Abstract: A learning model generation support apparatus 10 is an apparatus for supporting generation of a learning model to be utilized in odor detection using an odor sensor that reacts to a plurality of types of odors. The learning model generation support apparatus 10 includes a data acquisition unit 11 that acquires sensor data output by the odor sensor under specific measurement conditions and condition data specifying the measurement conditions, and inputs, as training data, the acquired sensor data and condition data to a machine learning engine 31 that generates the learning model, and a condition setting unit 12 that acquires a predictive accuracy output by the machine learning engine in response to input of the training data, and sets new measurement conditions for when the odor sensor newly outputs sensor data as training data, based on the acquired predictive accuracy.

Abstract: An input unit 81 inputs action data, in which a state of an environment and an action performed under the environment are associated with each other, a prediction model for predicting a state according to the action on the basis of the action data, and explanatory variables of objective functions for evaluating the state and the action together. A structure setting unit 82 sets a branch structure in which the objective functions are placed at lowermost nodes of a hierarchical mixtures of experts model. A learning unit 83 learns the objective functions including the explanatory variables and branching conditions at nodes of the hierarchical mixtures of experts model, on the basis of the states predicted with the prediction model applied to the action data divided in accordance with the branch structure.

Abstract: The learning unit 3 learns a regression equation based on learning data. The learning data includes a value for a state in a state space model at each time. Also, the learning data includes a set of combinations of a certain time, a value for the state at each of times from a time one time before the certain time to a time n times (n is an integer of 2 or more) before the certain time, a value for an input in the state space model at the certain time obtained one time before the certain time, and a value or values for one or more attributes at the certain time obtained one time before the certain time.

Abstract: An odor detection apparatus 100 includes a first odor sensor 10 provided with a sensitive membrane, a second odor sensor 20 provided with an identical sensitive membrane, and a control device 30. The control device 30 includes a sensor data acquisition unit 31 that acquires first sensor data output by the first odor sensor 10 and second sensor data output by the second odor sensor, a calculation processing unit 32 that calculates a difference between the first sensor data and the second sensor data, and a determination unit 33 that determines, when the sensitive membrane of one of the odor sensors is in a steady state, whether the sensitive membrane of the other odor sensor is in a steady state, based on the difference.

Abstract: A terminal apparatus 20 includes a sensor data collection unit 21 that collects sensor data from an odor sensor 40 that outputs the sensor data in reaction to a plurality of types of odors, an analyzer acquisition unit 22 that, in the case where an analyzer capable of analyzing a designated odor analysis target is transmitted thereto from a server apparatus 10 that holds a plurality of analyzers for analyzing odor analysis targets by analyzing the sensor data, acquires the analyzer transmitted thereto, an analysis execution unit 23 that executes analysis processing of the designated odor analysis target, by applying the acquired analyzer to the collected sensor data, and an analysis result holding unit 24 that holds information indicating a result of the analysis processing.

Abstract: A vehicle control system for controlling driving of a vehicle reflecting an environment and a characteristic of a user, while suppressing increase in learning time, is provided. The vehicle control system includes classification means for classifying, by using one or more attributes selected from accumulation means for accumulating data including attributes relating to driving of a vehicle, driving properties included in the data, learning means for learning a model representing the driving property, for each of types that are a result of classification by the classification means, and control information determination means for determining, by using the model learned for the type associated with a value of the attribute at time of driving of a control target vehicle, control information for the driving.

Abstract: A server apparatus 10 is communicably connected to a terminal apparatus 20 that collects sensor data from an odor sensor 40. The server apparatus 10 includes an analyzer holding unit 11 that holds a plurality of analyzers for analyzing specific odor analysis targets, based on sensor data, an analyzer management unit 12 that selects an analyzer, determines preprocessing to be performed on the sensor data, according to the selected analyzer, and causes the terminal apparatus 20 to execute the preprocessing, an analysis execution unit 13 that executes analysis processing of the designated odor analysis target, by applying the selected analyzer to the preprocessed sensor data from the terminal apparatus, and an analysis result transmission unit 14 that transmits information indicating a result of the analysis processing to the terminal apparatus 20.

