Abstract: A method can include receiving a low-resolution (LR) image, extracting a first feature embedding from the LR image, performing a first upsampling to the LR image by a first upsampling factor to generate a upsampled image, receiving a LR coordinate of a pixel within the LR image and a first cell size of the LR coordinate, generating a first residual image based on the first feature embedding, the LR coordinate, and the first cell size of the LR coordinate using a local implicit image function, and generating a first high-resolution (HR) image by combining the first residual image and the upsampled image via element-wise addition.

Abstract: An image processing method may include processing an original image to generate a plurality of activated patches, performing a first scanning operation on the plurality of activated patches according to a first scanning pattern to collect first scan data, performing a second scanning operation on the plurality of activated patches according to a second scanning pattern to collect second scan data, and processing at least the first scan data and the second scan data to generate an improved image. The first scanning pattern is different from the second scanning pattern.

Abstract: A method can include receiving a low-resolution (LR) image, extracting a first feature embedding from the LR image, performing a first upsampling to the LR image by a first upsampling factor to generate a upsampled image, receiving a LR coordinate of a pixel within the LR image and a first cell size of the LR coordinate, generating a first residual image based on the first feature embedding, the LR coordinate, and the first cell size of the LR coordinate using a local implicit image function, and generating a first high-resolution (HR) image by combining the first residual image and the upsampled image via element-wise addition.

Abstract: A neural network restores a low-quality (LQ) image having a given degradation type. The neural network includes a series of adapter layers, each adapter layer including a pre-trained module in parallel with an adapter module. The pre-trained module has been trained in a pre-training phase by images having multiple degradation types, and the adapter module has been trained in a fine-tuning phase subsequent to the pre-training phase by images having the given degradation type. In each adapter layer, a first output of the pre-trained module and a second output of the adapter module are added together to produce an output of the adapter layer. The neural network generates a high-quality (HQ) image restored from the LQ image based on outputs of the adapter layers.

Abstract: A method for determining a probability of a kidney stone in a subject being a uric-acid (UA) stone includes steps of: establishing, by using a machine learning algorithm, a prediction model based on a plurality of training data sets that are related to a plurality of patients, each of the plurality of training data sets at least including an estimated glomerular filtration rate (eGFR) and a value of urine pH; and feeding an input variable set into the prediction model so as to obtain the probability of the kidney stone in the subject being a UA stone. The input variable set is related to the subject and including an eGFR and a value of urine pH of the subject.

Abstract: A method for establishing a model to determine whether a subject has nephrolithiasis includes: grouping training data sets into a number N of preliminary groups, where N is a positive integer; obtaining a number N of preliminary models based on the preliminary groups; averaging the preliminary models to obtain an average model; and obtaining a prediction model for determining whether the subject has nephrolithiasis based on the average model.

Abstract: A method can include receiving a low-resolution (LR) image, extracting a first feature embedding from the LR image, performing a first upsampling to the LR image by a first upsampling factor to generate a upsampled image, receiving a LR coordinate of a pixel within the LR image and a first cell size of the LR coordinate, generating a first residual image based on the first feature embedding, the LR coordinate, and the first cell size of the LR coordinate using a local implicit image function, and generating a first high-resolution (HR) image by combining the first residual image and the upsampled image via element-wise addition.

Abstract: The present invention provides a method of laminating a film for a dye-sensitized cell. First, a composite film is taken by a robotic arm, in which the composite film includes a release layer, a protective layer and a hot glue layer between the release layer and the protective layer, and the release layer is removed by the robotic arm. Then, the hot glue layer is precisely attached to a substrate by a target positioning step. Next, the protective layer is removed by the robotic arm.

Abstract: The present invention provides a method of laminating a film for a dye-sensitized cell. First, a composite film is taken by a robotic arm, in which the composite film includes a release layer, a protective layer and a hot glue layer between the release layer and the protective layer, and the release layer is removed by the robotic arm. Then, the hot glue layer is precisely attached to a substrate by a target positioning step. Next, the protective layer is removed by the robotic arm.

Abstract: A method for conversion treating a surface of a magnesium alloy workpiece includes the following steps: providing a magnesium alloy workpiece, degreasing the magnesium alloy workpiece, acid pickling the magnesium alloy workpiece in a first acid pickling treatment step, surface conditioning the magnesium alloy workpiece in a first surface conditioning treatment step, acid pickling the magnesium alloy workpiece in a second acid pickling treatment step, surface conditioning the magnesium alloy workpiece in a second surface conditioning treatment step, pre-phosphating the magnesium alloy workpiece in a pre-phosphating treatment step, and phosphating the magnesium alloy workpiece in a phosphating treatment step to form a phosphating film on the surface of the magnesium alloy workpiece.

Abstract: A method for conversion treating a surface of a magnesium alloy workpiece includes the following steps: providing a magnesium alloy workpiece, degreasing the magnesium alloy workpiece, acid pickling the magnesium alloy workpiece in a first acid pickling treatment step, surface conditioning the magnesium alloy workpiece in a first surface conditioning treatment step, acid pickling the magnesium alloy workpiece in a second acid pickling treatment step, surface conditioning the magnesium alloy workpiece in a second surface conditioning treatment step, pre-phosphating the magnesium alloy workpiece in a pre-phosphating treatment step, and phosphating the magnesium alloy workpiece in a phosphating treatment step to form a phosphating film on the surface of the magnesium alloy workpiece.

Abstract: An apparatus for converting ultraviolet (UV) light into electricity is disclosed. It includes a UV-light-into-electricity conversion device and an electricity storage. The welding operation zone has a welding apparatus for welding a workpiece. This welding apparatus generates strong UV light during a welding operation. The UV-light-into-electricity conversion device can absorb the UV light and then convert into electricity. Finally, the electricity storage stores the electricity generated by the UV-light-into-electricity conversion device. Hence, it can convert the harmful UV light into useable electricity. The entire structure of this invention is simple. In addition, it is suitable for a fixed operation zone or a movable operation zone during a welding operation.