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    Water-leak state estimation system, method, and recording medium
    1.
    发明授权
    Water-leak state estimation system, method, and recording medium 有权

    公开(公告)号：US10228301B2

    公开(公告)日：2019-03-12

    申请号：US15573039

    申请日：2016-03-10

    Applicant: NEC CORPORATION

    Inventor： Yukitaka Kusumura ,  Sergey Tarasenko ,  Riki Eto ,  Yusuke Muraoka ,  Ryohei Fujimaki

    IPC: G01M3/28

    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.

    Hierarchical latent variable model estimation device, hierarchical latent variable model estimation method, and recording medium
    2.
    发明授权
    Hierarchical latent variable model estimation device, hierarchical latent variable model estimation method, and recording medium 有权
    分层潜变量模型估计装置，层级潜变量模型估计方法和记录介质

    公开(公告)号：US08909582B2

    公开(公告)日：2014-12-09

    申请号：US13758267

    申请日：2013-02-04

    Applicant: NEC Corporation

    Inventor： Riki Eto ,  Ryohei Fujimaki ,  Satoshi Morinaga

    IPC: G06N5/02 ,  G06N7/00

    CPC classification number: G06N7/005 ,  G06F17/18 ,  G06K9/00536 ,  G06N5/02 ,  G06N5/025

    Abstract: A hierarchical latent structure setting unit 81 sets 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 unit 82 computes 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 unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes 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, on the basis of the variational probability of the latent variable in the node.

    Abstract translation: 分层潜在结构设置单元81设置作为其中潜变量由树结构表示的结构的分层潜在结构，并且表示概率模型的分量位于树结构的最底层的节点处。 变分概率计算单元82计算作为潜在变量的路径潜变量的变分概率，所述潜变量包括在将根节点链接到分层潜在结构中的目标节点的路径中。 分量优化单元83针对所计算的变分概率优化每个分量。 门控功能优化单元84基于节点中的潜在变量的变分概率来优化门控功能模型，门控功能模型是根据层级潜在结构的节点中的多变量数据确定分支方向的模型。

    Prediction result display system, prediction result display method, and prediction result display program
    5.
    发明授权
    Prediction result display system, prediction result display method, and prediction result display program 有权

    公开(公告)号：US10949755B2

    公开(公告)日：2021-03-16

    申请号：US15544309

    申请日：2016-01-18

    Applicant: NEC Corporation

    Inventor： Yuki Chiba ,  Yousuke Motohashi ,  Ryohei Fujimaki ,  Satoshi Morinaga

    IPC: G06N5/02 ,  G06N20/00 ,  G06N5/04

    Abstract: An apparatus that extracts an explanatory variable used as a condition from a classification model classified by the condition for selecting a component used for prediction, displays the explanatory variable in association with any of dimensional axes of a multi-dimensional space in which a prediction value is displayed, specifies the component that corresponds to a position in the multi-dimensional space specified by each of the explanatory variables associated with the dimensional axis, displays the prediction value calculated based on the specified component, on the same position and displays the multi-dimensional space that corresponds to the position in which the prediction value is displayed, in a mode that corresponds to the component used for calculating the prediction value.

    Model estimation device, model estimation method, and information storage medium
    6.
    发明授权
    Model estimation device, model estimation method, and information storage medium 有权
    模型估计装置，模型估计方法和信息存储介质

    公开(公告)号：US09489632B2

    公开(公告)日：2016-11-08

    申请号：US14066265

    申请日：2013-10-29

    Applicant: NEC Corporation

    Inventor： Yusuke Muraoka ,  Ryohei Fujimaki

    IPC: G06N5/04 ,  G06N7/00

    CPC classification number: G06N7/005

    Abstract: A model estimation device includes: a data input unit; a state number setting unit; an initialization unit which sets initial values of a variational probability of a latent variable, a parameter, and the type of each component; a latent variable variational probability computation unit which computes the variational probability of the latent variable so as to maximize a lower bound of a marginal model posterior probability; a component optimization unit which estimates an optimal type of each component and a parameter thereof so as to maximize the lower bound of the marginal model posterior probability separated for each component of the latent variable model; an optimality determination unit which determines whether or not to continue the maximization of the lower bound of the marginal model posterior probability; and a result output unit which outputs a result.

