Predictive sensor method and apparatus

A microprocessor and electronics package employing predictive methodology was developed to accelerate the response time of slowly responding hydrogen sensors. The system developed improved sensor response time from approximately 90 seconds to 8.5 seconds. The microprocessor works in real-time providing accurate hydrogen concentration corrected for fluctuations in sensor output resulting from changes in atmospheric pressure and temperature. Following the successful development of the hydrogen sensor system, the system and predictive methodology was adapted to a commercial medical thermometer probe. Results of the experiment indicate that, with some customization of hardware and software, response time improvements are possible for medical thermometers as well as other slowly responding sensors

Electronic clinical predictive thermometer using logarithm for temperature prediction

A thermometer that rapidly predicts body temperature based on the temperature signals received from a temperature sensing probe when it comes into contact with the body. The logarithms of the differences between the temperature signals in a selected time frame are determined. A line is fit through the logarithms and the slope of the line is used as a system time constant in predicting the final temperature of the body. The time constant in conjunction with predetermined additional constants are used to compute the predicted temperature. Data quality in the time frame is monitored and if unacceptable, a different time frame of temperature signals is selected for use in prediction. The processor switches to a monitor mode if data quality over a limited number of time frames is unacceptable. Determining the start time on which the measurement time frame for prediction is based is performed by summing the second derivatives of temperature signals over time frames. When the sum of second derivatives in a particular time frame exceeds a threshold, the start time is established

A Neural Network Model for Wood Chip Thickness Distributions

Wood chip thickness is an important factor in pulp quality and yield. An artificial neural network model was developed and incorporated into a growth and yield simulator to predict wood chip thickness distributions from stand and tree characteristics. Models based on direct parameter estimation and parameter recovery were also developed for comparison to the neural network. Data were derived from 11,771 individual loblolly pine chip thickness measurements. Four stand ages, five dbh (diameter at breast height) classes, and three stem positions were used to predict the cumulative proportion of chip weight per chip thickness class. Results showed that the neural network model was superior to the two deterministic models on the basis of bias, root mean square error, and index of fit. Sensitivity analyses for the neural network model demonstrated that thicker chips were produced by younger stands and lower stem positions. The neural network was combined with a growth and yield simulator to demonstrate its use as a tool for procurement foresters and mill managers in predicting yields from stands of given characteristics