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arXiv 2023年

作者： Ma, Haoxiang Han, Shuo Kamhoua, Charles A. Fu, Jie The Department of Electrical & Computer Engineering University of Florida GainesvilleFL32611 United States The Department of Electrical & Computer Engineering University of Illinois at Chicago ChicagoIL60607 United States The U.S. Army Research Laboratory RomeNY13440 United States

This paper is concerned with the optimal allocation of detection resources (sensors) to mitigate multi-stage attacks, in the presence of the defender's uncertainty in the attacker's intention. We model the attack planning problem using a Markov decision process and characterize the uncertainty in the attacker's intention using a finite set of reward functions-each reward represents a type of the attacker. Based on this modeling framework, we employ the paradigm of the worst-case absolute regret minimization from robust game theory and develop mixed-integer linear program (MILP) formulations for solving the worst-case regret minimizing sensor allocation strategies for two classes of attack-defend interactions: one where the defender and attacker engage in a zero-sum game, and another where they engage in a non-zero-sum game. We demonstrate the effectiveness of our framework using a stochastic gridworld example. © 2023, CC BY.

关键词： Markov processes

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Machine vision Analysis for Multiplex Surface-Enhanced Raman Scattering Sensing

Machine Vision Analysis for Multiplex Surface-Enhanced Raman...

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CLEO: Science and Innovations, S and I 2022

作者： Hou, Xingwei Sivashanmugan, Kundan Zhao, Yong Wang, Alan X. School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States State Key Laboratory of Precision Measurement Technology and Instrument School of Precision Instruments and Opto-Electronics Engineering Tianjin University Tianjin300072 China School of Electrical Engineering The Key Laboratory of Measurement Technology and Instrumentation of Hebei Province Yanshan University Hebei Qinhuangdao066004 China

We report a multiplex sensing method by machine vision analysis of surface-enhanced Raman scattering coupled with thin layer chromatography. This machine vision method can detect five different analytes at 1 ppm conce... 详细信息

ISBN: (纸本)9781557528209

关键词： Thin layer chromatography

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Collaborative multiagent decision making for lane-free autonomous driving 20

Collaborative multiagent decision making for lane-free auton...

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20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021

作者： Troullinos, Dimitrios Chalkiadakis, Georgios Papamichail, Ioannis Papageorgiou, Markos Dynamic Systems and Simulation Laboratory Technical University of Crete Greece School of Electrical and Computer Engineering Technical University of Crete Greece

ISBN: (纸本)9781713832621

This paper addresses the problem of collaborative multi-agent autonomous driving of connected and automated vehicles (CAVs) in lane-free highway scenarios. We eliminate the lane-changing task, i.e., CAVs may be located in any arbitrary lateral position within the road boundaries, hence allowing for better utilization of the available road capacity. As a consequence, vehicles operate in a much more complex environment, and the need for the individual CAVs to select actions that are efficient for the group as a whole is highly desired. We formulate this environment as a multiagent collaboration problem represented via a coordination graph, thus decomposing the problem with local utility functions, based on the interactions between vehicles. We produce a tractable and scalable solution by estimating the joint action of all vehicles via the anytime max-plus algorithm, with local utility functions provided by potential fields, designed to promote collision avoidance. Specifically, the fields have an ellipsoid form that is most suitable for lane-free highway environments. This novel use of max-plus with potential fields gives rise to a coordinated control policy that exploits only local information specific to each CAV. Our experimental evaluation confirms the effectiveness of our approach: lane-free movement allows for increased traffic flow rates, and vehicles are able to achieve speeds that are both high and close to their desired ones, even in demanding environments with high traffic flow. © 2021 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Autonomous vehicles

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Robust Output Feedback Control Design for Inertia Emulation by Wind Turbine Generators

Robust Output Feedback Control Design for Inertia Emulation ...

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IEEE General Meeting Power& Energy Society

作者： Samaneh Morovati Yichen Zhang Seddik Djouadi Kevin Tomsovic Andrew Wintenberg Mohammed Olama Electrical Engineering and Computer Science The University of Tennessee Knoxville Energy system devision Argonne National Laboratory Electrical Engineering and Computer Science University of Michigan Ann Arbor Computational Sciences and Engineering Division Oak Ridge National Laboratory

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408240

Wind generation has gained widespread use as a renewable energy source. Most wind turbines and other renewables connected to the grid through converters result in a reduction in the natural inertial response to grid frequency changes. The doubly-fed induction generator (DFIG) can be controlled to compensate for this reduction and, in fact, provide faster response than traditional synchronous machines. This paper proposes to design observer based output feedback linear quadratic regulator (LQR) and H∞ control laws to realize the inertia emulation function and deliver fast frequency support. The aim is to track the reference speed by a diesel synchronous generator (DSG) in order to reach the desired inertia. The control signal is computed based on a reduced order model using the balanced truncation technique. A comparison with selective modal analysis (SMA) and balanced truncation model reduction techniques is presented. Comprehensive results show the effective emulation of synthetic inertia by implementing the control laws on a nonlinear three-phase diesel-wind system. The proposed technique is analyzed for different short circuit ratio (SCR) scenarios.

