公开(公告)号：US20180276527A1

公开(公告)日：2018-09-27

申请号：US15886428

申请日：2018-02-01

Applicant: Hitachi, Ltd.

Inventor： Toru MOTOYA ,  Goichi ONO ,  Hidehiro TOYODA

IPC: G06N3/04 ,  G06F17/16 ,  G06N3/08 ,  G06K9/00 ,  G06F17/28

CPC classification number: G06N3/04 ,  G06F17/16 ,  G06F17/2715 ,  G06F17/2785 ,  G06F17/28 ,  G06K9/00624 ,  G06K9/00973 ,  G06K9/00993 ,  G06K9/4628 ,  G06K9/627 ,  G06N3/0454 ,  G06N3/08

Abstract: In a processing method using a convolutional neural network, the neural network includes a convolution calculation unit that performs a convolution calculation by using a matrix vector product and a pooling calculation unit that performs a maximum value sampling calculation. A threshold value is set related to the matrix data for the convolution calculation, the matrix data is divided into a first and second halves based on the threshold value. The convolution calculation unit divides a first half convolution calculation by using the first half of the matrix data and a second half convolution calculation by using the second half of the matrix data into two and executes the calculations. The pooling calculation unit selects vector data to which the matrix vector product convolution calculation is to be performed in the second half convolution calculation, along with the maximum value sampling calculation.

公开(公告)号：US20240022701A1

公开(公告)日：2024-01-18

申请号：US18218261

申请日：2023-07-05

Applicant: Hitachi, Ltd.

Inventor： Yasutaka SERIZAWA ,  Hisanori MATSUMOTO ,  Goichi ONO

IPC: H04N13/296 ,  G06V20/52 ,  G06T7/80

CPC classification number: H04N13/296 ,  G06V20/52 ,  G06T7/85 ,  G06T2207/30232 ,  G06T2207/30244

Abstract: The state monitoring system includes: a 3D camera configured to acquire an image in a work area; and information processing apparatus connected to the 3D camera and including a processing unit and a storage unit. The processing unit calculates a camera setting parameter that determines an imaging condition of the 3D camera with respect to a monitoring target as a target to be monitored in the work area and stores the camera setting parameter in the storage unit, determines the imaging condition of the 3D camera by applying the camera setting parameter corresponding to the monitoring target with reference to the camera setting parameter stored in the storage unit, acquires an image of the monitoring target from the 3D camera configured to image the monitoring target in the determined imaging condition, and determines a state of the monitoring target based on the acquired image of the monitoring target.

公开(公告)号：US20230377313A1

公开(公告)日：2023-11-23

申请号：US18197281

申请日：2023-05-15

Applicant: Hitachi, Ltd.

Inventor： Takashi OSHIMA ,  Keisuke YAMAMOTO ,  Goichi ONO

IPC: G06V10/764 ,  G06T5/10

CPC classification number: G06V10/764 ,  G06T5/10 ,  G06T2207/20052 ,  G06T2207/20056 ,  G06T2207/20084

Abstract: The time-series signal of the sensor is transformed to the spectral intensity by fast Fourier transform (FFT) or the like, and the one-dimensional data of the spectral intensity is generated. A pseudo image is generated, for example, by repeatedly arranging the one-dimensional data in the vertical direction, or by arranging the one-dimensional data for a plurality of sensors in the vertical direction. The state of the facility is identified by analyzing the pseudo image with an image analysis unit such as a convolutional neural network.

公开(公告)号：US20220391698A1

公开(公告)日：2022-12-08

申请号：US17748710

申请日：2022-05-19

Applicant: Hitachi, Ltd.

Inventor： Takashi OSHIMA ,  Goichi ONO ,  Akira KITAYAMA ,  Ming LIU

IPC: G06N3/08 ,  G06N3/04 ,  G06V10/77 ,  G06V10/776 ,  G06V10/82

Abstract: Provided is a training recognition device that implements training of a DNN for article recognition that does not require manual annotation for an image for training and can reduce power consumption, time, and hardware amount required for training. The training recognition device includes: an image conversion unit that inputs a simulation image and an actual site image into a generative adversarial network and converts the simulation image into an artificial site image; a pre-trained feature extraction unit that inputs the simulation image to a trained deep neural network trained using the simulation image and annotation data for the simulation image and outputs a feature point of the simulation image at time of re-training; a re-training feature extraction unit that inputs the artificial site image to a deep neural network for re-training, re-trains a difference between the simulation image and the artificial site image, and outputs a feature point of the artificial site image; an error calculation unit for feature extraction unit that calculates a difference between the feature point output by the re-training feature extraction unit and the feature point output by the pre-trained feature extraction unit; a coefficient update unit for feature extraction unit that updates a coefficient of the re-training feature extraction unit used for re-training based on the difference; and a re-training identification unit that re-trains a method for identifying an article based on a feature point output from the deep neural network for re-training of the coefficient updated by the coefficient update unit for feature extraction unit.

公开(公告)号：US20180204118A1

公开(公告)日：2018-07-19

申请号：US15846987

申请日：2017-12-19

Applicant: Hitachi, Ltd.

Inventor： Goichi ONO

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06F11/1479 ,  G06N3/0454 ,  G06N3/063

Abstract: In a calculation system in which a neural network performing calculation using input data and a weight parameter is implemented in a calculation device including a calculation circuit and an internal memory and an external memory, the weight parameter is divided into two, i.e., a first weight parameter and a second weight parameter, and the first weight parameter is stored in the internal memory of the calculation device, and the second weight parameter is stored in the external memory.

