Abstract: A machine learning method related to these embodiments includes: first generating a pseudo attractor from each of plural series data sets, the pseudo attractor being a set of points in N-dimensional space, each of the points including N values sampled at an equal interval; second generating a series data set of Betti numbers from each of plural pseudo attractors generated in the first generating, each of the Betti numbers being a number of holes for a radius of a N-dimensional sphere in the N-dimensional space; and performing machine learning for each of plural series data sets of Betti numbers generated in the second generating, the series data set of Betti numbers being used as input in the machine learning.

Abstract: A plan determination method of which process is executed by a computer, the process includes receiving lead time for each raw material for a product, the lead time being indicative of time interval between a time at which an order of the each raw material is ordered and a time of arrival of the each raw material; and calculating an order quantity of the each raw material and a production quantity of the product which cause a cost relating to manufacturing of the product to be minimized, by using the received lead time for the each raw material and a predicted demand quantity of the product.

Abstract: A non-transitory computer-readable recording medium stores a model generation program that causes a computer to execute a process. The process includes obtaining measurement data on measurement points sequentially measured along a measurement path curve generated from a Hilbert curve laid out in a normalized space in which measurement target ranges of respective plurality of control parameters related to control of an object to be measured are normalized, the measurement points being more in number in a specific area of the measurement target ranges than in an area other than the specific area, and the numbers of measurement points lying on two sides in each group of two adjacent sides of the measurement path curve and where a control parameter changes in opposite directions being balanced; and generating a control model of the object to be measured on the basis of the obtained measurement data of the measurement points.

Abstract: A model generating device for generating model data regarding an operation state of a target object to be controlled, includes a memory and a processor coupled to the memory and configured to acquire first measurement data regarding the operation state of the target object under each of a plurality of measurement conditions set in a first sequence, acquire second measurement data regarding the operation state of the target object under each of the plurality of measurement conditions set in a second sequence different from the first sequence, estimate third measurement data indicating the operation state in a case where the target object enters a steady state in each of the plurality of measurement conditions, based on the first measurement data and the second measurement data, generate the model data regarding the operation state of the target object based on the third measurement data, and output the model data.

Abstract: A planning method for planning to order a product, the planning method being executed by a computer, the planning method includes: generating a predicted value of a demand of the product at each plurality of prices based on a selling price and a number of sales in association with each other for each past sale date; calculating an order quantity that yields a highest profit, for each of the plurality of prices, using the predicted value of the demand of the product at the plurality of prices; storing, for the plurality of prices, the calculated order quantity and a profit in association with each other into a memory; and identifying a combination of a price and an order quantity that yields the highest profit, with reference to the memory.

Abstract: A system includes: circuitry configured to receive a condition regarding a constraint condition of a product, acquire past requirement values for the product, predict, for each of a plurality of periods, requirement value for the product by calculating the requirement value for each of the plurality of periods based on the acquired past requirement values, generate, based on the predicted requirement value for each of the plurality of periods, a probability distribution of the constraint condition for each of a plurality of requested arrangements each of which indicates requested quantities of the product for each of the plurality of periods, and output at least one of the plurality of requested arrangements, based on the generated probability distribution and the received condition regarding the constraint condition.

Abstract: In this disclosure, equations to be solved in the model predictive control are transformed by using an off-line algebraic simplification method into a matrix operational expression representing a product of a coefficient matrix and a vector regarding solution inputs within a control horizon is equal to a function vector regarding target values of output states and the output states. The size of the coefficient matrix is reduced compared with the conventional matrix. Then, the matrix operational expression is solved in an online plant control apparatus with present output states and present target values of the output stats of a plant to be controlled, by the direct method, to output the solution to the plant.

Abstract: An order quantity determination method includes: accepting lead time from product order to arrival; calculating a stock quantity of the product by a processor based on an arrival quantity of the product and a demand forecast value of the product, the arrival quantity of the product is calculated based on the accepted lead time and order time of the product; and calculating an order quantity of the product by a processor based on a cost for holding the calculated stock quantity of the product, a price of the product, and the demand forecast value of the product.

Abstract: In this disclosure, equations to be solved in the model predictive control are transformed by using an off-line algebraic simplification method into a matrix operational expression representing a product of a coefficient matrix and a vector regarding solution inputs within a control horizon is equal to a function vector regarding target values of output states and the output states. The size of the coefficient matrix is reduced compared with the conventional matrix. Then, the matrix operational expression is solved in an online plant control apparatus with present output states and present target values of the output stats of a plant to be controlled, by the direct method, to output the solution to the plant.

Abstract: An order quantity determining method includes: receiving restriction information used for calculating a profit based on an order quantity of a product; specifying a combination of order quantities of a k-th period to a (k+H)-th period for which the profit is the highest by using the restriction information, by a processor; and outputting the order quantity of the k-th period based on the specified combination.

Abstract: An engine control apparatus controls an engine, by generating first and second instruction values to adjust an MAF of fresh air supplied to the engine and an MAP indicating a pressure of air supplied to the engine to respective target values, based on measured values of a first sensor which detects the MAF and a second sensor which detects the MAP, regardless of limiting conditions related to a totally closed or fully open state of an EGR and an VNT, switching a supplying destination of the measured values of the first and second sensors for a certain time after the generated first or second instruction value saturates, and generating the first and second instruction values to adjust the MAF and the MAP to the respective target values, based on the measured values of the first and second sensors, under the limiting conditions, when the switching occurs.

Abstract: In this disclosure, equations to be solved in the model predictive control are transformed by using an off-line algebraic simplification method into a matrix operational expression representing a product of a coefficient matrix and a vector regarding solution inputs within a control horizon is equal to a function vector regarding target values of output states and the output states. The size of the coefficient matrix is reduced compared with the conventional matrix. Then, the matrix operational expression is solved in an online plant control apparatus with present output states and present target values of the output stats of a plant to be controlled, by the direct method, to output the solution to the plant.

Abstract: In this disclosure, equations to be solved in the model predictive control are transformed by using an off-line algebraic simplification method into a matrix operational expression representing a product of a coefficient matrix and a vector regarding solution inputs within a control horizon is equal to a function vector regarding target values of output states and the output states. The size of the coefficient matrix is reduced compared with the conventional matrix. Then, the matrix operational expression is solved in an online plant control apparatus with present output states and present target values of the output stats of a plant to be controlled, by the direct method, to output the solution to the plant.

Abstract: A thermofluid simulation method includes referring to a storage unit storing a simulation result corresponding to a predetermined analysis range including partial spaces set in a space subject to simulation, and a first reduced order model generated based on the simulation result, generating a second reduced order model derived from the simulation result thus referred to for an analysis range having an arrangement of partial spaces matching an arrangement of the partial spaces of the predetermined analysis range in the space subject to simulation, and storing the generated second reduced order model in the storage unit, and interpolating reduced order models among reduced order models stored in the storage unit based on a boundary condition to form a reduced order model group corresponding to variable ranges of the boundary condition, the reduced order model group including the first, and the second reduced order models.

Abstract: In this disclosure, equations to be solved in the model predictive control are transformed by using an off-line algebraic simplification method into a matrix operational expression representing a product of a coefficient matrix and a vector regarding solution inputs within a control horizon is equal to a function vector regarding target values of output states and the output states. The size of the coefficient matrix is reduced compared with the conventional matrix. Then, the matrix operational expression is solved in an online plant control apparatus with present output states and present target values of the output stats of a plant to be controlled, by the direct method, to output the solution to the plant.