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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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science China(Information sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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Enhanced pH sensing with Ce-doped YTixOy sensing membrane in high-performance electrolyte–insulator–semiconductor devices

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Materials Chemistry and Physics 2024年 311卷

作者： Singh, Kanishk Tai, Li-Chia Her, Jim-Long Pan, Tung-Ming Department of Electronics Engineering Chang Gung University Taoyuan33302 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Division of Natural Science Center for General Education Chang Gung University Taoyuan33302 Taiwan Division of Urology Chang Gung Memorial Hospital Taoyuan33305 Taiwan

Electrolyte-insulator-semiconductor (EIS)-based sensors are a novel class of electronic chips designed for biochemical sensing that offer a direct electronic readout, making them part of a new generation of sensing technologies. To enhance the pH sensitivity, in this research we fabricated the CeYTixOy-based EIS sensors with different Ce concentrations (1 %, 5 %, and 10%) by the solution-processing method. The crystal orientation, surface topography, and chemical composition of the sensing film were examined by X-ray diffraction, atomic force microscopy, and X-ray photoelectron spectroscopy, respectively. The CeYTixOy-based EIS sensor treated with an optimal 5% Ce concentration displays a Super-Nernstian pH response of 66.59 mV/pH with an excellent linearity of 0.99. In the context of reliability, the device exhibited a smaller hysteresis voltage of 7 mV and a lower drift rate of 0.75 mV/h. This result may be attributed to the optimal Ce content in the CeYTixOy leads to the formation of redox couple Ce4+/Ce3+ resulting in less than one electron transferred per proton in the redox reaction. © 2023 Elsevier B.V.

关键词： Cerium compounds

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Real-time encryption of medical images in IoMT using a novel biometric and chaotic approach

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Knowledge-Based Systems 2025年 323卷

作者： Mansouri, Ali Sun, Pin Lv, Chengzhi Zhu, Yinghua Zhao, Xudong Ge, Hongwei Sun, Changkai School of Control Science and Engineering Dalian University of Technology Dalian116023 China Discipline Planning Department Shanghai Ninth People's Hospital Shanghai Jiaotong University School of Medicine. Shanghai200011 China Research Center Dalian Dermatosis Hospital Dalian116021 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education Dalian University of Technology Dalian116023 China College of Computer Science and Technology Dalian University of Technology Dalian116023 China

The rapid expansion of digital medical imaging and the Internet of Medical Things (IoMT) has raised significant concerns regarding medical image security and patient privacy. Existing encryption methods often struggle to balance computational efficiency with robust security in IoMT environments. This study introduced a novel image encryption system tailored for medical images in IoMT applications. The proposed algorithm employs a multipart secret key system, combining a cloud-based fingerprint dataset for biometric security with an image-derived key to enhance the sensitivity to changes in the plain image. The secret key serves as the input to a newly designed chaotic map that functions as a pseudorandom number generator. The encryption process integrates random value addition, confusion at both decimal and binary levels, and binary-level diffusion. Performance evaluations demonstrated the highly chaotic nature of the proposed map, achieving a Lyapunov exponent close to 500. Security analysis confirms the scheme's strong resistance to attacks, high noise and data loss recovery rates, and superior performance compared with existing encryption methods. The algorithm achieved a near-zero correlation coefficient, a number of pixel changing rates of 99.652%, and a unified averaged changed intensity of 33.640%, rendering it highly suitable for real-time deployment on resource-constrained IoMT devices. © 2025 Elsevier B.V.

