公开(公告)号：US20210182702A1

公开(公告)日：2021-06-17

申请号：US16761071

申请日：2018-08-17

Applicant: NEC CORPORATION

Inventor： Shinji ITO ,  Ryohei FUJIMAKI

IPC: G06N5/04 ,  G06N20/00 ,  G06Q30/02

Abstract: A learning unit 81 generates a plurality of sample groups from samples to be used for learning, and generates a plurality of prediction models while inhibiting overlapping of a sample group to be used for learning among the generated sample groups. An optimization unit 82 generates an objective function based on an explained variable predicted by the prediction model and based on a constraint condition for optimization, and optimizes a generated objective function. An evaluation unit 83 evaluates an optimization result by using a sample group that has not been used in learning of a prediction model used for generating an objective function targeted for the optimization.

公开(公告)号：US20190026660A1

公开(公告)日：2019-01-24

申请号：US16072228

申请日：2017-02-01

Applicant: NEC Corporation

Inventor： Akihiro YABE ,  Ryohei FUJIMAKI

IPC: G06Q10/04 ,  G06Q30/02 ,  G06N7/00

Abstract: An optimization system according to the present invention includes: a memory; and one processor being coupled to the memory and accepting an indicator probabilistically indicating a range of a prediction error related to a predicted value of the sales quantity, the predicted value being calculated with the prediction formula when a prediction formula predicting a sales quantity of a commodity is expressed by a function of a price of the commodity; optimizing the price to maximize the sales amount acquired by the objective function under a constraint with an objective function acquiring a sales amount including and being determined by the sales quantity and the price; and taking the predicted value and optimizing the price to increase a minimum value of the sales amount within the range of the prediction error, the range being indicated by the indicator.

公开(公告)号：US20180336476A1

公开(公告)日：2018-11-22

申请号：US15778690

申请日：2016-08-29

Applicant: NEC Corporation

Inventor： Ryohei FUJIMAKI ,  Shinji ITO

IPC: G06N5/02 ,  G06N99/00

CPC classification number: G06Q10/04

Abstract: Provided is an information processing system which perform suitable optimization even if there are input data not observed in mathematical optimization. A learning unit 71 learns a predictive model on the basis of an explained variable and explanatory variables, the predictive model representing a relationship between explained variable and explanatory variables and being expressed by a function of the explanatory variables. A visualization unit 72 visualizes the predictive model. When receiving the operation from the user, an optimization unit 73 calculates an objective variable optimizing an objective function under constraints, the objective function using, as an argument, a predictive model visualized by the visualization unit 72.

公开(公告)号：US20170075372A1

公开(公告)日：2017-03-16

申请号：US15125394

申请日：2015-02-27

Applicant: NEC Corporation

Inventor： Yosuke MOTOHASHI ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA ,  Riki ETO

IPC: G05F1/66 ,  G05B13/02

CPC classification number: G05F1/66 ,  G05B13/026 ,  G05B13/048 ,  G05B15/02 ,  G05B2219/25011 ,  G05B2219/40458 ,  G06N7/005 ,  G06Q10/04 ,  G06Q50/06

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 translation: 公开了能够高精度地预测能量的能量估计装置。 所述能量估计装置具有预测单元，其基于能量量与表示能够影响所述能量的信息的一个或多个说明变量之间的关系，预测与表示预测对象的预测信息有关的能量。 上述关系是基于特定学习信息计算的，其中学习信息中表示上述能量量的客观变量与一个或多个解释变量相关联，其与上述预测信息相匹配或类似。

公开(公告)号：US20160232213A1

公开(公告)日：2016-08-11

申请号：US15024802

申请日：2014-09-11

Applicant: NEC CORPORATION

Inventor： Satoshi MORINAGA ,  Ryohei FUJIMAKI

IPC: G06F17/30

Abstract: This invention helps improve the precision of data mining. This information processing system is provided with an attribute-generating means and an evaluating means, as follows. From among a plurality of inputted attributes, the attribute-generating means selects a combination of attributes to serve as operands for a function that defines an operation that takes a plurality of operands. The attribute-generating means applies said function to that combination of attributes to generate a new attribute that is the result of applying that function to that combination of attributes. The evaluating means inputs said new attribute to an analysis engine, which executes an analysis process on the basis of the attribute, and determines whether or not information outputted by said analysis engine satisfies a prescribed requirement.

