A controller for operating building equipment of a building. The controller includes one or more processors. The controller includes one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include comparing one or more model parameters of predictive models describing zones of the building to determine one or more zone groups for the building. The operations include generating one or more zone group models corresponding to the one or more zone groups. The operations include operating the building equipment using the one or more zone group models to affect a variable state or condition of the building.

Provided is a state data of the technical system are captured and fed into a controller, which is configurable by control parameters, in order to control the technical system on the basis of the state data. Furthermore, complexity data quantifying a present computation complexity for the controller are captured and transmitted to a control planner. The control planner takes the complexity data as a basis for ascertaining an updated control parameter that renders the control currently more performant, according to a predefined performance measure, than as a result of the previous control parameter. The controller is then reconfigured by the updated control parameter.

The present application describes an integrated module. The integrated module includes a microcontroller, an inertial measurement unit (IMU), a low-power accelerometer, and an environmental sensor. A distance between the environmental sensor and the IMU is greater than a distance between the IMU and the low-power accelerometer. The present application also describes a method of making an integrated module. The present application also describes a tamper detection system.

A building energy system for a building includes an energy storage system (ESS) configured to store energy received from an energy source and provide the stored energy to one or more pieces of building equipment to operate the one or more pieces of building equipment. The system includes a processing circuit configured to collect building data, determine whether to retrain a trained load prediction model based on at least some of the building data, retrain the trained load prediction model based on the building data in response to a determination to retrain the trained load prediction model, determine a load prediction for the building based on the retrained load prediction model, and operate the ESS to store the energy received from the energy source or provide the stored energy to the one or more pieces of building equipment to operate the one or more pieces of building equipment.

A vibration analysis system for predicting performance of a building system includes one or more memory devices configured to store instructions that, when executed on one or more processor, cause the one or more processors to receive vibration data from the building equipment, generate a performance prediction for the building equipment, generate a performance explanation for the performance prediction, and cause a user interface to display the performance prediction and the performance explanation.

A prediction control development device according to one aspect of the present invention generates a prediction model of a control amount by analyzing first time-series data of the control amount and provides the generated prediction model to a controller. The prediction control development device acquires second time-series data showing transition of a value of the control amount during prediction control using the prediction model, and evaluates prediction accuracy of the prediction model based on a difference between a prediction value by the prediction model and a value of the second time-series data. When the prediction accuracy of the prediction model is not allowable, the prediction control development device analyzes the second time-series data to newly generate a prediction model of the control amount, and provides the newly generated prediction model to the controller.

An information processing apparatus of an embodiment includes one or more hardware processors. The one or more hardware processors receive input of parameter of a model to be estimated by machine learning and input of first input data. The one or more hardware processors train, by using the first input data as training data, the model using a cost function for which a cost is smaller as a change in the parameter is smaller.

Insects can be localized and classified using a predictive model. To begin, image data is obtained that corresponds to the insects. Using a predictive model, samples of the image data are evaluated to determine whether the image portions include an insect and, if so, into what category the insect should be classified (e.g., male/female, species A/species B, etc.).

System and method for improving operation of an industrial automation system, which includes a control system that controls operation of an industrial automation process. The control system includes a feature extraction block that determines extracted features by transforming process data determined during operation of an industrial automation process based at least in part on feature extraction parameters; a feature selection block that determines selected features by selecting a subset of the extracted features based at least in part on feature selection parameters, in which the selected features are expected to be representative of the operation of the industrial automation process; and a clustering block that determines a first expected operational state of the industrial automation system by mapping the selected features into a feature space based at least in part on feature selection parameters.

A method inspects weld quality in-situ. The method obtains a plurality of sequenced images of an in-progress welding process and generates a multi-dimensional data input based on the plurality of sequenced images and/or one or more weld process control parameters. The parameters may include: (i) shield gas flow rate, temperature, and pressure; (ii) voltage, amperage, wire feed rate and temperature (if applicable); (iii) part preheat/inter-pass temperature; and (iv) part and weld torch relative velocity). The method generates defect probability and analytics information by applying one or more computer vision techniques on the multi-dimensional data input. The analytics information includes predictive insights on quality features of the in-progress welding process. The method then generates a 3-D visualization of one or more as-welded regions, based on the analytics information, and the plurality of sequenced images. The 3-D visualization displays the quality features for virtual inspection and/or for determining weld quality.