Abstract: A server apparatus 10 is communicably connected to a terminal apparatus 20 that collects sensor data from an odor sensor 40. The server apparatus 10 includes an analyzer holding unit 11 that holds a plurality of analyzers for analyzing specific odor analysis targets, based on sensor data, an analyzer management unit 12 that determines preprocessing to be performed on the sensor data, by selecting an analyzer according to the environment of the odor sensor 40, and causes the terminal apparatus 20 to execute the preprocessing, an analysis execution unit 13 that executes analysis processing of the designated odor analysis target, by applying the selected analyzer to the preprocessed sensor data, and an analysis result transmission unit 14 that transmits information indicating a result of the analysis processing to the terminal apparatus 20.

Abstract: An ensemble control system 80 combines different types of plant control. A plurality of subcontrollers 81 output actions for the plant control based on a prediction result by a predictor. A combiner or switch 82 combines or switches actions to maximize prediction or control performance as best control action based on the actions output by each subcontroller 81. Subcontrollers 81 include at least two types of subcontrollers. A first type subcontroller is an optimization-based subcontroller which optimizes an objective function that is a cost function to be minimized for calculating actions and outputs a control action. A second type subcontroller is a prediction-subcontroller which predicts based on machine learning models and outputs a predicted action.

Abstract: Provided is a model estimation system that can estimate a discrete time state space model having controllability. The model estimation system of the present invention estimates a model of a system that is represented by an ordinary differential equation with all the coefficients being non-zero, and with which input data and a state at each time can be obtained. When an order of the ordinary differential equation and input data and a state at multiple past times in the system are inputted, a model expression construction means 22 constructs an expression representing a model by using a first matrix that is a matrix according to the order and has only some elements as unknown elements and a second matrix that is a matrix according to the order and has only some element as an unknown element. A model estimation means 23 uses input data and a state at multiple past times, to estimate the model by learning unknown elements of the first matrix and an unknown element of the second matrix in the expression.

Abstract: This information processing device is equipped with: an actual travel data acquisition means that acquires actual travel data, which is travel data obtained by the driving of a vehicle by a driver; a simulated travel data acquisition means that uses travel environment data indicating the travel environment associated with the travel, and a driver model that determines the operation of the vehicle with respect to the travel environment, to acquire simulated travel data, which is travel data obtained from a simulator that simulates the driving of the vehicle by the driver; and a comparison means that compares the values of multiple indices of the actual driving data and the values of multiple indices of the simulated travel data, and that outputs the comparison results.

Abstract: An expert model unit 81 generates predicted expert control actions based on an expert model which is a machine learning model trained using data collected when an expert operated a plant which is a control target or a plant of the same or similar characteristics. A transformer 82 constructs metrics or error measures involving the predicted expert control actions from the expert model unit 81 as an objective term. A combiner 83 collects different objective terms from the transformer 82 and a learner which outputs machine-learning models as objective terms and computes an optimal set of weights or combinations of the objective terms to construct an aggregated cost function for use in an optimizer.

Abstract: A linear parameter varying model estimation means (83) estimates a linear parameter varying model of a target system based on input data and output data of the target system collected under a condition around each endpoint of an operating region. When a determination is made that the prediction performance is not good, a data addition instruction means (85) outputs a message indicating an instruction for adding input data and output data of the target system collected under a condition corresponding to a point in the operating region. When the input data and the output data of the target system are additionally input, the linear parameter varying model estimation means (83) further uses the input data and the output data to estimate the linear parameter varying model.

