Abstract: A novel dopant according to the present disclosure includes an anion represented by the following Formula (1) and a counter cation. In Formula (1), R1 and R2 may be each at least one group selected from a nitro group, a cyano group, an acyl group, a carboxyl group, an alkoxycarbonyl group, a haloalkyl group, a sulfo group, an alkylsulfonyl group, an halosulfonyl group, and a haloalkylsulfonyl group, or may be a group formed by R1 and R2 bonded to each other [—SO2-L-SO2—] (where L represents a haloalkylene group). The counter cation may be a radical cation represented by Formula (2), where R1 and R2 represent electron-withdrawing groups that may be bonded to each other to form a heterocycle, and R3 to R5 represent a hydrogen atom, a hydrocarbon group that may have a substituent, or a heterocyclic group that may have a substituent. The dopant is capable of forming an electroconductive composition that shows a high conductivity.

Abstract: The present invention provides steel foil for battery containers, the steel foil being characterized in that the maximum value of the maximum principal strain in uniaxial deformation is not less than 0.25 and the maximum value of the maximum principal strain in planar strain deformation is not less than 0.1. The present invention is able to provide steel foil for battery containers that is capable of restraining cracking of a base material when subjected to a severe forming process for battery containers.

Abstract: An iridium-containing oxide having a total pore volume of 0.20 cm3/g or more, calculated by a BJH method from nitrogen adsorption/desorption isotherm measurement, and a pore distribution having an average pore diameter of 7.0 nm or more.

Abstract: A defect inspecting device including an acquisition unit, an image generating unit, an inspection unit, and a setting unit. The image generating unit generates one or more feature extraction images by applying to an inspection image a learned classifier. The inspection unit identifies a region corresponding to a defect based on one or more determination parameters and a binarized image generated based on the feature extraction image. The setting unit provisionally sets the determination parameters on premise of a post-adjustment from a user when a number of image data for learning corresponding to the features is less than a threshold value in a case where the region corresponding to the defect is identified based on the pre-learned features, and sets the determination parameters according to designation from the user in a case where the region corresponding to the defect is identified based on a feature other than the pre-learned features.

Abstract: This image processing device, by superimposing a defect image 52 on a background image 51, with the original image of an inspection subject as the background image 51, and performing image processing in which the display format of the defect image 52 or the display format of the background image 51 is altered, a plurality of composite images 50 having different ways of seeing the defect image 52 with respect to the background image 51 are generated (step 403), the advisability of detecting the defect image 52 from each of the plurality of composite images 50 is verified (step 404), the detectable range 60 of the defect image 52 is estimated on the basis of the detection advisability verification results (step 405), and the detectable range 60 is displayed (step 406).

Abstract: An inspection system includes: a light emitting device configured to illuminate a target object; a collimator lens arranged between the light emitting device and the target object; and an imaging device configured to image the target object. The light emitting device is capable of changing a light emission position. The inspection system further includes an image analysis unit configured to generate an analysis image in which a value of each pixel corresponds to a normal direction of a surface of the target object appearing in the pixel, by analyzing a plurality of captured images obtained individually from a plurality of times of imaging where the light emission positions are different from each other. As a result, time and effort in adjustment for inspection can be reduced, and defect detection accuracy is improved.

Abstract: A convolutional neural network performs: a first masking process of masking a pixel region not to be inspected, by computing pixel values of corresponding pixels of an inspection image and of a mask image; an intermediate process for extracting a feature image from the inspection image that has been subjected to the first masking process; and a second masking process of masking the pixel region not to be inspected, by computing the pixel values of corresponding pixels of the inspection image that has been subjected to the intermediate process and of the mask image that has been subjected to a process identical to the intermediate process.

Abstract: The supported catalyst synthesis device according to the present disclosure includes a first source for a liquid containing a reducing agent; a second source for a liquid containing elements to constitute single-metal fine particles or solid solution fine particles to be supported; a third source for a liquid containing support particles; a reaction unit that joins flows of these liquids; a liquid feed route connecting between the first source and the reaction unit; a liquid feed route connecting between the second source and the reaction unit; a liquid feed route connecting between the third source and the reaction unit; and a collection unit, connected to the reaction unit via a pipe, to collect a produced reaction product, and further includes a pressure adjustment mechanism connected to the collection unit.

Abstract: An image generating part generating feature extraction images by applying an identification part, which has completed learning, that has executed learning in advance to extract features using learning image data to an inspection image, an inspection part specifying an area corresponding to a defect on the basis of judgment parameters for judging presence/absence of a defect in the inspection target object and a binary image generated on the basis of the feature extraction images, and a setting part calculating an image score based on a density of a color of pixels of a setting image using the setting image that is the binary image in which an area corresponding to the defect is specified and updating the judgment parameters such that a difference between an image score of the inside of the area and an image score of the outside of the area becomes relatively large are included.

