Abstract: A method may include obtaining position data and associated time entries for multiple individuals. The method may also include, for each of the individuals, associating instances of the position data with categories of locations to generate a context vector, where the context vector includes text strings describing a context of a given individual. The method may also include, for each of the individuals, squeezing the context vector to combine consecutive categories of locations to generate a count vector, where the count vector includes the text strings and a count of the consecutive categories of locations. The method may also include classifying each of the individuals based on a comparison of the count vector to a known class of individuals, and adjusting at least one factor directing flow of multiple people in a different manner based on the classification.

Abstract: A method may include obtaining position data and associated time entries for multiple individuals. The method may also include, for each of the individuals, associating instances of the position data with categories of locations to generate a context vector, where the context vector includes text strings describing a context of a given individual. The method may also include, for each of the individuals, squeezing the context vector to combine consecutive categories of locations to generate a count vector, where the count vector includes the text strings and a count of the consecutive categories of locations. The method may also include classifying each of the individuals based on a comparison of the count vector to a known class of individuals, and adjusting at least one factor directing flow of multiple people in a different manner based on the classification.

Abstract: A computing apparatus to classify anomalies in images, by unsupervised anomaly detection on an input dataset of the images to detect anomaly portions from said images to generate, for an image in the dataset, a corresponding mask image transmitting a detected anomaly portion in the image and blocking anomaly-free portions; train a classifier ANN, including, in a first epoch process processing a masked version of the input dataset with the classifier ANN, the masked version including the image of the input dataset masked by the corresponding mask image, and training the classifier ANN to classify anomaly portions into one of plural classes by minimising a cross entropy loss function using generated labels as ground truths; extracting, from the classifier ANN, a latent feature representation of the image of the masked version in the input dataset; and in a second epoch process generating a set of pseudo labels corresponding to the masked version of the input dataset by applying an unsupervised clustering algori

Abstract: An analyzer configured to acquire a chromatogram or spectrum by performing a predetermined analysis of a sample and perform a qualitative or quantitative analysis of components contained in the sample. The analyzer includes: a peak detection unit configured, based on information regarding a plurality of target components that need to be checked whether contained in the sample or that need to be quantified, to detect a peak or peaks in the chromatogram or spectrum acquired by the predetermined analysis of the sample corresponding to one of the target components, configured to acquire peak information regarding each of the peak or peaks, and configured to obtain confidence information for each of the peak or peaks, the confidence information being an indicative value of certainty of detecting a peak; and a display processing unit configured to display on a display unit a list of at least a part of the target components.

Abstract: A computer implemented method including acquiring a live image of a subject physical sample of a product or material; inputting the live image to a trained generator neural network to generate a defect-free reconstruction of the live image; comparing the defect-free reconstruction of the live image with the live image to determine a difference; and identifying a defect corresponding to the subject physical sample at a location of the determined difference.

Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Abstract: A computing apparatus to classify anomalies in images, by unsupervised anomaly detection on an input dataset of the images to detect anomaly portions from said images to generate, for an image in the dataset, a corresponding mask image transmitting a detected anomaly portion in the image and blocking anomaly-free portions; train a classifier ANN, including, in a first epoch process processing a masked version of the input dataset with the classifier ANN, the masked version including the image of the input dataset masked by the corresponding mask image, and training the classifier ANN to classify anomaly portions into one of plural classes by minimising a cross entropy loss function using generated labels as ground truths; extracting, from the classifier ANN, a latent feature representation of the image of the masked version in the input dataset; and in a second epoch process generating a set of pseudo labels corresponding to the masked version of the input dataset by applying an unsupervised clustering algori

Abstract: An analyzer configured to acquire a chromatogram or spectrum by performing a predetermined analysis of a sample and perform a qualitative or quantitative analysis of components contained in the sample. The analyzer includes: a peak detection unit configured, based on information regarding a plurality of target components that need to be checked whether contained in the sample or that need to be quantified, to detect a peak or peaks in the chromatogram or spectrum acquired by the predetermined analysis of the sample corresponding to one of the target components, configured to acquire peak information regarding each of the peak or peaks, and configured to obtain confidence information for each of the peak or peaks, the confidence information being an indicative value of certainty of detecting a peak; and a display processing unit configured to display on a display unit a list of at least a part of the target components.

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.

Abstract: An information processing method implemented by a computer, the information processing method includes: acquiring an image group generated by imaging a data group according to each of a plurality of imaging methods; for each of the acquired image groups, calculating a score of the imaging method used to generate the image group, based on distribution of a first feature value group in a feature value space, and distribution of a second feature value group in the feature value space, the first feature value group being a plurality of feature values output when the image group is input to a trained model outputting feature values corresponding to input images, the second feature value group being a plurality of feature values output when a reference image group is input to the trained model; and outputting the score of the imaging method, the score being calculated for each of the image groups.

Abstract: A computer implemented method including acquiring a live image of a subject physical sample of a product or material; inputting the live image to a trained generator neural network to generate a defect-free reconstruction of the live image; comparing the defect-free reconstruction of the live image with the live image to determine a difference; and identifying a defect corresponding to the subject physical sample at a location of the determined difference.

Abstract: A learning data generation apparatus includes a memory and a processor configured to perform determination of a region of interest in each of a plurality of images related to a learning target for machine learning in accordance with a result of image matching between the plurality of images, apply an obscuring processing to a specific region other than the region of interest in each of the plurality of images, and generate learning data including the plurality of images to which the obscuring processing is applied.

Abstract: An information processing method implemented by a computer, the information processing method includes: acquiring an image group generated by imaging a data group according to each of a plurality of imaging methods; for each of the acquired image groups, calculating a score of the imaging method used to generate the image group, based on distribution of a first feature value group in a feature value space, and distribution of a second feature value group in the feature value space, the first feature value group being a plurality of feature values output when the image group is input to a trained model outputting feature values corresponding to input images, the second feature value group being a plurality of feature values output when a reference image group is input to the trained model; and outputting the score of the imaging method, the score being calculated for each of the image groups.

Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Abstract: A non-transitory computer-readable recording medium records a measurement control program that controls a sensor that measures a water level, the measurement control program causing a computer to execute processing comprising: specifying a predicted peak value of the water level based on a prediction result for a change in the water level; calculating a reference value of the water level at which measurement of the water level is started, according to the predicted peak value that has been specified; specifying a timing at which the water level is predicted to reach the reference value that has been calculated, based on the prediction result; and controlling a start timing of measurement of the water level by the sensor based on the timing that has been specified.

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.

Abstract: A computer-implemented learning method includes inputting a plurality of pieces of input data and labels representing the plurality of pieces of input data into an encoder configured to output context variables associated with each of the plurality of pieces of input data, inputting the plurality of pieces of input data and the context variables output by the encoder into a decoder configured to output decision labels associated with the plurality of pieces of input data respectively, and learning parameters of the encoder and the decoder so that each of the decision labels matches with a corresponding label of the labels representing the plurality of the plurality of pieces of input data.

Abstract: A learning data generation apparatus includes a memory and a processor configured to perform determination of a region of interest in each of a plurality of images related to a learning target for machine learning in accordance with a result of image matching between the plurality of images, apply an obscuring processing to a specific region other than the region of interest in each of the plurality of images, and generate learning data including the plurality of images to which the obscuring processing is applied.