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Distributed formation control of manipulators' end-effector with internal model-based disturbance rejection

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arXiv 2021年

作者： Wu, Haiwen Jayawardhana, Bayu de Marina, Hector Garcia Xu, Dabo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain The School of Automation Nanjing University of Science and Technology Nanjing210094 China

This paper addresses the problem of end-effector formation control for manipulators that are subjected to external disturbances: input disturbance torques and disturbance forces at each end-effector. The disturbances are assumed to be non-vanishing and are superposition of finite number of sinusoidal and step signals. The formation control objective is achieved by assigning virtual springs between end-effectors, by adding damping terms at joints, and by incorporating internal model-based dynamic compensators to counteract the effect of the disturbances;all of which presents a clear physical interpretation of the proposed approach. Simulation results are presented to illustrate the effectiveness of the proposed approach. © 2021, CC BY.

关键词： End effectors

Distributed formation control for manipulator end-effectors

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arXiv 2021年

作者： Wu, Haiwen Jayawardhana, Bayu De Marina, Hector Garcia Xu, Dabo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain School of Automation Nanjing University of Science and Technology Nanjing210094 China

We present three classes of distributed formation controllers for achieving and maintaining the 2D/3D formation shape of manipulator end-effectors to cope with different scenarios due to availability of modeling parameters. We firstly present a distributed formation controller for manipulators whose system parameters are perfectly known. The formation control objective is achieved by assigning virtual springs between end-effectors and by adding damping terms at joints, which provides a clear physical interpretation of the proposed solution. Subsequently, we extend it to the case where manipulator kinematic and system parameters are not exactly known. An extra integrator and an adaptive estimator are introduced for gravitational compensation and stabilization, respectively. Simulation results with planar manipulators and with seven degree-of-freedom humanoid manipulator arms are presented to illustrate the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

Hardware-Oriented Algebraic Fault Attack Framework with Multiple Fault Injection Support

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Hardware-Oriented Algebraic Fault Attack Framework with Mult...

Workshop on Fault Diagnosis and Tolerance in Cryptography (FDTC)

作者： Maël Gay Tobias Paxian Devanshi Upadhyaya Bernd Becker Ilia Polian Chair of Hardware Oriented Computer Science University of Stuttgart Stuttgart Germany Chair of Computer Architecture University of Freiburg Freiburg Germany

The evaluation of fault attacks on security-critical hardware implementations of cryptographic primitives is an important concern. In such regards, we have created a framework for automated construction of fault attacks on hardware realization of ciphers. The framework can be used to quickly evaluate any cipher implementations, including any optimisations. It takes the circuit description of the cipher and the fault model as input. The output of the framework is a set of algebraic equations, such as conjunctive normal form (CNF) clauses, which is then fed to a SAT solver. We consider both attacking an actual implementation of a cipher on an field-programmable gate array (FPGA) platform using a fault injector and the evaluation of an early design of the cipher using idealized fault models. We report the successful application of our hardware-oriented framework to a collection of ciphers, including the advanced encryption standard (AES), and the lightweight block ciphers LED and PRESENT. The corresponding results and a discussion of the impact to different fault models on our framework are shown. Moreover, we report significant improvements compared to similar frameworks, such as speedups or more advanced features. Our framework is the first algebraic fault attack (AFA) tool to evaluate the state-of-the art cipher LED-64, PRESENT and full-scale AES using only hardware-oriented structural cipher descriptions.

关键词： Circuit faults Integrated circuit modeling Ciphers Mathematical model Hardware Analytical models Tools

Construction Progress Monitoring In Smart Cities Using Deep Alexnet

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Construction Progress Monitoring In Smart Cities Using Deep ...

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： A. Rajavel S. Karkuzhali Sarvdaman Sharma Sitesh Kumar Singh Senthilkumar P S. Jhansi Ida Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Business Systems R.M.K Engineering College India

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle.

