A method and a device for configuring a controller and to a method and a controller for controlling a technical system by means of a data-based control model is provided, in particular a model based on reinforcement learning. This data-based control model is configured using a model-predictive control model. Configuration parameters of the data-based control model are set by mapping the model-predictive control model onto the data-based control model in such a way that the data-based control model reproduces the output data of the model predictive control model depending on state data of the technical system read in, and determines optimized control parameters configured in this way. A computationally intensive training procedure for configuring the data-based control model can thus be avoided.

A method of controlling an industrial gas production facility comprising: receiving time-dependent power data receiving time-dependent operational characteristic data; defining one or more power constraints for the operational parameters of the power network; defining one or more process constraints for the operational parameters of each industrial gas plant; generating, based on the power data, the operational characteristic data, the one or more power constraints and the one or more process constraints, control set point values for the one or more industrial gas plants to achieve a pre-determined production parameter for the industrial gas production facility; and sending the control set point values to a control system to control the one or more industrial gas plants by adjusting one or more control set points of the industrial gas plants to achieve the pre-determined production parameter for the industrial gas production facility.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving operational parameters for one or more automation devices, wherein the one or more automation devices are configured to implement control logic generated based on a decision tree. The operations also include receiving an output by the decision tree based on the operational parameters. Further, the operations include determining the output is an anomalous output based on a constraint associated with the decision tree. Further still, the operations include generating an updated decision tree based on the anomalous output. Even further, the operations include generating updated control logic for the one or more automation devices based on the updated decision tree. Even further, the operations include sending the updated control logic to the one or more automation devices.

A system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

A model learning unit learns a prediction model on the basis of learning data, a target setting unit sets a target output parameter value by interpolating between a goal output parameter value and an output parameter value which is the closest to the goal output parameter value in output parameter values in the learning data, a processing condition search unit estimates input parameter values which corresponds to the goal output parameter value and the target output parameter value, a model learning unit updates the prediction model by using a set of the estimated input parameter value and an output parameter value which is a result of processing that a processing device performs as additional learning data.

A system and method 300 for optimization of compactibility of sand in a foundry is disclosed, which ensure that compactibility of sand is maintained within desired values through the different stages of the foundry. A method to forecast compactibility of sand at downstream stages of the foundry based on sand compactibility data obtained from a sample drawn from an operation unit associated the different stages of sand molding and casting operations and supporting data sensed by one or more sensors related to at least one parameter of the sand, additives and environment of the operation unit from where the sample is collected. Based on predicted compactibility of the sand at one or more of the different stages, the compactibility set point is optimized and adjusted at a compactibility controller.

A method for controlling a vehicle based on a prediction from a semantic map is presented. The method includes receiving a snapshot of an environment from one or more sensors. The method also includes generating the semantic map based on the snapshot and predicting an action of a dynamic object in the snapshot based on one or more surrounding objects. The method still further includes controlling an action of the vehicle based on the predicted action.

A system for performing projective tests includes a web server, a database server, and an artificial intelligence (AI) server. The web server is coupled with an electronic data network and configured to provide a man-machine interface via the electronic data network to a remote client. The database server manages test and training data and is coupled with the web server. The AI server is coupled with the web server and the database server, and configured to execute one or more AI algorithms. The man-machine interface provides administrative tools to control a content of at least one projective test where the content may include at least one projective stimulus comprising at least one of an image, a video, an audio file, and a text file. The man-machine interface provides administrative tools that control a display associated with the projective test. The man-machine interface includes a plurality of web pages for providing interactive displays that allow a remote client to view and execute the projective test. The projective test includes an interactive display component for selecting a portion of projective stimuli and an interactive prompt configured to allow entry of additional data related to the selected portion. The system executes an AI algorithm to generate a score based on the selected portion and the response to the prompt. The system executes a second AI algorithm to associate characteristics to a user based on the selected portion of the projective stimuli, the response to the prompt, and scores from the past AI algorithm. The man-machine interface includes a plurality of web pages for providing interactive displays that allow a remote client to view and engage with their predicted scores and characteristics.

A method and system for controlling elevator dispatch is provided. User data, including user behavior, is collected from a number of users over a specified time period. Elevator use data for a number of elevators in a building is also collected over the specified time period. Applying the user data and elevator use data, an elevator dispatch model is constructed that predicts future elevator use according to predicted user needs. An elevator control system dispatches the elevators according to the dispatch model. The elevator dispatch model is refined according to feedback data collected from users over a subsequent time period.

A control device includes: a first generator generating a first command position of a control object on a plane in each control cycle based on a target trajectory; a first control part generating a first operation amount by model predictive control using a first dynamic characteristic model and the first command position; a second generator generating a second command position of the control object on an orthogonal axis in each control cycle; and a second control part generating a second operation amount by model predictive control using a second dynamic characteristic model and the second command position. Based on shape data indicating a surface shape of an object and the first command position, the second generator generates the second command position so that a distance between the control object and a surface of the object is constant.