Abstract: A functional film 1 according to an embodiment includes: a substrate (10) containing an acetyl cellulose resin; a primer layer (20) containing a filler (40) and disposed on the substrate (10); and a functional layer (30) containing the filler (40) and disposed on the primer layer (20). The substrate (10) has a permeation layer (12) formed by permeation of the resin contained in the primer layer (20) into part of the substrate (10). The permeation layer (12) has a thickness of 0.1 ?m or more and 3.5 ?m or less. The primer layer (20) has a thickness of 0.1 ?m or more and 1.0 ?m or less. The filler (40) is silica particles having an average particle size of 20 nm or more and 80 nm or less. The filler (40) in the primer layer (20) has a density of ? or more and ¾ or less of the density of the filler (40) in the functional layer (30). The primer layer (20) and the functional layer (30) each contain a resin. The resin component of the primer layer (20) is the same as the resin component of the functional layer 30.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining service life data of a part including a maintenance operation count of the part. The life expectancy of the part is formulated as a function of a life span of the part and the maintenance operation count at a point in time based on the obtained service life data and data interpolated therefrom. A life expectancy model is built based on the function and a plurality of life expectancies are predicted by applying simulated inputs to the life expectancy model. The life expectancies are produced after verification to indicate when to replace the part.

Abstract: A method for interactive test-schedule adjustment is provided. The steps include: displaying a table including the importance and period of each test item; displaying a first optimum schedule obtained from an optimal solution based on values entered by a user into the table, the schedule including a use schedule of each test facilities used for each of the multiple test items; displaying a second optimum schedule obtained from an optimal solution recalculated by mathematical programming after a use schedule of a test item the importance of which is changed on the first optimum schedule is changed; and displaying a third optimum schedule based on neighborhood solutions obtained by a search method using a history of optimal solutions if no agreement between the users is obtained on the second schedule.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: modeling for a subject process by machine learning with adaptive inputs. In one embodiment, the modeling may include: generating a model by use of machine learning with training data from measurements of successive components of a process to be modeled in order to predict measurements of a succeeding component within a statistically meaningful prediction range; adjusting the generated model by use of machine learning with less-deviation inducing measurements from a preceding component in case the measurement of the succeeding component is out of the prediction range; and presenting the adjusted model as a prediction model for the process.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining service life data of a part including a maintenance operation count of the part. The life expectancy of the part is formulated as a function of a life span of the part and the maintenance operation count at a point in time based on the obtained service life data and data interpolated therefrom. A life expectancy model is built based on the function and a plurality of life expectancies are predicted by applying simulated inputs to the life expectancy model. The life expectancies are produced after verification to indicate when to replace the part.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: modeling for a subject process by machine learning with adaptive inputs. In one embodiment, the modeling may include: generating a model by use of machine learning with training data from measurements of successive components of a process to be modeled in order to predict measurements of a succeeding component within a statistically meaningful prediction range; adjusting the generated model by use of machine learning with less-deviation inducing measurements from a preceding component in case the measurement of the succeeding component is out of the prediction range; and presenting the adjusted model as a prediction model for the process.

Abstract: A method for interactive test-schedule adjustment is provided. The steps include: displaying a table including the importance and period of each test item; displaying a first optimum schedule obtained from an optimal solution based on values entered by a user into the table, the schedule including a use schedule of each test facilities used for each of the multiple test items; displaying a second optimum schedule obtained from an optimal solution recalculated by mathematical programming after a use schedule of a test item the importance of which is changed on the first optimum schedule is changed; and displaying a third optimum schedule based on neighborhood solutions obtained by a search method using a history of optimal solutions if no agreement between the users is obtained on the second schedule.

Abstract: A system, program and related computer implemented method for selecting a controller and constructing it to automate one or more processes is provided. The method comprises the steps of: constructing a controller from a plant described by an LPV model to which a scheduling parameter is given by: a) calculating one or more ? gaps and a mean value of each candidate of a plant parameter vector of said LPV model based on a requirement of preselected constraints; b) choosing a candidate via information gathered by proof based on the magnitude of differences between the ? gaps; c) converting the LPV model into LMI and finding controllers, according to an H? criterion, at all combination end points of a maximum value and a minimum value of the plant parameter vector thus chosen; and d) choosing and constructing said chosen controller for said plant.