公开(公告)号：US20220222506A1

公开(公告)日：2022-07-14

申请号：US17513968

申请日：2021-10-29

Applicant: Hitachi, Ltd.

Inventor： Takashi OSHIMA ,  Goichi ONO ,  Tadashi KISHIMOTO ,  Masaru KOKUBO

IPC: G06N3/04

Abstract: An information processing device includes a parallel deep neural network configured to input a captured image of an article to deep neural network models respectively corresponding to a plurality of articles and perform inferences about the plurality of articles in parallel using the deep neural network models, a new article determination unit configured to determine whether an article included in the image is an unlearned article based on learned model information about the articles and the image, and a new article learning unit configured to learn a deep neural network model corresponding to the article determined to be unlearned based on the image and initial model configuration information about the deep neural network model when the article included in the image is determined to be an unlearned article. The new article learning unit adds the learned deep neural network model to the deep neural network models.

公开(公告)号：US20230298045A1

公开(公告)日：2023-09-21

申请号：US18080880

申请日：2022-12-14

Applicant: Hitachi, Ltd.

Inventor： Kenji KOGO ,  Naohiro KOHMU ,  Akira KITAYAMA ,  Goichi ONO

IPC: G06Q30/018

CPC classification number: G06Q30/018

Abstract: Provided is a new technique that enables authentication of sources as well as storage and distribution conditions of food. A preferred aspect of the invention is a food authentication method to be executed by an information processing apparatus including a processor, a memory, an input and output apparatus, and a storage apparatus, the food authentication method including: a first step of setting unique information of food measured at a first time as a unique information initial value; a second step of acquiring environmental information that is associated with the food and that is measured at a second time after the first time; a third step of setting unique information of the food measured at a third time after the second time as a unique information measurement value; a fourth step of calculating a prediction value of the unique information based on the unique information initial value and the environmental information and setting the prediction value as a unique information prediction value; and a fifth step of performing authentication of the food based on the unique information measurement value and the unique information prediction value.

公开(公告)号：US20210037084A1

公开(公告)日：2021-02-04

申请号：US16937262

申请日：2020-07-23

Applicant: Hitachi, Ltd.

Inventor： Riu HIRAI ,  Goichi ONO ,  Daisuke ISHII ,  Yuji OGATA

IPC: H04L29/08 ,  G06N20/00

Abstract: To improve learning accuracy, while avoiding transferring of a dataset from an edge terminal to a cloud server. A management device accessible to a target object to be managed has a processor executing a program, a storage device storing the program, and a communication interface communicable with the target object. The processor executes a reception process for receiving first environmental information representing a first environment of the target object, a first generation process for generating relevant information representing relevancy between the first environmental information and second environmental information representing a second environment of the target object, a second generation process for generating a first learned model for inference by the target object in the first environment based on the relevant information and a second learned model for inference by the target object in the second environment, and a transmission process for transmitting the first learned model to the target object.

公开(公告)号：US20180247182A1

公开(公告)日：2018-08-30

申请号：US15881056

申请日：2018-01-26

Applicant: Hitachi, Ltd.

Inventor： Toru MOTOYA ,  Goichi ONO ,  Hidehiro TOYODA

IPC: G06N3/04 ,  G06N3/08 ,  G06K9/46 ,  G06K9/62

CPC classification number: G06N3/04 ,  G06K9/00973 ,  G06K9/4604 ,  G06K9/4628 ,  G06K9/4652 ,  G06K9/6265 ,  G06K9/6285 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/08

Abstract: An information processing apparatus having an input device for receiving data, an operation unit for constituting a convolutional neural network for processing data, a storage area for storing data to be used by the operation unit and an output device for outputting a result of the processing. The convolutional neural network is provided with a first intermediate layer for performing a first processing including a first inner product operation and a second intermediate layer for performing a second processing including a second inner product operation, and is configured so that the bit width of first filter data for the first inner product operation and the bit width of second filter data for the second inner product operation are different from each other.

公开(公告)号：US20160064099A1

公开(公告)日：2016-03-03

申请号：US14644906

申请日：2015-03-11

Applicant: Hitachi, Ltd.

Inventor： MASANAO YAMAOKA ,  Goichi ONO ,  Chihiro YOSHIMURA ,  Masato HAYASHI

IPC: G11C29/08 ,  G11C29/00

CPC classification number: G11C29/08 ,  G06N7/005 ,  G06N99/002 ,  G11C29/76

Abstract: A semiconductor device capable of easily and properly detecting a defective element unit(s) and a quality management method for the semiconductor device are suggested. A semiconducting device simulating interactions between nodes in an interaction model is equipped with a quality management unit for managing the quality of each element unit provided corresponding to each node, wherein the quality management unit executes a specified quality test of each element unit, compares test results of the quality test with pre-given results to be obtained from the quality test, and detects a defective memory cell(s) and a defective element unit(s) based on the comparison results.

Abstract translation: 建议能够容易且适当地检测缺陷元件单元的半导体器件和半导体器件的质量管理方法。 模拟交互模型中节点之间的交互的半导体装置配备有用于管理对应于每个节点提供的每个元素单元的质量的质量管理单元，其中质量管理单元执行每个元素单元的指定质量测试，比较测试结果 的质量测试，并从质量测试中获得预先给定的结果，并且基于比较结果来检测有缺陷的存储单元和有缺陷的元件单元。