Abstract: This invention provides a water-leakage state estimation system configured to estimate a state of a water leakage in a specific area of a water distribution network. A learning unit is configured to: receive labeled data, which is labeled so as to separate past flow rate data into abnormal values and normal values, and past environment state condition data; build a prediction model for predicting the normal values in the labeled data through learning; and determine a score parameter defining a length of a period involving data to be verified through learning as well. A water-leakage estimation unit is configured to: compare predicted flow rate data obtained by supplying current environment condition data into the prediction model and current flow rate data to produce error values; and calculate an average value of the error values in the period of a window width defined by the score parameter to estimate a water-leakage score representing a state of the water-leakage in the specific area.

Abstract: A learner unit 81 learns a quantity model for a quantity the user is interest in based on data acquired from dynamics and surroundings of a plant which is a control target. A cost function designing unit 82 designs a cost function to be used in the derivation of solutions to optimally control the plant so as to include at least the quantity model as terms.

Abstract: An initial value determination means 71 determines an initial value of a scheduling parameter of a target system. Furthermore, a convergence determination means 75 determines whether the value of a predetermined evaluation function has converged. Until it is determined that the value of the predetermined evaluation function has converged, a state variable calculation means 72 repeatedly calculates a value of a state variable, a regression coefficient calculation means 73 repeatedly calculates a value of a regression coefficient, and a scheduling parameter prediction model derivation means repeatedly derives a scheduling parameter prediction model and calculates the value of the scheduling parameter. When the value of the predetermined evaluation function converges, a model estimation means 76 estimates a linear parameter-varying model of the target system on the basis of the value of the state variable and the value of the scheduling parameter at that point in time.

Abstract: This invention provides a water-leakage state estimation system configured to estimate a state of a water leakage in a specific area of a water distribution network. A learning unit is configured to: receive labeled data, which is labeled so as to separate past flow rate data into abnormal values and normal values, and past environment state condition data; build a prediction model for predicting the normal values in the labeled data through learning; and determine a score parameter defining a length of a period involving data to be verified through learning as well. A water-leakage estimation unit is configured to: compare predicted flow rate data obtained by supplying current environment condition data into the prediction model and current flow rate data to produce error values; and calculate an average value of the error values in the period of a window width defined by the score parameter to estimate a water-leakage score representing a state of the water-leakage in the specific area.

Abstract: A price estimation device that can predict a price with a high degree of precision is disclosed. Said price estimation device has a price-predicting means that predicts a price pertaining to second information in a target second time period by applying rule information to said second information, which includes explanatory variables. Said rule information represents the relationship between the explanatory variables and the price, said relationship having been extracted on the basis of a first-information set comprising first information in which explanatory-variable values are associated with price values. The explanatory variables include an attribute that represents a length of time, determined on the basis of a first time period in which a specific event occurs, pertaining to a target object associated with the aforementioned first information or the abovementioned second information.

Abstract: An energy-amount estimation device that can predict an energy amount with a high degree of precision is disclosed. Said energy-amount estimation device has a prediction unit that, on the basis of the relationship between energy amount and one or more explanatory variables representing information that can influence said energy amount, predicts an energy amount pertaining to prediction information that indicates a prediction target. The aforementioned relationship is computed on the basis of specific learning information, within learning information in which an objective variable representing the aforementioned energy amount is associated with the one or more explanatory variables, that matches or is similar to the aforementioned prediction information.

Abstract: A model estimation device 100 includes a hidden variable variational probability calculation processing unit 104 for acquiring parameters of a hidden variable model and calculating a constrained hidden variable variational probability as a hidden variable posterior probability close to a previously-given distribution by use of the parameters, a model parameter optimization processing unit 105 for optimizing the parameters of the hidden variable model by use of the constrained hidden variable variational probability, and an optimality determination processing unit 106 for determining whether a marginalized log likelihood function using the optimized parameters is converged, wherein when it is determined that the marginalized log likelihood function is converged, the constrained hidden variable variational probability and the parameters used for the marginalized log likelihood function are output.