Abstract: A system is configured so that signals passed through an all-pass filter using warp parameter ? are whitening-filtered, and so that the frequency axis is restored by an all-pass filter using warp parameter ?. This optimizes the whitening filter by determining the optimum ?. First, the AR order p is automatically estimated using ?=0. A spectral distance d? is computed using a discrete Fourier transform spectrum value passed through an all-pass filter using warp parameter ? and a discrete AR spectrum value passed through an all-pass filter using warp parameter ?. The ? which minimizes the spectral distance d? is set as the warp parameter, and a whitening filter is configured in which the warp parameter has been optimized.

Abstract: Method, article of manufacture and system using minimum data to determine whether a sliding-mode control should be applied in a plant. First measure the plant in an open-loop control fashion, and using the measured data, describe a state equation of the plant by system identification and order determination methods. Then design a switching hyperplane for sliding-mode control. Next, calculate higher order statistics on the difference between an output of a linear model on the hyperplane and an output of the sliding-mode control model in the measured data; When any of the higher order moments is larger than a predetermined threshold, configure a controller as a sum of the linear control input term and the nonlinear control input term. If both higher-order moments are smaller than the threshold, then configure the controller using only a linear control input term.

Abstract: A method and system for processing Kalman Filter. The system includes: an Unscented Kalman Filter; and a processor device configured to: non-uniform a phase duration of a signal outputted from a plant; inputting the signal to the Unscented Kalman Filter; and restore non-uniformed phase duration of an estimated value calculated in the Unscented Kalman Filter to the phase duration.

Abstract: A processing system, method, and program for a modeling system. A math equation including a differential equation is prepared. A CAS solves the differential equation normally and by using an interval coefficient in accordance with an interval analysis technique. A coefficient vector of the two solutions are compared to determine if a distance between the vectors is equal to or less than a preset value, and if so, the CAS solves the math equation including a differential equation with the target coefficient set to zero. The coefficient vectors of the solution thus obtained and the normally solved solution are compared to determine if the distance between the vectors is equal to or less than a preset value, and if so, the differential equation is simplified by omitting a term of the target coefficient from the differential equation.

Abstract: A method and system for configuring a PID controller.

Abstract: A system is configured so that signals passed through an all-pass filter using warp parameter ? are whitening-filtered, and so that the frequency axis is restored by an all-pass filter using warp parameter ?. This optimizes the whitening filter by determining the optimum ?. First, the AR order p is automatically estimated using ?=0. A spectral distance d? is computed using a discrete Fourier transform spectrum value passed through an all-pass filter using warp parameter ? and a discrete AR spectrum value passed through an all-pass filter using warp parameter ?. The ? which minimizes the spectral distance d? is set as the warp parameter, and a whitening filter is configured in which the warp parameter has been optimized.

Abstract: A system, program and related computer implemented method for selecting a controller and constructing it to automate one or more processes is provided. The method comprises the steps of: constructing a controller from a plant described by an LPV model to which a scheduling parameter is given by: a) calculating one or more v gaps and a mean value of each candidate of a plant parameter vector of said LPV model based on a requirement of preselected constraints; b) choosing a candidate via information gathered by proof based on the magnitude of differences between the v gaps; c) converting the LPV model into LMI and finding controllers, according to an H? criterion, at all combination end points of a maximum value and a minimum value of the plant parameter vector thus chosen; and d) choosing and constructing said chosen controller for said plant.

Abstract: [Object] To generate a signal pattern generation apparatus capable of performing suitable processing even when an abrupt increase in the number of accesses occurs while the number of accesses is large, having an adequate long-tail characteristic, and capable of providing adequate fractality with respect to a plurality of ranges with respect to time and providing a suitable short-range distribution characteristic.

Abstract: Method, article of manufacture and system using minimum data to determine whether a sliding-mode control should be applied in a plant. First measure the plant in an open-loop control fashion, and using the measured data, describe a state equation of the plant by system identification and order determination methods. Then design a switching hyperplane for sliding-mode control. Next, calculate higher order statistics on the difference between an output of a linear model on the hyperplane and an output of the sliding-mode control model in the measured data; When any of the higher order moments is larger than a predetermined threshold, configure a controller as a sum of the linear control input term and the nonlinear control input term. If both higher-order moments are smaller than the threshold, then configure the controller using only a linear control input term.

Abstract: A method and system for configuring a PID controller.

Abstract: Systems and methods for distribution-transition estimation of key performance indicator (KPI).

Abstract: A method and system for determining a variable compensation amount according to the degree of achievement by avoiding stop at the stage where value brought about to a client by a solution is qualitatively evaluated, avoiding compensation calculation from being performed by accumulation of cost such as labor cost, and quantitatively estimating the effect from the viewpoint of a set KPI in consideration of risk to achievement of the KPI.