Posters (Sessions 8 & 11)
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Modern societies are witnessing the prevalence of a wide assortment of distributed cyber-physical systems (CPS) built upon network infrastructure. International standards for mission-critical CPS applications, such as industrial process control systems and avionics, require their network infrastructure to provide deterministic delay performance. However, the problem of organically integrating CPS theories with network performance analysis remains largely unexplored.
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Rapid and customized part realization imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, they can very rarely be directly measured and/or controlled in a production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters.
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Internet of Things (IoT) can have a huge impact in engineering by leveraging state-of-the-art information and communication technologies (ICT). In practice, however, deploying IoT platforms to handle domain-specific engineering information along with sensor data of different types remains to be a challenging problem. To effectively support data sharing and interoperability and facilitate access by engineering tools, engineering modeling information and sensor data of different types need to be managed properly.
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As evidenced by the recent cyberattacks against Ukrainian power grids, attack strategies have advanced and Stuxnet-like malware agents will continue to emerge. Currently, the measures to audit the critical cyber assets of power infrastructure do not provide a quantitative guidance that can be used to address security protection improvement in the audit process. Investing in security protection is often limited to compliance enforcement on the reliability standards. Technologies could provide more security logs to automate the assessment of the ongoing health of cyber systems.
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A 4-degree-of-freedom powered exercise and/or rehabilitation machine has been designed for studies in physical human-machine interaction. The machine, called CSU 4OptimX, is fitted with 4-axis motion and force sensors and a robust impedance control system. A visualization system is used to provide motion reference paths for the human to follow. The control system uses real-time optimization to generate human motion reference paths, machine reference paths and a set of impedance parameters for each axis.
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Overview: Communication networks, which convey system measurements from sensors to controllers, play a key role in cyber physical systems (CPSs) such as smart grids, unmanned aerial vehicles (UAVs), and robotic networks. In contrast to traditional communication networks such as the Internet or cellular networks, CPS communication networks aims at stabilizing or optimizing the physical dynamics, thus re-quiring new theoretical foundations and design methodologies.
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Vehicle systems, being either ground/air/water vehicles, require hundreds/thousands of battery cells to meet their power and energy needs. We have been developing comprehensive management solutions of such large-scale batteries with the joint consideration of peak power, operation time, and battery life for vehicle electrification.
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Historically, software assurance technologies and robust fault-tolerant control (RFTC) theory were developed under different assumptions and models. The software assurance technologies are often model-based that require the profile of the physical dynamics and the observation of the system state, which may not be available when physical defects exist. On the other hand, though the existing RFTC techniques can efficiently compensate for the physical damage, it is critical to guarantee that the control software and the sensor data are not compromised.
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A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. Current field phenotyping systems rely on exhaustive coverage of a breeding experiment. This works well in relatively small proof-of-concept trials, but will fail as the size of the field gets larger.
