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

作者： Long Kang Yang Yang Byung-Ju Yi PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xi'an Modern Control Technology Research Institute Xi'an China School of Automation Nanjing University of Information Science and Technology Nanjing China Department of the Electronic Systems Engineering Hanyang University Ansan South Korea

This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages.

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Lidar-Based Multiple Object Tracking with Occlusion Handling

Lidar-Based Multiple Object Tracking with Occlusion Handling

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

作者： Ruo-Tsz Ho Chieh-Chih Wang Wen-Chieh Lin Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Occlusion remains an issue in multiple object tracking, which could cause ambiguity in object detection, such as incorrect or missing detection. Under occlusion, a track could experience an early termination, resulting in identity switches and/or fragmentation. To recover from different lengths of occlusions, the track should be maintained by considering its occlusion status. To address the issues mentioned above, we propose an indicator that can model the track's occlusion extent via geometric information provided by LiDAR data. Through incorporating the indicator into the track management and data association process, it is feasible to prevent tracks from premature termination. The proposed method is evaluated on the collected dataset which undergoes frequent and severe occlusions. Compared to the state-of-the-art probabilistic tracking approach, our approach achieves improvements of 3.26% in MOTA and 5.36% in IDF1. Additionally, we obtain 9.89% improvements in IDF1 specifically for objects experiencing severe occlusions.

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Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

arXiv

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arXiv 2021年

作者： Ramos, Guilherme Pedro Aguiar, A. Kar, Soummya Pequito, Sérgio The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal The Department of Electrical and Computer Engineering Carnegie Mellon University United States The Delft University of Technology The Delft Center for Systems and Control Netherlands

Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. Yet, it can raise privacy concerns in situations where the agents' states contain sensitive information. In this paper, we propose a novel (noiseless) privacy preserving distributed algorithms for multi-agent systems to reach an average consensus. The main idea of the algorithms is that each agent runs a (small) network with a crafted structure and dynamics to form a network of networks (i.e., the connection between the newly created networks and their interconnections respecting the initial network connections). Together with a re-weighting of the dynamic parameters dictating the inter-agent dynamics and the initial states, we show that it is possible to ensure that the value of each node converges to the consensus value of the original network. Furthermore, we show that, under mild assumptions, it is possible to craft the dynamics such that the design can be achieved in a distributed fashion. Finally, we illustrate the proposed algorithm with examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping

arXiv

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arXiv 2022年

作者： Rikos, Apostolos I. Oliva, Gabriele Hadjicostis, Christoforos N. Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Unit of Automatic Control Department of Engineering Università Campus Bio-Medico di Roma via Álvaro del Portillo 21 Rome00128 Italy The Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

In this paper, we present a distributed algorithm which implements the k-means algorithm in a distributed fashion for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck. Also, in order to preserve available resources, nodes are able to distributively determine whether they can terminate the operation of the proposed algorithm. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents to mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm. © 2022, CC BY.

关键词： K-means clustering

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Suppressing the endemic equilibrium in SIS epidemics: A state dependent approach

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IFAC-PapersOnLine 2021年第15期54卷 163-168页

作者： Yuan Wang Sebin Gracy Hideaki Ishii Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure and focuses on the effect of social distancing to regulate the epidemic level. We demonstrate that if each subpopulation is informed of its infection rate and reduces interactions accordingly, the fraction of the subpopulation infected stays below half for all time instants. To this end, we first modify the basic SIS model by introducing a state dependent parameter representing the frequency of interactions between subpopulations. Thereafter, we show that for this modified SIS model, the spectral radius of a suitably-defined matrix being not greater than one causes all the agents, regardless of their initial sickness levels, to converge to the healthy state; assuming non-trivial disease spread, the spectral radius being greater than one leads to the existence of a unique endemic equilibrium, which is also asymptotically stable. Finally, by leveraging the aforementioned results, we show that the fraction of (sub)populations infected never exceeds half.

