Panasonic Intellectual Property Corporation of America

Panasonic Intellectual Property Management Co., Ltd.

Tsubakimoto Chain Company

Yohei Nakata

Yasunori Ishii

Kazuma Takeuchi

Masahiko Saito

Sotaro Tsukizawa

Naoyuki Harada

Takuya Yamaguchi

Ryota Fujimura

Shuhei Matsui

Toshitaka Ogo

Shinji Ohyama

Kensuke Wakasugi

Masaki Takahashi

Yuji Kurematsu

Hiroaki Urabe

Tomoyuki Okuno

An information processing device includes a processor. The processor obtains a first classification threshold for classifying data into at least one of a plurality of classes, and outputs a classification result of classifying the data into at least one of the plurality of classes based on an output of a trained classification model and the first classification threshold. The first classification threshold is obtained by a second transform performed on a second classification threshold, the second transform being an inverse transform of a first transform. The first transform corresponds to transforming the output of the trained classification model into a classification probability value of each of a plurality of unit classes constituting the plurality of classes. The second classification threshold is set based on the classification probability values of the plurality of unit classes.

Information processing device, information processing method, and recording medium

An information processing apparatus includes: an obtainer that obtains sensing data; a common neutral network (NN) that inputs the sensing data into an inference model to obtain a result of inference and information on a processing time for a plurality of tasks subsequent to the processing performed by the inference model; and an NN inference computation management unit that determines a task schedule for a task processing unit that processes the plurality of subsequent tasks to process the plurality of subsequent tasks on the basis of the information on the processing time for the plurality of subsequent tasks and inputs the result of the inference into the task processing unit to process the plurality of subsequent tasks according to the determined task schedule.

Information processing apparatus, information processing method, and recording medium for performing inference processing using an inference model

An information processing method includes acquiring first output data for input data of first learning model, reference data for the input data, and second output data for the input data of second learning model obtained by converting first learning model; calculating first difference data corresponding to a difference between the first difference data and the reference data and second difference data corresponding to a difference between the second output data and the reference data; and training first learning model with use of the first difference data and the second difference data.

Information processing method and information processing system for reducing a difference between output data of a first learning model and output data of a second learning model

In a data processing method executed by a computer: inputting, in a third trained model, first output data corresponding to first input data for a first trained model to obtain second output data, the third trained model being acquired through training in which (i) output data of the first trained model is used as training data, and (ii) output data of a second trained model acquired by converting the first trained model is used as label data; obtaining first label data of the first input data; and retraining the first trained model using first differential data corresponding to differences between the second output data and the first label data.

Data processing method and data processing system

An information processing device includes a processor. The processor obtains an input image, inputs the input image to a machine learning model that executes classification likelihood calculation processing to obtain, for each of candidate objects in the input image, likelihoods of belonging to the plurality of classes, executes first determination on whether or not each of the candidate objects is classified as a first class of the plurality of classes using a likelihood of belonging to the first class that is a likelihood having a negative correlation with likelihoods of belonging to other classes, executes second determination on whether or not each of the candidate objects that have been determined in the first determination as a non-first class is classified as the other classes, and outputting a result of classifying the candidate objects included in the input image using a result of the second determination.

An information processing device is an information processing device including a processor. The processor obtains a detection result of a first detector for detecting a first target in first sensing data; and based on the detection result of the first detector, determines a setting of processing by a second detector for detecting a second target in second sensing data next in an order after the first sensing data, the second target being different from the first target.

Detection device, detection method, and recording medium for detecting an object in an image

An information processing device includes a processor. This processor: obtains at least one of first sensor data output from a first sensor and used to determine an ambient environment of a device in which a third sensor is placed, and second sensor data used to determine an orientation of this device; determines a tilt of a plane in a sensing direction of the third sensor with respect to the orientation of the device based on the at least one of the first sensor data and the second sensor data; determines, in accordance with the tilt determined, a processing target area of third sensor data output from the third sensor and used for object detection processing in the sensing direction of the third sensor; and executes the object detection processing using the processing target area determined, of the third sensor data.

