Tyco Fire & Security Gmbh

Sensormatic Electronics Corporation

Johnson Controls Tyco IP Holdings Llp

Santle Camilus Kulandai Samy

Young M Lee

Chenlu Zhang

Michael James Risbeck

Rajiv Ramanasankaran

Zhanhong Jiang

Yohai Falik

Deepak Chembakassery Rajendran

Prantik Sen

Jaume Amores

Rajkiran Kumar Gottumukkal

Saman Cyrus

Youngchoon Park

A system includes a plurality of devices of building equipment, an additional device of building equipment, and a computing system. The computing system is configured to process data from the plurality of devices to extract common features of the plurality of devices, train a global model based on the common features, obtain additional data from the additional device, adapt the global model for the additional device based on the additional data to obtain an adapted model for the additional device, predict a status of the additional device using the adapted model, and affect an operation of the additional device based on the status.

Predictive modeling and control system for building equipment with multi-device predictive model generation

A method includes automatically selecting a prediction horizon used by the predictive model by performing evaluations of model performance at successively narrower ranges of possible prediction horizons until the prediction horizon is determined based on results of the evaluations. The method may also include using the predictive model with the prediction horizon to perform an automated control action, which may include at least one of controlling or monitoring the building equipment.

Building equipment control system with automated horizon selection

A method includes obtaining a fault prediction model for building equipment, predicting, with the fault prediction model, both (i) whether a fault will occur during a first prediction bin and (ii) whether a fault will occur during a second prediction bin, performing a first mitigating action for the building equipment if the fault is predicted to occur during the first prediction bin, and performing a second mitigating action for the building equipment if the fault is predicted to occur during the second prediction bin.

Building equipment control system with modular models

Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Object detection systems and methods including an object detection model using a tailored training dataset

A building system of a building including one or more memory devices having instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive tags describing points of the building. The instructions cause the one or more processors to map the tags to classes of a schema of a graph data structure, perform clustering to generate clusters of the points. The instructions cause the one or more processors to identify, based on the clusters, relationships in the schema of the graph data structure between the tags mapped to the classes of the schema of the graph data structure. The instructions cause the one or more processors to construct the graph data structure in the schema based on the tags mapped to the classes and the relationships in the schema of the graph data structure.

Building system that maps points into a graph schema

A system located in a building. The system including a processing circuit configured to receive tags describing points of a piece of building equipment, the piece of building equipment connected to the system. The processing circuit configured to map the tags to classes of a schema of a graph data structure. The processing circuit configured to perform clustering to generate clusters of the points. The processing circuit configured to identify, based on the clusters, relationships in the schema of the graph data structure between the tags mapped to the classes of the schema of the graph data structure. The processing circuit configured to communicate data to a second system based at least in part on the tags mapped to the classes and the relationships.

Gateway system that maps points into a graph schema

A system may be configured to dynamically update deployed machine learning models. In some aspects, the system may receive sampled video information, generate first object detection information based on a cloud model and the sampled video information, and generate second object detection information based on a first edge model and the sampled video information. Further, the system may select, based on the first object detection information and the second object detection information, a plurality of training images from the sampled video information, detect motion information corresponding to motion of one or more detected objects within the plurality of training images, generate a plurality of annotated images based at least in part on the first object detection information and the motion information, and generate a second edge model based upon training the first edge model using the plurality of annotated images.

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Santa Clara, CA, United States of America

Santle Camilus Kulandai Samy