关键词： Wind energy generation Renewable energy sources Regulators Modal analysis Emulation Observers Reduced order systems

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Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 368-380页

作者： Shuyang Xu Xingying Chen Jun Xie Saifur Rahman Jixiang Wang Hongxun Hui Tao Chen College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China Department of Electrical and Computer Engineering Viginia TechAlington VA 22203 USAand also with the Advanced Research InstituteViginia TechArlingtonVA 22203 USA State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 519031China Collegue of Electrical Engineering Southeast UniversityNanjing 210009China.DOI:10.17775/CSEEJPES.2019.01750

Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)*** typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market *** those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive *** this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)*** simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)*** bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR *** and GenCos optimize their bidding strategies via the RL *** modified optimization approach in this procedure can prevent the training results from falling into local optimum *** ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)*** on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential *** show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand *** models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions

关键词： Agent-based modeling and simulation(ABMS) electricity market residential demand response(RDR) reinforcement learning(RL)

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Enhanced Redundant Weighted Clustered Scheme for Damaged S-UAV

Enhanced Redundant Weighted Clustered Scheme for Damaged S-U...

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IEEE International Conference on Communications (ICC)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Cyprus and Department of Electrical & Electronic Engineering Science University of Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

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Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement

arXiv

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arXiv 2023年

作者： Wang, Jing Li, Jinagyun Chen, Chen Zhang, Yisi Shen, Haoran Zhang, Tianxiang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing China Center for Research in Computer Vision University of Central Florida Orlando United States

The Few-Shot Segmentation (FSS) aims to accomplish the novel class segmentation task with a few annotated images. Current FSS research based on meta-learning focuses on designing a complex interaction mechanism between the query and support feature. However, unlike humans who can rapidly learn new things from limited samples, the existing approach relies solely on fixed feature matching to tackle new tasks, lacking adaptability. In this paper, we propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes. In detail, we design the Prototype Adaptive Module (PAM), which utilizes accurate category information provided by the support set to derive class prototypes, enhancing class-specific information in the multi-stage representation. In addition, our approach is compatible with diverse FSS methods with different backbones by simply inserting PAM between the layers of the encoder. Experiments demonstrate that our method effectively improves the performance of the FSS models (e.g., MSANet, HDMNet, FPTrans, and DCAMA) and achieves new state-of-the-art (SOTA) results (i.e., 72.4% and 79.1% mIoU on PASCAL-5i 1-shot and 5-shot settings, 52.7% and 60.0% mIoU on COCO-20i 1-shot and 5-shot settings). Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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A Hybrid Swarm Intelligence Model for Efficient Query Optimization in Distributed Databases Using Genetic and Ant Colony Algorithms

A Hybrid Swarm Intelligence Model for Efficient Query Optimi...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Jayasri, Nemala Santosh Kumar, B. Singh, Navdeep Singh, Ajeet Al-Rubaye, Taqi Mohammed Khattab Rao, Mallikarjuna Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technologies The Islamic University Department of Medical Laboratory Technology Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

Optimisation of queries in distributed databases is more imperative to enhance the processing rate and use of resources with limited computational abilities. This work presents a novel GA-ACO hybrid swarm intelligence model to address the query optimization problem. The optimization effort of the proposed model starts with GA for an initial population of probable paths for query execution which uses the concept of evolution to select diverse solutions. After that, ACO strengthens this set by the application of pheromone-based reinforcement that allows adjusting of a query paths toward the best solution or its approximation with the following number values. In this way the enhancement of the convergence rates and the scalability of the proposed solution when distributed databases are used can be explained. The model was tested with artificial query logs with DEAP being used for the implementation of the genetic algorithm while ant colony operations were implemented using Python built ACO libraries. Performance analysis shows the success of the developed model in decreasing query response time as follows: This essentially complex hybrid mechanism shows potential for future use in other areas with large data sets and efficient query handling. © 2025 IEEE.

关键词： Ant colony optimization

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Multiple Redundant K-Means Clustered Scheme Based on Weighted Cluster Head Selection for Damaged S-UAV

Multiple Redundant K-Means Clustered Scheme Based on Weighte...

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IEEE Conference on Global Communications (GLOBECOM)

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATLAB. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.

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Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems ⁎

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IFAC-PapersOnLine 2024年第15期58卷 265-270页

作者： Patrick J.W. Koelewijn Rajiv Singh Peter Seiler Roland Tóth Sioux Technologies B.V. Eindhoven The Netherlands Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands The MathWorks Inc. Natick USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples.

关键词： Reduced-order modeling Neural Networks Linear Parameter-Varying Models

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