Abstract: A convolutional neural network performs: a first masking process of masking a pixel region not to be inspected, by computing pixel values of corresponding pixels of an inspection image and of a mask image; an intermediate process for extracting a feature image from the inspection image that has been subjected to the first masking process; and a second masking process of masking the pixel region not to be inspected, by computing the pixel values of corresponding pixels of the inspection image that has been subjected to the intermediate process and of the mask image that has been subjected to a process identical to the intermediate process.

Abstract: A defect inspecting device including an acquisition unit, an image generating unit, an inspection unit, and a setting unit. The image generating unit generates one or more feature extraction images by applying to an inspection image a learned classifier. The inspection unit identifies a region corresponding to a defect based on one or more determination parameters and a binarized image generated based on the feature extraction image. The setting unit provisionally sets the determination parameters on premise of a post-adjustment from a user when a number of image data for learning corresponding to the features is less than a threshold value in a case where the region corresponding to the defect is identified based on the pre-learned features, and sets the determination parameters according to designation from the user in a case where the region corresponding to the defect is identified based on a feature other than the pre-learned features.

Abstract: This image processing device, by superimposing a defect image 52 on a background image 51, with the original image of an inspection subject as the background image 51, and performing image processing in which the display format of the defect image 52 or the display format of the background image 51 is altered, a plurality of composite images 50 having different ways of seeing the defect image 52 with respect to the background image 51 are generated (step 403), the advisability of detecting the defect image 52 from each of the plurality of composite images 50 is verified (step 404), the detectable range 60 of the defect image 52 is estimated on the basis of the detection advisability verification results (step 405), and the detectable range 60 is displayed (step 406).

Abstract: Provided is an image processing system which determines at least one object using a low function device and a high function device having higher calculation capacity than the low function device. The low function device includes a part configured to output a first determination result which identifies the objects by applying a first convolution neural network to captured images of the objects, and a part configured to output the captured image to the high function device when the first determination result meets a predetermined condition. The high function device includes a part configured to apply a second convolution neural network preliminarily learned from a sample which is at least partially common to the first neural network to the captured image and to output a second determination result which identifies the object.

Abstract: An image processing device includes: a feature detection image generating unit that generates multiple feature detection images corresponding to multiple classes by applying a convolutional neural network having the classes learned previously to an input image; a post-processing unit that generates a measurement result by performing a post-process on at least some feature detection images of the multiple feature detection images on the basis of a setting parameter; and a user interface unit that receives an input of the setting parameter while presenting a user at least one of at least some of the feature detection images which are generated by the feature detection image generating unit and the measurement result which is generated by causing the post-processing unit to perform the post-process using at least some of the feature detection images which are generated by the feature detection image generating unit.

Abstract: The disclosure includes: a memory section, storing a learned model and an internal parameter set in the learned model; an acquisition section, acquiring an image of an inspection object photographed under predetermined conditions; a preprocessing section, generating a predetermined preprocessing filter according to a feature value in a preprocessing image being an image of the inspection object that is acquired by the acquisition section and that includes a defect, and a feature value corresponding to the internal parameter, and generating a preprocessed image by applying the generated preprocessing filter to an inspection image being an image of the inspection object that is acquired by the acquisition section and converting the inspection image; and an inspection section, inspecting the preprocessed image for presence or absence of the defect of the inspection object by using the stored learned model.

Abstract: The present disclosure provides an automatic transaction device including: a device casing that is formed at side faces of the device; a door that can be opened and closed to form a device opening at one face of the device; and a device base plate that is formed at a base of the device, a first gap is present between an internal unit of the device and the device casing, the device base plate includes plural fixing member holes disposed in the first gap for fixing the device to a floor, and the fixing member holes are disposed at the device opening side with respect to a backmost position as viewed from the device opening.

Abstract: There is provided a method of manufacturing a cylindrical container using a metal sheet on at least one surface of which the metal is exposed. The method includes: obtaining a blank having a hexagonal shape from the metal sheet; and processing the blank into a cylindrical shape by pressing a central part of the blank with a punch in a state in which a peripheral part of the blank is clamped between a die for drawing process and a blank holder. The method is characterized by the following features. At least one of the die for drawing process and the blank holder has a groove-formed area at a portion of a surface thereof. The portion corresponds to a side of the blank. The groove-formed area is formed with a plurality of grooves along the circumferential direction.

Abstract: An image generating part generating feature extraction images by applying an identification part, which has completed learning, that has executed learning in advance to extract features using learning image data to an inspection image, an inspection part specifying an area corresponding to a defect on the basis of judgment parameters for judging presence/absence of a defect in the inspection target object and a binary image generated on the basis of the feature extraction images, and a setting part calculating an image score based on a density of a color of pixels of a setting image using the setting image that is the binary image in which an area corresponding to the defect is specified and updating the judgment parameters such that a difference between an image score of the inside of the area and an image score of the outside of the area becomes relatively large are included.