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Enhancing media literacy: The effectiveness of (Human) annotations and bias visualizations on bias detection

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Information Processing & Management 2025年第6期62卷

作者： Timo Spinde Fei Wu Wolfgang Gaissmaier Gianluca Demartini Isao Echizen Helge Giese Department of Computer Science University of Göttingen Göttingen Germany Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Psychology University of Konstanz Konstanz Germany Cluster for the Advanced Study of Collective Behavior University of Konstanz Konstanz Germany School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia National Institute of Informatics Tokyo Japan Heisenberg Chair for Medical Risk Literacy and Evidence-Based Decisions Charité – Universitätsmedizin Berlin Berlin Germany

Marking biased texts effectively increases media bias awareness, but its sustainability across new topics and unmarked news remains unclear, and the role of AI-generated bias labels is untested. This study examines how news consumers learn to perceive media bias from human- and AI-generated labels and identify biased language through highlighting, neutral rephrasing, and political orientation cues. We conducted two experiments with a teaching phase exposing them to various bias-labeling conditions and a testing phase evaluating their ability to classify biased sentences and detect biased text in unlabeled news on new topics. We find that, compared to the control group, both human- and AI-generated sentential bias labels significantly improve bias classification ( p < .001), though human labels are more effective ( d = 0.42 vs. d = 0.23). Additionally, among all teaching interventions, participants best detect biased sentences when taught with biased sentence or phrase labels ( p < .001), while politicized phrase labels reduce accuracy. The effectiveness of different media literacy interventions remains independent of political ideology, but conservative participants are generally less accurate ( p = .011), suggesting an interaction between political inclinations and bias detection. Our research provides a novel experimental framework into assessing the generalizability of media bias awareness and offer practical implications for designing bias indicators in news-reading platforms and media literacy curricula.

关键词： News literacy Media bias Language processing Text perception

Linking performance data and geospatial information of manufacturing assets through standard representations

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Linking performance data and geospatial information of manuf...

ASME 2020 International Design Engineering Technical Conferences and computers and Information in Engineering Conference, IDETC-CIE 2020

作者： Hanke, Aaron Vernica, Teodor Bernstein, William Z. Associate Systems Integration Division National Institute of Standards and Technology GaithersburgMD20899 United States TU-Dresden Chair of Engineering Design and CAD Dresden01069 Germany Aarhus University Department of Computer Science Aarhus N8200 Denmark Systems Integration Division National Institute of Standards and Technology GaithersburgMD20899 United States

ISBN: (纸本)9780791883983

Interoperability across emerging visualization modalities, including augmented reality (AR) and virtual reality (VR), remains a challenge with respect to industrial applications. One critical issue relates to the lack of standard approaches for coordinating geospatial representations that are required to facilitate AR/VR scenes with domain-specific information in the form of time-series data, solid models, among other data types. In this paper, we focus on the linking of manufacturing asset data via the MTConnect standard with geospatial data via the IndoorGML standard. To this end, we demonstrate the utility of this integration through two visualization-based prototype implementations, including one focused on (i) monitoring production facilities to improve situational awareness and (ii) evaluating and delivering suggested navigation paths in production facilities. We then comment on implications of such standards-driven approaches for related domains, including AR prototype development and automatic guided vehicles. Copyright © 2020 ASME

关键词： Augmented reality

Characterization of Novel Lignocellulosic Spinifex littoreus Fibers and Their Composites

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Journal of Bionic Engineering 2020年第2期17卷 393-404页

作者： Milan Soosai Christopher Thankian Winowlin Jappes Jebas Thangiah Rajini Nagarajan Mayandi Kalimuthu Sikiru Oluwarotimi Ismail Faruq Mohammad Department of Mechanical Engineering Cape Institute of TechnologyLevengipuramTirunelveli 627114Tamil NaduIndia Department of Mechatronics Engineering Kamaraj College of Engineering&Technology VellakulamMadurai dist-622701Tamil NaduIndia Centre for Composite Materials Department of Mechanical EngineeringKalasalingam UniversityKrishnankoilVirudhunagar 626126Tamil NaduIndia Centre for Engineering Research Department of EngineeringSchool of Engineering and Computer ScienceUniversity of HertfordshireHatfieldHertfordshire AL109ABUK Surfactants Research Chair Department of ChemistryCollege of ScienceKing Saud UniversityRiyadh 11451Kingdom of Saudi Arabia

