Browse Topic: Integrated circuits
As per Committee/Henry E. Harschburger recommendations
Along with unique and challenging development concerns, target hardware deployment concerns exist for artificial intelligence (AI) and machine learning (ML) applications. Those deployment concerns should be addressed in the planning phase and consist of the issues surrounding the target hardware selection and the certifiability/qualifiable of the target hardware for the AI/ML model deployment. These concerns center around certification issues identified for multi-core processors (MCP), where those MCP issues are amplified for graphics processor units (GPUs) when they are used for general computing. While the use of complex graphics processors for general computing is being reconciled for flight critical applications, the reduction of these concerns is possible through design specific target hardware choices, e.g., selection of Field Programmable Gate Array (FPGA) devices or other certifiable approaches. This paper explores these concerns and proposes design specific target hardware
Wear debris monitoring and analysis is a common practice for the condition assessment of engine and transmission health. Oil debris monitoring (ODM) and electronic chip detectors (ECD) are two common methods deployed for continuous monitoring of oil wetted component health in-flight. This study evaluates the diagnostic performance of the two sensing technologies within controlled rolling element bearing (REB) fault experiments. Progressive visual inspection of the REB spall progression through failure provided a ground truth against which both systems could be compared. Quantifiable metrics of reliability, diagnostic accuracy, provided maintenance interval were defined to create a framework for condition-based maintenance (CBM) program decision making. In summary, it was found that the ODM sensor system provided earlier fault notice, but more so, vastly outperformed the ECD in reliability and avoidance of false positives.
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A device that monitors health conditions in the body using a person's sweat has been developed by Penn State and Xiangtan University researchers, according to Huanyu “Larry” Cheng, assistant professor of engineering science and mechanics, Penn State.
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