A computer-executable method relating to weeds, and a computer system. The method comprises: receiving an image (S11); recognizing one or more plants in the image in order to obtain the classification and/or names of the plants, and determining whether the plants are weeds (S12); and in response to determining that at least one plant is a weed, outputting information indicating that the at least one plant is a weed (S13).

A method and device for testing product quality are disclosed. The method for testing product quality comprises: acquiring an image of a product to be tested; testing the image by using a pre-trained neural network model to obtain a testing result output by the neural network model; when the testing result indicates that the product to be tested is a defective product, performing a secondary judgment on the testing result according to position information of defective feature pixels in the image in the testing result, and determining whether the product to be tested is qualified according to a secondary judgment result. The method has high test accuracy, ensures the quality of product and facilitates reducing the labor cost of test.

A fault state detection apparatus includes an input unit and a processing unit. The input unit receives condition monitoring data. The processing unit implements a trained machine learning algorithm to analyze the received condition monitoring data to determine if the received condition monitoring data is associated with a fault state. The trained machine learning algorithm was trained on the basis of a plurality of non-fault state condition monitoring data and associated ground truth information and on the basis of a plurality of fault state condition monitoring data and associated ground truth information. A subset of the plurality of fault state condition monitoring data was generated from one or more non-fault state condition monitoring data. Generation of fault state conditioning monitoring data in the subset of the plurality of fault state condition monitoring data comprises a transformation of non-fault state condition monitoring data to fault state condition monitoring data.

A tire-enhancement product has a container comprising a dissolvable packaging material; and a solute encased in the container that is inert to the solute. The container is configured to be placed in an interior volume of a tire, to which solvent can be added. The container is configured to dissolve when placed in a predetermined solvent, and the solute is configured to mix with the solvent to form a tire-enhancement mixture.

A system and method for statistical analysis of PV photovoltaic panel images for quantifying the percentage of reduction of electricity generation or the percentage of electricity generation of PV photovoltaic panels due to the soiling level of PV photovoltaic panels by means of a system for statistical analysis of PV photovoltaic panel images, using a camera capable of photographing the panels in the visible spectrum, a clean and properly functioning PV photovoltaic panel without the presence of soiling, shading, or operational faults, a string of soiled PV photovoltaic panels to be evaluated, a computer and an method for image analysis.

A learning data generation device that can generate learning data suitable for learning of an identification model. The learning data generation device has a function of cutting out part of first image data as second image data, a function of generating a two-dimensional graphic corresponding to the area of the second image data and representing a pseudo defect, a function of generating third image data by combining the second image data and the two-dimensional graphic, and a function of assigning a label corresponding to the two-dimensional graphic to the third image data. By using the third image data for learning of the identification model, a highly accurate identification model can be generated.

The present disclosure provides an electronic apparatus capable of capturing an image without being affected by an abnormality on a display surface.

The electronic apparatus includes: a display unit; an imaging unit that is disposed on an opposite side to a display surface of the display unit; an abnormality detection unit that detects an abnormality on the display surface; and a display control unit that highlights a position where the abnormality detected by the abnormality detection unit occurs on the display unit.

A process includes: (i) receiving image data, dividing a portion of the image data into a plurality of grids including a first grid, a second grid, a third grid, and a fourth grid, dividing the portion of the image data into a plurality of areas including (a) a first area including the first grid and the third grid, and (b) a second area including the second grid and the fourth grid; (ii) detecting an object relating to the first grid; and (iii) when the object is detected in the first grid, generating a signal for controlling discharge of fluid from a fluid discharging unit so that fluid flows through the first area including the first grid.

The present disclosure relates to a portable production machine, a mobile terminal, a production system having a portable production machine and a mobile terminal, and a method for capturing data. According to aspects of the disclosure, on the production machine - which can be, for example, a welding machine or cutting machine - production data is automatically captured, organized into data sets, and converted into data set images, which are displayed on a display device. The displayed data set images are captured with a mobile terminal and transmitted to a central server. As a result, a plurality of production machines can be incorporated into a data system without the need for any changes to the hardware in the production machines.

Some example embodiments relate to a super resolution scanning electron microscope (SEM) image implementing device and/or a method thereof. Provided a super resolution scanning electron microscope (SEM) image implementing device comprising a processor configured to crop a low resolution SEM image to generate a first cropped image and a second cropped image, to upscale the first cropped image and the second cropped image to generate a first upscaled image and a second upscaled image, and to cancel noise from the first upscaled image and the second upscaled image to generate a first noise canceled image and a second noise canceled image.