Abstract: A flip-flop circuit includes a first inverter configured to receive a first clock signal and output a second clock signal, a second inverter configured to receive the second clock signal and output a third clock signal, a master stage, and a slave stage including a first feedback inverter and a first transmission gate. The first feedback inverter includes a first transistor configured to receive the first clock signal and a second transistor configured to receive the second clock signal, and the first transmission gate includes first and second input terminals configured to receive the second and third clock signals.

Abstract: A control method in use of a flyback power converter is disclosed to provide an operation power source supplying power to a power controller controlling a main power switch. The flyback power converter has a transformer with a primary winding and an auxiliary winding. The main power switch and the primary winding are connected in series. A chopper switch and a buffer inductor are connected in series between the auxiliary winding and the power controller. The power controller turns ON the main power switch for an ON time to energize the transformer, and turns ON the chopper switch for at least a time period during the ON time, so that during the time period the buffer inductor conducts an induced current flowing from the auxiliary winding and through the chopper switch, to build up the operation power source.

Abstract: Circuits, systems, and methods are described herein for increasing a hold time of a master-slave flip-flop. A flip-flop includes circuitry configured to receive a scan input signal and generate a delayed scan input signal; a master latch configured to receive a data signal and the delayed scan input signal; and a slave latch coupled to the master latch, the master latch selectively providing one of the data signal or the delayed scan input signal to the slave latch based on a scan enable signal received by the master latch.

Abstract: A machine learning model training method includes receiving a problem type to be processed, determining a machine learning model corresponding to the problem type, receiving sample data to train the determined machine learning model, analyzing training results of the determined machine learning model obtained after training is completed and displaying the training results that meet preset conditions, and providing a machine learning model corresponding to the training result that meets the preset conditions.

Abstract: An image defect detection method is used in an electronic device. The electronic device determines a training image feature set, and trains a Gaussian mixture model by using the feature set to obtain an image defect detection model and a reference error value. An image for analysis is input into the autoencoder to obtain a second implicit vector and a second reconstructed image, and to calculate a second reconstruction error. The electronic device obtains a test image feature of the image for analysis according to the second reconstruction error and the second implicit vector, and inputs the test image feature into the image defect detection model to obtain a prediction score. The image for analysis is determined to reveal a defect when the prediction score is less than or equal to the reference error value.

Abstract: A primary controller applied to a primary side of a power converter includes a current peak upper limit adjustment circuit and a gate control signal generation circuit. The current peak upper limit adjustment circuit increases a current peak upper limit of the primary side of the power converter when a gate control signal is enabled at a second valley of a voltage, and reduces the current peak upper limit when the gate control signal is enabled at an Nth valley of the voltage, wherein N is a positive integer. The gate control signal generation circuit is coupled to the current peak upper limit adjustment circuit, wherein the gate control signal generation circuit enables the gate control signal, disables the gate control signal according to the current peak upper limit, and the gate control signal is used for turning on a power switch of the primary side of the power converter.

Abstract: A control method in use of a flyback power converter is disclosed to provide an operation power source supplying power to a power controller controlling a main power switch. The flyback power converter has a transformer with a primary winding and an auxiliary winding. The main power switch and the primary winding are connected in series. A chopper switch and a buffer inductor are connected in series between the auxiliary winding and the power controller. The power controller turns ON the main power switch for an ON time to energize the transformer, and turns ON the chopper switch for at least a time period during the ON time, so that during the time period the buffer inductor conducts an induced current flowing from the auxiliary winding and through the chopper switch, to build up the operation power source.

Abstract: A method for determining a plant growth curve includes obtaining color images and depth images of a plant to be detected at different time points, performing alignment processing on each color image and each depth image to obtain an alignment image, detecting the color image through a pre-trained target detection model to obtain a target bounding box, calculating an area ratio of the target bounding box in the color image, determining a depth value of all pixel points in the target boundary frame according to the aligned image, performing denoising processing on each depth value to obtain a target depth value, generating a first growth curve of the plant to be detected according to the target depth values and corresponding time points, and generating a second growth curve of the plant to be detected according to the area ratios and the corresponding time points.

Abstract: A method for determining a height of a plant, an electronic device, and a storage medium are disclosed. In the method, a target image is obtained by mapping an obtained color image with an obtained depth image. The electronic device processes the color image by using a pre-trained mobilenet-ssd network, obtains a detection box appearance of the plant, and extracts target contours of the plant to be detected from the detection box. The electronic device determines a depth value of each of pixel points in the target contour according to the target image. Target depth values are obtained by performing a de-noising on depth values of the pixel points, and a height of the plant to be detected is determined according to the target depth value. The method improves accuracy of height determination of a plant.

Abstract: A package applied to a flyback power converter includes a first lead frame and a second lead frame. The first lead frame connects to at least one component of a primary side of the flyback power converter. An isolation distance exists between the first lead frame and the second lead frame. The primary side of the flyback power converter is galvanically isolated from and communicates with a secondary side of the flyback power converter through capacitive coupling effect between the first lead frame and the second lead frame.

