Monitoring and control of cyber-physical systems.
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Optimization algorithms used in a real-time and safety-critical context offer the potential for considerably advancing robotic and autonomous systems by improving their ability to execute complex missions. However, this promise cannot happen without proper attention to the considerably stronger operational constraints that real time, safety-critical applications must meet, unlike their non-real-time, desktop counterparts.
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This exhibition presents two projects. The first project is a framework and associated tooling for the extraction of system specifications from test data that has been automatically generated from automated executions of the system. The test data in our framework consists of sequences of inputs to and the corresponding outputs from the system. From an initial collection of test data, data-mining techniques are used to infer the invariants; then the system is instrumented with the invariants and new test data is generated to try to invalidate the mined invariants.
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Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction.
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Large scale applications of cyber physical systems (CPS) such as commercial buildings with Building Automation System (BAS)-based demand response (DR) can play a key role in alleviating demand peaks and associated grid stress, increased electricity unit cost, and carbon emissions. However, benefits of BAS alone are often limited because their demand peak reduction cannot be maintained long enough without unduly affecting occupant comfort. This project seeks to develop control algorithms to closely integrate battery storage-based DR with existing BAS capabilities.
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Cyber-physical processors work in harsh environments and often suffer very high operating temperatures. High temperatures accelerate processor aging and increase failure rate. Effective thermal-aware management is required to meet the computational demands of the application while reducing processor thermal stress.
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The objective of this project is to improve the performance of autonomous systems in dynamic environments by integrating perception, planning paradigms, learning, and databases. For the next generation of autonomous systems to be truly effective in terms of tangible performance improvements (e.g., long-term operations, complex and rapidly changing environments), a new level of intelligence must be attained.
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Computer systems are increasingly coming to be relied upon to augment or replace human
operators in controlling mechanical devices in contexts such as transportation systems, chemical
plants, and medical devices, where safety and correctness are critical. A central problem is how
to verify that such partially automated or fully autonomous cyber-physical systems (CPS) are
worthy of our trust. One promising approach involves synthesis of the computer implementation
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Robotic devices are excellent candidates for delivering repetitive and intensive practice that can restore functional use of the upper limbs, even years after a stroke. Rehabilitation of the wrist and hand in particular are critical for recovery of function, since hands are the primary interface with the world. However, robotic devices that focus on hand rehabilitation are limited due to excessive cost, complexity, or limited functionality. A design and control strategy for such devices that bridges this gap is critical.
