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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

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State-Space Piece-Wise Affine System Identification with Online Deterministic Annealing

State-Space Piece-Wise Affine System Identification with Onl...

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European Control Conference (ECC)

作者： Christos N. Mavridis Aris Kanellopoulos John S. Baras Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Department of Electrical and Computer Engineering and the Institute for Systems Research University of Maryland College Park USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose an online identification scheme for discrete-time piece-wise affine state-space models based on a system of adaptive algorithms running in two timescales. A stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale, estimating the partition of the augmented state-input space that defines the switching signal. At the same time, an adaptive identification algorithm, running at a higher timescale, updates the parameters of the local models based on the estimate of the switching signal. Identifiability conditions for the switched system are discussed and convergence results are given based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for piece-wise affine systems, the proposed approach progressively estimates the number of modes needed and is appropriate for online system identification using sequential data acquisition. This progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off, desired in practical applications. Experimental results validate the efficacy of the proposed methodology.

关键词： Switched systems Annealing Stochastic processes Switches Approximation algorithms Real-time systems System identification

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Path Planning and Optimization for Cuspidal 6R Manipulators

arXiv

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arXiv 2025年

作者： Elias, Alexander J. Wen, John T. Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

A cuspidal robot can move from one inverse kinematics (IK) solution to another without crossing a singularity. Multiple industrial robots are cuspidal. They tend to have a beautiful mechanical design, but they pose path planning challenges. A task-space path may have a valid IK solution for each point along the path, but a continuous joint-space path may depend on the choice of the IK solution or even be infeasible. This paper presents new analysis, path planning, and optimization methods to enhance the utility of cuspidal robots. We first demonstrate an efficient method to identify cuspidal robots and show, for the first time, that the ABB GoFa and certain robots with three parallel joint axes are cuspidal. We then propose a new path planning method for cuspidal robots by finding all IK solutions for each point along a task-space path and constructing a graph to connect each vertex corresponding to an IK solution. Graph edges are weighted based on the optimization metric, such as minimizing joint velocity. The optimal feasible path is the shortest path in the graph. This method can find non-singular paths as well as smooth paths which pass through singularities. Finally, this path planning method is incorporated into a path optimization algorithm. Given a fixed workspace toolpath, we optimize the offset of the toolpath in the robot base frame while ensuring continuous joint motion. Code examples are available in a publicly accessible repository. © 2025, CC BY.

关键词： Inverse kinematics

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Variational Covariance Smoothing for Dynamic Functional Connectivity Analysis 58

Variational Covariance Smoothing for Dynamic Functional Conn...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Abeysekara, Ruwanthi Simon, Jonathan Z. Babadi, Behtash University of Maryland Department of Electrical & Computer Engineering College ParkMD United States Institute for Systems Research University of Maryland College ParkMD United States University of Maryland Department of Biology College ParkMD United States

ISBN: (纸本)9798350354058

Functional connectivity is among the widely used metrics to assess the network-level attributes of brain function. While most existing analysis frameworks assume static functional connectivity during the course of an experiment, to capture neural dynamics over short time scales, a time-varying notion of functional connectivity is required. By revealing how neural networks reconfigure in response to changing external stimuli, internal states, and task demands, time-varying functional connectivity can be leveraged to study flexible cognition, such as working memory, attention, and decision-making. A major challenge in estimating time-varying functional connectivity from high-dimensional neural is the associated computational complexity. Existing methods trade off accuracy for computational efficiency, especially in applications that require real-time or near real-time processing. Here, we build on existing work using covariance-domain state-space models and introduce a framework based on variational inference that allows low-complexity estimation of time-varying functional connectivity and construction of confidence intervals. We validate the performance of the proposed method using simulation studies. Our results reveal significant gains in computational complexity compared to existing methods, while maintaining high accuracy. © 2024 IEEE.

关键词： State space methods

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Multi-Timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2023年第4期1卷 322-335页

作者： Wu, Sirui Lin, Jin Li, Jiarong Liu, Feng Song, Yonghua Xu, Yanhui Cheng, Xiang Yu, Zhipeng Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China

Renewablepower to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified timeframe. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of the RePtA VPP joining multiple markets over the conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor flexibility. © 2023 IEEE.

关键词： Ammonia

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Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning 13th

Prediction of Emission Characteristics of Spark Ignition (S....

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13th International Advanced Computing Conference, IACC 2023

作者： Kumbhar, Sujit Khot, Sanjay Jujare, Varsha Patil, Vishal Husainy, Avesahemad Shedbalkar, Koustubha Department of Technology Shivaji University Maharashtra Kolhapur416004 India Department of Automation and Robotics Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Computer Science and Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Electrical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India

ISBN: (纸本)9783031566998

In the current research work, a single cylinder spark (S.I)ignition engine were used for investigations of premium level gasoline-ethanol-alkane experimentally with different operating conditions e.g. variation spark ignition timing. The Engine Lab and PE3 software were used for engine control and data acquisition system. The data obtained after experimentation were used to predict the engine emissions for different operating conditions. The engine emission characteristics were predicted using three machine learning algorithmsviz linear regression, decision tree and random forest. It was found that emissions characteristics such as carbon monoxide, unburnt hydrocarbon found to be minimum for 24°bTDC experimentally as well as predicted by machine learning algorithms with different operating conditions than other spark timing positions such as 15°, 18°, 21°, 27°, 30° bTDC. All three machine learning algorithms gave better results but the random forest algorithm were more accurate than linear regression and decision trees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Paraffins

