Abstract: A pixel circuit and a display panel. The pixel circuit includes a driving circuit, a data write circuit, a storage circuit, and a first reset circuit; the driving circuit includes a control end, a first end, and a second end, and is configured to control a driving current flowing through the first end and the second end for driving a light-emitting element to emit light; the first reset circuit is configured to apply a first initialization voltage to the control end of the driving circuit under the control of a first reset control signal; and the first reset circuit includes an N-type oxide thin film transistor; where the pixel circuit further includes a third reset circuit configured to apply a holding voltage to the first terminal of the driving circuit under a control of a third reset control signal.

Abstract: A pixel circuit and a driving method therefor and a display panel are provided. The pixel circuit includes a driving circuit, a data writing circuit, a storage circuit, and a first reset circuit; the driving circuit includes a control terminal, a first terminal, and a second terminal, and is configured to control a driving current flowing through the first terminal and the second terminal for driving a light-emitting element to emit light; the data write circuit is configured to write a data signal into the control terminal of the driving circuit; the storage circuit is configured to store the data signal; the first reset circuit is configured to apply a first initialization voltage to the control terminal of the driving circuit; the driving circuit and the data write circuit each include an N-type thin film transistor; and the first reset circuit includes an N-type oxide thin film transistor.

Abstract: A pixel driving circuit includes: a data writing circuit, a compensation control circuit, a light emission control circuit, a voltage regulation circuit, a driving transistor, the compensation control circuit is connected with the driving transistor at a first node, the compensation control circuit is connected with the data writing circuit at a second node, the compensation control circuit, the light emission control circuit, the voltage regulation circuit are connected with the driving transistor at a third node; the compensation control circuit obtains a threshold voltage of the driving transistor, writes a third voltage into the second node, and writes a light emission voltage into the first node according to a variation in a voltage at the second node and the threshold voltage; the voltage regulation circuit maintains a voltage at the third node stable during the compensation control circuit writing the light emission voltage into the first node.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The display panel includes a substrate and a plurality of light-emitting units disposed on the substrate, wherein the light-emitting units includes a plurality of light-emitting devices arranged in an array, and a barrier is disposed between neighboring light-emitting devices; a dimming layer disposed on the light-exiting side of light-emitting unit, wherein the dimming layer includes a plurality of first refracting portions arranged in an array, and a second refracting portion is disposed between neighboring first refracting portions and contacted with the first refracting portions; and orthographic projections of the first refracting portions on the substrate overlap with an orthographic projection of the barrier on the substrate, and an orthographic projection of the second refracting portions on the substrate overlaps with orthographic projections of the light-emitting devices on the substrate.

Abstract: A pixel driving circuit, a method for driving the same, and a display panel are provided. The pixel driving circuit includes a driving circuit (1) and an initialization circuit (2). The driving circuit (1) is connected to a first node (N1), a second node (N2), and a third node (N3). The initialization circuit (2) is connected to the first node (N1), the initialization circuit (2) is further connected to the second node (N2) and/or the third node (N3), and the initialization circuit (2) is configured to provide a first initialization voltage to the first node (N1), provide a first reset voltage to the second node (N2) and/or provide a second reset voltage to the third node (N3), so as to control a driving transistor (T3) in the driving circuit (1) to be turned on.

Abstract: A pixel driving circuit includes: a data writing circuit, a compensation control circuit, a light emission control circuit, a voltage regulation circuit, a driving transistor, the compensation control circuit is connected with the driving transistor at a first node, the compensation control circuit is connected with the data writing circuit at a second node, the compensation control circuit, the light emission control circuit, the voltage regulation circuit are connected with the driving transistor at a third node; the compensation control circuit obtains a threshold voltage of the driving transistor, writes a third voltage into the second node, and writes a light emission voltage into the first node according to a variation in a voltage at the second node and the threshold voltage; the voltage regulation circuit maintains a voltage at the third node stable during the compensation control circuit writing the light emission voltage into the first node.

Abstract: A pixel circuit and a driving method therefor and a display panel are provided. The pixel circuit includes a driving circuit, a data writing circuit, a storage circuit, and a first reset circuit; the driving circuit includes a control terminal, a first terminal, and a second terminal, and is configured to control a driving current flowing through the first terminal and the second terminal for driving a light-emitting element to emit light; the data write circuit is configured to write a data signal into the control terminal of the driving circuit; the storage circuit is configured to store the data signal; the first reset circuit is configured to apply a first initialization voltage to the control terminal of the driving circuit; the driving circuit and the data write circuit each include an N-type thin film transistor; and the first reset circuit includes an N-type oxide thin film transistor.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: A dexrabeprazole sodium compound includes crystal forms B and C. The crystal form B has good stability and flowability. The crystal form C is an anhydrous crystal form and has good stability and low hygroscopicity. The crystal forms are suitable for preparing a dexrabeprazole sodium preparation.

Abstract: A dexrabeprazole sodium compound includes crystal forms B and C. The crystal form B has good stability and flowability. The crystal form C is an anhydrous crystal form and has good stability and low hygroscopicity. The crystal forms are suitable for preparing a dexrabeprazole sodium preparation.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.