关键词： Sensitive data

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Hardware-in-the-Loop Simulation of Vehicle-Manipulator Systems for Physical Interaction Tasks

Hardware-in-the-Loop Simulation of Vehicle-Manipulator Syste...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hemjyoti Das Bjørn Kåre Sæbø Kristin Y. Pettersen Christian Ott Faculty of Electrical Engineering and Information Technology Automation and Control Institute (ACIN) Technical University of Vienna TU Wien Vienna Austria Department of Engineering Cybernetics Centre for Autonomous Marine Operations and Systems (NTNU AMOS) NTNU Norwegian University of Science and Technology Trondheim Norway German Aerospace Center Institute of Robotics and Mechatronics Wessling Germany

Hardware-in-the-loop simulation (HILS) allows a more realistic evaluation of control approaches than what is possible with pure software simulations, but without the actual complexity of the complete system. This is important for some complex systems such as orbital robots, where testing of the system is typically not possible after its launch, and an on-ground replica is used to validate the performance of such a system. In this article, an impedance-matching approach is presented to match the end-effector dynamics of a fixed-base robot manipulator with that of a target vehicle-manipulator system (VMS), while taking into account the redundant nullspace dynamics in a connected real-time simulation framework. This approach ensures that the forces and torques exerted by the system on the environment matches with that of the simulated system. The contact wrenches used in our approach are not obtained from numerical simulations, but rather from real physical interaction, which is one of the main advantages of our approach. The effectiveness of our method is validated by demonstrating various physical interaction tasks with the environment, using a suspended aerial manipulator as the target system.

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Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements

arXiv

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arXiv 2024年

作者： Tucker, Kris Rege, Amit Kiran Smith, Conor Monteleoni, Claire Albash, Tameem Department of Applied Math University of Colorado Boulder United States Department of Computer Science University of Colorado Boulder United States Center for Quantum Information and Control University of New Mexico University of New Mexico AlbuquerqueNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States INRIA Centre de Recherche de Paris France Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States

We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised and unsupervised contexts. We demonstrate that the model benefits from larger training sets establishing that it is in fact "learning," and we show robustness to errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single particle relaxation. © 2024, CC BY.

关键词： Qubits

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Linear Quadratic Guidance Law for Joint Motion Planning of a Pursuer-Turret Assembly

arXiv

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arXiv 2024年

作者： Jha, Bhargav Bopardikar, Shaunak D. Von Moll, Alexander Casbeer, David Michigan State University East LansingMI48864 United States Air Force Research Laboratory WPAFB OH45433 United States Department of Electrical and Computer Engineering Michigan State University United States Control Science Center 2210 8th St.

This paper presents joint motion planning of a vehicle with an attachedrotating turret. The turret has a limited range as well as the field of *** objective is capture a maneuvering target such that at the terminal time itis withing the field-of-view and range limits. Catering to it, we present aminimum effort guidance law that commensurate for the turn rate abilities ofthe vehicle and the turret. The guidance law is obtained using linearizationabout the collision triangle and admits an analytical solution. Simulationresults are presented to exemplify the cooperation between the turret and thevehicle. © 2024, CC BY.

关键词： Motion planning

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach

Real-Time Object Detection and Robotic Manipulation for Agri...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai China The Center for Nondestructive Evaluation (CNDE) Iowa State University Ames IA United States

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

关键词： YOLO Visualization Crops Grasping Production Robustness Real-time systems

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SIFT-based Approach for Dorsal Hand Veins Images Recognition

SIFT-based Approach for Dorsal Hand Veins Images Recognition

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Dobroslav P. Egorov Pavel V. Mizinov Oleg V. Kravchenko Natalia S. Konnova Gavriil V. Osipenko Kotel'nikov Institute of Radio Engineering and Electronics of RAS Moscow Russian Department of information security Bauman Moscow State Technical University Moscow Russian Federation Federal Research Center Computer Science and Control of RAS Moscow Russian Federation Moscow Aviation Institute Moscow Russian Federation

In this paper we present a system for person identification using dorsal hand veins pattern images. A simple approach is developed based on scale-invariant feature transform (SIFT) method for image features extraction, fast approximate nearest neighbors algorithm (FLANN) for keypoints matching in a preselected region-of-interest (ROI), and subsequent utilization of dense layer neural network for accurate friend-or-foe predictions. The efficiency of the considered approach is evaluated using an open dataset, and confusion matrices are calculated. The obtained results confirm again that dorsal hand veins identification systems are particularly promising for high-security settings. The proposed approach is implemented in software for Orange pi single-board computers.

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