Abstract: Example implementations include a method, apparatus, and computer-readable medium for object detection, comprising detecting a first object in a first image frame and a second image frame, wherein the first object is bounded by region-of-interest (ROI) boundaries generated by an ROI detection model. The implementations further include calculating a speed of the first object using positions of the first object in the first and second image frame, identifying at least one image frame that should include the first object based on a calculated speed of the first object. The implementations further include determining that the at least one image frame should be added to a training dataset for the ROI detection model in response to detecting that the ROI detection model did not generate a ROI boundary in the at least one image frame, and subsequently re-training the ROI detection model using said training dataset.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for training a fault probability model using warranty claim data includes obtaining, by a processing circuit, a first data set for failed building devices based on warranty claim data associated with the building devices; receiving, by the processing circuit, design change data associated with the building devices and determining a design change date based on the design change data; comparing, by the processing circuit, a manufacturing date for each of the failed building devices with the design change date; removing, by the processing circuit, any building devices from the first data set in response to the manufacturing date preceding the design change date to create an updated first data set; generating, by the processing circuit, a training data set comprising the updated first data set; and training, by the processing circuit, a fault probability model using the training data set to produce a trained model.

Abstract: A system including a processing circuit configured to receive tags describing points of a piece of equipment, the piece of 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.

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

Abstract: 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.

Abstract: 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.

Abstract: Sensorized commercial buildings are a rich target for building a new class of applications that improve operational and energy efficiency of building operations that take into account human activities. Such applications, however, rarely experience widespread adoption due to the lack of a common descriptive schema that would enable porting these applications and systems to different buildings. Our demo presents Brick [4], a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. Using a web application, we will demonstrate real buildings that have been mapped to the Brick schema, and show application queries that extracts relevant metadata from these buildings. The attendees would be able to create example buildings and write their own queries.

Abstract: 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.

Abstract: A building system operates to receive building data for a building describing one or more conditions of the building and perform a first optimization with a multi-tiered model that predicts a first condition of the building based on a first control setting, the first optimization determining one or more first values of the first control setting. The building system operates to perform a second optimization with the multi-tiered model that predicts a second condition of the building based on a second control setting and the one or more first values of the first control setting, the second optimization determining one or more second values of the second control setting and operate building equipment based on the one or more first values of the first control setting and the one or more second values of the second control setting.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method includes training a conditional generator by operating a generative adversarial network that includes the conditional generator, generating, by the conditional generator, synthetic timeseries data corresponding to a plurality of fault types, wherein labels for the plurality of fault types are used as inputs to the conditional generator, training a fault prediction model using the synthetic timeseries data, and predicting a fault for building equipment by applying the fault prediction model to real timeseries data relating to the building equipment.

Abstract: The present disclosure relates to a building management system (BMS) with indoor navigation features. The BMS comprises a processing circuit and a database. The database comprises a building model, an entity model and spatial anchors. The processing circuit receives camera-derived data from an electronic device associated with a user. Further, a current location of the user within a building is determined by comparing the camera-derived data with the building model. Additionally, a target location may be determined using the entity model. Further, one or more spatial anchors between the target location and the current location are identified to determine a shortest navigable path between the current location and the target location. Subsequently, the spatial anchors are interconnected and provided to the electronic device, wherein the user associated with the electronic device navigates towards the target location by following the interconnected spatial anchors.

Abstract: 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.

Abstract: 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.

Abstract: 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.