A method for generating combined scenarios for testing an object detection unit, wherein the method comprises provision of first sensor data of a first scenario and of second sensor data of a second scenario, wherein the first sensor data and the second sensor data in each case are a point cloud comprising a plurality of points, wherein the method further comprises a classification of the respective points of the first sensor data and of the respective points of the second sensor data into relevant or not relevant and merging of the first sensor data and of the second sensor data for obtaining third sensor data of a combined scenario, wherein only relevant points of the first sensor data and relevant points of the second sensor data are merged to form third sensor data of the combined scenario.

A method, apparatus, computer system, and computer program product for managing a device. The method detects, by a computer system, a physical handling of the device to form a physical handling pattern for the device. The method determines, by the computer system, a baseline physical handling pattern for the device, wherein the baseline physical handling pattern for the device meets a set of handling metrics for the device. The method initiates, by the computer system, a set of actions in response to the physical handling pattern for the device deviating from the baseline physical handling pattern for the device.

In some examples, a system includes a memory configured to store a first image and a second image captured by one or more cameras mounted on one or more vehicles and store locations and orientations of the one or more cameras at times when the first and second images were captured. The system also includes processing circuitry configured to identify an existing landmark in the first and second images. The processing circuitry is also configured to determine a latitude, a longitude, and an altitude of the existing landmark based on the locations and orientations of the one or more cameras at the times when the images were captured. The processing circuitry is configured to create a file including the location of the existing landmark and pixel coordinates of the existing landmark in the first and second images.

Embodiments of the present invention provide an improvement to convention machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. Common characteristics of data from the identified emerging patterns are broadened in scope and used to generate a synthetic data set using a generative neural network approach. The resulting synthetic data set is narrowed based on analysis of the synthetic data as compared to the detected emerging patterns, and can then be used to further train one or more machine learning models for further pattern detection.

Disclosed are embodiments for preventing training data vulnerabilities in training data. In one embodiment, a method comprises receiving a first and second set of importance features for a first and second label output by a machine learning (ML) model; generating a first feature dictionary based on the first set of importance features and a second feature dictionary based on the second set of importance features; identifying a subset of labeled examples in a training dataset used to train the ML model based on the first feature dictionary and second feature dictionary; modifying the subset of labeled examples based on the first feature dictionary and second feature dictionary, the modifying generating a modified training data set; and retraining the ML model using the modified training data set.

An apparatus and method for generating learning data for combustion optimization is provided. The apparatus includes a data pre-processor to collect raw data including currently measured real-time data for boiler combustion and previously measured past data for the boiler combustion, and to perform pre-processing for the collected raw data, and a data analyzer to derive learning data from the raw data by analyzing the raw data. An apparatus for combustion optimization includes a management layer to collect currently measured real-time data for boiler combustion, to determine whether to perform combustion optimization, and to determine whether to tune a combustion model and a combustion controller; a data layer to derive learning data from raw data; a model layer to generate the combustion model/controller through the learning data; and an optimal layer to calculate a target value for combustion optimization and to output a control signal according to the calculated target value.

Embodiments of the present invention are directed to learning of an appropriate dictionary which has a high expression ability of minority data while preventing reduction of an expression ability of majority data. A dictionary generation apparatus which generates a dictionary used for discriminating whether data to be discriminated belongs to a specific category includes a generation unit configured to generate a first dictionary based on learning data belonging to the specific category and a selection unit configured to estimate a degree of matching of the learning data at each portion with the first dictionary using the generated first dictionary and select a portion of the learning data based on the estimated degree of matching, wherein the generation unit generates a second dictionary based on the selected portion of the learning data.

According to one aspect of an embodiment a learning apparatus includes a generating unit that generates a model. The model includes an encoder that encodes input information. The model includes a vector generating unit that generates a vector by applying a predetermined matrix to the information encoded by the encoder. The model includes a decoder that generates information corresponding to the information from the vector. The learning apparatus includes a learning unit that, when predetermined input information is input to the model, learns the model such that the model outputs output information corresponding to the input information and the predetermined matrix serves as a dictionary matrix of the input information.

A system includes a computer programmed to receive first sensor data from a first sensor, wherein the first sensor data is defined in part by a first data space that includes first parameters of the first sensor, and second sensor data from a second sensor, wherein the second sensor data is defined in part by a second data space that includes second parameters of the second sensor, to input the first sensor data and the second sensor data to a machine learning program to train the machine learning program to determine a domain translation of data from the first data space to the second data space, and then to input a set of training data received from the first sensor to the trained machine learning program to generate a training dataset based on the determined domain translation of data from the first data space to the second data space.

A machine learning apparatus calculates second values, based on first values each assigned to one variable value of each term in relation to a neuron in a layer following an input layer of a neural network, where the second values are assigned to variable-value combination patterns in relation to each following-layer neuron. Each second value is represented by a product of first values each assigned to a variable value included in the combination pattern in relation to the following-layer neuron. The apparatus then applies the second values as weights each to a numerical value when it is entered to the corresponding following-layer neuron, to calculate an output value of the neural network with the numerical values arranged in an input order. The apparatus updates reference values in a reference pattern and the first values based on input error that the output value exhibits with respect to training data.