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公开(公告)号：US12099922B2
公开(公告)日：2024-09-24
申请号：US16426763
申请日：2019-05-30
发明人： Kun Zhao , Takayuki Katsuki , Takayuki Yoshizumi
摘要： A computer-implemented method for detecting an operation tendency is disclosed. The method includes preparing a general model for generating a general anomaly score. The method also includes preparing a specific model, for generating a specific anomaly score, trained with a set of a plurality of operation data related to operation by a target operator. The method further includes receiving input operation data. The method includes also calculating a detection score related to the operation tendency by using a general anomaly score and a specific anomaly score generated for the input operation data. Further the method includes outputting a result based on the detection score.
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公开(公告)号：US20220318615A1
公开(公告)日：2022-10-06
申请号：US17223183
申请日：2021-04-06
摘要： A computer-implemented method for reconstructing time series data including irregular time intervals and missing values to predict future data from the time series data using a Recurrent Neural Network (RNN) is provided including obtaining irregular time series data X={x1, . . . , xt, . . . , xT} and time interval data Δ={δ1, . . . , δt, . . . , δT}, where xt is a D-dimensional feature vector, T is a total number of observations, δt is a D-dimensional time interval vector, and a d-th element δtd of δt represents a time interval from a last observation, replacing missing values in xt with imputed values using an imputation to obtain {tilde over (x)}t, rescaling data of the time interval δt to obtain rescaled time interval data φ(δt) by calculating φ(δt)=φ log(e+ max(0,ϕδt+bϕ))+bφ, where Wφ, Wϕ, bϕ, bφ are network parameters of a neural network and e is Napier's constant, and multiplying {tilde over (x)}t by φ(δt) to obtain {circumflex over (x)}t as regular time series data for input of the RNN.
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公开(公告)号：US20220013239A1
公开(公告)日：2022-01-13
申请号：US16926741
申请日：2020-07-12
摘要： A computer-implemented method, a computer program product, and a computer system for using a time-window based attention long short-term memory (TW-LSTM) network to analyze sequential data with time irregularity. A computer splits elapsed time into a predetermined number of time windows. The computer calculates average values of previous cell states in respective ones of the time windows and sets the average values as aggregated cell states for the respective ones of the time windows. The computer generates attention weights for the respective ones of the time windows. The computer calculates a new previous cell state, based on the aggregated cell states and the attention weights for the respective ones of the time windows. The computer updates a current cell state, based on the new previous cell state.
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公开(公告)号：US20200380354A1
公开(公告)日：2020-12-03
申请号：US16426763
申请日：2019-05-30
发明人： Kun Zhao , Takayuki Katsuki , Takayuki Yoshizumi
摘要： A computer-implemented method for detecting an operation tendency is disclosed. The method includes preparing a general model for generating a general anomaly score. The method also includes preparing a specific model, for generating a specific anomaly score, trained with a set of a plurality of operation data related to operation by a target operator. The method further includes receiving input operation data. The method includes also calculating a detection score related to the operation tendency by using a general anomaly score and a specific anomaly score generated for the input operation data. Further the method includes outputting a result based on the detection score.
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公开(公告)号：US10599788B2
公开(公告)日：2020-03-24
申请号：US14984794
申请日：2015-12-30
摘要： Target characteristic data may be predicted using an apparatus including a processor and one or more computer readable mediums collectively including instructions. When executed by the processor, the instructions cause the processor to obtain a plurality of physical structure data and a plurality of characteristic data, estimate at least one structural similarity between at least two physical structures that correspond with physical structure data among the plurality of physical structure data, and generate an estimation model for estimating a target characteristic data from a target physical structure data by using at least one characteristic data and corresponding at least one structural similarity between the target physical structure data and each of the plurality of the physical structure data.
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公开(公告)号：US20200027032A1
公开(公告)日：2020-01-23
申请号：US16039700
申请日：2018-07-19
摘要： A computer-implemented method for reducing computational costs for reducing computational costs to perform machine learning tasks includes generating one or more state partitioning candidates corresponding to a plurality of states associated with a partially observable Markov decision process (POMDP) model, determining that a given state partitioning candidate of the one or more state partitioning candidates satisfies a merge condition based on a state transition matrix for the given state partitioning candidate, and performing a machine learning task based on the POMDP model with merged states using the given state partitioning candidate.
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公开(公告)号：US10467546B2
公开(公告)日：2019-11-05
申请号：US14827521
申请日：2015-08-17
发明人： Takayuki Katsuki , Takayuki Osogami
摘要： An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section.
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公开(公告)号：US10170924B2
公开(公告)日：2019-01-01
申请号：US15992395
申请日：2018-05-30
发明人： Takayuki Katsuki , Tetsuro Morimura
摘要： A battery controller and method for controlling a battery include generating a battery capacity prediction model that characterizes a battery capacity decay rate. Future battery capacity for a battery under control is predicted based on the battery capacity prediction model and a present value of the battery capacity. One or more operational parameters of the battery under control is controlled based on the predicted future battery capacity.
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9.发明申请公开(公告)号：US20180307999A1
公开(公告)日：2018-10-25
申请号：US15804256
申请日：2017-11-06
IPC分类号： G06N7/00
CPC分类号： G06N7/005
摘要： A method including receiving designation of an input node for which a node value is generated from collected data, an option node to which a node value is arbitrarily provided, and an estimation target node to be a target of a node value estimation, in a graph including nodes and directional edges; and identifying a directional edge for which a conditional probability is to be acquired to measure the node value of the estimation target node, from among the directional edges, by traversing a directional edge that can propagate an effect to a node value from the estimation target node. The identifying includes, for the option node, traversing both a directional edge that can propagate an effect if a node value is provided to the option node and a directional edge that can propagate an effect if a node value is not provided to the option node.
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公开(公告)号：US20240119576A1
公开(公告)日：2024-04-11
申请号：US18045598
申请日：2022-10-11
CPC分类号： G06T7/0002 , G06T7/11 , G06T2207/20084 , G06T2207/20132 , G06T2207/30168
摘要： One or more systems, devices, computer program products, and/or computer-implemented methods provided herein relate to accurate anomaly detection in images using patched features. According to an embodiment, an extraction component can extract multiple layers of features from one or more patches of an image using a pretrained convolutional neural network (CNN). A feature mapping component can concatenate the features from the multiple layers to generate a tensor feature map comprising a one-dimensional feature vector for respective patches. A cropping component can perform center cropping on the tensor feature map. A calculation component can calculate a distance to a feature distribution mean for respective patches.
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