Modernized electrical grid automated to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.
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This project is developing methods for human context awareness in smart buildings, aging-in-place, smart authorization, and emergency response. We enable this using inference using radio frequency (RF) sensing networks, in which channel measurements are made by deployed wireless networking devices. In RF sensing networks, the network is the sensor. We are developing methods to learn human context for smart facilities and elder care, including:
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This project addresses architectural considerations in the design and operation of future power grids. This includes the consideration of sophisticated sensing, communication, and actuation capabilities on the system's reliability, price volatility, and economic and environmental efficiency.
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Reinforcement Learning (RL) is a paradigm for learning decision- making tasks that could enable cyber-physical systems (CPS) to learn and adapt to situations on-line. For an RL algorithm to be practical for CPS control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn effi- ciently in the face of noise, sensor/actuator delays and continuous state features. We describe TEXPLORE, a model-based RL method that ad- dresses these issues.
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The electric power grid is a complex cyber-physical system (CPS), whose reliable and secure operation is of paramount importance to national security and economic vitality. There is a growing and evolving threat of cyber-based attacks, both in numbers and sophistication, on the nation's critical infrastructures. Therefore, cyber security - encompassing attack prevention, detection, mitigation, and resilience - is critical in today's power grid and the emerging smart grid.
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Cyber-Physical Systems (CPS) encompass a large variety of systems including example future energy systems (e.g. smart grid), homeland security and emergency response, smart medical technologies, smart cars and air transportation. One of the most important challenges in the design and deployment of Cyber-Physical Systems is how to formally guarantee that they are amenable to effective human control.
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The objective of this research is an injection of new modeling techniques into the area of Cyber-Physical Systems (CPSs). The approach is to design new architectures for domain- specific modeling tools in order to permit feedback from analysis, validation, and verification engines to influence how CPSs are designed. This project outlines new research into the integration of existing, heterogeneous modeling languages in order to address problems in CPS design, rather than a single language used to design any CPS.
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Despite their importance within the energy sector, buildings have not kept pace with technological improvements and particularly the evolution of intelligent features. A primary obstacle in enabling intelligent buildings is their highly distributed and diverse nature.
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A hybrid system is a dynamical model that describes the coupled evolution of both continuous- valued variables and discrete patterns. A prime example of such a system is a power electronic circuit, where the semiconductor transistors behave as ideal switches whose switching actions effectively change the circuit topology (i.e., the discrete pattern) that in turn defines the dynamics of currents and voltages (i.e., the continuous variables) and hence the switching actions.
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Transforming the traditional, single-vehicle-based safety and efficiency control, next-generation vehicles are expected to form platoons for optimizing roadway usage and fuel efficiency while ensuring transportation safety. Two basic enablers of vehicle platooning are vehicular wireless networking and platoon control.
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Motivation: Reliable and resilient operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical. Efficient models and tools to perform timely fault diagnostics and prognostics are needed for curtailing systemic failures such as power blackouts. Varying system state caused by the fluctuating power consumption, dynamic control actions, physical component degradation, and interactions with possible software anomalies make the failure analysis, prediction, and mitigation difficult.
