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arXiv 2024年

作者： Dwivedi, Anmol Paternain, Santiago Tajer, Ali Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute TroyNY12180 United States

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system’s stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black-box counterparts. One important observation is that strategically removing transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts. © 2024, CC BY.

关键词： Electric load flow

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Towards Finding the Sources of Polymorphism in Polymorphic Gates (Invited)

Towards Finding the Sources of Polymorphism in Polymorphic G...

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Asia and South Pacific Design Automation Conference

作者： Timothy Dunlap Zelin Lu Gang Qu Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park MD USA

A polymorphic gate can change its functionality based on external conditions such as temperature, voltage, and external signals. Although this concept was proposed more than two decades ago and has found success in designing circuits for area minimization and security applications, how to construct polymorphic gates remains a challenge because they do not have the structure of conventional CMOS gates. Without knowing the causes of polymorphism, the majority of reported polymorphic gates are generated in the ad-hoc fashion, using evolutionary algorithms and the time-consuming Spice simulation. In this paper, we study thousands of candidate circuits that we have created for the sources of polymorphism. We observe several features that are not present in traditional CMOS gates. Circuit analysis suggests that these features are potential sources of polymorphism, which is confirmed as they exist in the polymorphic gates reported in the literature. Furthermore, we demonstrate with examples that polymorphic gates can be effectively constructed using these features as guidance.

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3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring systems

3DG: A Framework for Using Generative AI for Handling Sparse...

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Zhang, Liang Lin, Jionghao Borchers, Conrad Cao, Meng Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Memphis MemphisTN38152 United States

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN’s superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology. © 2024 CEUR-WS. All rights reserved.

关键词： Generative adversarial networks

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TTT: A Temporal Refinement Heuristic for Tenuously Tractable Discrete Time Reachability Problems

arXiv

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arXiv 2024年

作者： Sidrane, Chelsea Tumova, Jana The Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden

Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show that the system is generally functioning as desired, but a formal tool like reachability analysis can provide a guarantee of correctness. For linear systems, reachability analysis is straightforward and fast, but as more complex components are added to the control system such as nonlinear dynamics or a neural network controller, reachability analysis may slow down or become overly conservative. To address these challenges, much literature has focused on spatial refinement, e.g., tuning the discretization of the input sets and intermediate reachable sets. However, this paper addresses a different dimension: temporal refinement. The basic idea of temporal refinement is to automatically choose when along the horizon of the reachability problem to execute slow symbolic queries which incur less approximation error versus fast concrete queries which incur more approximation error. Temporal refinement can be combined with other refinement approaches and offers an additional "tuning knob" with which to trade off tractability and tightness in approximate reachable set computation. Here, we introduce an automatic framework for performing temporal refinement and we demonstrate the effectiveness of this technique on computing approximate reachable sets for nonlinear systems with neural network control policies. We demonstrate the calculation of reachable sets of varying approximation error under varying computational budget and show that our algorithm is able to generate approximate reachable sets with a similar amount of error to the baseline approach in 20-70% less time. © 2024, CC BY.

关键词： Linear systems

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Counterclockwise Dissipativity, Potential Games and Evolutionary Nash Equilibrium Learning

arXiv

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arXiv 2024年

作者： Martins, Nuno C. Certório, Jair Hankins, Matthew S. The Department of Electrical and Computer Engineering The Institute for Systems Research The University of Maryland College Park United States

We use system-theoretic passivity methods to study evolutionary Nash equilibria learning in large populations of agents engaged in strategic, non-cooperative interactions. The agents follow learning rules (rules for short) that capture their strategic preferences and a payoff mechanism ascribes payoffs to the available strategies. The population's aggregate strategic profile is the state of an associated evolutionary dynamical system. Evolutionary Nash equilibrium learning refers to the convergence of this state to the Nash equilibria set of the payoff mechanism. Most approaches consider memoryless payoff mechanisms, such as potential games. Recently, methods using δ-passivity and equilibrium independent passivity (EIP) have introduced dynamic payoff mechanisms. However, δ-passivity does not hold when agents follow rules exhibiting "imitation" behavior, such as in replicator dynamics. Conversely, EIP applies to the replicator dynamics but not to δ-passive rules. We address this gap using counterclockwise dissipativity (CCW). First, we prove that continuous memoryless payoff mechanisms are CCW if and only if they are potential games. Subsequently, under (possibly dynamic) CCW payoff mechanisms, we establish evolutionary Nash equilibrium learning for any rule within a convex cone spanned by imitation rules and continuous δ-passive *** Codes 92D10, 92D25 © 2024, CC BY-NC-ND.

