Nec Corporation

Riki Eto

Junko Watanabe

Shigeo Kimura

Ryohei Fujimaki

Yoshio Kameda

Hidetaka Hane

Shintarou Tsuchiya

Satoshi Morinaga

Wemer Wee

Hiromi Shimizu

Ryota Suzuki

Yusuke Kikuchi

Yukitaka Kusumura

Yusuke Muraoka

Noriyuki Tonouchi

So Yamada

Sergey Tarasenko

Yosuke Motohashi

An information processing apparatus () includes a use environment information acquisition unit (), a model selection unit (), and a prediction unit (). The use environment information acquisition unit () acquires use environment information indicating a use environment of a physical system having input-output. The model selection unit () selects, from a storage unit storing a plurality of prediction models of the physical system in association with section information indicating a section based on the use environment, a prediction model being associated with section information of a section matching the use environment indicated by the use environment information. The prediction unit () performs prediction based on output of the physical system by use of the selected prediction model.

Information processing apparatus, information processing method, and program

A learning model generation support apparatusis 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 apparatusincludes a data acquisition unitthat 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 enginethat generates the learning model, and a condition setting unitthat 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.

Learning model generation support apparatus, learning model generation support method, and computer-readable recording medium

An information processing apparatus () acquires time-series data () output by a sensor () and computes a plurality of feature constants θand a contribution value ξrepresenting contribution with respect to the time-series data () for each feature constant θ. Thereafter, the information processing apparatus () outputs information in which a set Θ of the feature constants θand a set Ξ of the contribution values ξare associated with each other as a feature value of a target gas. As the feature constant θ, a velocity constant β or a time constant τ that is a reciprocal of the velocity constant can be adopted.

Information processing apparatus, control method, and non-transitory storage medium

An information processing apparatus () includes a model generating unit () and a feature value computation unit (). The model generating unit () generates an Auto-Regressive with eXogenous input (ARX) model of a smell sensor by use of input data controlling an input operation of gas including a smell component being a measurement target, and output data acquired by inputting the gas to the smell sensor, based on the input data. The feature value computation unit () computes a transfer function of the smell sensor relating to the smell component by subjecting the ARX model to Z-Transform, and further computes a first-order lag transfer function feature value of the smell sensor relating to the smell component by subjecting the transfer function to partial fraction decomposition.

In a noise removing apparatus, a data acquisition unit acquires sets of odor data measured using a sensor with respect to a plurality of objects, each set of odor data representing features of an odor of an object by respective rates of a plurality of odor molecules. A noise component extraction unit extract a noise component using a set of odor data. A noise removing unit removes the noise component from each set of odor data to be processed.

Noise removing apparatus, noise removing method, and recording medium

An information processing apparatus () includes a sensor output data acquisition unit (), a prediction equation generation unit (), and an operation setting unit (). The sensor output data acquisition unit () acquires sensor output data for each sampling length of an odor sensor with respect to a target gas. The prediction equation generation unit () generates, by using the sensor output data for each sampling length, a prediction equation for making a prediction for an odor component of the target gas. The operation setting unit () determines, by using the prediction equation, a sampling length for operating the odor sensor.

Information processing apparatus, sensor operation optimization method, and program

A server apparatusis communicably connected to a terminal apparatusthat collects sensor data from an odor sensor. The server apparatusincludes an analyzer holding unitthat holds a plurality of analyzers for analyzing specific odor analysis targets, based on sensor data, an analyzer management unitthat determines preprocessing to be performed on the sensor data, by selecting an analyzer according to the environment of the odor sensor, and causes the terminal apparatusto execute the preprocessing, an analysis execution unitthat executes analysis processing of the designated odor analysis target, by applying the selected analyzer to the preprocessed sensor data, and an analysis result transmission unitthat transmits information indicating a result of the analysis processing to the terminal apparatus

Server apparatus, odor sensor data analysis method, and computer-readable recording medium

An information processing apparatus () acquires a feature vector () obtained based on signal data () of a detected value of a sensor () that senses gas to be measured. The information processing apparatus () decomposes the feature vector () into a product of a coefficient vector and a feature matrix by using a non-negative matrix factorization (NMF). The detected value of the sensor () changes according to an attachment and a detachment of a molecule contained in a sensed gas. A value of each element of the feature vector () is equal to or greater than zero.

Gas feature vector decomposition

A model estimation system estimates a model of a system represented by an ordinary differential equation with all 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 unit 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 one element as an unknown element. A model estimation unit uses input data and a state at multiple past times, to estimate the model by learning unknown elements of the first matrix and the unknown element of the second matrix.

Model estimation system, method, and program

An expert model unitgenerates 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 transformerconstructs metrics or error measures involving the predicted expert control actions from the expert model unitas an objective term. A combinercollects different objective terms from the transformerand 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.

Control objective integration system, control objective integration method and control objective integration program

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.

Vehicle control system, vehicle control method, and program recording medium

A server apparatusis communicably connected to a terminal apparatusthat collects sensor data from an odor sensor. The server apparatusincludes an analyzer holding unitthat holds a plurality of analyzers for analyzing specific odor analysis targets, based on sensor data, an analyzer management unitthat selects an analyzer, determines preprocessing to be performed on the sensor data, according to the selected analyzer, and causes the terminal apparatusto execute the preprocessing, an analysis execution unitthat 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 unitthat transmits information indicating a result of the analysis processing to the terminal apparatus

Server apparatus, odor sensor data analysis method, and computer readable recording medium for unfixed odor analysis targets

A terminal apparatusincludes a sensor data collection unitthat collects sensor data from an odor sensorthat outputs the sensor data in reaction to a plurality of types of odors, an analyzer acquisition unitthat, in the case where an analyzer capable of analyzing a designated odor analysis target is transmitted thereto from a server apparatusthat holds a plurality of analyzers for analyzing odor analysis targets by analyzing the sensor data, acquires the analyzer transmitted thereto, an analysis execution unitthat executes analysis processing of the designated odor analysis target, by applying the acquired analyzer to the collected sensor data, and an analysis result holding unitthat holds information indicating a result of the analysis processing.

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.

Water-leak state estimation system, method, and recording medium

A hierarchical latent structure setting unitsets a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure. A variational probability computation unitcomputes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unitoptimizes each of the components for the computed variational probability. A gating function optimization unitoptimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, based on the variational probability of the latent variable in the node.

Hierarchical latent variable model estimation device, hierarchical latent variable model estimation method, supply amount prediction device, supply amount prediction method, and recording medium

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Tokyo, Japan

Riki Eto