Abstract translation: 本发明有助于提高数据挖掘的精度。 该信息处理系统具有如下的属性生成单元和评价单元。 从多个输入的属性中，属性生成装置选择属性的组合以用作定义执行多个操作数的操作的函数的操作数。 属性生成装置将所述功能应用于属性的组合以生成作为将该功能应用于该属性组合的结果的新属性。 评估装置将所述新属性输入到基于属性执行分析处理的分析引擎，并且确定所述分析引擎输出的信息是否满足规定的要求。

公开(公告)号：US20150120254A1

公开(公告)日：2015-04-30

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。

公开(公告)号：US20200042872A1

公开(公告)日：2020-02-06

申请号：US16339934

申请日：2017-08-16

Applicant: NEC CORPORATION

Inventor： Yusuke MURAOKA ,  Ryohei FUJIMAKI ,  Zhao SONG

IPC: G06N3/08 ,  G06N7/00 ,  G06Q10/06 ,  G06F17/18

Abstract: A parameter estimation unit 81 estimates parameters of a neural network model that maximize the lower limit of a log marginal likelihood related to observation value data and hidden layer nodes. A variational probability estimation unit 82 estimates parameters of the variational probability of nodes that maximize the lower limit of the log marginal likelihood. A node deletion determination unit 83 determines nodes to be deleted on the basis of the variational probability of which the parameters have been estimated, and deletes nodes determined to correspond to the nodes to be deleted. A convergence determination unit 84 determines the convergence of the neural network model on the basis of the change in the variational probability.

公开(公告)号：US20180218380A1

公开(公告)日：2018-08-02

申请号：US15763668

申请日：2016-08-09

Applicant: NEC Corporation

Inventor： Ryohei FUJIMAKI

IPC: G06Q30/02 ,  G06F17/11 ,  G06F15/18

CPC classification number: G06Q30/0202 ,  G06F17/11 ,  G06N5/003 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/10 ,  G06Q30/0201 ,  G06Q30/0206

Abstract: A predictive model reception unit 81 receives a predictive model that is learned based on an explained variable and an explanatory variable, indicates a relationship between the explained variable and the explanatory variable, and is represented by a function of the explanatory variable. An optimization unit 82 calculates, for an objective function having the received predictive model as an argument, an objective variable that optimizes the objective function, under a constraint condition.

公开(公告)号：US20180136076A1

公开(公告)日：2018-05-17

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

CPC classification number: 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.

公开(公告)号：US20170161628A1

公开(公告)日：2017-06-08

申请号：US15307229

申请日：2015-04-15

Applicant: NEC CORPORATION

Inventor： Yuki CHIBA ,  Yousuke MOTOHASHI ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA

IPC: G06N7/00 ,  G06N99/00 ,  G06F17/14 ,  G06F17/21 ,  G06F17/18 ,  G06N7/02

CPC classification number: G06N7/005 ,  G05B23/0283 ,  G06F17/14 ,  G06F17/18 ,  G06F17/211 ,  G06N7/023 ,  G06N20/00

Abstract: An estimation data input unit 90 inputs estimation data including one or more explanatory variables which are information that may influence deterioration of an object. A component determination unit 91 determines a component to be used for estimation of deterioration of the object based on a hierarchical latent structure, which is a structure in which latent variables are represented by a tree structure and each of the components representing a probability model is assigned to each node at the lowest level of the tree structure, a gate function to determine a branch direction at each node of the hierarchical latent structure, and the estimation data. A deterioration estimation unit 92 estimates the deterioration of the object based on the component determined by the component determination unit 91 and the estimation data.