公开(公告)号：US20200241511A1

公开(公告)日：2020-07-30

申请号：US16262778

申请日：2019-01-30

Applicant: Hitachi, Ltd.

Inventor： Shuai ZHENG ,  Chetan GUPTA ,  Susumu SERITA

IPC: G05B19/418 ,  G06N3/04 ,  G06N3/08

Abstract: Example implementations described herein are directed to a system for manufacturing dispatching using reinforcement learning and transfer learning. The systems and methods described herein can be deployed in factories for manufacturing dispatching for reducing job-due related costs. In particular, example implementations described herein can be used to reduce massive data collection and reduce model training time, which can eventually improve dispatching efficiency and reduce factory cost.

公开(公告)号：US20180246958A1

公开(公告)日：2018-08-30

申请号：US15756321

申请日：2016-03-28

Applicant: HITACHI, LTD.

Inventor： Susumu SERITA ,  Yoshiyuki TAJIMA ,  Tomoaki AKITOMI ,  Fumiya KUDO

IPC: G06F17/30

CPC classification number: G06F16/353 ,  G06F16/00

Abstract: Provided is a technique for extracting a factor (event pattern) that has an influence on an objective index (objective variable). A data analysis device according to the present disclosure performs: a process of generating, with respect to explanatory variable data included in data to be analyzed, a time-series pattern in a predetermined range; a process of calculating a correlation value between the time-series pattern and at least one item of objective variable data included in the data to be analyzed; and a process of outputting, together with the correlation value, the time-series pattern corresponding to the correlation value as an analysis result.

公开(公告)号：US20230334406A1

公开(公告)日：2023-10-19

申请号：US18115081

申请日：2023-02-28

Applicant: Hitachi, Ltd.

Inventor： Shunichi AKATSUKA ,  Susumu SERITA ,  Toshihiro KUJIRAI

IPC: G06Q10/0637 ,  G06Q10/0631 ,  G06Q50/30

CPC classification number: G06Q10/06375 ,  G06Q10/06315 ,  G06Q50/30

Abstract: An inference apparatus includes: an inference module configured to infer a modification plan for modification target data by inputting, for each of agents, a state of the each of the agents to a policy model of the each of the agents which is related to the modification target data, and by acquiring an action of the each of the agents, and store, as experience data, the state and the action of each of the agents as well as a reward earned by taking the action; an evaluation module configured to calculate an evaluation value for each of the agents, the evaluation value being a probability at which the action is selected under the state; and a modification module configured to modify the experience data based on the evaluation value of each of the agents calculated by the evaluation module.

公开(公告)号：US20180253479A1

公开(公告)日：2018-09-06

申请号：US15756436

申请日：2016-03-17

Applicant: Hitachi, Ltd.

Inventor： Fumiya KUDO ,  Tomoaki AKITOMI ,  Susumu SERITA ,  Yu KITANO

IPC: G06F17/30

CPC classification number: G06F16/258 ,  G06F16/221 ,  G06F16/2358 ,  G06F16/254 ,  G06F16/285

Abstract: This data conversion system is characterized by including: a storage unit that stores a column including a plurality of data elements; a range specification module that specifies the range of each of the data elements of the column; an information amount evaluation module that calculates the information amount of the data element within the specified range of the column; and a change point detection module that detects a point at which a change in the information amount according to a change in the specified range satisfies a predetermined condition.

公开(公告)号：US20220012585A1

公开(公告)日：2022-01-13

申请号：US16925538

申请日：2020-07-10

Applicant: Hitachi, Ltd.

Inventor： Hamed KHORASGANI ,  Chi ZHANG ,  Susumu SERITA ,  Chetan GUPTA

IPC: G06N3/08 ,  G06N3/04

Abstract: Example implementations described herein involve a new reinforcement learning algorithm to address short-term goals. In the training step, the proposed solution learns the system dynamic model (short-term prediction) in a linear format in terms of actions. It also learns the expected rewards (long-term prediction) in a linear format in terms of actions. In the application step, the proposed solution uses the learned models plus simple optimization algorithms to find actions that satisfy both short-term goals and long-term goals. Through the example implementations, there is no need to design sensitive reward functions for achieving short-term and long-term goals concurrently. Further, there is better performance in achieving short-term and long-term goals compared to the traditional reward modification methods, and it is possible to modify the short-term goals without time-consuming retraining.

