초록 ▼

A method and system for detecting errors in a process such as laboratory analysis of patient specimens and generation of test results is described. The steps of the method include collecting data elements having a range of values from the process. The number of data elements having values within pre

A method and system for detecting errors in a process such as laboratory analysis of patient specimens and generation of test results is described. The steps of the method include collecting data elements having a range of values from the process. The number of data elements having values within predetermined intervals of the range are then counted. The counts of the data elements are applied as inputs to nodes of a neural network, each count being applied to a node representing the predetermined interval corresponding to the count. Output is then generated from the neural network based on the inputs, the output indicative of whether an error in the process (such as bias error or a precision error) has occurred. If the technology is used with a laboratory instrument, the output is generated in real time and available immediately for automatic or manual correction of the instrument.

대표청구항 ▼

A method and system for detecting errors in a process such as laboratory analysis of patient specimens and generation of test results is described. The steps of the method include collecting data elements having a range of values from the process. The number of data elements having values within pre

A method and system for detecting errors in a process such as laboratory analysis of patient specimens and generation of test results is described. The steps of the method include collecting data elements having a range of values from the process. The number of data elements having values within predetermined intervals of the range are then counted. The counts of the data elements are applied as inputs to nodes of a neural network, each count being applied to a node representing the predetermined interval corresponding to the count. Output is then generated from the neural network based on the inputs, the output indicative of whether an error in the process (such as bias error or a precision error) has occurred. If the technology is used with a laboratory instrument, the output is generated in real time and available immediately for automatic or manual correction of the instrument. d Data Validation." Presented at the Fifth International Joint ISA POWID/EPRI Controls and Instrumentation Conference, La Jolla, California, Jun. 19-21, 1995.* "Pneumatic and Thermal State Estimators for Production Engine Control and Diagnostics" by Peter J. Maloney and Peter M. Olin, SAE Technical Paper Series 980517, International Congress and Exposition, Feb. 23-26, 1998, Copyright 1998 Society of Automotive Engineers, Inc. (ISSN 0148-7191). "MSET Modeling Of Crystal River-3 Venturi Flow Meters" by J. P. Herzog, S. W. Wegerich, K. C. Gross, and F. K. Bockhorst, 6th International Conference on Nuclear Engineering, ICONE-6169, May 10-14, 1998, Copyright .COPYRGT. 1998 ASME (12 pp). "Application Of A New Technique For Modeling System Behavior" by Paul J. O'Sullivan, presented at the ISA Symposium, Edmonton, Alberta, May 1, 1991, .COPYRGT. Copyright 1991 Instrument Society of America (21 pp.). "A Universal, Fault-Tolerant, Non-Linear Analytic Network For Modeling And Fault Detection," by J. E. Mott, R. W. King, L. R. Monson, D. L. Olson, and J. D. Staffon, Proceedings of the 8th Power Plant Dynamics, Control & Testing Symposium, Knoxville, Tennessee, May 27-29, 1992 (14 pp.). ModelWare� Product Review by Robert D. Flori, reprinted from Computerized Investing, Sep./Oct. 1992, vol. XI, No. 5, copyright by The American Association of Individual Investors (pp. 8-10). ModelWare� A New Approach to Prediction by Ken Tucker, reprinted from PC AI Magazine, Jan./Feb. 1993, pp. 14, 15, 30. "Similarity Based Regression: Applied Advanced Pattern Recognition for Power Plant Analysis," by E. J. Hansen and M. B. Caudill, presented at the 1994 EPRI Heat Rate Improvement Conference (9 pp.). "Applied Pattern Recognition For Plant Monitoring And Data Validation," by Ron Griebenow, E. J. Hansen and A. L. Sudduth, presented at the Fifth International Joint ISA POWID/EPRI Controls and Instrumentation Conference, La Jolla, California, Jun. 19-21, 1995 (11 pp.). "Model-Based Nuclear Power Plant Monitoring And Fault Detection: Theoretical Foundations," by Ralph M. Singer, Kenny C. Gross, James P. Herzog, Ronald W. King, and Stephan Wegerich, presented at the International Conference on Intelligent System Application to Power Systems (ISAP'97), Jul. 6-10, 1997, Seoul, Korea (pp. 60-65). "Application Of A Model-Based Fault Detection System To Nuclear Plant Signals," by K. C. Gross, R. M. Singer, S. W. Wegerich, J. P. Herzog, R. VanAlstine, and F. Bockhorst, presented at the International Conference on Intelligent System Application to Power Systems (ISAP '97), Jul. 6-10, 1997, Seoul, Korea (pp. 66-70). puterized Investing, Sep./Oct. 1992, vol. XI, No. 5, copyright by The American Association of Individual Investors (pp. 8-10). ModelWare� A New Approach to Prediction by Ken Tucker, reprinted from PC AI Magazine, Jan./Feb. 1993, pp. 14, 15, 30. "Similarity Based Regression: Applied Advanced Pattern Recognition for Power Plant Analysis," by E. J. Hansen and M. B. Caudill, presented at the 1994 EPRI Heat Rate Improvement Conference (9 pp.). "Applied Pattern Recognition For Plant Monitoring And Data Validation," by Ron Griebenow, E. J. Hansen and A. L. Sudduth, presented at the Fifth International Joint ISA POWID/EPRI Controls and Instrumentation Conference, La Jolla, California, Jun. 19-21, 1995 (11 pp.). "Model-Based Nuclear Power Plant Monitoring And Fault Detection: Theoretical Foundations," by Ralph M. Singer, Kenny C. Gross, James P. Herzog, Ronald W. King, and Stephan Wegerich, presented at the International Conference on Intelligent System Application to Power Systems (ISAP'97), Jul. 6-10, 1997, Seoul, Korea (pp. 60-65). "Application Of A Model-Based Fault Detection System To Nuclear Plant Signals," by K. C. Gross, R. M. Singer, S. W. Wegerich, J. P. Herzog, R. VanAlstine, and F. Bockhorst, presented at the International Conference on Intelligent System Application to Power Systems (ISAP '97), Jul. 6-10, 1997, Seoul, Korea (pp. 66-70).