It selects learning data that is effective for learning a learning model. An identification information assignment device comprising: a processor; and memory, wherein, using the memory, the processor: acquiring a plurality of image data; selecting a part of the plurality of image data as learning data; assigning identification information to the selected image data by using a learning model which is recorded in the memory; and updating the learning model by using the selected image data to which the identification information is assigned, wherein identification information is assigned to a rest of the plurality of image data by using the updated learning model, the rest of the plurality of image data being different from the selected image data.

Disclosed are methods, devices, and computer-readable media for operating paired or grouped drone devices. In one embodiment, a method is disclosed comprising capturing a first image by a camera installed on a first drone device; processing the first image using an artificial intelligence (AI) engine embedded in the first drone device, the processing comprising generating a first inference output; transmitting the first inference output to a second drone device; receiving a second inference output from the second drone device, the second inference output associated with a second image captured by the second drone device; and transmitting the first image to a processor based on the first inference output and second interference output.

An apparatus, methods and systems for data collection in an industrial environment are disclosed. A monitoring system can include a data collector coupled to a plurality of sensors to collect data, a data storage structured to store a plurality of data collection management plans, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data and select one of the plurality of data collection management plans, wherein the selected one of the plurality of data collection management plans is selected is at least in part based on a data analysis of received data from the plurality of sensors.

An image object classification optimizing method and system are disclosed. The method is executed by a processor coupled to a memory. The method includes steps: providing an image file including at least one image object; performing a process of characteristics enhancement on the image object; performing a process of characteristics classification on the enhanced image object by an odd number of two-dimensional masks whose sizes are sequentially doubled, based on a plurality of characteristic parameters of a preferred classification model, to generate a plurality of classification results; and estimating variabilities of the plurality of classification results, sorting the variabilities, and selecting at least one of the classification results whose variability is lower than a variation tolerance as at least one optimization result, according to the sorting result.

The present technology provides systems, methods and computer program instructions implementing machine learning techniques to enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided. In implementations an automated technology for automated creation of movement assessments from labeled video and continually learning audio, video or other feedback for use with machine learning techniques enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided.

A method of ascertaining disparities in sensor data uses at least one neural network implemented in a controller of a vehicle. The method involves capturing (101) a learning data record from temporally successive raw sensor data, evaluating (102) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining (103) expected sensor data, comparing (104) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining (105) a disparity between the currently captured sensor data and the ascertained expected sensor data.

Various embodiments of the present disclosure relate to an electronic device and a feedback information acquisition method therefor. The feedback information acquisition method of the electronic device includes: acquiring input feedback information of a user and first response information of the user, which are related to a specific function; training a feedback estimation model by using the input feedback information and the first response information; acquiring second response information of the user related to the specific function; and acquiring feedback information related to the specific function by applying the second response information to the trained feedback estimation model.

A data cleaning method is provided. The method incudes: acquiring a training face dataset, the training face dataset including a plurality of training images each labeled with a person ID; acquiring a candidate face verification model and training the candidate face verification model by the plurality of training images; acquiring a plurality of feature embeddings from the candidate face verification model after training, and determining a similarity between the average feature embedding of one person ID and each image labeled as the same person ID; extracting at least one training image whose similarity is smaller than a similarity threshold from the plurality of training images; and excluding the at least one extracted training image in response to receiving a confirmation signal.

Systems and methods for road typology scene annotation are provided. A method for road typology scene annotation includes receiving an image having a road scene. The image is received from an imaging device. The method populates, using a machine learning model, a set of attribute settings with values representing the road scene. An annotation interface is implemented and configured to adjust values of the attribute settings to correspond with the road scene. Based on the values of the attribute settings, a simulated overhead view of the respective road scene is generated.

Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.