A system for performing a task according to a reference trajectory is provided. The system includes at least one actuator configured to change a state of the system according to a control input, and a memory configured to store a model of dynamics of the system including a known part of the dynamics of the system as a function of the state of the system and the control input to the system and an unknown part of the dynamics of the system as a function of the state of the system, wherein the unknown part of the dynamics of the system is represented by parameters of a probabilistic distribution including a first-order moment and a second-order moment of the probabilistic distribution. The system also includes a control system configured to recursively determine and submit the control input to the actuator to change the state of the system.

Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to include one or more particular data elements in a computer-based reasoning model and determining two probability density or mass functions (“PDMFs”), one for the data set including the one or more particular data elements, once for the data set excluding it. Surprisal is determined based on those two PDMFs, and inclusion in the computer-based reasoning model is determined based on surprisal. A system is later controlled using the computer-based reasoning model.

Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.

Provided is an industrial device for building and/or processing a knowledge graph, with at least one sensor and/or at least one data source configured for providing raw data, with an ETL component, configured for converting the raw data into triple statements, using mapping rules, with a triple store, storing the triple statements as a dynamically changing knowledge graph (with a learning component, configured for processing the triple statements in a learning mode, and for performing an inference in an inference mode, and with a control component, configured for switching between different modes of operation of the learning component.

Distributed data sampling, including: receiving a sampling target; generating, based on one or more sensors, sampled data; determining, based on the sampling target, a value for the sampled data; and determining, based on the value for the sampled data, whether to provide the sampled data to a remotely disposed computing device.

In a quality control method applied in manufacturing, product information of a product is obtained. Manufacturing parameters corresponding to the product information are queried. The manufacturing parameters are input into a product quality prediction model which is trained to obtain the value of at least one quality inspection of each product. If such quality inspection value is not equal to a standard value or is not within a standard value range, an incorrect manufacturing parameter is identified from all the manufacturing parameters applicable to each product, the incorrect manufacturing parameter being output when identified.

In order to control a technical system, a system state of the technical system is continually detected. By a trained first control model, a subsequent state of the technical system is predicted on the basis of a sensed system state. Then, a distance value is determined for a distance between the predicted subsequent state and an actually occurring system state. Furthermore, a second control model is trained by the trained first control model to predict the distance value on the basis of a sensed system state and on the basis of a control action for controlling the technical system. A subsequent state predicted by the first control model is then modified on the basis of a distance value predicted by the trained second control model. The modified subsequent state is output in order to control the technical system.

Provided is a system for autonomous control of dynamical systems. The system may include one or more processors programmed or configured to receive data associated with a host platform, construct a run-time dynamic envelope based on the data associated with the host platform, and construct a Worldview Relational Interaction Map (WRIM) based on the run-time dynamic envelope, wherein the WRIM comprises a coordinate system corresponding to an area (e.g., a volume associated with the area) of an environment of the host platform, wherein the coordinate system comprises a plurality of elements, wherein each element of the plurality of elements includes one or more data attributes associated with a predicted momentum exchange resulting from the host platform co-occupying the element with an entity. A method and computer program product are also disclosed.

A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

A method includes determining that conditions of a processing chamber have changed since a trained machine learning model associated with the processing chamber was trained. The method further includes determining whether a change in the conditions of the processing chamber is a gradual change or a sudden change. Responsive to determining that the change in the conditions of the processing chamber is a gradual change, the method further includes performing a first training process to generate a new machine learning model. Responsive to determining that the change in the conditions of the processing chamber is a sudden change, the method further includes performing a second training process to generate the new machine learning model. The first training process is different from the second training process.