Nec Corporation

Mitsubishi Electric Corporation

Jfe Steel Corporation

Rakuten, Inc.

Ryosuke Sakai

Soma Shiraishi

Tomokazu Kaneko

Katsuhiko Takahashi

Takami Sato

Yoshikuni Miyata

Tomoyuki Yokota

Junji Shimamura

Yu Nabeto

Makoto Terao

Yota Kuronuma

Yoshihiro Mitsuka

Motofumi Awa

In a learning data generation device, an acquisition means acquires a first image corresponding to an image obtained by photographing one or more objects arranged in a predetermined arrangement state, and first information corresponding to information indicating the predetermined arrangement state in the first image. An image deformation means deforms a second image corresponding to an image of a desired object. An image generation means generates a fourth image having an arrangement state that matches or is similar to the predetermined arrangement state indicated by the first image, by pasting a deformed second image to the first image or a third image corresponding to the image of a position where the desired object is arranged. A learning data generation means generates data including the fourth image and second information corresponding to the information indicating the arrangement state of the object in the fourth image, as learning data.

Learning data generation device, learning data generation method and recording medium

The learning deviceA includes an acquiring meansA, a conversion meansA, and a learning meansA. The acquiring meansA is configured to acquire a combination of a first label that is a unique label for each feature point of an object and a feature point image in which a feature point corresponding to the first label is imaged. The conversion meansA is configured to convert the first label to a second label that is set to a same label for feature points of the object with at least one of a congruence relation in appearance or a mirror symmetry relation to one another. The learning meansA is configured to learn an inference engine based on the second label, the feature point image, and correct answer data regarding a position of the feature point, the inference engine being configured to perform an inference on the position of the feature point included in an image that is inputted to the inference engine.

Learning device, control method, and storage medium

An information processing deviceincludes a feature point acquiring meansA and a label determining meansA. The feature point acquiring meansA is configured to acquire, based on a captured image 'Im' captured by an imaging unitA, a combination of positions of feature points of a symmetrical object with a symmetry and second labels defined to integrate or change first labels that are unique labels of the feature points based on the symmetry of the symmetrical object. The label determination meansA is configured to determine a first label to be assigned to each of the feature points based on additional information to break the symmetry and the second labels.

Information processing device, control method, and storage medium

A data analysis apparatus performs to acquire series data having character strings related to an information system to be analyzed as elements and series data having numerical values indicating a state of a device constituting the information system as elements, each of the series data having an index that enables comparison of element order relations within series and between series, classify the elements of each piece of the series data into classification classes, and output series data having classification values indicating classification classes as elements, integrate series data having classification values of the character strings as elements and series data having classification values of the numerical values as elements into one piece of series data, and detect an occurrence of a frequent pattern which is a combination of frequently occurring elements, using the one piece of series data obtained through integration.

Data analysis apparatus, data analysis system, and non-transitory computer-readable storage medium

An image acquisition unitacquires a plurality of images. The plurality of images include an object to be inferred. An image cut-out unitcuts out an object region including the object from each of the plurality of images acquired by the image acquisition unit. An importance generation unitgenerates importance information by processing the object region cut out by the image cut-out unit. The importance information indicates the importance of the object region when an object inference model is generated, and is generated for each object region, that is, for each image acquired by the image acquisition unit. A learning data generation unitstores a plurality of object regions cut out by the image cut-out unitand a plurality of pieces of importance information generated by the importance generation unitin a learning data storage unitas at least a part of the learning data.

Generating learning data from important, cut-out object regions

Disclosed is a clad steel plate with further improved low temperature toughness along with excellent HIC resistance while ensuring a tensile strength of 535 MPa or more. A clad steel plate includes: a base steel; and a clad metal made of a corrosion resistant alloy bonded to one surface of the base steel, in which the base steel has: a chemical composition with appropriately controlled values of ACR and P; and a steel microstructure in which bainite is present in an area fraction of 94% or more at a &#xbd; thickness position in a thickness direction of the base steel, and with an average crystal grain size of 25 &#x3bc;m or less, and shear strength at a bonded interface between the base steel and the cladding metal is 300 MPa or more.

Clad steel plate and method of producing the same

The information processing device includes a registration information obtaining unit, a data generating unit and a transmission processing unit. The registration information obtaining unit obtains user identification information, a login password, and attribute information in association with one another as user registration information. The data generating unit generates incomplete attribute information and complementary attribute information such that the attribute information can be reconstructed by combining the incomplete attribute information and the complementary attribute information with each other. The transmission processing unit transmits the user identification information, the login password, the incomplete attribute information, and the complementary attribute information to another information processing device. The transmission processing unit transmits the user identification information, the login password, and the incomplete attribute information to the another information processing device, and the transmission processing unit transmits the complementary attribute information in response to a request from the another information processing device.

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Tokyo, Japan

Ryosuke Sakai