Abstract: A method for detecting a tumor from images which are required to be shrunken in resolution obtains one or more first images. Then, the method segments or divides the detection images into a number of detection image blocks according to an input size of training data of a convolutional neural network architecture, before segmenting, each of the plurality of detection image blocks comprising coordinate values. The detection image blocks are input into a preset tumor detection model to generate image blocks of a result of the detection images. The method merges the image blocks into a single image according to the coordinate values of each detection image block. Colors of normal areas, abnormal areas, and overlapping areas of the abnormal areas are all different. The method generates a final detection according to color depths in the image. A tumor detection device and a non-transitory storage medium are provided.

Abstract: For the benefit of pedestrians, a method for identifying and locating positions of obstacles moving on a pedestrian sidewalk acquires an image of the sidewalk and processes the image to divide it. The divided image comprises classifications of objects in the image on a pixel by pixel basis. The classifying of objects in the divided image comprises the sidewalk classification, and classification of the obstacles appears in the image. Pixels surrounding the obstacles are acquired in terms of number and classifications. Positions of the obstacles are determined based on a preset threshold, the classifications of adjacent pixels of the obstacles, and the pixel number of the adjacent pixel in each object classification. An apparatus and a system applying the method are also disclosed.

Abstract: A method for optimizing the conversion of a deep learning model to process other data, applied in a device, includes converting a first deep learning model to obtain a second deep learning model, obtaining a weighting arrangement of the two models according to their deep learning frameworks and performing a quantization on the two models. A similarity in weighting between the two models is analyzed to produce a weighting analysis based on the first and second weighting arrangement and the first and second model quantization result weighting. The two models are tested to establish a model performance analysis. One or more suggestions for optimization are obtained based on the weighting analysis and the model performance analysis, and are applied to optimize the second deep learning model, an optimized second deep learning model being employed to process the other data.

Abstract: A data labeling model training method, an electronic device employing the method, and a storage medium are provided. The method acquires medical image data. An improved quality of the medical image data to be used for training the data labeling model is obtained by filtering the medical data, so as to enable training with higher-quality training material. The data labeling model is used to label medical data with improved efficiency and accuracy.

Abstract: An image processing method and an electronic device are disclosed, the method acquires training data in response to receiving an image processing instruction and trains a deep learning model with the training data by using a preset deep learning framework to obtain an initial model. A data type of the initial model is converted to increase the data processing speed of the model. A correction layer is added to the converted initial model and the initial model is optimized by training weights of the correction layer to obtain an image processing model to further increase the data processing speed. After acquiring an image from the image processing instruction, the image processing model can be used to process the image and an image processing result can be outputted. The image is processed based on the optimized image processing model, and the data processing speed and accuracy are guaranteed.

Abstract: A product defect detection method which includes acquiring a detection image of a product to be detected is provided. The method further includes dividing the detection image into a first preset number of detection blocks. Once a detection result of each detection block is obtained by inputting each detection block into a preset defect recognition model, according to a position of each detection block in the detection image, a detection result of the product is determined according to the detection result of each detection block.

Abstract: A method for automatically selecting a suitable network model for machine deep learning includes identifying a category of an input content and selecting a training set based on the identified category. Several matched network models are selected based on the identified category. A test network model is selected based on performance data corresponding to the matched network models, and the selected model is trained based on the input content and the selected training set. When an output result in a display interface is not satisfactory, an optimization operation is executed for adjusting the structure of the test network model. An apparatus, an electronic device, and a storage medium applying the method are also disclosed.

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: 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.

Abstract: A method for processing data based on a neural network trained by different methods includes: dividing sample data into a training set and a test set; training a predetermined neural network to obtain a first detection model based on the training set; testing the first detection model based on the test set to count a first precision rate; cleaning the training set and the test set according to selected cleaning method; adjusting the first detection model by a predetermined rule and training adjusted first detection model based on cleaned training set to obtain a second detection model; testing the second detection model based on cleaned test set to count a second precision rate; selecting the first detection model or the second detection model as a final detection model based on a comparison between the first precision rate and the second precision rate.

Abstract: A method for blood serum protein activity preservation is provided. The method comprises the steps of mixing blood serum with one or more protectants selected from the group consisting of albumin, triglyceride, glycerol, dextran, propylene glycol, galactose, alginate, and trehalose; and lyophilizing the mixture.

