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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Singular Perturbation and Small-Signal Stability for Inverter Networks
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Computing Essential Sets for Convex and Nonconvex Scenario Problems: Theory and Application
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A Distributed-Optimization-Based Architecture for Management of Interconnected Energy Hubs
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Time-Varying Optimization of Networked Systems With Human Preferences
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Distributed and Localized Model-Predictive Control–Part I: Synthesis and Implementation
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Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework
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Power Converters Based Advanced Experimental Platform for Integrated Study of Power and Controls
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Cyber Attack Resilient Distance Protection and Circuit Breaker Control for Digital Substations
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A Physically Inspired Data-Driven Model for Electricity Theft Detection With Smart Meter Data
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An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation
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High-Efficiency Bilateral S–SP Compensated Multiload IPT System With Constant-Voltage Outputs
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An Efficient Subcycle Alternative to Instantaneous Power Metrics
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Encrypted Decentralized Multi-Agent Optimization for Privacy Preservation in Cyber-Physical Systems
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An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions
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Data-Driven Decentralized Online System Identification-Based Integral Model-Predictive Voltage and Frequency Control in Microgrids
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Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings
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A Rainbow Coverage Path Planning for a Patrolling Mobile Robot With Circular Sensing Range
In this paper, we proposed a coverage path (CP) planning approach for a mobile robot moving in a dynamically changing environment. Our family of algorithms begins by finding the first path around the center of interior obstacles/disks of a given target region (TR) through the Graham scan algorithm. The first path is then used as a foundational path to generate a collision free, first order differentiable, and observable CP through a complete set of input and output transformation algorithms which together form the rainbow CP planning approach. The last algorithm of the rainbow CP planning approach finds a sufficient number of observation points on the CP needed to observe the TR. The novelty of our rainbow CP planning approach is that it partitions the TR into different shapes with different properties needed to obtain complete coverage while achieving first order path differentiability. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and computes the collision-free CP within a quadratic runtime. The proposed method can be readily generalized to address problems of higher dimensions, and it is scalable with respect to the size of TR and the number of robots. Computer simulations are used to illustrate the effectiveness and correctness of the proposed approach.