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3rd International Conference on Electronic Information engineering and computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

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Design, automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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RoboFold: A platform for Protein-folding Inspired Robot Swarms *

RoboFold: A platform for Protein-folding Inspired Robot Swar...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Feng Zhang Yuping Gu Shuai Yuan Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China School of Computer Science and Engineering and School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China

Natural swarms have inspired various controlling algorithms for swarm robotics, while few of them were programmed in a similar way as the natural swarms. Defining a reliable programming method is still a daunting challenge in swarm robotics. In nature, the information of 3D protein structure is programmed in the sequence of amino acids of the protein. To mimic the programming method of protein folding, we present a platform for investigating protein-folding inspired programming method for robot swarms. This platform includes three parts: a vibration machine, protein-like robot sequences, and recording devices. The preliminary results presented here demonstrated the effectiveness of this platform and the feasibility of protein-folding inspired programming method for robot swarms.

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Robotic Learning of Haptic Skills from Expert Demonstration for Contact-Rich Manufacturing Tasks

Robotic Learning of Haptic Skills from Expert Demonstration ...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Sara Hamdan Yusuf Aydin Erhan Oztop Cagatay Basdogan Robotics and Mechatronics Laboratory and the KUIS-AI Center Koc University Istanbul Turkey Department of Electrical and Electronics Engineering MEF University Istanbul Turkey Department of Computer Science Ozyegin University Istanbul Turkey SISReC/OTRI Osaka University Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

We propose a learning from demonstration (LfD) approach that utilizes an interaction (admittance) controller and two force sensors for the robot to learn the force applied by an expert from demonstrations in contact-rich tasks such as robotic polishing. Our goal is to equip the robot with the haptic expertise of an expert by using a machine learning (ML) approach while providing the flexibility for the user to intervene in the task at any point when necessary by using an interaction controller. The utilization of two force sensors, a pivotal concept in this study, allows us to gather environmental data crucial for effectively training our system to accommodate workpieces with diverse material and surface properties and maintain the contact of polisher with their surfaces. In the demonstration phase of our approach where an expert guiding the robot to perform a polishing task, we record the force applied by the human (F h ) and the interaction force (F int ) via two separate force sensors for the polishing trajectory followed by the expert to extract information about the environment (F env =F h −F int ). An admittance controller, which takes the interaction force as the input is used to output a reference velocity to be tracked by the internal motion controller (PID) of the robot to regulate the interactions between the polisher and the surface of a workpiece. A multilayer perceptron (MLP) model was trained to learn the human force profile based on the inputs of Cartesian position and velocity of the polisher, environmental force (F env ), and friction coefficient between the polisher and the surface to the model. During the deployment phase, in which the robot executes the task autonomously, the human force estimated by our system $\left({{{\hat F}_h}}\right)$ is utilized to balance the reaction forces coming from the environment and calculate the force $\left({{{\hat F}_h} - {F_{env}}}\right)$ needs to be inputted to the admittance controller to gene

关键词： Surface cleaning Force Robot sensing systems Real-time systems Haptic interfaces Trajectory Manufacturing Force sensors Admittance Robots

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Multisensor fusion estimation of nonlinear systems with intermittent observations and heavy-tailed noises

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Science China(Information Sciences) 2022年第09期65卷 173-190页

作者： Bo XIAO Q.M.Jonathan WU Liping YAN School of Artificial Intelligence Beijing University of Posts and Telecommunications Department of Electrical and Computer Engineering University of Windsor Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Inspired by the robust student t-distribution based nonlinear filter(RSTNF), a student tdistribution and unscented transform(UT) based filter for state estimation of heavy-tailed nonlinear dynamic systems, a modified RSTNF for intermittent observations is derived. The fusion estimation for nonlinear multisensor systems with intermittent observations and heavy-tailed measurement and process noises is *** this work, the centralized fusion, the sequential fusion, and the na¨?ve distributed fusion algorithms are presented, respectively. Theoretical analysis shows that the presented algorithms are effective, which are the efficient extension of the classical unscented Kalman filter(UKF) or the cubature Kalman filter(CKF) based algorithms with Gaussian noises. Simulation results show that the presented algorithms are effective and feasible.

