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Manipulating the Posterior Support of a Discrete Bayesian Estimator Under Full Sensor control

IFAC-PapersOnLine 2023年第2期56卷 252-257页

作者： Daniel Selvaratnam Farhad Farokhi Iman Shames Henrik Sandberg Decision and Control Systems School of Electrical Engineering & Computer Science KTH Royal Institute of Technology 114 28 Stockholm Sweden Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3010 Australia College of Engineering & Computer Science Australian National University Canberra ACT 2601 Australia

The asymptotic implausibility problem is introduced from the perspective of an adversary that seeks to drive the belief of a recursive Bayesian estimator away from a particular set of parameter values. It is assumed that the adversary controls all sensors informing the estimator, and can transmit false measurements stochastically according to a fixed distribution of its choice. First, we outline a method for verifying whether a given distribution solves the problem. We then consider the class of spoofing attacks, and show that the asymptotic implausibility problem has a solution if and only if it can be solved by a spoofing attack. Attention is restricted to finite parameter and observation spaces.

关键词： Network Security Bayesian Estimation Cyber-Physical Systems Network Systems Resilient control Systems Signal Processing

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Integrated fault-tolerant control of assembly and automated guided vehicle-based transportation layers

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International Journal of computer Integrated Manufacturing 2022年第4-5期35卷 409-426页

作者： Pawel Majdzik Marcin Witczak Bogdan Lipiec Zbigniew Banaszak a Department of Electronics and Computer Science Koszalin University of Technology Koszalin Polandb Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland

ABSTRACTABSTRACTThe paper concerns the digital twin design and fault-tolerant control of a production system. The real laboratory system under consideration consists of two subsystems: an assembly part and an automated guided vehicle transportation one. The proposed approach focuses, simultaneously, on both concurrency and synchronization problems as they are inevitable in such a kind of modern systems. The research contribution deals with the development of an analytical description of both an assembly system and a multiple automated guided vehicles transportation layer along with algorithms that are able to determine an optimum sequence of starting individual operations. Subsequently, diagnostics and fault-tolerant control schemes are elaborated and applied to the two-layer system being investigated. The core advantage of the proposed approach is that it can compensate or eliminate the impact of some faults, e.g. delays occurring in the assembly unit or reduced velocities of transportation means. This makes it possible to keep the desired schedule as far as possible. The paper concludes with a series of illustrative use cases and comparisons which exhibit the performance of the novel approach in both cases, i.e. faulty and fault-free.

关键词： Fault-tolerant control assembly system automated guided vehicles system digital twin concurrency synchronization predictive control

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MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson's disease diagnosis

MEST: Multi-plane Embedding and Spatial-temporal Transformer...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Jin Du, Hao Bi, Qian Liao, Haiyan Pan, Yi Central South University School of Computer Science and Engineering Changsha China Hunan Frist Normal University School of Mathematics and Statistics Changsha China Central South University Department of Radiology The Second Xiangya Hospital Changsha China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Faculty of Computer Science and Control Engineering Shenzhen China

ISBN: (纸本)9781665468190

Parkinson's disease (PD) is a common neurodegenerative disorder that impairs the patient's quality of life. Medical imaging technology has provided a variety of neuroimages for PD diagnosis studies. However, how to effectively integrate the rich representations from multi-modality data is still a challenging task. To address this challenging task, we propose a multiplane embedding and spatial-temporal Transformer (MEST) framework for PD diagnosis. Firstly, we project structural magnetic resonance imaging (sMRI) into 2D images from coronal, sagittal and axial directions, respectively. Then, the multi-plane dynamic images are learned by pre-trained VGG11 and attention mechanism for representation learning. Afterwards, time series information of functional magnetic resonance imaging (fMRI) is used to construct dynamic functional connection images. To capture information changes in brain, spatial-temporal connectivity Transformer (SCTransformer) is utilized to address spatial-temporal redundancy and dependencies. To integrate multimodality data, ensemble learning is designed based on majority voting strategy to perform PD diagnosis. We evaluate the proposed method on 279 subjects from an in-house and Parkinsons Progression Markers Initiative (PPMI) dataset. Experimental results show that the MEST achieves promising performance with accuracies of 0.856 and 0.885, and outperforms some state-of-the-art methods. © 2022 IEEE.

关键词： Embeddings

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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Funnel control Under Hard and Soft Output Constraints

Funnel Control Under Hard and Soft Output Constraints

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IEEE Conference on Decision and control

作者： Farhad Mehdifar Charalampos P. Bechlioulis Dimos V. Dimarogonas Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering Division of Systems and Control University of Patras Patra Greece

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a funnel control method under time-varying hard and soft output constraints. First, an online funnel planning scheme is designed that generates a constraint consistent funnel, which always respects hard (safety) constraints, and soft (performance) constraints are met only when they are not conflicting with the hard constraints. Next, the prescribed performance control method is employed for designing a robust low-complexity funnel-based controller for uncertain nonlinear Euler-Lagrangian systems such that the outputs always remain within the planned constraint consistent funnels. Finally, the results are verified with a simulation example of a mobile robot tracking a moving object while staying in a box-constrained safe space.

关键词： Aerospace electronics Planning Safety Trajectory Complexity theory Mobile robots Nonlinear systems

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Path Planning for Coverage Spraying of Plants in Outdoor Wheeled Mobile Robots

Path Planning for Coverage Spraying of Plants in Outdoor Whe...

