Abstract: A metal porous body includes: a metal framework; and a separation wall formed in one piece with the metal framework, the separation wall being composed of the same material as a material of the metal framework. A plurality of cells each having a polyhedral shape exist inside the metal porous body. A side of the polyhedral shape is constituted of the metal framework. A location in which an opening of a cell of the plurality of cells is closed by the separation wall exists inside the metal porous body, the opening being defined by a side of a polyhedral shape of the cell.

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes identifying, among pieces of feature information of a plurality of pieces of time-series data, a plurality of pieces of feature information similar to feature information of processing target time-series data, obtaining a predicted value distribution of the processing target time-series data based on each of the plurality of pieces of feature information, obtaining a combined distribution from the predicted value distribution, and determining a range of predicted value for the processing target time-series data based on the combined distribution.

Abstract: A computer-readable recording medium stores a correspondence relationship determination program for causing a computer to execute a process. The process includes: acquiring, using a first machine learning model that infers individual figure information regarding one line segment in a first plurality of line segments included in first figure data and a second machine learning model that infers information regarding a relative relationship between the one line segment in the first plurality of line segments and another line segment in the first figure data, an inference result of each of a plurality of items that includes the individual figure information and the information regarding the relative relationship; and determining correspondence relationships between the first plurality of line segments and a second plurality of line segments included in second figure data different from the first figure data based on the inference result of each of the plurality of items.

Abstract: A recording medium stores a reinforcement learning program for causing a computer to execute a process. The process includes: calculating a second demand amount after a certain period of time and a reliability of the second demand amount based on a current first demand amount for a service provided in a predetermined environment; determining an action to be performed for the environment in accordance with a machine learning model based on input data that includes the second demand amount, the reliability, and a current first state of the environment; executing the determined action for the environment; and updating, based on a second state of the environment after the action is performed and a reward, a parameter of the model by constrained reinforcement learning in which the reward is increased in a range that satisfies a constraint on the state of the environment.

Abstract: An engine intake system control device configured to control an intake system of an engine, having a map function that inputs at least a fuel injection pressure of the engine, a fresh air flow, and a compressor outlet temperature of a supercharger, and outputs a control gain; and a control unit that inputs the control gain and a deviation between a controlled variable of the intake system of the engine and a target value thereof, and controls a manipulated variable of the intake system of the engine.

Abstract: A process includes obtaining, according to a predetermined update order of a policy parameter of each agent, a degree of influence on a constraint specific to a first agent and a degree of influence on a system-wide constraint in which a degree of influence by an updated policy parameter of a second agent previous to the first agent in the update order is shared, and in a case where an update width of the policy parameter of the first agent exists in both ranges respectively determined depending on the degree of influence on the constraint specific to the first agent and the degree of influence on the system-wide constraint, updating the policy parameter of the first agent and causing the degree of influence on the system-wide constraints by the updated policy parameter to be shared with a third agent next to the first agent in the update order.

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes identifying, among pieces of feature information of a plurality of pieces of time-series data, a plurality of pieces of feature information similar to feature information of processing target time-series data, obtaining a predicted value distribution of the processing target time-series data based on each of the plurality of pieces of feature information, obtaining a combined distribution from the predicted value distribution, and determining a range of predicted value for the processing target time-series data based on the combined distribution.

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes obtaining a third matrix by changing at least one of a position of a target object already installed or a scale of the target object in a two-dimensional second matrix, obtaining three-dimensional second data by superimposing a two-dimensional first matrix and the third matrix, the first matrix being provided for each facility already installed and indicating a position and a scale thereof, and predicting a degree of influence of the target object by inputting the second data, a type of day of week, and time to a machine learning model trained with three-dimensional first data, a type of day of week, and time as input, and with a degree of influence of the target object as output, the first data being obtained by superimposing the first matrix and the second matrix.

Abstract: An engine control device includes a model that, based on engine operating condition and first-type operation amount, reproduces at least one index from among various indexes of combustion state of engine, and a processor that executes a process including deciding on second-type operation amount, by optimization using the model so as to treat at least one of the indexes, which are reproduced by the model, as estimated value of control amount, and ensure that the estimated value of the control amount follows control target value, associating the second-type operation amount with the control target value and the engine operating condition, rewriting a learning control table in which operation amount corresponding to the control target value and the engine operating condition is registered, and calculating operation amount according to the learning control table based on the control target value and the engine operating condition.

Abstract: A method including acquiring a first test pattern in which an operation variable for an engine test is changed in time series, inputting a first operation variable to a mathematical model representing a time series response obtained by inputting a test pattern as a simulation of the engine test, monitoring a first monitoring parameter obtained by inputting the first operation variable to the mathematical model, holding the first operation variable until the first monitoring parameter is less than a first threshold value, creating a history of the first operation variable in the simulation as a second test pattern, monitoring a second monitoring parameter obtained by inputting a second operation variable to a real engine based on the second test pattern, holding the second operation variable until the second monitoring parameter is less than a second threshold value, acquiring time series data of the second operation variable and a controlled variable.

