Abstract: A method and computer-implemented system for optimizing a product. Based on the “voice of the customer”, aspects of the product that are critical to the customer satisfaction, and target values, therefore, are identified. Aspects are characterized in terms of their contributing factors. Each contributing factor is characterized in a transfer function in terms of control and noise factors impacting the contributing factors. Contributing factors are optimized during product design by shifting nominal design values for control factors with respect to the transfer function such that target contributing factors are attained with minimum variability due to existing noises and variability in control. Where target values cannot be obtained through design optimization, conventional methods of manufacturing optimization are implemented. The extent to which the target values are attained and maintained over the life of the product are assessed.

Abstract: A system 10, which receives a computer, aided model or design 18 and which probabilistically analyzes the model 18 by use of a modified Latin Hypercube sampling technique and combined MARS and Kriging simulation methodologies, thereby allowing a simulation to be conducted at a most probable point of operation and allowing products having desired characteristics and attributes to be created.

Abstract: A method and computer-implemented system for optimizing a product. Based on the “voice of the customer”, aspects of the product that are critical to the customer satisfaction, and target values, therefore, are identified. Aspects are characterized in terms of their contributing factors. Each contributing factor is characterized in a transfer function in terms of control and noise factors impacting the contributing factors. Contributing factors are optimized during product design by shifting nominal design values for control factors with respect to the transfer function such that target contributing factors are attained with minimum variability due to existing noises and variability in control. Where target values cannot be obtained through design optimization, conventional methods of manufacturing optimization are implemented. The extent to which the target values are attained and maintained over the life of the product are assessed.

Abstract: A system 10, which receives a computer, aided model or design 18 and which probabilistically analyzes the model 18 by use of a modified Latin Hypercube sampling technique and combined MARS and Kriging simulation methodologies, thereby allowing a simulation to be conducted at a most probable point of operation and allowing products having desired characteristics and attributes to be created.

Abstract: A system and method for processing test measurements collected from an internal combustion engine that is cold-tested for diagnostic purposes. Test measurements are collected from an engine. The test measurements are "pre-processed" by filtering and subsampling techniques so that Principle Component Analysis can be applied to condense the quantity of test measurements while still retaining a statistically accurate indication of a majority of the original measurements. The pre-processed test measurements are then passed through one or more classifiers including: a Neural Network classifier, a Fuzzy Logic classifier, a cluster-based classifier (or "Spherical" classifier) and a Genetic Program classifier. Results from these classifiers can be used to obtain a verdict about an engine (i.e., whether the engine is "normal" or "faulty").

Abstract: System and method for filtering a misfire data stream having numerical values that indicate whether a firing of a cylinder was a misfire or a proper firing, to yield an optimum measurement misfire-rate for accurate and rapid determination of whether an engine is misfiring beyond a threshold level.