounds based on induced matrix norms and induced matrix log norms. Copyright © 2023, The Authors. All rights reserved.

关键词： State feedback

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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Survey of security, performance, and profitability of Monero: a browser-based cryptocurrency

Survey of security, performance, and profitability of Monero...

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electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Floarea Pitu Nicoleta Cristina Gaitan Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control (MANSiD) Suceava Romania

Information is the key to success in every domain; this statement also applies to cryptocurrencies and blockchain domains. Beginning with Bitcoin back in 2009, which the anonymous Japanese Satoshi Nakamoto launched, blockchain technology emerged. This technology has revolutionized and continues to do so in digital transactions. In addition, this technology led to the further development of other cryptocurrencies that are active at these moments on the market. One class of cryptocurrencies obtained differently from classical bitcoin is those obtained due to web-based mining. The most known coins of this type are Monero, Dash, or Electroneum. This paper aims to analyze the web-based mining process of Monero and the whole process’s security. In addition, we want to explore the performance of the mining processes used in the user’s web browser. Moreover, finally, we aim to research the profitability of coins obtained due to web-browser mining, having as an object of study the cryptocurrency Monero.

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Improved Malaria Cells Detection Using Deep Convolutional Neural Network

Improved Malaria Cells Detection Using Deep Convolutional Ne...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Sarmad Nozad Mahmood Swash Sami Mohammed Ayad Ghany Ismaeel Hülya Gükalp Clarke Iman Nozad Mahmood Dlnya Abdulahad Aziz Sameer Alani Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk Iraq Electrical and Electronics Engineering Ondokuz Mayıs University Samsun Turkey Kirkuk General Education Directorate Kirkuk Iraq Computer Center University of Anbar Anbar Iraq

This research presents a deep convolutional neural network (CNN) as a solution for identifying malarial cells that are infected. The AI model suggested in this work comprises a three-layered CNN and a two-layered dense neural network. The model can capture both minor and significant features by utilizing CNN, thereby extracting a maximum amount of information from the input data. The model is trained over 20 epochs and evaluated using the binary cross entropy loss function and accuracy metric to assess its performance. Remarkably, the proposed model achieved an impressive accuracy of 96% and maintained a loss value below 0.2 for both the training and validation datasets. Ultimately, this research demonstrates promising potential for automating the detection of malaria through parasite cell counting.

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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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Stabilizing control Design by Integrable Differential Passive Outputs*

Stabilizing Control Design by Integrable Differential Passiv...

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IEEE Conference on Decision and control

作者： Morteza Nazari Monfared Yu Kawano Michele Cucuzzella Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Graduate School of Advance Science and Engineering Hiroshima University Higashi-hiroshima Japan Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen AG the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Differential passivity of a nonlinear system has been introduced as passivity of its variational system. Applying standard passivity-based control techniques to differentially passive systems leads to controllers for the corresponding variational systems. However, it is challenging to construct controllers for the original nonlinear systems if the differential passive outputs are non-exact differential one-forms. In this letter, our objective is to provide a systematic procedure to address this issue when differential passive outputs are integrable, i.e., when non-exact differential passive outputs can be made exact by multiplying them by suitable integrating factors. In particular, under suitable detectability assumptions, we propose one static and two dynamic state feedback stabilizing controllers, where each dynamic controller has a form of input-and output-shaping, respectively. We illustrate their effectiveness by stabilization of counter-current heat exchangers.

关键词： Heating systems State feedback Systematics control design Color Nonlinear systems Standards

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IsoCost-Based Dynamic Programming for Solving Infinite Horizon Optimal control Problems

arXiv

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arXiv 2022年

作者： Rahimi, Saeed Lafmejani, Amir Salimi Kalhor, Ahmad School of Mechanical Engineering University of Tehran Tehran Iran School of Electrical Computer and Energy Engineering Arizona State University United States Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

An innovative numerical algorithm for solving infinite-horizon optimal control problems is introduced in this paper, using the IsoCost-HyperSurface (ICHS) concept. In the state space of an optimal control system, an ICHS is defined as a set of state points having a certain amount of the cost value. In this paper, it is proved that for a certain cost amount, the ICHS resulting from the infinite-horizon optimal control solution, surrounds all other ICHSs resulting from non-optimal control strategies. Regarding this geometric feature, the novel Isocost Dynamic Programming (IDP) algorithm is introduced to search for optimal control solutions. As an illustration of the introduced ICHS concepts and to demonstrate the effectiveness of the proposed IDP algorithm, several simulated examples are presented. The results are compared with those of conventional DP. These comparisons demonstrate that the proposed algorithm has better relative optimality compared to the DP algorithm with 18 % lower cumulative cost value, by comparing results from the closed-loop control of two nonlinear systems with random initial conditions. More significantly, when compared to the DP algorithm, the IDP was able to enhance computational performance by reducing the execution time by 21 % while using less *** Codes Dynamic programming in optimal control and differential games(49L20) © 2022, CC BY.

关键词： Dynamic programming

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Egcnet: Edge-Guided Context Real-Time Semantic Segmentation Network

SSRN

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SSRN 2023年

作者： Yu, Kaili Chen, Zhenxue Ye, Mengting Guo, Yixin Wu, Q.M. Jonathan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Shandong University Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Semantic segmentation is widely needed in many application areas. Driven by the development of deep learning, semantic segmentation methods have made significant progress in accuracy, robustness, and speed. Based on the short-term dense concatenate network, this paper proposes an edge-guided context real-time semantic segmentation network (EGCNet), which further improves the detail branch, extracts edge information from feature maps, and enhances the network’s ability to extract edge information through edge-assisted training. Because the top-level feature map contains minimal detailed information, the network only extracts the boundary of the low-level feature map such that the useful information is utilized as much as possible while reducing the computational load. In addition, we improve the attention-refining module by embedding position information, allowing it to capture the target structure more accurately. EGCNet achieves 71 FPS and 78.6\% mIoU on the Cityscapes dataset at a low computational cost, making it an efficient real-time semantic segmentation network. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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