Abstract: A method for training an autoencoder implemented in an electronic device includes obtaining a stereoscopic image as the vehicle is in motion, the stereoscopic image includes a left image and a right image; generating a stereo disparity map according to the left image; generating a predicted right image according to the left image and the stereo disparity map; and calculating a first mean square error between the predicted right image and the right image.

Abstract: A method for detecting medical images implemented in an electronic device includes obtaining at least one image to be detected; obtaining a reconstructed image by inputting the at least one image to be detected as a target image into a pre-trained variational autoencoder model; determining a target area according to pixel values of pixels in the reconstructed image and the target image; obtaining a feature area and a lesion category of the feature area by inputting the target image into a pre-trained convolutional neural network model; when there is a feature area corresponding to the target area in the target image, determining a lesion area and a corresponding lesion category based on the target area and the feature area, and generating a detection result of the image to be detected.

Abstract: A secondary-side control method of a flyback power converter includes a primary controller included in the flyback power converter generating a first gate control signal to turn on a power switch at a first predetermined valley of a first voltage after the primary controller enters a start-up mode; and a secondary controller included in the flyback power converter generating a trigger pulse to a synchronous switch at a second predetermined valley of a second voltage to make the primary controller enter a secondary-side control mode from the start-up mode after the secondary controller detects a first coupling voltage corresponding to the first gate control signal on the second voltage.

Abstract: A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.

Abstract: A flyback power converter includes: a power transformer, a primary side control circuit, a secondary side control circuit, and an active clamp snubber including a snubber switch and a control signal generation circuit. The control signal generation circuit controls the snubber switch to be conductive during a soft switching period in an OFF period of a primary side switch within a switching period of the switching signal, whereby the primary side switch achieves soft switching. A starting time point of the soft switching period is determined by a current threshold, so that a secondary side current is not lower than the current threshold at the starting time point, whereby the secondary side control circuit keeps the SR switch conductive at the starting time point. The secondary side control circuit turns OFF the SR switch when the secondary side current is lower than the current threshold.

Abstract: A flyback power converter circuit includes a transformer, a blocking switch, a primary side switch, a primary side controller circuit and a secondary side controller circuit. The transformer is coupled between an input voltage and an internal output voltage in an isolated manner. The blocking switch controls the electric connection between the internal output voltage and an external output voltage. In a standby mode, the internal output voltage is regulated to a standby voltage, and the blocking switch is controlled to be OFF; in an operation mode, the internal output voltage is regulated to an operating voltage, and the blocking switch is controlled to be ON, such that the external output voltage has the operating voltage. The standby voltage is smaller than the operating voltage, so that the power consumption of the flyback power converter circuit is reduced in the standby mode.

Abstract: A method of detecting product defects obtains an image of a product and sets a region of interest (ROI) of the image. A first contour of a first target object is detected in the region of interest. The image is detected according to the first contour to obtain a corrected image. A position difference between the first contour and a second target object in the region of interest is obtained. A second contour of the second target object is detected in the corrected image according to the position difference. A first image area corresponding to the first contour and a second image area corresponding to the second contour are segmented and input into an autoencoder. According to outputs of the autoencoder, whether the product is defective is determined. A detection result of the product is output. The method can detect defects on products quickly and accurately.

Abstract: A method for detecting defects in images, is employed in a computer device, and stored in a storage medium. The method trains an autoencoder model using unblemished images, inputting an image to be detected into the autoencoder model, and obtaining a reconstructed image. An image error is calculated between the image to be detected and the reconstructed image, and the image error is inputted into a student's t-distribution and a calculation result is obtained. In response that the calculation result falls within a preset defect determination criterion range, the image to be detected is determined to be an unblemished image. In response that the calculation result does not fall within the preset defect determination criterion range, the image to be detected is determined to be a defective image. The method improves the efficiency and accuracy of defect detection.

Abstract: A method for detecting cells in images using an autoencoder, a computer device, and a storage medium extracts a first feature vector from each of a plurality of sample medical images. The first feature vector is inputted into an autoencoder, and a first latent feature of each of the plurality of sample medical images is extracted. A first predicted value of a number of cells in each of the plurality of sample medical images is generated based on the first latent feature. The first latent feature is inputted into the autoencoder, and a plurality of reconstructed images are obtained. The autoencoder is optimized based on the plurality of reconstructed images and the first predicted value. This method can be run in the computer device to improve efficiency of detection from images.

Abstract: A method for selecting a deep learning network which is optimal for solving an image processing task obtaining a type of the image processing task, selecting a data set according to the type of problem, and dividing selected data set into training data and test data. Similarities between different training data are calculated, and a batch size of the training data is adjusted according to the similarities of the training data. A plurality of deep learning networks is selected according to the type of problem, and the plurality of deep learning networks is trained through the training data to obtain network models. Each of the network models is tested through the test data, and the optimal deep learning network with the best test result is selected from the plurality of deep learning networks appropriate for image processing.