Abstract: A system and method to assist aircraft pilots with rapid decision-making in cases where the pilot needs to make a flight diversion at low altitudes due to an emergency (for example, loss of thrust). Once an emergency need for diversion is detected, the system and method generates a list of alternative airports the plane can reach given: (i) the current conditions of the plane; (ii) a real-emergency time simulation of evolving conditions of the plane; (iii) the environment at potential landing sites; and (iv) the environment on the flight path to those sites. For airports potentially within reach, the system and method provides a confidence scores for successful landings for alternative simulated landing options. The simulations and confidence scores take into account aircraft position, altitude, speed, and possible further problems with the aircraft for the both the current flight path and for each simulated alternative.

Abstract: A system and method to assist aircraft pilots with rapid decision-making in cases where the pilot needs to make a flight diversion at low altitudes due to an emergency (for example, loss of thrust). Once an emergency need for diversion is detected, the system and method generates a list of alternative airports the plane can reach given: (i) the current conditions of the plane; (ii) a real-emergency time simulation of evolving conditions of the plane; (iii) the environment at potential landing sites; and (iv) the environment on the flight path to those sites. For airports potentially within reach, the system and method provides a confidence scores for successful landings for alternative simulated landing options. The simulations and confidence scores take into account aircraft position, altitude, speed, and possible further problems with the aircraft for the both the current flight path and for each simulated alternative.

Abstract: A system and method for determining the cause of a trend shift in physiological data received from a patient under observation includes receiving physiological data on a plurality of measured physiological parameters from the patient and performing a statistical analysis on a portion of the physiological data to determine a measured shift over a confidence interval in each of the plurality of physiological parameters. A signature shift is defined for each of the plurality of physiological parameters that is indicative of a pre-determined medical condition and the measured shift confidence interval of each of the plurality of physiological parameters is compared to these signature shifts. From this comparison between the measured shift confidence interval and the signature shift of each of the plurality of physiological parameters, a physiological assessment is formulated.

Abstract: A system and method for determining a reference baseline of a patient and measuring trend shifts in physiological data to generate alerts acquires and receives physiological data from a patient under observation. Initial physiological data is received during a lock-in period and monitored physiological data is received during a diagnosis period. Shifts in the physiological data are measured by comparing the monitored physiological data to the initial physiological data. An alert is generated for shifts that exceed at least one of a pre-determined size and rate (i.e., a “threshold value”).

Abstract: A system of patient health condition monitoring includes a device configured to measure a health parameter of a patient and a computer. The computer is programmed to receive an input based on the measured health parameter, determine a first moving average value for a first period of time based on the measured health parameter and determine a second moving average value for a second period of time based on the measured health parameter, the second period of time different than the first period of time. The computer is further programmed to calculate a difference between the first and second moving average values and store the difference in computer memory.

Abstract: Automated systems and methods for diagnosing the cause of a trend shift in performance data associated with a system including selecting a fuzzy model describing how the mean associated with a parameter shifts when a predetermined problem occurs and receiving performance data associated with the system. The diagnostic systems and methods also including applying a preferred set of piecewise regressions. The diagnostic systems and methods further including selecting a start date and selecting data sets near the start date and an end date that have a relatively large size without violating normal scatter. The diagnostic systems and methods still further including measuring the mean shift between a plurality of samples using one or more statistical tests and combining the results with the fuzzy model to achieve a diagnosis.

Abstract: Automated systems and methods for diagnosing the cause of a trend shift in performance data associated with a system including selecting a fuzzy model describing how the mean associated with a parameter shifts when a predetermined problem occurs and receiving performance data associated with the system. The diagnostic systems and methods also including applying a preferred set of piecewise regressions. The diagnostic systems and methods further including selecting a start date and selecting data sets near the start date and an end date that have a relatively large size without violating normal scatter. The diagnostic systems and methods still further including measuring the mean shift between a plurality of samples using one or more statistical tests and combining the results with the fuzzy model to achieve a diagnosis.

Abstract: The present invention discloses an error log analysis system comprising a diagnostic unit and a training unit. The training unit includes a plurality of historical error logs generated during abnormal operation or failure from a plurality of machines, and the actual fixes (repair solutions) associated with the abnormal events or failures. A block finding unit identifies sections of each error log that are in common with sections of other historical error logs. The common sections are then labelled as blocks. Each block is then weighted with a numerical value that is indicative of its value in diagnosing a fault. In the diagnostic unit, new error logs associated with a device failure or abnormal operation are received and compared against the blocks of the historical error logs stored in the training unit.