关键词： state fusion estimation nonlinear systems heavy-tailed noise intermittent observations multivariate t-distribution

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Deep Learning for Fault Detection and Diagnosis: Application to Photovoltaic System 19

Deep Learning for Fault Detection and Diagnosis: Application...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Marweni, Manel Fezai, Radhia Hajji, Mansour Mansouri, Majdi National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Im-age 5019 Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

With expanding requests for effectiveness and product quality and advancing integration of au-tomatic control systems in high-cost and safety-critical processes, Fault Detection and Diagnosis (FDD) in photo-voltaic (PV) have gained a very important role. However, PV systems can be subject to failures. These faults may include the distinctive system components such as PV modules, converters/inverters, connection lines, which can lead to a reduce in the operation performance. Thus, FDD is one of the key technologies to improve the efficiency of grid-connected PV performances. The most well-known data-driven based FDD methods are Deep Learning (DL)-basd approaches. The main advantages of DL algorithms, are that they can learn automatically high- level features from PV data in a high-order. This paper therefore presents the appli-cation of DL-based tools for fault diagnosis purposes and provides a comparative study using PV systems. These techniques include Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and BIdirectional Long-Short Term Memory (BILSTM). The implemented FDD based frameworks are validated using an emulated Grid-Connected PV (GCPV) system. The results show that the DL-based diagnosis methods are able to detect and diagnose faults under different operating modes. © 2022 IEEE.

关键词： Long short-term memory

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Type Synthesis of Single-DOF Multi-Loop-Coupled Planar Continuum Morphing Mechanisms

Type Synthesis of Single-DOF Multi-Loop-Coupled Planar Conti...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Jieyu Wang Yuxiang Li Yingzhong Tian Fengfeng Xi Guangbo Hao Yinjun Zhao Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Aerospace Engineering Toronto Metropolitan University Toronto ON Canada School of Engineering Electrical and Electronic Engineering University College Cork Cork Ireland School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

This paper investigates a single degree-of-freedom (DOF) multi-loop-coupled planar continuum morphing mechanism (MPCMM), which can be used in broad application prospects in fields such as robotics, aerospace, and biomedical devices. The mechanism is proposed based on the generalized kinematic chains. The design process mainly includes determining the design requirements, selecting the generalized kinematic chains based on the design requirements, modifying the chains, and choosing the most appropriate chain to construct MPCMM. In addition, the vector equation method is used to analyze the mechanisms. Finally, the MPCMM is applied to a variable camber wing trailing edge.

关键词： Couplings Kinematics Vectors Robots

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Distributed Feedback Optimization of Networked Nonlinear Systems Using Relative Output Measurements*

Distributed Feedback Optimization of Networked Nonlinear Sys...

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European Control Conference (ECC)

作者： Zhengyan Qin Tao Liu Tengfei Liu Zhong-Ping Jiang Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong HKU Shenzhen Institute of Research and Innovation Shenzhen China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Electrical and Computer Engineering New York University Brooklyn NY USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the distributed feedback optimization problem of nonlinear multi-agent systems. In such systems, each agent can measure the relative outputs between itself and its neighbors but lacks access to their absolute states and internal controller states. By combining distributed optimization and singular perturbation methods, a novel distributed controller design is presented, that relies solely on each agent's real-time gradient values of its local objective function and its relative output measurements to neighboring agents. The boundedness of the closed-loop signals and the convergence of the agent outputs to the minimizer of the total cost are proved rigorously. A numerical example is conducted to validate the effectiveness of the proposed approach.

关键词： Costs Perturbation methods Europe Distributed feedback devices Linear programming Real-time systems Nonlinear systems

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Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt engineering

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Zhang, Ping Shen, Xuemin Sherman School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Auburn University Auburn United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering University of Waterloo Canada

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services can become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modal Generative Semantic Communications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize a series of cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perpetual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward. © 2024, CC BY.

关键词： Semantics

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