An information processing method includes: deciding a configuration of a machine learning model, using a processor; performing first determination as to whether the machine learning model in the decided configuration meets a first performance requirement on hardware performance; performing learning using the machine learning model in the configuration determined to meet the first performance requirement, performing second determination as to whether a learned model obtained by the learning meets a second performance requirement on evaluation value of output of a machine learning model, and when the learned model is determined to meet the second performance requirement, outputting information indicating that both performance requirements are met; and when it is determined that the first performance requirement is not met, changing the configuration of the machine learning model, and performing the first determination as to whether the machine learning model in the changed configuration meets the first performance requirement.

Information processing method and information processing system

An information processing method including the following executed using a computer: obtaining a neural network model that solves a regression problem; obtaining input data and label data corresponding to the input data; compressing a network of the neural network model to obtain a compressed model; and changing the label data and the number of nodes in the neural network model, based on information indicating performance of the compressed model, the number of nodes being assigned to the regression problem, the information being calculated using the label data and output data which is obtained by inputting the input data to the compressed model.

An information processing method including the following executed using a computer: obtaining a neural network model that solves a regression problem; obtaining input data and label data corresponding to the input data; compressing a network of the neural network model to obtain a compressed model; transforming the regression problem to be solved by the neural network model into a classification problem, based on information indicating performance of the compressed model, the information being calculated using the label data and output data which is obtained by inputting the input data to the compressed model; and changing a network configuration of the neural network model and transforming the label data, in accordance with the transformation from the regression problem to the classification problem.

An information processing apparatus is configured to distribute, to a vehicle, a map showing a road shape of a road on which the vehicle runs makes, for each of a plurality of areas and determine whether it is necessary to update the map of that area. The determination is based on at least either an amount of change from the road shape shown by the map to the road shape at a present time in each of the plurality of areas or the number of occurrences of a predetermined action of a vehicle having run through in each of the plurality of areas.

Information processing apparatus, vehicle, information processing method, running control method, and map updating method

An apparatus includes: an image capture sensor configured to perform imaging by employing a rolling shutter method; a stand configured to move the image capture sensor; a processor; and a memory. A first still image is obtained by using the image capture sensor while the image capture sensor is moved by the stand at a first constant speed. A second still image is obtained by using the image capture sensor while the image capture sensor is moved by the stand at a second constant speed or is remained stationary. Inclination information, which indicates an inclination difference between an image of an object in the first still image and an image of the object in the second still image, is specified. Distance information, which indicates a distance, is generated by using the specified inclination information.

Distance measuring apparatus and distance measuring method

A chain guide is provided which has a simple structure and improved durability, and which enables reduction in weight and in material cost, and moreover which does not inhibit smooth flow of resin inside a metal mold, and with which dimension errors, contour errors, internal distortion, defects and the like are reduced and concentration of internal stresses or repeatedly applied bending stresses can be prevented. A reinforcing rib of a body part includes a lateral rib that has an independent zone not connected to other parts of the reinforcing rib over a length of 20% or more of a distance between support portions at two locations of the rib. The independent zone is provided on a side further away from a guide surface than a midpoint of the height of the support wall.

Chain guide

A tensioner with a simple structure is provided, which is capable of keeping oil pressure in a first high oil-pressure chamber stable and holding a chain favorably. The tensioner includes a plunger, a housing, a check valve, an inner sleeve, and a biasing member. In the housing, a second high oil-pressure chamber and a unit setting part are formed. In the unit setting part, a relief valve unit is set. A gap between an inner circumferential surface of a plunger hole and an outer circumferential surface of the inner sleeve functions as part of an oil flow passage that connects the first high oil-pressure chamber and the second high oil-pressure chamber.

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Osaka, Japan

Yohei Nakata