Sartorius Stedim Data Analytics Ab

Mks Instruments, Inc.

Umetrics Ab

Swetree Technologies Ab

Johan Trygg

Magnus Hertzberg

Svante Bjarne Wold

Svante Wold

Göran Sandberg

Pär Jonsson

Jarmo Schrader

Karin Johansson

Rickard Sjögren

Lennart Eriksson

Rickard Sjoegren

Rishikeshi Bhalerao

Tuula Teeri

Henrik Aspeborg

Björn Sundberg

Lars Wallbäcks

Ann Karlsson

An example method comprises receiving a new observation characterizing at least one parameter of an entity; inputting the new observation to a deep neural network having hidden layers; obtaining a second set of intermediate output values that are output from at least one of the hidden layers by inputting the received new observation to the deep neural network; mapping the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier.

Computer-implemented method, computer program product and system for anomaly detection and/or predictive maintenance

A computer-implemented method for analysis of cell images comprises obtaining a deep neural network and a training dataset, the deep neural network comprising a plurality of hidden layers; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers; constructing a latent variable model using the first sets of intermediate output values, the latent variable model mapping the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than the sets of the intermediate outputs; obtaining a second set of intermediate output values by inputting a received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether the received new cell image is an outlier.

Computer-implemented method, computer program product and system for analysis of cell images

A computer-implemented method for data analysis is provided. A deep neural network () is provided for processing images and at least a part of a training dataset used for training the deep neural network, the deep neural network comprising a plurality of hidden layers, the training dataset including possible observations that can be input to the deep neural network; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers, each of the first sets of intermediate output values obtained by inputting a different one of the possible input images included in said at least the part of the training dataset; constructing a latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than a dimension of the sets of the intermediate outputs; receiving an observation to be input to the deep neural network; obtaining a second set of intermediate output values that are output from said at least one of the plurality of hidden layers by inputting the received observation to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether or not the received observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values.

Computer-implemented method, computer program product and system for data analysis

A method and system for partitioning (clustering) large amounts of data in a relatively short processing time. The method involves providing a first data matrix and a second data matrix where each of the first and second data matrices includes one or more variables, and a plurality of data points. The method also involves determining a first score from the first data matrix using a partial least squares (PLS) analysis or orthogonal PLS (OPLS) analysis and partitioning the first and second data matrices (e.g., row-wise) into a first group and a second group based on the sorted first score, the variance of the first data matrix, and a variance of the first and second groups relative to the variances of the first and second data matrices.

Hierarchically organizing data using a partial least squares analysis (PLS-trees)

The invention provides a method and an arrangement for filtering or pre-processing most any type of multivariate data exemplified by NIR or NMR spectra measured on samples in order to remove systematic noise such as base-line variation and multiplicative scatter effects. This is accomplished by differentiating the spectra to first or second derivatives, by Multiplicative Signal Correction (MSC), or by similar filtering methods. The pre-processing may, however, also remove information from the spectra, as well as other multiple measurement arrays, regarding (Y) (the response variables). Provided is a variant of PLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given (y) vector or (Y) matrix. Hence, ensuring that the signal correction removes as little information as possible regarding (Y). A filter according to the present invention is named Orthogonal Partial Least Squares (OPLS).

Orthogonal signal projection

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Umeå, Sweden

Johan Trygg