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Semi-Autonomous Interaction Framework for Contact-Based Operations with Commercial UAVs IN GNSS-Denied Environments

Semi-Autonomous Interaction Framework for Contact-Based Oper...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： A. González-Morgado Q. Zhang G. Damigos E. Cuniato T. Hui E. Sahin G. Nikolakopoulos R. Siegwart M. Fumagalli A. Ollero G. Heredia GRVC Robotics Labs University of Seville Spain 3D Media Group Tampere University Finland Department of Computer Electrical and Space Ericsson Research and the Robotics and AI Group Autonomous Systems Lab (ASL) at ETH Zurich Switzerland Department of Electrical and Photonics Engineering Technical University of Denmark Denmark Department of Computer Electrical and Space Robotics and AI Group

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Unmanned aerial vehicles (UAVs) are being increasingly established for autonomous contacted-based inspection of industrial assets, reducing the risk of errors by human operators. However, challenges remain in the navigation under unreliable Global Navigation Satellite System (GNSS) signals, or in the detection and interaction with the inspection surface, which limits the autonomy level of current UAV industrial technologies. This paper presents a semi-autonomous framework which combines automated target detection and interaction in GNSS-denied environments, with supervision commands by a human operator, to increase both safety and reliability. Our framework is composed of: (i) a camera-based onboard odometry solution for positioning the UAV in GNSSdenied conditions, (ii) a target-detection and filtering algorithm which estimates the orientation and position of the interaction target and (iii) a visual servoing strategy for approaching and contacting the target. The proposed framework is developed completely in ROS, and can be used with any commercial UAV. The framework is validated through outdoor flights, where a UAV detects and contacts a target on a vertical pipe.

关键词： Vibrations Global navigation satellite system Three-dimensional displays Switches Inspection Autonomous aerial vehicles Visual servoing Safety Odometry Reliability

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Energy Efficiency Optimization in Massive MIMO systems with Low-Resolution ADCs 2

Energy Efficiency Optimization in Massive MIMO Systems with ...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Musa, Abdulwaheed Adebayo, Abdullateef Ola Peace Balogun, Oluwasijibomi Kwara State University Department of Electrical and Computer Engineering Malete Nigeria Kwara State University Centre for Artificial Intelligence and Machine Learning Systems Malete Nigeria University of Johannesburg Institute for Intelligent Systems South Africa

ISBN: (纸本)9798350358834

Massive multiple-input multiple-output (MIMO) is a promising 5G technology, but the high energy consumption from numerous radio components is a key challenge. Prior work has explored higher than 4-bit ADCs, but very low 1-2 bit remain uninvestigated. This paper provides an in-depth examination of the resolution versus power savings and signal quality degradation trade-offs with 1-3-bit analog-to-digital converters (ADCs) in Massive MIMO systems. Mathematical modeling and simulations quantify the exponential reduction in power consumption along with losses in key performance metrics like signal-to-interference-plus-noise ratio (SINR), throughput, and spectral efficiency with lower ADC resolutions stemming from increased quantization noise. Advanced linear detection techniques are shown to effectively recover the signal quality degradation. The analysis provides valuable insights on jointly optimizing ADC configuration and signal processing to maximize overall system efficiency. Energy savings of over 50% are demonstrated with 1-bit ADCs along with the ability to maintain acceptable performance metrics using linear detectors. The practical implementation feasibility is also discussed. This work enables ultra-low-resolution ADC deployments in energy-efficient Massive MIMO systems. © 2023 IEEE.

关键词： Energy efficiency

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Improved Bound for Robust Causal Bandits with Linear Models

arXiv

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arXiv 2024年

作者： Yan, Zirui Mukherjee, Arpan Varici, Burak Tajer, Ali Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

This paper investigates the robustness of causal bandits (CBs) in the face of temporal model fluctuations. This setting deviates from the existing literature's widely-adopted assumption of constant causal models. The focus is on causal systems with linear structural equation models (SEMs). The SEMs and the time-varying pre- and post-interventional statistical models are all unknown and subject to variations over time. The goal is to design a sequence of interventions that incur the smallest cumulative regret compared to an oracle aware of the entire causal model and its fluctuations. A robust CB algorithm is proposed, and its cumulative regret is analyzed by establishing both upper and lower bounds on the regret. It is shown that in a graph with maximum in-degree d, length of the largest causal path L, and an aggregate model deviation C, the regret is upper bounded by O (dL-1 2 ( √ T + C)) and lower bounded by Ω(d L2 -2 max{ √ T , d2C}). The proposed algorithm achieves nearly optimal O ( √ T) regret when C is o( √ T), maintaining sub-linear regret for a broad range of C. Copyright © 2024, The Authors. All rights reserved.

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