Abstract: Bumpers for maintaining a gap between a light sheet and a translucent material in backlighting applications, as well as installation kits comprising light sheets and such bumpers. In some embodiments, the bumper comprises a base part having a central body, one or more bumper members, and at least one arm section which extends radially outward from the central body and connects to a respective bumper member opposite the central body. The central body may have an opening for receiving a fastener therethrough, with each bumper further comprising a cap part for covering the opening. Bumper member profiles are also specified with respect to height, radial extension, and convex curvature parameters.

Abstract: Rack-type backlight light emitting element (LEE) printed circuit boards (PCBs) with at least two layers to provide redundant current paths. This enables a PCB strip to be broken at designated perforations in the field in order to accommodate pipes or fixtures that require formation of a gap in the rack array. The entire length and width of an array can be sized in the field. A plurality of PCB arrays can be serially chained together with the system. A chain of PCB strips is snapped into clips, which in turn snap into rails that are fastened to a surface. The mounting system includes various types of clips, such as end clips, clips for different LEE patterns on the PCBs, and mounting clips to secure the rails to a surface.

Abstract: Embodiments of the present disclosure provide a method and a device for detecting defect of a meal box, a server, a device, and a storage medium. The method includes: receiving a detection request including an image of the meal box sent by a user terminal, the image of the meal box being obtained by an image acquirer of the user terminal; and performing defect recognition based on the image of the meal box and a defect detection model in response to the detection request.

Abstract: Provided are a method for detecting display screen quality, an apparatus, an electronic device and a storage medium. The method includes: receiving a quality detection request sent by a console deployed on a display screen production line, the quality detection request including a display screen image collected by an image collecting device on the display screen production line; inputting the display screen image into a defect detection model to obtain a defect detection result, the defect detection model being obtained by training historical defective display screen images using a structure of deep convolutional neural networks and an object detection algorithm; and determining, according to the defect detection result, a defect on a display screen corresponding to the display screen image, a defect category corresponding to the defect, and a position corresponding to the defect.

Abstract: Rack-type backlight light emitting element (LEE) printed circuit boards (PCBs) with at least two layers to provide redundant current paths. This enables a PCB strip to be broken at designated perforations in the field in order to accommodate pipes or fixtures that require formation of a gap in the rack array. The entire length and width of an array can be sized in the field. A plurality of PCB arrays can be serially chained together with the system. A chain of PCB strips is snapped into clips, which in turn snap into rails that are fastened to a surface. The mounting system includes various types of clips, such as end clips, clips for different LEE patterns on the PCBs, and mounting clips to secure the rails to a surface.

Abstract: A display screen quality detection method, an apparatus, an electronic device and a storage medium. The method includes receiving a quality detection request sent by a console deployed on a display screen production line, where the quality detection request includes a display screen image captured by an image capturing device on the display screen production line, performing image preprocessing on the display screen image, and inputting the preprocessed display screen image into a defect detection model to obtain a defect detection result, where the defect detection model is obtained by training with a historical defect display screen image using a deep convolutional neural network structure and an instance segmentation algorithm, determining, according to the defect detection result, quality of a display screen corresponding to the display screen image. The technical solution has high defect detection accuracy, good system performance, and high business expansion capability.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

Abstract: A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.

Abstract: LED lighting sheets feature foldable light-sheets including multiple illumination modules electrically interconnected such that unremoved illumination units remain energized upon removal of one or more by horizontal, vertical and or diagonal cuts. A new and non-obvious conductor geometry forms a 3D mesh that is resistant to cutting from any direction in 45 degree increments. Relatively constant illumination levels are maintained in the remaining LEE's by using relatively high resistance series connected current limiting resistors for each LEE. Each current limiting resistor may be, for example, ten times the resistance of the entire lower ribbon conductive layer. Tunable color LEE's are included.

Abstract: Provided are a method and an apparatus for component fault detection based on an image, and a specific implementation is: when it is determined that an image shot by an image pickup apparatus for a component to be tested with a first shooting parameter does not meet a preset condition, adjusting the first shooting parameter to a second shooting parameter; controlling the image pickup apparatus to shoot for the component to be tested with the second shooting parameter to obtain a first image that meets the preset condition; and performing fault detection on the component to be tested according to the first image. The image pickup apparatus can be adjusted in real time, so that the image can be used for fault detection only when meeting the preset condition, thereby the image is kept stable, and the accuracy rate of component fault identification based on an image is improved.