This innovative work presents mechanical,physical and chemical characterization and analysis of newly extracted fiber from naturally resourced plant stem,named Spinifex littoreus fibers(SLF).This is a novel natural,biodegradable and sustainable reinforcement for an improved ***,the chemical constituents of SLF,such as cellulose,lignin,moisture and wax content were *** raw SLF surfaces were modified by chemical treatment with sodium hydroxide(NaOH),calcium hydroxide(Ca(OH)2)and silane.A polyester matrix was reinforced with all the treated SLF,before the mechanical properties(tensile strengths)of the composites were *** all the surface chemically treated SLF/polyester composite samples,the Ca(OH)2 treated sample exhibited the highest tensile *** microscopic examination was carried out to validate this ***,this analysis established the mechanism of failure of the tensile fractured composite samples,using Scanning Electron Microscope(SEM),among other techniques.

关键词： cellulosic fiber Spinifex littoreus fibers XRD FTIR SEM tensile strength

ENAS4D: Efficient multi-stage CNN architecture search for dynamic inference

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arXiv 2020年

作者： Yuan, Zhihang Liu, Xin Wu, Bingzhe Sun, Guangyu Peking University Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

Dynamic inference is a feasible way to reduce the computational cost of convolutional neural network(CNN), which can dynamically adjust the computation for each input sample. One of the ways to achieve dynamic inference is to use multistage neural network, which contains a sub-network with prediction layer at each stage. The inference of a input sample can exit from early stage if the prediction of the stage is confident enough. However, design a multi-stage CNN architecture is a non-trivial task. In this paper, we introduce a general framework, ENAS4D, which can efficiently search for optimal multi-stage CNN architecture for dynamic inference in a well-designed search space. Firstly, we propose a method to construct the search space with multi-stage convolution. The search space include different numbers of layers, different kernel sizes and different numbers of channels for each stage and the resolution of input samples. Then, we train a once-for-all network that supports to sample diverse multi-stage CNN architecture. A specialized multi-stage network can be obtained from the once-for-all network without additional training. Finally, we devise a method to efficiently search for the optimal multi-stage network that trades the accuracy off the computational cost taking the advantage of once-for-all network. The experiments on the ImageNet classification task demonstrate that the multi-stage CNNs searched by ENAS4D consistently outperform the state-of-the-art method for dyanmic inference. In particular, the network achieves 74.4% ImageNet top-1 accuracy under 185M average MACs. Copyright © 2020, The Authors. All rights reserved.

关键词： Memory architecture

HyGCN: A GCN accelerator with hybrid architecture

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arXiv 2020年

作者： Yan, Mingyu Deng, Lei Hu, Xing Liang, Ling Feng, Yujing Ye, Xiaochun Zhang, Zhimin Fan, Dongrui Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences University of California Santa Barbara United States

Inspired by the great success of neural networks, graph convolutional neural networks (GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct execution patterns. The Aggregation phase, behaves as graph processing, showing a dynamic and irregular execution pattern. The Combination phase, acts more like the neural networks, presenting a static and regular execution pattern. The hybrid execution patterns of GCNs require a design that alleviates irregularity and exploits regularity. Moreover, to achieve higher performance and energy efficiency, the design needs to leverage the high intra-vertex parallelism in Aggregation phase, the highly reusable inter-vertex data in Combination phase, and the opportunity to fuse phase-by-phase execution introduced by the new features of GCNs. However, existing architectures fail to address these demands. In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509× speedup with 2500× energy reduction and average 6.5× speedup with 10× energy reduction, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency