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1
US2021029255A1
MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND MACHINE LEARNING PROGRAM
Publication/Patent Number: US2021029255A1 Publication Date: 2021-01-28 Application Number: 16/923,509 Filing Date: 2020-07-08 Inventor: Kiriyama, Tomohiro   Saito, Koichi   Sugai, Shun   Kowase, Kazuhiko   Kashiwagura, Naoshi   Assignee: KONICA MINOLTA, INC.   IPC: H04N1/00 Abstract: A machine learning device learns an action of a driving source in a transport device continuously transporting at least two transported objects along a transport path, and includes: a hardware processor that: acquires position information of the at least two transported objects on the transport path on the basis of a result of detection by a sensor provided in the transport path; calculates a reward on the basis of the position information acquired, according to a predetermined rule; learns an action by calculating an action value in reinforcement learning on the basis of the position information acquired and the reward calculated; and generates and outputs control information that causes the driving source to perform an action determined on the basis of a learning result.
2
US2021088985A1
MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND MACHINE LEARNING PROGRAM
Publication/Patent Number: US2021088985A1 Publication Date: 2021-03-25 Application Number: 16/991,088 Filing Date: 2020-08-12 Inventor: Sugai, Shun   Saito, Koichi   Assignee: KONICA MINOLTA, INC.   IPC: G05B13/02 Abstract: A machine learning device generates a control parameter of image formation in an image forming device including an image forming part that forms an image on a paper sheet and an image reading part that reads the image formed on the paper sheet, and the machine learning device includes: a first hardware processor that generates the control parameter on the basis of machine learning; a second hardware processor that receives input of an image including a read image that is formed by the image forming part according to the control parameter and read by the image reading part, the second hardware processor making a determination relating to the read image on the basis of machine learning; and a third hardware processor that causes the first hardware processor and/or the second hardware processor to learn oil the basis of a determination result by the second hardware processor.
3
US2021089926A1
MACHINE LEARNING METHOD AND MACHINE LEARNING APPARATUS
Publication/Patent Number: US2021089926A1 Publication Date: 2021-03-25 Application Number: 17/112,135 Filing Date: 2020-12-04 Inventor: Nakajima, Hiroaki   Takahashi, Yu   Assignee: YAMAHA CORPORATION   IPC: G06N3/08 Abstract: A machine learning apparatus includes a memory storing instructions and a processor that implements the stored instructions to execute a plurality of tasks. The tasks include an obtaining task that obtains a mixture signal containing a first component and a second component, a first generating task that generates a first signal that emphasize the first component inputting a mixture signal to a neural network, a second generating task that generates a second signal by modifying the first signal, a calculating task that calculates an evaluation index from the second signal, and a training task that trains the neural network with the evaluation index.
4
US2021012193A1
MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE
Publication/Patent Number: US2021012193A1 Publication Date: 2021-01-14 Application Number: 16/921,944 Filing Date: 2020-07-07 Inventor: Yasutomi, Suguru   Katoh, Takashi   Uemura, Kento   Assignee: FUJITSU LIMITED   IPC: G06N3/08 Abstract: A machine learning method includes: calculating, by a computer, a first loss function based on a first distribution and a previously set second distribution, the first distribution being a distribution of a feature amount output from an intermediate layer when first data is input to an input layer of a model that has the input layer, the intermediate layer, and an output layer; calculating a second loss function based on second data and correct data corresponding to the first data, the second data being output from the output layer when the first data is input to the input layer of the model; and training the model based on both the first loss function and the second loss function.
5
US2021158226A1
MACHINE LEARNING SYSTEM, MACHINE LEARNING METHOD, AND PROGRAM
Publication/Patent Number: US2021158226A1 Publication Date: 2021-05-27 Application Number: 17/047,028 Filing Date: 2019-04-12 Inventor: Niwa, Kenta   Kleijn, Willem Bastiaan   Assignee: NIPPON TELEGRAPH AND TELEPHONE CORPORATION   VICTORIA UNIVERSITY OF WELLINGTON   IPC: G06N20/20 Abstract: Machine learning techniques which allow machine learning to be performed even when a cost function is not a convex function are provided. A machine learning system includes a plurality of node portions which learn mapping that uses one common primal variable by machine learning based on their respective input data while sending and receiving information to and from each other. The machine learning is performed so as to minimize, instead of a cost function of a non-convex function originally corresponding to the machine learning, a proxy convex function serving as an upper bound on the cost function. The proxy convex function is represented by a formula of a first-order gradient of the cost function with respect to the primal variable or by a formula of a first-order gradient and a formula of a second-order gradient of the cost function with respect to the primal variable.
6
EP3767552A1
MACHINE LEARNING METHOD, PROGRAM, AND MACHINE LEARNING DEVICE
Publication/Patent Number: EP3767552A1 Publication Date: 2021-01-20 Application Number: 20184191.3 Filing Date: 2020-07-06 Inventor: Yasutomi, Suguru   Katoh, Takashi   Uemura, Kento   Assignee: FUJITSU LIMITED   IPC: G06N3/08 Abstract: A machine learning method includes: calculating, by a computer, a first loss function based on a first distribution and a previously set second distribution, the first distribution being a distribution of a feature amount output from an intermediate layer when first data is input to an input layer of a model that has the input layer, the intermediate layer, and an output layer; calculating a second loss function based on second data and correct data corresponding to the first data, the second data being output from the output layer when the first data is input to the input layer of the model; and training the model based on both the first loss function and the second loss function.
7
US2021064994A1
MACHINE LEARNING DEVICE AND MACHINE LEARNING METHOD
Publication/Patent Number: US2021064994A1 Publication Date: 2021-03-04 Application Number: 16/922,395 Filing Date: 2020-07-07 Inventor: Tomaru, Tatsuya   Assignee: HITACHI, LTD.   IPC: G06N3/08 Abstract: A machine learning device includes a general arithmetic device that calculates data, and a reservoir arithmetic device including an input unit, an output unit, and one or more nodes. The reservoir arithmetic device performs a certain calculation on data and performs calculation in response to an input value input through an input unit using the dynamics of the nodes. Each node i outputs a measurement outcome zi(tk) at a time point tk. The general arithmetic device calculates y(tk)=Σizi(tk)wi. In the calculation of y(tk), in addition to zi(tk) at the time point zi(tk), the term zi(tk′) at a time point tk′ (tk′≠tk) is included. Thus, the calculation of y(tk) is performed by redundantly using zi(tk) at different time points, with the range of sum with respect to the subscript i being i=1, . . . qn, where q is redundancy.
8
US2021097440A1
MACHINE LEARNING APPARATUS, MACHINE LEARNING METHOD, AND INDUSTRIAL MACHINE
Publication/Patent Number: US2021097440A1 Publication Date: 2021-04-01 Application Number: 17/027,480 Filing Date: 2020-09-21 Inventor: Li, Zhenxing   Minami, Hiroshi   Hada, Keita   Maeda, Kazuomi   Assignee: FANUC CORPORATION   IPC: G06N20/00 Abstract: A machine learning apparatus determines a control parameter of an active vibration isolation apparatus on which an industrial machine is mounted. The industrial machine includes a movable part, a drive source that drives the movable part, and a drive source control section that controls the drive source to position the movable part at a command position. The machine learning apparatus includes: an acquiring section that acquires, as teacher data, a positional deviation, which is a difference between the command position and an actual position of the movable part; a storage section that stores a learning model that outputs the control parameter corresponding to a state quantity concerning the industrial machine; and a learning section that updates the learning model using the teacher data.
9
US2021174227A1
MACHINE LEARNING APPARATUS, CONTROL DEVICE, MACHINING SYSTEM, AND MACHINE LEARNING METHOD FOR LEARNING CORRECTION AMOUNT OF WORKPIECE MODEL
Publication/Patent Number: US2021174227A1 Publication Date: 2021-06-10 Application Number: 17/105,609 Filing Date: 2020-11-26 Inventor: Ogawa, Kenichi   Nagatomi, Takashi   Assignee: FANUC CORPORATION   IPC: G06N5/04 Abstract: A machine learning apparatus capable of reducing an error between a machined workpiece and a target shape when the workpiece is machined based on a workpiece model modeling the target shape of the workpiece. A machine learning apparatus includes a state observation section configured to observe machining state data of a machine tool configured to machine the workpiece, and measurement data of an error between a shape of the workpiece machined by the machine tool based on the workpiece model and the target shape, as a state variable representing a current state of environment in which the workpiece is machined, and a learning section configured to learn the correction amount in association with the error by using the state variable.
10
US2021073376A1
LEARNING INPUT PREPROCESSING TO HARDEN MACHINE LEARNING MODELS
Publication/Patent Number: US2021073376A1 Publication Date: 2021-03-11 Application Number: 16/566,862 Filing Date: 2019-09-10 Inventor: Tran, Ngoc Minh   Sinn, Mathieu   Nicolae, Maria-irina   Wistuba, Martin   Rawat, Ambrish   Buesser, Beat   Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION   IPC: G06F21/55 Abstract: Various embodiments are provided for securing machine learning models by one or more processors in a computing system. One or more hardened machine learning models that are secured against adversarial attacks are provided by applying one or more of a plurality of combinations of selected preprocessing operations from one or more machine learning models, a data set used for hardening the one or more machine learning models, a list of preprocessors, and a selected number of learners.