Abstract: A method for detecting a tumor from images which are required to be shrunken in resolution obtains one or more first images. Then, the method segments or divides the detection images into a number of detection image blocks according to an input size of training data of a convolutional neural network architecture, before segmenting, each of the plurality of detection image blocks comprising coordinate values. The detection image blocks are input into a preset tumor detection model to generate image blocks of a result of the detection images. The method merges the image blocks into a single image according to the coordinate values of each detection image block. Colors of normal areas, abnormal areas, and overlapping areas of the abnormal areas are all different. The method generates a final detection according to color depths in the image. A tumor detection device and a non-transitory storage medium are provided.

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 method for accelerating deep learning includes calling up an entire deep learning architecture. Such architecture includes a data operation program of a convolutional layer and a data operation program of a fully connecting layer. A data operation program of the convolutional layer is obtained, the data operation program of the fully connecting layer is discarded, and the data operation program of the convolutional layer is loaded to a first processor. The data operation program for the fully connecting layer is then applied in similar manner to a second processor of the user terminal, the second processor continuing to perform operations on the fully connecting layer, thereby completing the entire deep learning architecture and training on the user terminal.

Abstract: A method of extracting nutrients from a plant includes the steps of: pulverizing a water soluble nutrient-based plant part of a first plant material of the plant so as to form a first pulverized plant part; pulverizing a lipid soluble nutrient-based plant part of a second plant material of the plant so as to form a second pulverized plant part; subjecting the first pulverized plant part to a distillation so as to obtain a distillate and a first residue that contains a water soluble nutrient; and immersing the second pulverized plant part in the distillate to form a first mixture followed by distillation of the first mixture, so as to obtain a second residue that contains a lipid soluble nutrient.

Abstract: A method for blood plasma protein activity preservation is provided. The method comprises the steps of mixing blood plasma with two or more protectants selected from the group consisting of triglyceride, glycerol, propylene glycol, alanine, serine, glycine, alginate, and sucrose to obtain a mixture; and lyophilizing the mixture.

Abstract: The present invention provides a stirrer set, comprising: a fitting tube having an opening portion and a hollow rotating shaft fitted into the opening portion, the hollow rotating shaft having an external wall which is annularly recessed to form at least one annular groove. The present invention also provides a stirring device, wherein a drive element comprising a power transmission element, such as a belt, may be rotatably driven to cause, via the power transmission element, the stirrer set to rotate for performing a stirring function. As such, the stirring device provides higher design flexibility when dealing with high-flux samples, and the element replacement rates can be significantly reduced. Thus, the costs related to manufacturing, repair, maintenance and other processes can be reduced in an effective manner.

Abstract: A method of extracting nutrients from a plant includes the steps of: pulverizing a water soluble nutrient-based plant part of a first plant material of the plant so as to form a first pulverized plant part; pulverizing a lipid soluble nutrient-based plant part of a second plant material of the plant so as to form a second pulverized plant part; subjecting the first pulverized plant part to a distillation so as to obtain a distillate and a first residue that contains a water soluble nutrient; and immersing the second pulverized plant part in the distillate to form a first mixture followed by distillation of the first mixture, so as to obtain a second residue that contains a lipid soluble nutrient.

Abstract: A method of stabilizing thrombin in a thrombin solution is provided. The method comprises heating the thrombin solution at a temperature of 35-85° C. for 1 to 20 seconds, and quenching the heating. Also provided is a stabilized thrombin composition. The stabilized thrombin composition is characterized in that it is prepared by heating a thrombin solution at a temperature of 35-85° C. for 1 to 20 seconds, and quenching the heating. The thrombin solution may be further lyophilized to obtain a stabilized thrombin composition in lyophilized form.

Abstract: The invention is directed to inter-helix inductor devices. The inter-helix inductor device includes a dielectric substrate. An input end is disposed on the first surface of the dielectric substrate. A clockwise winding coil has one end connecting to the input end and at least one winding turn through the dielectric substrate. A counter clockwise winding coil includes at least one winding turn through the dielectric substrate, wherein the clockwise and counter clockwise winding coils are connected by an interconnection. An output end is disposed on the dielectric substrate, connects one end of the counter clockwise winding coil, and is adjacent to the input end.