In at least one embodiment, the present description is directed to a computer system, having a processor to at least: electronically receive a model for one or more operating conditions, and facility operating data; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model, including: determining model predicted values, comparing the model predicted values to the facility operating data, removing bias facility operating data from the facility operating data of the plurality of facilities, and constructing, based at least in part on the non-biased facility operating a data, an updated model with one or more updated coefficients; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the no-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

The present invention relates generally to a system and method for measuring the structural characteristics of an object. The object is subjected to an energy application processes and provides an objective, quantitative measurement of structural characteristics of an object. The system may include a device, for example, a percussion instrument, capable of being reproducibly placed against the object undergoing such measurement for reproducible positioning. The invention provides for a system and methods for analyzing measured characteristics utilizing machine learning to create a system for predicting pathologies from measurements.

An electronic device for smoke estimation is provided. The electronic device receives a first image of a plurality of images of a physical space. The electronic device detects smoke in the physical space based on an application of a trained neural network model on the received first image. The electronic device generates a heatmap of the physical space based on the detected smoke in the physical space, and further based on an output of the trained neural network model corresponding to the detection of the smoke. The electronic device estimates a level of the smoke in the physical space based on a normalization of the generated heatmap.

A computer-implemented method for training a deterministic autoencoder. The autoencoder is configured to compress sample data representing objects and subsequently to reconstruct the sample data again, wherein the autoencoder is further configured to generate data representing additional objects. The method comprises the following steps: providing training data representing objects; and training the autoencoder on the basis of the training data, wherein the training of the autoencoder takes place on the basis of a probability distribution and a loss function, and wherein the loss function has a reconstruction term and a regularization term.

There is provided a data processing apparatus for performing machine learning. The data processing apparatus includes convolution circuitry for convolving a plurality of neighbouring regions of input data using a kernel to produce convolution outputs. Max-pooling circuitry determines and selects the largest of the convolution outputs as a pooled output and prediction circuitry performs a size prediction of the convolution outputs based on the neighbouring regions, wherein the size prediction is performed prior to the max-pooling circuitry determining the largest of the convolution outputs and adjusts a behaviour of the convolution circuitry based on the size prediction.

In an embodiment of the present invention, a memory stores case performances on anomaly handling of a plurality of apparatuses. A hardware processor performs: identification processing of identifying an anomaly cause of an abnormal apparatus in the plurality of apparatuses; selection processing of selecting an estimation model for estimating a handling method suitable for the anomaly cause from a plurality of estimation models based on the case performance of the abnormal apparatus; estimation processing of estimating a handling method suitable for the anomaly cause among a plurality of handling methods based on the selected estimation model; and output processing of outputting information indicating the estimated handling method.

An information processing apparatus includes: a memory that stores first and second series data; and a processor that performs machine learning of a state space model and an identification model, by calculating a loss function for each model, based on the first and second series data. The state space model includes: an encoder that calculates a state to be inferred based on either one of at least part of the first series data or at least part of the second series data; a decoder that reconstructs at least part of the first and second series data from the state; and a transition predictor that predicts a transition of the state. The identification model identifies whether the state is based on the first series data or the second series data. The loss function of the state space model includes a term that deteriorates accuracy of identification by the identification model.

A spectrum analysis apparatus is an apparatus for analyzing an analysis object on the basis of a spectrum of light generated in the analysis object containing any one or two or more of a plurality of reference objects, and includes an array conversion unit, a processing unit, a learning unit, and an analysis unit. The array conversion unit generates two-dimensional array data on the basis of a spectrum of light generated in the reference object or the analysis object. The processing unit includes a deep neural network. The analysis unit causes the array conversion unit to generate the two-dimensional array data on the basis of the spectrum of light generated in the analysis object, inputs the two-dimensional array data to the deep neural network, and analyzes the analysis object on the basis of data output from the deep neural network.

To implement a highly accurate prediction analysis that does not depend on data amount. A data analysis apparatus has a processor that is configured to execute: an acquisition processing of acquiring a first statistical model based on a distribution of actual measurement results of a group and a second statistical model based on a distribution of a first actual measurement result of first samples having a smaller number of samples than the number of samples of the group; a calculation processing of calculating correction information indicating a difference between the first statistical model and the second statistical model; a learning processing of generating a first prediction model by performing machine learning using the first actual measurement result and first feature amount data corresponding to the first actual measurement result; and a correction processing of correcting a first prediction result , and outputting a second prediction result.

An estimation apparatus according to one embodiment is an estimation method for estimating a parameter of a model for obtaining a state transition probability used in model-based reinforcement learning and causes a computer to perform: an input procedure in which first data indicating a state transition history in a situation where an action of the model-based reinforcement learning is not performed and second data indicating, when an action prompting a transition to a predetermined state is performed, a degree of accepting the transition to the predetermined state are input; and an estimation procedure in which a parameter of the model is estimated by using the first data and the second data.