Shimadzu Corporation

Yusuke Tagawa

Yuki Ishikawa

Wataru Takahashi

Yoshihiro Ueno

Hiroshi Oohara

Kazuho Tawada

Kaname Hasebe

A model estimator () estimates a model function of an analyzing system () based on target observation data and reference observation data. For this estimation, the amount of variation between the model of the target system and that of a reference model is estimated from the target observation data and reference observation data. The model function of the target model is estimated after the observation data are corrected based on the estimated amount of variation. A parameter determiner () calculates an acquisition function based on the mean and covariance of the model function, and determines a parameter value for the next observation, using the acquisition function. A data acquirer () sets the parameter value in the analyzing system () and acquires a corresponding observed value. A loop process with the feedback of the observation data is repeated to determine an optimal parameter value. In the case of a parameter search using a multi-task Bayesian optimization method, the present technique improves the efficiency of the parameter search even if the correlation between the target observation data and reference observation data is low.

Parameter-searching method, parameter-searching device, and program for parameter search

This production method of a trained model () includes a step of optimizing a training model () so that a value of a loss function (A) becomes small. The loss function (A) is configured to output a relatively large value in a case where the contrast of the predetermined area (PA) in a brightness-adjusted image adjusted in brightness becomes high with respect to training data () than in a case where the contrast of the predetermined area in the brightness-adjusted image () becomes low with respect to the training data.

Production method of learned model, brightness adjustment method, and image processing apparatus

A mass spectrometer according to one aspect of the present invention includes an ion source (), a mass separator (), and a detector (), the mass spectrometer further including: a parameter optimization unit () configured to optimize a parameter value using a Bayesian optimization method based on a result obtained by making measurements while changing values of device parameters including a plurality of parameters that affects ionization efficiency in the ion source (), a display processor () configured to display a sensitivity model which is a posterior distribution indicating a relationship between a plurality of parameters in all or some of the device parameters and signal strength estimated during the optimization of the device parameters, expressing as a graph like a heat map or an array of a plurality of the graphs on a display unit (), and to sequentially update the sensitivity model, and a file creation unit () configured to a user to designate a position on the displayed graph, and to create a method file containing a measurement condition used for sample measurement, based on a combination of values of parameters corresponding to the designated position.

Mass spectrometer and program for mass spectrometer

A mass spectrometer according to an aspect of the present invention includes, to optimize N (where N is an integer of 2 or more) parameters that affect ionization efficiency in an ion source (), a measurement controller () that causes respective units to repeatedly execute measurement on a sample containing a target component while changing values of the N parameters or a value set of M (where M is an integer smaller than N) parameters, in a plurality of stages, and a parameter determiner () that sequentially finds an optimum value for each parameter based on a result of the measurement executed under control of the measurement controller (). At least one parameter whose physical quantity is temperature is optimized prior to all of the parameters whose physical quantities are other than temperature.

Mass spectrometer and mass spectrometry method

A mass spectrometer includes an ionization unit, a mass separation unit, a detection unit, a first measurement control unit configured to control the ionization unit to repeatedly execute a first measurement on a target sample while changing values of a plurality of parameters defined as device parameters, a second measurement control unit configured to control the ionization unit to set a value of each of the plurality of parameters to a predetermined reference value and execute a second measurement on the target sample at two or more time points before, after, or in a middle of repetition of the first measurement, a correction processing unit configured to correct results of the first measurements using results of the second measurements, and a device parameter-related information acquisition unit configured to determine the plurality of parameters using the corrected measurement results or acquire reference information for determining the plurality of parameters.

This dynamic image processing device () for a head mounted display includes an attitude detection means () capable of detecting the attitude of an imaging device affixed to the head of a user, a first image deviation amount calculation means () that calculates a first image deviation amount (G) in the yawing and pitching directions of the imaging device based on the detection result of the attitude detection means, a second image deviation amount calculation means () that calculates a second image deviation amount (G) between a past image () and a current frame image () based on the first image deviation amount, the current frame image captured by the imaging device, and the past image, and an image synthesis means () that corrects the past image based on the second image deviation amount and synthesizes the past image and the current frame image.

Dynamic image processing device for head mounted display, dynamic image processing method for head mounted display and head mounted display system

A generation method of a digital hologram includes steps of emitting coherent light from a coherent light source, imaging a hologram that is an interference pattern of an object beam and a reference beam due to the emission light from the light source, and setting a plurality of wavelengths of the illumination light that generates the hologram detected by the detector, and wherein the plurality of wavelength are specified by the wavelength setting step based on a magnification percentage X of a conjugate image set up by a user not to disturb visibility of an image when a real image and the conjugate image reconstructed by a predetermined calculation means relative to structures of observation targets are superimposed to a corresponding real image so that a shortest wavelength &#x3bb;and a longest wavelength &#x3bb;satisfy the expression &#x3bb;/&#x3bb;&#x2265;(1/X+1).

Digital holography device and digital hologram generation method

In an X-ray inspecting apparatus, a rotational fluctuation amount of a stage is calculated around a power transmission part of the stage and a stage drive unit as a base point, i.e., the X-axis and Y-axis sliding parts, in accordance with detected positional information from a position detecting sensor. Then, a stage shift amount is calculated in accordance with the rotational fluctuation amount and a distance between the base point and an imaging position on the stage. Here, the stage shift amount corresponds to a positional deviation of the stage at the imaging position caused by an attitude variation of the stage in a yawing direction, and thus is an error in repeated positioning. Accordingly, a tomographic image with high resolution can be generated in consideration of the error in repeated positioning.

Radiation inspecting apparatus

Initial values A^P^M^. . . A^P^M^of parameters representing a geometric relationship between an X-ray tube, a stage and a flat panel X-ray detector are estimated, a least squares solution (p^)of characteristic point three-dimensional coordinates is estimated, and only limited parameters are updated until reprojection square errors converge. Thus, based on known radiographic conditions, initial values of the parameters are estimated, and a nonlinear optimization operation is carried out on only the parameters considered, in view of mechanisms and drive characteristics of the apparatus, to have large errors between the initial values of the parameters and the parameters at a time when radiography is actually carried out. As a result, the calculation can be speeded up, while securing the convergence accuracy of the nonlinear optimization operation, by using the radiographic conditions, i.e. information on tomography.

Growing community of inventors

Kyoto, Japan

Yusuke Tagawa