关键词： Epidemic processes Infection reduction Characterization of endemic equilibrium Suppressing endemic equilibrium

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Wireless sensor network for street lighting control 20

Wireless sensor network for street lighting control

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3rd International Scientific and Practical Conference, DEFIN 2020

作者： Burtsev, Alexander Kolesnichenko, Dmitry Vodovozov, Alexander Akhmetov, T.R. LLC Systems and Technologies Vologda Russia Department of Control and Computer Systems Vologda State University Vologda160000 Russia Kazan State Power Engineering University Kazan Russia

ISBN: (纸本)9781450375306

Automation of street lighting systems is an effective way to reduce energy consumption. Savings are achieved through controlled switching on/off of lights, which simultaneously leads to changes in lighting that are undesirable for pedestrians and drivers, and contradicts current standards. To ensure public safety, lights should be turned on in advance when pedestrians approach or vehicles appear within sight. A street lighting system based on a wireless sensor network is considered as a solution to the problem. The main concept of such a system is to create a wireless network of smart lights that actively interact with each other to predict the approach of pedestrians and transport. In this system, each lamp collects information and participates in the exchange and processing of data to detect approaching objects. The wireless sensor network of lighting fixtures is a distributed, self-organizing network with many sensors and actuators connected to each other via a radio channel. © 2020 ACM.

关键词： Wireless sensor networks

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Biologically plausible robust control with Neural Network weight reset for unmanned aircraft systems under impulsive disturbances

Biologically plausible robust control with Neural Network we...

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American control Conference (ACC)

作者： Ignacio Rubio Scola Luis Rodolfo Garcia Carrillo Andrew T. Sornborger João P. Hespanha Department of Industrial Products Engineering INTI CONICET and UNR Rosario Argentina Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Los Alamos National Laboratory Los Alamos NM USA Center for Control Dynamical Systems and Computation University of California Santa Barbara USA

Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implementation of such controllers in real-world scenarios. In this work the limbic system inspired control (LISIC) framework is revisited, introducing three contributions that facilitate the implementation of this type of controller in real-time. First, an extension enabling the implementation of LISIC to the domain of SISO affine systems is proposed. Second, a strategy for resetting the controller’s Neural Network (NN) weights is developed, in such a way that now it is possible to deal with piece-wise smooth references and impulsive perturbations. And third, for the case when a nominal model of the system is available, a technique is proposed to compute a set of optimal NN reset weight values by solving a convex constrained optimization problem. Numerical simulations addressing the stabilization of an unmanned aircraft system via the robust LISIC demonstrate the advantages obtained when adopting the extension to SISO systems and the two NN weight reset strategies.

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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Monitoring of Energy Processes in the Electrohydraulic Complex with a Variable-Frequency Electric Drive in Unsteady Operation Modes

Monitoring of Energy Processes in the Electrohydraulic Compl...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Mohamed Zaidan Qawaqzeh Viktoriya Kovalchuk Tetyana Korenkova Oleksandr Serdiuk Oleksandr Bilyk Department of Electrical and Electronics Engineering Al-Balqa’ Applied University Al-Salt Jordan Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department Of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350346848

An energy model of an electrohydraulic complex has been developed. It allows analyzing the energy processes in the power channel in dynamic modes during the development of cavitation and turbulent oscillations in the pipeline network. It has been shown that the nature of energy processes in the considered cases is periodic constant power fluctuations in the analyzed time interval. The variable component in technological and energy signals of the electrohydraulic complex has been assessed. Their frequency analysis has been performed. The coefficient of power controllability of the electrohydraulic complex and its critical values, whereat the development of emergency modes is possible, have been determined.

关键词： Time-frequency analysis Pipelines Process control controllability Hydrodynamics Harmonic analysis Mathematical models

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The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

arXiv

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arXiv 2022年

作者： Aquilina, Matthew Ciantar, Keith George Galea, Christian Camilleri, Kenneth P. Farrugia, Reuben A. Abela, John Department of Communications & Computer Engineering Faculty of ICT University of Malta Malta Msida Malta Deanery of Molecular Genetic & Population Health Sciences University of Edinburgh Scotland Edinburgh United Kingdom Alpha Centre Ascent 90/3 Tarxien Road Luqa Malta Department of Systems & Control Engineering Faculty of Engineering University of Malta Malta Msida Malta Department of Computer Information Systems Faculty of ICT University of Malta Malta Msida Malta

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution – high-resolution (LR – HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information that could be used to improve the SR process. On the other hand, followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network, using a metadata insertion block to insert prediction vectors into SR network feature maps. Through comprehensive testing, we prove that state-of-the-art contrastive and iterative prediction schemes can be successfully combined with high-performance SR networks such as RCAN and HAN within our framework. We show that our hybrid models consistently achieve stronger SR performance than both their non-blind and blind counterparts. Furthermore, we demonstrate our framework’s robustness by predicting degradations and super-resolving images from a complex pipeline of blurring, noise and compression. Our framework is available at: https://***/um-dsrg/RUMpy. Copyright © 2022, The Authors. All rights reserved.

关键词： Metadata

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