Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Technologies and techniques for automatically executing a security function of a surveillance system with respect to at least one object of interest based on spatially resolved sensor data for environment perception generated by at least one sensor device. Spatially resolved sensor data, is received. A trained machine-learning computer vision model is accessed to determine at least one surveillance parameter. The model includes a set of parameters set to values learned as a result of a training process based on a synthetic training data set, which is generated by rendering at least one model of the at least one object of interest according to at least one render parameter. The generation of the training data set is determined based on at least one environment parameter being characteristic for the environment to be surveilled and/or for the sensor device. The security function is executed depending on the at least one surveillance parameter.

A multisensory system provides both temporal and spatial coverage capacities for auto-detection of bird collision events. The system includes an apparatus having a first circuitry to capture and store a series of images or video of a blade of a wind turbine; and a memory to store the images from the first circuitry. The apparatus also has one or more sensors to continuously sense vibration of the blade or for acoustic recordings; and a second circuitry to analyze the sensor data stream and/or the series of images or video to identify a cause of the vibration and to trigger the camera(s). A communication interface transmits data from the second circuitry to another device, wherein the second circuitry applies artificial intelligence or machine learning to control sensitivity of the one or more sensors.

An image processing method, an image processing device, an electronic device, and a storage medium are provided. The method comprises: performing image segmentation on a first image which is to be processed, obtaining an initial mask image according to an image segmentation result, in response to determining that the first image satisfies a preset sky area replacement condition according to the initial mask image, obtaining a target mask image by performing guided filtering on the initial mask image by using a greyscale image of the first image as a guide image, acquiring a target sky scene, and obtaining a second image by performing replacement on the sky area in first the image according to the target mask image and the target sky scene.

A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Electronic devices and/or operating methods of the electronic device to provide visual localization based on outdoor three-dimensional (3D) map information may be provided. Such electronic devices may be configured to acquire two-dimensional (2D) image information about an outdoor environment, generate 3D map information about the outdoor environment based on the 2D image information, and determine a position of a point in the 3D map information corresponding to a query image.

The present disclosure describes a method, apparatus, and storage medium for annotating image. The method includes extracting, by a device, a visual feature of an image through a generative adversarial network model, and sequentially inputting M pieces of random noise into the generative adversarial network model. In response to each of the M pieces of random noise being inputted into the generative adversarial network model, the method includes performing a determinantal point process (DPP) on the visual feature of the image and the each random noise through the generative adversarial network model to obtain N tag subsets, and selecting a distinct tag subset from the N tag subsets through the generative adversarial network model. The method also includes outputting M distinct tag subsets through the generative adversarial network model after the M pieces of random noise are inputted into the generative adversarial network model.

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

A farming machine is configured to identify and treat plants in a field. The farming machine includes one or more light sensors for measuring a characteristic of light. The one or more light sensors are coupled to the farming machine and are directed a substantially upwards orientation away from the plants. A control system adjusts settings of an image acquisition system based on a characteristic of light measured by the one or more light sensors. The image acquisition system captures an image of a plant using one or more image sensors coupled to the farming machine, the one or more image sensors directed in a substantially downwards orientation towards the plants. The control system identifies a plant in the image and actuates a treatment mechanism to treat the identified plant.

A user can confirm the congestion degree of a congestion confirmation area at the present moment by a congestion confirmation system which includes mobile communication devices and a congestion confirmation server. The mobile communication device periodically detects and transmits the current location information to the congestion confirmation server. Receiving the current location information, the congestion confirmation server stores the current location information in a database in association with a current time. The mobile communication device sends a congestion confirmation request designating one of the congestion confirmation areas. In response to this request, the congestion confirmation server refers to the database and transmits the location information of mobile communication devices currently located in the congestion confirmation area as designated. Receiving the location information of the mobile communication devices, the mobile communication device which has sent the congestion confirmation request displays a map including the mobile communication devices.