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Robotics, control and Automation (ICRCA), International Conference on

作者： Weixin Zhang Zhengxin Yu Shixin Mao Xuefeng Zhou Jia Pan Haifei Zhu School of ElectRomechanical Engineering Guangdong University of Technology Guangzhou China Jiutian Innovation (Guangdong) Intelligent Technology Co Ltd Foshan China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China Department of Computer Science The University of Hong Kong Hong Kong China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In contemporary agricultural practices and pest management, the deployment of outdoor wheeled mobile robots for precise and efficient plant spraying is crucial to optimize chemical application and mitigate environmental impact. This study introduces an integrated path planning framework designed to systematically tackle the challenges of obstacle avoidance, adherence to kinematic constraints, and optimization of spraying operations, while ensuring complete coverage of target plants. The framework comprises three key components: a local route generator utilizing principal component analysis to minimize movement required for spraying, thereby enhancing efficiency; a global path optimizer that effectively sequences and connects the generated routes to produce collision-free paths; and a path regulator that fine-tunes the resulting paths to comply with the robot's kinematic constraints. Extensive simulations across various outdoor scenarios demonstrate that the proposed framework achieves comprehensive coverage, reduces path lengths, and improves operational efficiency, presenting a viable solution for advanced robotic spraying applications.

关键词： Regulators Robot kinematics Spraying Kinematics Path planning Real-time systems Mobile robots Collision avoidance Optimization Principal component analysis

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Numerical Modeling of Oxygen Diffusion in Tissue Spheroids Undergoing Fusion Using Functional Representation and Finite Volumes

arXiv

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arXiv 2025年

作者： Vilinski-Mazur, Katherine Kirillov, Bogdan Rogozin, Oleg Kolomenskiy, Dmitry Center for Materials Technologies Skolkovo Institute of Science and Technology Moscow Russia Center for Precision Genome Editing and Genetic Technologies for Biomedicine Institute of Gene Biology Russian Academy of Sciences Moscow Russia Federal Research Center"Computer Science and Control" Russian Academy of Sciences Moscow Russia

A three-dimensional cell culture called a spheroid serves as a foundational entity in a wide variety of modern tissue engineering applications, including 3D-bioprinting and preclinical drug testing. Lack of oxygen within tissue spheroids hinders metabolism of cells and eventually leads to cell death. Prevention of necrosis is crucial to success of tissue engineering methods and such prevention requires estimation of cell viability in the spheroid. We propose a novel approach for numerical modeling of diffusion in tissue spheroids during their fusion. The approach is based on numerical solutions of partial differential equations and the application of Function Representations (FRep) framework for geometric modeling. We present modeling of oxygen diffusion based on meshes derived from the geometry of fusing spheroids, a method for selecting optimal spheroid size, and several statistics for estimating cellular viability. Our findings provide insights into oxygen diffusion in three-dimensional cell cultures thus improving the robustness of biotechnological methods that employ tissue spheroids. © 2025, CC BY.

关键词： Cell death

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Event-Triggered-Based Robust control of Heterogeneous Connected Vehicles Platoon with Communication Delay and Random Packet Loss

Event-Triggered-Based Robust Control of Heterogeneous Connec...

引用

Conference on Vehicle control and Intelligence (CVCI)

作者： Chen Liu Shuangkun Ru Lin Qi Jiayi Fang Ting Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

With the development of vehicle autonomous driving technology and wireless network communication technology, the mix platoon of connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs) is becoming inevitable. The abnormal V2V communication and uncertain vehicle dynamics may affect the CAV control performance. Considering time-varying communication delays and random packet loss, an event-trigger-based robust control strategy is proposed based on the stability theory of stochastic time-delay systems. The Bernoulli packet loss model is constructed to solve the random packet loss problem. The robust controller with the event-triggered mechanism designed using the Lyapunov functional stability theory of stochastic systems ensures the stability of the mix platoon with vehicle heterogeneity and the stability of CAV. The simulation comparison has verified that the proposed control strategy can better suppress the fluctuation of the preceding vehicle and more quickly reach the desired value when both time delay and packet loss are present.

关键词： Robust control Fluctuations Wireless networks Packet loss Vehicular ad hoc networks Stability analysis Delays Vehicle dynamics Autonomous vehicles Time-varying systems

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Time-Robust Path Planning with Piece-Wise Linear Trajectory for Signal Temporal Logic Specifications

Time-Robust Path Planning with Piece-Wise Linear Trajectory ...

引用

American control Conference (ACC)

作者： Nhan-Khanh Le Erfaun Noorani Sandra Hirche John S. Baras Chair of Information-oriented Control (ITR) School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Real-world scenarios are characterized by timing uncertainties, e.g., delays, and disturbances. Algorithms with temporal robustness are crucial in guaranteeing the successful execution of tasks and missions in such scenarios. We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in Sig-nal Temporal Logic (STL). In contrast to prior approaches that rely on discretized trajectories with fixed time-steps, we leverage Piece-Wise Linear (PWL) signals for the synthesis. PWL signals represent a trajectory through a sequence of time-stamped waypoints. This allows us to encode the STL formula into a Mixed-Integer Linear Program (MILP) with fewer variables. This reduction is more pronounced for specifications with a long planning horizon. To that end, we define time-robustness for PWL signals. Subsequently, we propose quantitative semantics for PWL signals according to the recursive syntax of STL and prove their soundness. We then propose an encoding strategy to transform our semantics into a MILP. Our simulations showcase the soundness and the performance of our algorithm.

关键词： Uncertainty Semantics Transforms Syntactics Encoding Robustness Trajectory

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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