Abstract: LED lighting sheets feature foldable light-sheets including multiple illumination modules electrically interconnected such that unremoved illumination units remain energized upon removal of one or more by horizontal, vertical and or diagonal cuts. A new and non-obvious conductor geometry forms a 3D mesh that is resistant to cutting from any direction in 45 degree increments. Relatively constant illumination levels are maintained in the remaining LEE's by using relatively high resistance series connected current limiting resistors for each LEE. Each current limiting resistor may be, for example, ten times the resistance of the entire lower ribbon conductive layer. Tunable color LEE's are included.

Abstract: The method and apparatus for detecting a peripheral circuit of a display screen provided by the present disclosure receive a quality detection request sent by a console deployed on a production line of the peripheral circuit of the display screen, where the quality detection request includes a peripheral circuit image of the display screen captured by an image capturing device on the production line of the peripheral circuit of the display screen; zoom in or out on the peripheral circuit image of the display screen to obtain an image to be detected a size of which is consistent with an input size requirement of a defect detection model; input the image to be detected into the defect detection model to obtain a defect detection result; and determine quality of the peripheral circuit of the display screen according to the defect detection result.

Abstract: The disclosure provides a method for detecting a display screen peripheral circuit, by receiving a quality detection request sent by a console deployed on a production line of the display screen peripheral circuit, where the quality detection request includes an image of the display screen peripheral circuit captured by an image capturing device on the production line of the display screen peripheral circuit; enlarging or reducing the image of the display screen peripheral circuit to obtain an image to be detected, where the defect detection model is obtained by using a historical image of a defective display screen peripheral circuit to perform object detection FAST RCNN algorithm training; inputting the image to be detected into the defect detection model to obtain a defect detection result; and determining, according to the defect detection result, quality of a display screen peripheral circuit corresponding to the image of the display screen peripheral circuit.

Abstract: Provided are a method for detecting display screen quality, an apparatus, an electronic device and a storage medium. The method includes: receiving a quality detection request sent by a console deployed on a display screen production line, the quality detection request including a display screen image collected by an image collecting device on the display screen production line; inputting the display screen image into a defect detection model to obtain a defect detection result, the defect detection model being obtained by training historical defective display screen images using a structure of deep convolutional neural networks and an object detection algorithm; and determining, according to the defect detection result, a defect on a display screen corresponding to the display screen image, a defect category corresponding to the defect, and a position corresponding to the defect.

Abstract: The present disclosure provides a display screen quality detection method, an apparatus, an electronic device and a storage medium, where the method comprises: receiving a quality detection request sent by a console deployed on a display screen production line, where the quality detection request includes a display screen image captured by an image capturing device on the display screen production line, performing image preprocessing on the display screen image, and inputting the preprocessed display screen image into a defect detection model to obtain a defect detection result, where the defect detection model is obtained by training with a historical defect display screen image using a deep convolutional neural network structure and an instance segmentation algorithm, determining, according to the defect detection result, quality of a display screen corresponding to the display screen image.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

Abstract: The present disclosure provides a method for preparing heterogeneous hematopoietic stem and progenitor cells using non-mobilized peripheral blood, which uses a capsule culture system to capture and proliferate rare hematopoietic stem and progenitor cells in non-mobilized peripheral blood, and prepares heterogeneous hematopoietic stem and progenitor cell clones. The present disclosure captures the rare heterogeneous stem cells in non-mobilized peripheral blood and morphologically verifies the presence of heterogeneous hematopoietic stem and progenitor cells in non-mobilized peripheral blood. The method of the present disclosure has the characteristics of hematopoietic reconstitution, drug development, transplantation and immunotherapy, gene editing of cell types, and the like. The method of the present disclosure provides a reliable cell source for patient-specific functional hematopoietic stem cells, and actively promotes the clinical application of non-mobilized hematopoietic stem and progenitor cells.

Abstract: LED lighting sheets feature foldable light-sheets including multiple illumination modules electrically interconnected such that unremoved illumination units remain energized upon removal of one or more by horizontal, vertical and or diagonal cuts. A new and non-obvious conductor geometry forms a 3D mesh that is resistant to cutting from any direction in 45 degree increments. Relatively constant illumination levels are maintained in the remaining LEE's by using relatively high resistance series connected current limiting resistors for each LEE. Each current limiting resistor may be, for example, ten times the resistance of the entire lower ribbon conductive layer. Tunable color LEE's are included.

Abstract: Embodiments of the present disclosure provide a method and a device for detecting defect of a meal box, a server, a device, and a storage medium. The method includes: receiving a detection request including an image of the meal box sent by a user terminal, the image of the meal box being obtained by an image acquirer of the user terminal; and performing defect recognition based on the image of the meal box and a defect detection model in response to the detection request.

Abstract: A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.