Abstract: In a parameter adjustment device, the feature calculation unit calculates a feature of machining by simulating an operation of a machine tool from the tool travel command. The evaluation index calculation unit calculates evaluation index values for evaluating a machining result from the feature of machining. The first optimal solution search unit infers the evaluation index values corresponding to a first search command value generation parameter set by using a first learning result, and, by using a result of the inference, searches for command value generation parameter set candidates that simultaneously optimize the respective evaluation index values. The display control unit displays the feature of machining calculated when the command value generation parameter set candidates are set on a command value generation device that generates the tool travel command, and the respective evaluation index values.

Abstract: A method of testing a subject for impairment includes presenting the subject with a display of a smoothly moving object, repeatedly moving over a tracking path and, while presenting the display to the subject, measuring the subject's right eye positions and measuring the subject's left eye positions. The method further includes generating a disconjugacy metric by comparing the measured right eye positions with the measured left eye positions, comparing the disconjugacy metric with a predetermined baseline to determine whether the disconjugacy metric is indicative of an impairment, and generating a report based on the disconjugacy metric.

Abstract: A machining condition searching method includes: generating a machining condition to be set in a machining device; collecting a machining state; collecting the machining result performed under the machining condition; calculating an evaluation value of the machining on the basis of the machining result; constructing an evaluation value prediction model predicting, on the basis of the machining condition, the machining state, and the evaluation value, an evaluation value corresponding to the machining condition that has not been tried; and constructing the evaluation value prediction model on the basis of a change degree in the relationship between the machining condition and the evaluation value, and performs weighting based on the machining state on the evaluation value prediction model. The machining condition to be tried next is generated using a predictive value of the evaluation value. Each of the above processes is repeatedly performed until it is determined to end a search.

Abstract: A method of testing a subject for impairment includes presenting the subject with a display of a smoothly moving object, repeatedly moving over a tracking path and, while presenting the display to the subject, measuring the subject's right eye positions and measuring the subject's left eye positions. The method further includes generating a disconjugacy metric by comparing the measured right eye positions with the measured left eye positions, comparing the disconjugacy metric with a predetermined baseline to determine whether the disconjugacy metric is indicative of an impairment, and generating a report based on the disconjugacy metric.

Abstract: A validation method determining device includes a data acquiring unit to acquire training data to be used for training of a predictor and test data to be used for measurement of prediction accuracy of the predictor, a validation method recommending unit to recommend a validation method for extracting the training data and validation data corresponding to a prediction target using data type information indicating data types of the training data, the test data, and column data constituting both the data, and a validation method determining unit to determine a validation method to be used for estimation of prediction performance of the predictor from a recommended validation method.

Abstract: Included are: a machining data acquiring unit to acquire machining data including data related to time-series machining conditions to perform machining for a plurality of steps and data related to a machining state for the plurality of steps on a basis of the machining conditions; an analysis unit to calculate an importance with respect to machining data; an extraction unit to extract transfer learning data for performing transfer learning of a first machine learning model on a basis of the importance; and a learning unit to perform the transfer learning of the first machine learning model by using the transfer learning data, and generate a second machine learning model that receives, as an input, data related to the machining conditions in time series for the plurality of steps and outputs data related to a machining state after execution for the plurality of steps.

Abstract: A machining condition searching method includes: generating a machining condition to be set in a machining device; collecting a machining state; collecting the machining result performed under the machining condition; calculating an evaluation value of the machining on the basis of the machining result; constructing an evaluation value prediction model predicting, on the basis of the machining condition, the machining state, and the evaluation value, an evaluation value corresponding to the machining condition that has not been tried; and constructing the evaluation value prediction model on the basis of a change degree in the relationship between the machining condition and the evaluation value, and performs weighting based on the machining state on the evaluation value prediction model. The machining condition to be tried next is generated using a predictive value of the evaluation value. Each of the above processes is repeatedly performed until it is determined to end a search.

Abstract: The feature amount extraction device includes: a data acquisition unit to acquire time-sequential data as input time-sequential data; a multiple-filter application unit, including multiple digital filters, to apply each of the digital filters to the input time-sequential data acquired by the data acquisition unit, and output, for each of the digital filters, filter response time-sequential data that is time-sequential data including a time-sequential feature or a frequency feature after having undergone the application; and a feature amount extracting unit to extract feature amounts for a plurality of pieces of the filter response time-sequential data output from the multiple-filter application unit for each of the plurality of pieces of the filter response time-sequential data, and output the extracted feature amounts as feature amount data.

Abstract: A system and method for testing a subject for cognitive or oculomotor impairment includes presenting the subject with a display of an object, while presenting the display to the subject, and the subject's head moves horizontally or vertically within a predefined range of movement rates, measuring the subject's right eye positions and/or the subject's left eye positions. The system generates a metric by statistical analysis of the measurements of subject's right eye positions or the subject's left eye positions, and generates a report based on the metric. In some embodiments, the system is configured to train the subject to improve their performance, and thereby remediate or reduce cognitive and oculomotor impairment.

Abstract: A method of testing a subject for impairment includes presenting the subject with a display of a smoothly moving object, repeatedly moving over a tracking path and, while presenting the display to the subject, measuring the subject's right eye positions and measuring the subject's left eye positions. The method further includes generating a disconjugacy metric by comparing the measured right eye positions with the measured left eye positions, comparing the disconjugacy metric with a predetermined baseline to determine whether the disconjugacy metric is indicative of an impairment, and generating a report based on the disconjugacy metric.

Abstract: A system and method for testing a subject for cognitive or oculomotor impairment includes presenting the subject with a display of an object, while presenting the display to the subject, and the subject's head moves horizontally or vertically within a predefined range of movement rates, measuring the subject's right eye positions and/or the subject's left eye positions. The system generates a metric by statistical analysis of the measurements of subject's right eye positions or the subject's left eye positions, and generates a report based on the metric. In some embodiments, the system is configured to train the subject to improve their performance, and thereby remediate or reduce cognitive and oculomotor impairment.

Abstract: A method of testing a subject for impairment includes presenting the subject with a display of a moving object, repeatedly moving over a tracking path and, while presenting the display to the subject, measuring the subject's measuring the subject's gaze positions. A computer system generates tracking error data corresponding to differences in the measured gaze positions and corresponding positions of the moving object, filters the tracking error data to remove data meeting one or more predefined thresholds so as to generate filtered tracking error data, and generates a representation (e.g., a visual representation) of the filtered tracking error data, the representation indicating frequency and amplitude of anticipatory saccades in subject's visual tracking of the moving object.

Abstract: A method of testing a subject for impairment includes presenting the subject with a display of a smoothly moving object, repeatedly moving over a tracking path and, while presenting the display to the subject, measuring the subject's right eye positions and measuring the subject's left eye positions. The method further includes generating a disconjugacy metric by comparing the measured right eye positions with the measured left eye positions, comparing the disconjugacy metric with a predetermined baseline to determine whether the disconjugacy metric is indicative of an impairment, and generating a report based on the disconjugacy metric.