Abstract: A non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process, the process includes obtaining training data that includes shape information of a heat sink that serves as an explanatory variable and heat distribution information of the heat sink that serves as an objective variable, and executing, based on the training data, machine learning of a machine learning model according to a loss function that includes an expression that constrains a temperature relationship of a plurality of positions in the heat sink.

Abstract: An information processing apparatus determines a group, which includes at least two nodes, based on similarity between a plurality of first behavior sets using node information which indicates the plurality of first behavior sets corresponding to the plurality of nodes. The information processing apparatus assigns a second behavior set to the group. The information processing apparatus calculates an evaluation value for each behavior included in the second behavior set without calculating evaluation values for behaviors included in the at least two first behavior sets corresponding to the at least two nodes. The information processing apparatus calculates a probability distribution of the behaviors included in the second behavior set based on the evaluation values.

Abstract: An information processing apparatus calculates a plurality of first evaluation values respectively corresponding to a plurality of actions on the basis of probability distribution information indicating the selection probability of each of the plurality of actions. When the plurality of first evaluation values include a negative evaluation value, the information processing apparatus converts the plurality of first evaluation values to a plurality of second evaluation values that are non-negative, using a negative reference value. The information processing apparatus updates the selection probability of each of the plurality of actions on the basis of the plurality of second evaluation values.

Abstract: A control device includes a controller configured to: obtain a driving condition and an actual value of a controlled amount of an engine; use a transformed engine model to calculate an estimate value of a future controlled amount, from the obtained driving condition and a candidate value of an operation amount for controlling the controlled amount, the transformed engine model in which an activation function of each neuron is transformed using a linear inequality function having a binary variable, the transformed engine model for which a weight coefficient, bias, and upper and lower limits of an input/output variable of the each neuron are set based on an engine model that has a multilayer neural structure and the activation function of a ReLU structure, receives the driving condition and operation amount as input, and predicts dynamic characteristics of the engine; determine a value of the operation amount, based on error between the calculated estimate value and a target value of the controlled amount ident

Abstract: A non-transitory computer-readable recording medium stores a function generation program for causing a computer to execute a process, the process includes acquiring manipulation data generated based on manipulated variable distribution information that represents distribution of values of manipulated variables, and measurement data measured when a control object device is controlled based on the manipulation data, and by performing inverse reinforcement learning by using the manipulation data and the measurement data, generating a reward function that includes evaluation indices for the manipulated variable distribution information and coefficient distribution information that represents distribution of the values of coefficients of the evaluation indices.

Abstract: An information processing apparatus generates a data set including a plurality of records each of which indicates one out of a plurality of behaviors. The information processing apparatus calculates a first evaluation value for a first behavior that appears in the data set, based on a distribution of appearance frequency of first behaviors in the data set. The information processing apparatus updates at least a part of the records so that the appearance frequency of a first behavior whose first evaluation value exceeds a threshold increases. The information processing apparatus calculates a second evaluation value for a second behavior that appears in the updated data set based on a distribution of the appearance frequency of second behaviors in the updated data set.

Abstract: A non-transitory computer-readable storage medium storing a n EMI calculation program that causes at least one computer to execute a process, the process includes inputting circuit information of a first circuit to a machine learning model; acquiring an EMI value at a certain frequency of the first circuit; selecting, based on an impedance characteristic of the first circuit and the EMI value at the certain frequency, first EMI information from a plurality of pieces of EMI information in each of which an impedance characteristic of each of a plurality of circuits is associated with EMI values at a plurality of frequencies of each of the plurality of circuits; and acquiring an EMI value at another frequency different from the certain frequency of the first circuit based on the EMI value at the certain frequency and the first EMI information.

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes in a case of calculating an equilibrium solution of selection probabilities of a plurality of strategies using replicator dynamics, calculating a differential value of a calculation result based on the replicator dynamics using, as an input, respective selection probabilities of the plurality of strategies and respective gains when a game is performed with the respective selection probabilities, and adjusting the respective selection probabilities after elapse of a predetermined time based on the differential value.

Abstract: A non-transitory computer-readable storage medium storing a shape data output program that causes at least one computer to execute process, the process includes, normalizing each shape data of a plurality of pieces of shape data for each component in each coordinate axis direction to create unit shape data; classifying the plurality of pieces of shape data based on the created unit shape data of each of the pieces of shape data; specifying, based on dimensions of sites of each shape data in classified group, a dimensional relationship between different sites of the shape data in the group; and outputting information indicating the specified dimensional relationship in association with the unit shape data of the shape data in the group.

Abstract: A control device calculates a first estimate value of a first monitored amount of a mobile object, from sensor information of a controlled object, using a transformed first model obtained by transformation of a first model having a multi-layered neural structure and activation functions of a ReLU structure, and predicting dynamic behavior of the mobile object. The transformed first model is a model for which weight coefficients, biases, and upper and lower limit values of input/output variables of neurons are set based on the first model and in which activation functions of the neurons are transformed using a linear inequality function having a binary variable. The control device calculates a second estimate value of a second monitored amount of the controlled object from a candidate value of an operation amount for controlling the second monitored amount.