The terms denote engineering domains that have high CPS content.
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The goal of this research project is to develop a scalable cyber-physical system (CPS) framework
for seamless integration of physical and computational systems for bridge lifecycle monitoring.
Bridge monitoring includes many different components, such as computer-aided design (CAD)
system, bridge management system (BMS), structural health monitoring (SHM) system, and
various analysis tools. In current practice, these components are isolated from each other and
sharing of information across systems is very limited. Information sharing and system integration
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Computational simulation is one of the most im- portant ways of reproducing the dynamic responses of a Cyber Physical System using a model of the system. The simulation discovers areas of differential system performance and allows linking such performance back to system characteristics. In the medical domain, patient simulators are used to train physicians in patient management. One critical question is how to verify these systems under realistic human (physician) input.
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Modern cyber-physical systems are monitored and controlled by multi-core platforms, and thermal management of multi-core chips is critical as overheated cores thereon will suffer from exponentially decaying lifetime and unacceptable performance degradation. To meet the timing and system lifetime reliability requirements under dynamic workloads and operating environment, we need a real-time thermal management (RTM) scheme that predicts run-time temperature and actuates effective thermal control without compromising task deadlines.
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Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges. In particular, and of special importance to the automotive industry, is the transition from conventional powertrains to (plug-in) hybrid and electric vehicles, all of which are subject to environmental and operational variations.
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Millions of mobile applications (apps) are being developed in domains such as energy, health, security, and entertainment. The US FDA expects that there will be 500 million smart phone users downloading healthcare related apps by the end of 2015. Many of these apps will perform interventions to control human physiological parameters such as blood pressure and heart rate. The intervention aspects of the apps can cause dependency problems, e.g., multiple interventions of multiple apps can increase or decrease each other's effects, some of which can be harmful to the user.
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Cyber-Physical Systems (CPS) that contain self-modifying smart components can improve and self-repair, but sometimes at the cost of impeding model-based Verification and Validation (V&V). In this work, we focus on maintaining short and long range V&V capability in a system containing self-adaptive smart components. In this work, we focus on smart component based in-flight control adaptation of damaged Flapping-Wing Micro Air Vehicles (FW-MAV).
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It's common in controller design to assume that the controller reads the sensors and writes to the actuators at the same time instant. This assumption is often violated in practice because the controller executes its code sequentially on a microprocessor. If the microprocessor is "fast enough," often the controller will still work. However, if the sensing and control are done by two different devices that must communicate across a network, the resulting timing uncertainty due to network delays and clock offsets will often destabilize the controller.
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The timely and accurate in-service identification of faults in mechanical structures, such as airplanes, can play a vitally important role in avoiding catastrophes. One major challenge, however, is that the sensing system relies on high frequency signals, the coordination of which is difficult to achieve throughout a large structure. To tackle this fundamental issue, the research team will take advantage of 3D printing technology to fabricate integrated sensor-structure components.
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Despite their importance within the energy sector, buildings have not kept pace with technological improvements and particularly the introduction of intelligent features. A primary obstacle in enabling intelligent buildings is their highly distributed and diffuse nature. To address this challenge, a modular approach will be investigated for building design, construction, and operation that would completely transform the building industry.