11
US2021055240A1
MACHINE LEARNING DEVICE, CONTROL SYSTEM, AND MACHINE LEARNING METHOD
Publication/Patent Number: US2021055240A1 Publication Date: 2021-02-25 Application Number: 16/939,430 Filing Date: 2020-07-27 Inventor: Konishi, Kouki   Assignee: FANUC CORPORATION   IPC: G01N25/20 Abstract: A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.
12
US10901374B2
Machine learning device, control device, and machine learning method
Publication/Patent Number: US10901374B2 Publication Date: 2021-01-26 Application Number: 16/435,840 Filing Date: 2019-06-10 Inventor: Tsuneki, Ryoutarou   Ikai, Satoshi   Sonoda, Naoto   Assignee: FANUC CORPORATION   IPC: G05B13/02 Abstract: The settling time of machine learning is shortened. A machine learning device is configured to perform machine learning related to optimization of coefficients of a transfer function of an IIR filter of a feedforward calculation unit with respect to a servo control device configured to control a servo motor configured to drive an axis of a machine tool, a robot, or an industrial machine using feedforward control by a feedforward calculation unit having the IIR filter. The machine learning device represents a zero-point at which the transfer function of the IIR filter is zero and a pole at which the transfer function diverges infinitely in polar coordinates using a radius r and an angle θ, respectively, and searches for and learns, within a predetermined search range, the radius r and the angle θ to thereby perform the optimization of the coefficients of the transfer function of the IIR filter.
13
US11029650B2
Machine learning device, control system, and machine learning method
Publication/Patent Number: US11029650B2 Publication Date: 2021-06-08 Application Number: 16/510,027 Filing Date: 2019-07-12 Inventor: Tsuneki, Ryoutarou   Ikai, Satoshi   Shimoda, Takaki   Assignee: FANUC CORPORATION   IPC: G05B13/02 Abstract: Setting of parameters that determine filter characteristics is facilitated. Machine learning of optimizing the coefficients of a filter provided in a motor control device that controls rotation of a motor for a machine tool, a robot, or an industrial machine is performed on the basis of measurement information of an external measuring instrument provided outside the motor control device and a control command input to the motor control device.
14
US2021065025A1
MACHINE LEARNING DEVICE, RECEIVING DEVICE AND MACHINE LEARNING METHOD
Publication/Patent Number: US2021065025A1 Publication Date: 2021-03-04 Application Number: 16/991,285 Filing Date: 2020-08-12 Inventor: Kurihara, Kenichiro   Akimoto, Shinji   Miyachi, Motoyoshi   Assignee: FANUC CORPORATION   IPC: G06N5/04 Abstract: To enable adjustment of digital filters suited to disturbances occurring in the surroundings. A receiving device includes: a digital filter that eliminates or attenuates a disturbance included in a signal received through a communication line; a coefficient adjusting unit that adjusts a coefficient of the digital filter based on operation schedule information of a device causing the disturbance in the communication line; and an information table that records a combination of operation information included in the operation schedule information and a coefficient of the digital filter corresponding to the operation information or correction information of the coefficient, in which the coefficient adjusting unit calculates the coefficient of the digital filter or the correction information of the coefficient from the information table based on the operation information included in the operation schedule information, and adjusts the coefficient of the digital filter.
15
US10901396B2
Machine learning device, control device, and machine learning method
Publication/Patent Number: US10901396B2 Publication Date: 2021-01-26 Application Number: 16/376,025 Filing Date: 2019-04-05 Inventor: Liang, Yao   Tsuneki, Ryoutarou   Assignee: FANUC CORPORATION   IPC: G05B19/00 Abstract: A machine learning device performs machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device that includes a compensation generation unit configured to generate a compensation value to be added to a control command for controlling a servo motor and a limiting unit configured to limit the compensation value or the control command to which the compensation value is added so as to fall within a setting range. During a machine learning operation, when the compensation value or the control command is outside the setting range and the limiting unit limits the compensation value or the control command so as to fall within the setting range, the machine learning device applies the compensation value to the learning and continues with a new search to optimize the compensation value generated by the compensation generation unit.