关键词： Nash equilibrium

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Robust Optimization for the Day-Ahead Scheduling of Cascaded Hydroelectric systems

Robust Optimization for the Day-Ahead Scheduling of Cascaded...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Zhong, Zhiming Fan, Neng Wu, Lei University of Arizona Department of Systems and Industrial Engineering TucsonAZ United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Uncertain electricity prices resulting from the re-structuring of electricity market have brought new opportu-nities and challenges for hydroelectric producers. This paper presents a data-driven robust optimization approach for the day-ahead scheduling of cascaded hydroelectric systems (CHS) with electricity price uncertainty. In this paper, a minimum volume enclosing ellipsoid (MVEE) is adopted to construct an ellipsoidal uncertainty set that fully identifies and exploits the historical characteristics data. A second-order conic optimization formulation of the robust counterpart is derived for efficient computation. A real-world case study is conducted to demonstrate the capability of the proposed optimization approach compared with the traditional robust optimization approach. Response to Reviewers We express our sincere thanks to the committee numbers and all the anonymous review-ers for their constructive comments on our manuscript. We have revised the manuscript based on the comments and suggestions. The detailed one-to-one responses are summarized below. The texts highlighted in blue are our response to the reviewers comments. Reviewer 1 Comment 1.1 - The innovation of this paper is not clear, as the modeling and conversion techniques have been well established. The authors are suggested to consider the case where CHS are distributed at locations with different electricity prices. Reply: Thanks for your comment. A point-by-point description on the major innovations of this paper is provided at the end of introduction section in page 2. Our contribution is to apply ellipsoidal uncertainty set and second-order cone optimization method to first capture the time-correlation characteristic of uncertain electricity prices for hydroelectric producers. Testing the performance of the proposed optimization technique in various cases is indeed important. Unfortunately, due to the page limitation, we are unable to supplement more numerical examples in this paper, so that

关键词： Scheduling

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Safe Collective Control Under Noisy Inputs and Competing Constraints via Non-Smooth Barrier Functions

Safe Collective Control Under Noisy Inputs and Competing Con...

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European Control Conference (ECC)

作者： Clinton Enwerem John S. Baras Department of Electrical & Computer Engineering Institute for Systems Research University of Maryland College Park MD USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We consider the problem of safely coordinating ensembles of identical autonomous agents to conduct complex missions with conflicting safety requirements and under noisy control inputs. Using non-smooth control barrier functions (CBFs) and stochastic model-predictive control as springboards, and by adopting an extrinsic approach where the ensemble is treated as a unified dynamic entity, we devise a method to synthesize safety-aware control inputs for uncertain collectives. Drawing upon stochastic CBF theory and recent developments in Boolean CBF composition, our method proceeds by smoothing a Boolean-composed CBF and solving a stochastic optimization problem where each agent's forcing term is restricted to the affine subspace of control inputs certified by the combined CBF. For the smoothing step, we employ a polynomial approximation scheme, providing evidence for its advantage in generating more conservative yet sufficiently-filtered control inputs than the smoother but more aggressive equivalents produced from an approximation technique based on the log-sum-exp function. To further demonstrate the utility of the proposed method, we present an upper bound for the expected CBF approximation error, along with results from simulations of a single-integrator collective under velocity perturbations. Lastly, we compare these results with those obtained using a naive state-feedback controller lacking safety filters.

关键词： Smoothing methods Upper bound Stochastic processes Control systems Polynomials Robustness Safety

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FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field

IEEE Transactions on Field Robotics

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IEEE Transactions on Field robotics 2024年 1卷 1-26页

作者： Nikolaos Stathoulopoulos Björn Lindqvist Anton Koval Ali-Akbar Agha-Mohammadi George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

In this article, a novel approach for merging 3-D point cloud maps in the context of egocentric multirobot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the general iterative closest point (GICP) point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments.

关键词： Robots Point cloud compression Feature extraction Three-dimensional displays Robot sensing systems Robot kinematics Accuracy

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A Hybrid Feature Selection Framework for Predicting Students Performance

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computers, Materials & Continua 2022年第1期70卷 1893-1920页

作者： Maryam Zaffar Manzoor Ahmed Hashmani Raja Habib KS Quraishi Muhammad Irfan Samar Alqhtani Mohammed Hamdi High Performance Cloud Computing(HPC3) Department of Computer and Information SciencesUniversiti Teknologi PETRONAS32610SeriIskandarMalaysia Department of Computer Science and Information Technology University of LahorePakistan Department of Process Engineering Pakistan Institute of Engineering&Applied SciencesNiloreIslamabadPakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia College of Computer Science and Information Systems Najran University61441NajranSaudi Arabia

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic *** are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal *** this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of *** main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic *** key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha

关键词： Educational data mining feature selection hybrid feature selection

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Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms 48

Wildfire Spread Prediction Model Calibration Using Metaheuri...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pereira, Jorge Mendes, Jerome Junior, Jorge S. S. Viegas, Carlos Paulo, Joao Ruivo University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Pólo Ii CoimbraPT-3030-290 Portugal University of Coimbra Association for the Development of Industrial Aerodynamics CoimbraPT-3030-289 Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model's selected parameters. © 2022 IEEE.

关键词： Forecasting

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