    Abstract translation: 模型估计装置包括：数据输入单元; 状态号设定单元; 初始化单元，其设定潜变量的变分概率的初始值，参数以及各成分的种类; 潜变量变异概率计算单元，其计算潜变量的变分概率，以便最大化边际模型后验概率的下限; 估计每个分量的最佳类型及其参数的分量优化单元，以便最大化潜在变量模型的每个分量分离的边际模型后验概率的下限; 确定是否继续边际模型后验概率的下限的最大化的最优性确定单元; 以及输出结果的结果输出单元。

    Model estimation device and model estimation method
    7.
    发明授权
    Model estimation device and model estimation method 有权
    模型估计装置和模型估计方法

    公开(公告)号：US09355196B2

    公开(公告)日：2016-05-31

    申请号：US14066281

    申请日：2013-10-29

    Applicant: NEC Corporation

    Inventor： Yusuke Muraoka ,  Ryohei Fujimaki

    IPC: G06F17/50

    CPC classification number: G06F17/50 ,  G06F17/18 ,  G06K9/6226 ,  G06K9/6278 ,  G06N7/005

    Abstract: A model estimation device includes: a data input unit 101; a state number setting unit; an initialization unit; a latent variable variational probability computation unit which computes a variational probability of a latent variable so as to maximize a lower bound of a model posterior probability limited in degree of freedom; a component optimization unit which estimates an optimal type of each component and a parameter thereof so as to maximize the lower bound of the model posterior probability limited in degree of freedom and separated for each component of a latent variable model; a free parameter selection variable computation unit which computes the free parameter selection variable; an optimality determination unit which determines whether or not to continue the maximization of the lower bound of the model posterior probability; and a result output unit.

    Abstract translation: 模型估计装置包括：数据输入单元101; 状态号设定单元; 一个初始化单元; 潜变量变异概率计算单元，其计算潜变量的变分概率，以便最大限度地限制自由度的模型后验概率的下界; 估计每个分量的最佳类型及其参数的分量优化单元，以便最大限度地限制自由度的模型后验概率的下限并对潜变量模型的每个分量分离; 自由参数选择变量计算单元，其计算自由参数选择变量; 确定是否继续最大化模型后验概率的下限的最优性确定单元; 和结果输出单元。

    HEALTH GUIDANCE RECEIVER SELECTION CONDITION GENERATION SUPPORT DEVICE
    8.
    发明申请
    HEALTH GUIDANCE RECEIVER SELECTION CONDITION GENERATION SUPPORT DEVICE 审中-公开
    健康指导接收者选择条件生成支持设备

    公开(公告)号：US20150074019A1

    公开(公告)日：2015-03-12

    申请号：US14002550

    申请日：2013-04-03

    Applicant: NEC Corporation

    Inventor： Yuki Kosaka ,  Masataka Andou ,  Ryohei Fujimaki

    IPC: G06F19/00 ,  G06N99/00

    CPC classification number: G16H50/20 ,  G06F19/328 ,  G06N20/00 ,  G06Q10/10

    Abstract: A memory that stores health checkup data of a person and a label value representing whether or not the person fell under a predetermined health guidance criterion in the subsequent period, and a processor connected with the memory are provided. The processor learns a discriminant model with use of the health checkup data of each person and the label value. The discriminant model, in which health checkup items of the health checkup data are used as explanatory variables, is represented as a polynomial including the explanatory variables and coefficients of the respective explanatory variables, and is used for discriminating whether or not the person falls under the health guidance criterion in the subsequent period. The processor generates, as a selection condition, combinations of the health checkup items as the explanatory variables and values of the coefficients in the discriminant model after learning.

    Abstract translation: 存储人员的身体检查数据的存储器和表示在随后的时间段内是否处于预定的健康指导准则的人的标签值，以及与存储器连接的处理器。 处理器使用每个人的健康检查数据和标签值来学习判别模型。 将健康检查数据的健康检查项目用作解释变量的判别模型被表示为包括各个解释变量的解释变量和系数的多项式，并且用于区分该人是否落在 健康指导标准。 处理器作为选择条件生成健康检查项目的组合作为解释变量和学习后判别模型中的系数值。

    Hierarchical latent variable model estimation device, hierarchical latent variable model estimation method, supply amount prediction device, supply amount prediction method, and recording medium
    10.
    发明授权
    Hierarchical latent variable model estimation device, hierarchical latent variable model estimation method, supply amount prediction device, supply amount prediction method, and recording medium 有权
    层次潜变量模型估计装置，层次潜变量模型估计方法，供给量预测装置，供给量预测方法和记录介质

    公开(公告)号：US09324026B2

    公开(公告)日：2016-04-26

    申请号：US14032295

    申请日：2013-09-20

    Applicant: NEC Corporation

    Inventor： Yosuke Motohashi ,  Satoshi Morinaga ,  Ryohei Fujimaki ,  Riki Eto

    IPC: G06N5/02 ,  G06N99/00

    CPC classification number: G06N5/02 ,  G06N7/005 ,  G06N99/005

    Abstract: A hierarchical latent structure setting unit 81 sets 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 unit 82 computes 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 unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes 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.

    Abstract translation: 分层潜在结构设置单元81设置作为其中潜变量由树结构表示的结构的分层潜在结构，并且表示概率模型的分量位于树结构的最底层的节点处。 变分概率计算单元82计算作为潜在变量的路径潜变量的变分概率，所述潜变量包括在将根节点链接到分层潜在结构中的目标节点的路径中。 分量优化单元83针对所计算的变分概率优化每个分量。 门控功能优化单元84基于节点中的潜在变量的变分概率来优化门控功能模型，门控功能模型是根据层级潜在结构的节点中的多变量数据确定分支方向的模型。

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