16
US10891478B2
Method for correction of the eyes image using machine learning and method for machine learning
Publication/Patent Number: US10891478B2 Publication Date: 2021-01-12 Application Number: 15/567,365 Filing Date: 2016-03-03 Inventor: Kononenko, Daniil Sergeyevich   Lempitsky, Victor Sergeyevich   Assignee: Skolkovo Institute Of Science And Technology   IPC: G06K9/00 Abstract: The present invention refers to automatics and computing technology, namely to the field of processing images and video data, namely to correction the eyes image of interlocutors in course of video chats, video conferences with the purpose of gaze redirection. A method of correction of the image of eyes wherein the method obtains, at least, one frame with a face of a person, whereupon determines positions of eyes of the person in the image and forms two rectangular areas closely circumscribing the eyes, and finally replaces color components of each pixel in the eye areas for color components of a pixel shifted according to prediction of the predictor of machine learning. Technical effect of the present invention is rising of correction accuracy of the image of eyes with the purpose of gaze redirection, with decrease of resources required for the process of handling a video image.
17
US11029651B2
Machine learning device, control system, control device, and machine learning method
Publication/Patent Number: US11029651B2 Publication Date: 2021-06-08 Application Number: 16/101,968 Filing Date: 2018-08-13 Inventor: Tajima, Daisuke   Morita, Yuuki   Assignee: FANUC CORPORATION   IPC: G01L5/24 Abstract: A machine learning device includes: a state information acquisition unit configured to cause the control device to execute a tapping program to acquire from the control device, state information including a torque command value with respect to the spindle motor, a drive state including deceleration, a ratio of a movement distance in acceleration and a movement distance in deceleration; an action information output unit configured to output action information including adjustment information of the ratio of the movement distance in acceleration and the movement distance in deceleration, to the control device; a reward output unit configured to output a reward value in reinforcement learning based on a torque command value in deceleration, and a target torque command value in deceleration; and a value function update unit configured to update an action value function based on the reward value, the state information, and the action information.
18
US10891520B2
Machine learning device, inspection device and machine learning method
Publication/Patent Number: US10891520B2 Publication Date: 2021-01-12 Application Number: 16/029,827 Filing Date: 2018-07-09 Inventor: Namiki, Yuta   Assignee: FANUC CORPORATION   IPC: G06K9/62 Abstract: A machine learning device that creates training data to be used in machine learning includes: an image input unit that inputs an image which was obtained by capturing an inspection target on which a symbol indicating a defect is marked; and a creation unit that creates the training data based on the inputted image, in which the creation unit: creates training data consisting of a training image which is the image as inputted, and a label that retains a value of OK which signifies not having a defect, in a case of there not being the symbol in the image inputted; and creates training data consisting of a training image generated based on the image inputted, and a label that retains a value of NG signifying having a defect, in a case of there being the symbol in the image inputted.
19
US10984757B2
Machine learning method, machine learning system, and display system
Publication/Patent Number: US10984757B2 Publication Date: 2021-04-20 Application Number: 16/607,815 Filing Date: 2018-05-07 Inventor: Okamoto, Yuki   Assignee: Semiconductor Energy Laboratory Co., Ltd.   IPC: G09G5/37 Abstract: To improve the display quality of a display device. To provide a method of correcting image data input to the display device. To provide a novel image correction method or an image correction system. Machine learning for a neural network correcting image data input to the display device is performed by the following method: second image data based on an image that is displayed on the display device by input of first image data to the display device is obtained; third image data is generated by obtaining a difference between the first image data and the second image data; fourth image data is generated by adding the first image data and the third image data; and a weight coefficient is updated so that output data obtained by input of the first image data to the neural network is close to the fourth image data.
20
US2021087033A1
MACHINE LEARNING METHOD, FORKLIFT CONTROL METHOD, AND MACHINE LEARNING APPARATUS
Publication/Patent Number: US2021087033A1 Publication Date: 2021-03-25 Application Number: 17/007,504 Filing Date: 2020-08-31 Inventor: Kimura, Nobutaka   Seto, Kouichi   Iwamoto, Yutaka   Ito, Hidekazu   Ara, Koji   Assignee: HITACHI, LTD.   HITACHI SOLUTIONS, LTD.   IPC: B66F9/075 Abstract: A forklift which carries a cargo is includes a machine learning apparatus which executes a procedure of: accepting input of learning data of a first category group and evaluation data of a second category group; extracting the learning data of at least one category from the first category group and calculating parameters of the estimation models for controlling the forklift by using the extracted learning data; extracting the evaluation data of at least one category from the second category group and evaluating the estimation models, for which the parameters are calculated, by using the extracted evaluation data; and outputting an estimation model M whose data evaluation result is equal to or larger than a specified threshold value, from among the estimation models for which the parameters are calculated, and a category of the evaluation data used for the evaluation of the estimation model M.
Total 500 pages