A method for performing automatic visual inspection includes: capturing visual information of an object using a scanning system including a plurality of cameras; extracting, by a computing system including a processor and memory, one or more feature maps from the visual information using one or more feature extractors; classifying, by the computing system, the object by supplying the one or more feature maps to a complex classifier to compute a classification of the object, the complex classifier including: a plurality of simple classifiers, each simple classifier of the plurality of simple classifiers being configured to compute outputs representing a characteristic of the object; and one or more logical operators configured to combine the outputs of the simple classifiers to compute the classification of the object; and outputting, by the computing system, the classification of the object as a result of the automatic visual inspection.

Various embodiments described herein relate to techniques for forecasting with state transitions and confidence factors. In this regard, a system is configured to segment data associated with one or more assets to determine a set of classifications for one or more attributes related to the one or more assets. The system is also configured to generate a state machine associated with a Markov chain model based on the set of classifications for the data. Furthermore, the system is configured to perform a machine learning process associated with the state machine to determine one or more behavior changes associated with the one or more attributes related to the one or more assets. The system is also configured to predict, based on the one or more behavior changes associated with the one or more attributes related to the one or more assets, a change in demand data for the one or more assets during a future interval of time.

A plant material trimmer (PMT) that cuts/separates plant leaf material from plant flower material. The PMT has a trimming assembly that includes a motor with a shaft. A propeller is attached to the motor shaft, above the motor. A twisted metal stand wire rope cutting element is attached to the propeller. An upward extending pin is located at each end of the propeller and the cutting element's ends interface with the pins. A trimming platform is located above the trimming assembly. The platform has multiple elliptical beveled slots extending therethrough. Above the platform is a conveyor assembly with a belt that rotates about two axles. A plurality of push panels are attached to the belt. A conveyor motor produces a forward movement of the belt and push panels which sweep across the platform. Wet or dry plant material is placed on the platform, and the push panels sweeping across the platform cause the plant material down through the slots. Upon exiting the slots the plant material is immediately cut by the rotating propeller and cutting element. The cut plant then falls into a mesh fabric bag located below.

Various systems and methods for implementing distributed object detection processing are described herein. An object detection system includes a plurality of computer vision accelerators to process a respective plurality of portions of an input image and produce a list of detected objects in the respective plurality of portions of the input image; and a processor subsystem to: combine the list of detected objects from each of the plurality of computer vision accelerators, to produce a combined list of detected objects; sort the combined list of detected objects; and remove duplicate entries in the combined list of detected objects to produce an output list of detected objects.

A method for performing automatic visual inspection includes: capturing visual information of an object using a scanning system including a plurality of cameras; extracting, by a computing system including a processor and memory, one or more feature maps from the visual information using one or more feature extractors; classifying, by the computing system, the object by supplying the one or more feature maps to a complex classifier to compute a classification of the object, the complex classifier including: a plurality of simple classifiers, each simple classifier of the plurality of simple classifiers being configured to compute outputs representing a characteristic of the object; and one or more logical operators configured to combine the outputs of the simple classifiers to compute the classification of the object; and outputting, by the computing system, the classification of the object as a result of the automatic visual inspection.

A system for generating a false alarm rule for preventing a false alarm that occurs at a building includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The building events include a first non-alarm event, a second non-alarm event different than the first non-alarm event, and a false alarm event. The processing circuit is configured to generate an event sequence based on the events, where the event sequence includes the first non-alarm event and the second non-alarm event and indicates a relationship between the first non-alarm event and the second non-alarm event that is indicative of a situation at the building that causes the false alarm event to occur. The processing circuit is configured to generate the false alarm rule based on the event sequence. The false alarm rule includes a recommendation for preventing the false alarm event from occurring.

A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The processing circuit is configured to determine, based on the events, whether a false alarm rule has triggered, where the false alarm rule indicates relationships between one or more of the events that is indicative of a situation at the building site that causes the false alarm, generate a parameter update for at least one of the plurality of building devices in response to determining that the false alarm rule has triggered, and implement the parameter update by providing, via the communications interface, the parameter update to the at least one of the building devices.

An object information registration apparatus that registers information of a first object that is a reference object of object recognition holds a first object image that is an image of the first object and recognition method information related to the first object, selects one or more partial regions included in the first object image, sets a recognition method corresponding to each of the one or more partial regions, acquires feature information of each of the one or more partial regions from the first object image based on the set recognition method, and stores the one or more partial regions, the set recognition method, and the acquired feature information in the recognition method information in association with each other.

A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

Example implementations described herein involve a question and answer based interface for the automatic construction of an object detection system. In example implementations, the interface aids in configuring an analytics server to conduct image analytics for a selected camera through the generation of a base framework with glue modules to implement the analytics.