公开(公告)号：US20190271543A1

公开(公告)日：2019-09-05

申请号：US16332210

申请日：2017-08-07

Applicant: Hitachi, Ltd.

Inventor： Susumu SERITA ,  Chetan GUPTA

IPC: G01B21/22 ,  B62J99/00

Abstract: Example implementations described herein are directed to a system for lean angle estimation without requiring specialized calibration. In example implementations, the mobile device sensor data can be utilized without any additional specialized data or configuration to estimate the lean angle of a motorcycle. The lean angle is determined based on a determination of a base attitude of a mobile device and a measured attitude of the mobile device.

公开(公告)号：US20220405161A1

公开(公告)日：2022-12-22

申请号：US17838983

申请日：2022-06-13

Applicant: Hitachi, Ltd.

Inventor： Susumu SERITA

IPC: G06F11/07

Abstract: A data selection device assists selection of suitable training data used for sign detection, and includes: a storage unit configured to store time-series sensor data acquired from a sensor with respect to a failure prediction target device; a data classification unit configured to classify the time-series sensor data into a first data set and a second data set while allowing the first data set and the second data set to overlap each other; a training data selection unit configured to select a subset of the second data set based on a value range of the first data set; a training data evaluation unit configured to calculate an evaluation index indicating a suitability of a failure prediction model as training data based on the selected subset; and a data selection condition search unit configured to search for the value range of the first data set that maximizes the evaluation index.

公开(公告)号：US20210056484A1

公开(公告)日：2021-02-25

申请号：US16547349

申请日：2019-08-21

Applicant: Hitachi, Ltd.

Inventor： Susumu SERITA ,  Chetan Gupta

IPC: G06Q10/06 ,  G06Q10/08 ,  G06F17/50 ,  G06N20/00

Abstract: Example implementations described herein involve methods and systems with one or more machines on a factory floor. Example implementations involve, in response to received orders, determining an initial scheduling policy for internal processes to meet the order and a due date policy for the order; a) executing a simulation involving scheduling decisions and due date quotations based on the initial scheduling policy and the due date policy; b) executing a machine learning process on the simulation results to update the scheduling policy and the due date policy by evaluating the scheduling decisions and the due date quotations according to a scoring function which is common for evaluating the scheduling decisions and evaluating the due date quotations; iteratively executing a) and b) until a finalized scheduling policy and the due date policy is determined; and output the finalized scheduling policy and the due date policy in response to the order.

公开(公告)号：US20200004219A1

公开(公告)日：2020-01-02

申请号：US16025865

申请日：2018-07-02

Applicant: Hitachi, Ltd.

Inventor： Qiyao WANG ,  Susumu SERITA ,  Chetan GUPTA

IPC: G05B19/406

Abstract: Example implementations described herein are directed to systems and methods for defect rate analytics to reduce defectiveness in manufacturing. In an example implementation, a method include determining, from data associated with each feature for a manufacturing process, the data feature indicative of process defects detected based on the feature, an estimated condition for the feature that reduces a defect rate of the process defects, the estimated condition indicating the data into a first group and second group; calculating the rate reduction of the defect rate based on a difference in defects between the first group and the second group; for the rate reduction meeting a target confidence level for a target defect rate, applying the estimated condition to the manufacturing process associated with each of the features. In example implementations, the defect rate analytics reduce defectiveness in manufacturing with independent processes and/or dependent processes.

公开(公告)号：US20200380447A1

公开(公告)日：2020-12-03

申请号：US16428141

申请日：2019-05-31

Applicant: Hitachi, Ltd.

Inventor： Qiyao WANG ,  Haiyan WANG ,  Susumu SERITA ,  Takashi SAEKI ,  Chetan GUPTA

IPC: G06Q10/06 ,  H04L29/08 ,  G06N20/00

Abstract: Example implementations described herein involve systems and methods involving a plurality of sensors monitoring one or more processes, the sensors providing sensor data, which can include determining a probability map of the sensor data from a database and a functional relationship between key performance indicators (KPIs) of the one or more processes and the sensor data; executing a search on the probability map to determine constrained and continuous ranges for the sensor data that optimize KPIs for the one or more processes based on the functional relationship; and generating a recommendation for the one or more processes that fit within the constrained and continuous range of the sensor data.