Abstract: A method for processing images, an electronic device, and a storage medium are provided. A head portrait of a subject is obtained from a camera device. A hair region and a scalp region are identified from the head portrait. A proportion of the scalp region is calculated. The proportion of the scalp region is compared with a preset value, and baldness of the subject is determined accordingly. If found to be bald, complementary color processing is performed by processing the scalp region using a hair color of the hair region, and an updated head portrait is obtained after finishing the complementary color processing. The method automatically detects baldness and supplements the hair color in the scalp region.

Abstract: A floating bridge structure includes a substrate, a floating bridge layer, and at least one support. The floating bridge layer is on the substrate and substantially parallel to an upper surface of the substrate. The support extends on a vertical surface from the substrate to the floating bridge layer, in which the vertical surface is substantially perpendicular to the upper surface of the substrate.

Abstract: A method for making a graphene nanoribbon composite structure includes providing a substrate including a plurality of protrusions spaced apart from each other. A graphene film is grown on a growth substrate, an adhesive layer is on a surface of the graphene film away from the growth substrate. After removing the growth substrate, the graphene film and the adhesive layer are cleaned with water or an organic solvent. The graphene film, the adhesive layer, and the substrate are combined and then are dried, so that a plurality of wrinkles are formed near the plurality of protrusions. The adhesive layer is removed, and after etching a surface of the graphene film away from the substrate, the graphene films except for the plurality of wrinkles are removed, to form a plurality of graphene nanoribbons.

Abstract: A method for generating defective image of products applied in an electronic device includes generating first input data according to flawless sample images and a first noise vector, using an autoencoder as a generator of a Generative Adversarial Network (GAN), inputting the first input data to the generator, and generating images for training in defects. The method further includes calculating a first loss value between the flawless sample images and the defect training images, inputting the defect training images into a discriminator of the GAN, and calculating a second loss value. The method further includes obtaining an optimized GAN and taking the optimized GAN as a defective image adversarial network, obtaining flawless testing images, inputting the flawless testing images and a second noise into a generator of the defective image adversarial network, and generating images of defects by processing the flawless testing images and the second noise.

Abstract: A method for detecting obstacles implemented in an electronic device includes obtaining detection point data from at least one sensor, the detection point data comprising a first position coordinate of each of a plurality of detection points; transforming the first position coordinate of each of the plurality of detection points to a second position coordinate of each of the plurality of detection points relative to an ego vehicle; determining at least one target object according to a plurality of second position coordinates; calculating target object data of the at least one target object according to a second position coordinate of each of the plurality of detection points relative to the ego vehicle; tracking the at least one target object according to the target object data.

Abstract: A semiconductor with 3D flash memory storing cells includes a stack structure in each storing cell, a blocking layer, at least one floating gate layer, a tunnel dielectric layer, and a channel layer. The stack structure includes at least one control gate layer, at least one dielectric layer, and at least one erasing layer. The blocking layer is coplanar with the control gate layer. The floating layer is received in the blocking layer, and insulates the control gate layer by the blocking layers. The tunnel dielectric layer covers sides of the blocking layer and the floating gate layer. The channel layer is placed on a side of the tunnel electric layer. When the storing cell executes a data reading and writing process, a voltage is applied on the erasing layer to reduce a series resistance of the channel layer for rapid conduction by the semiconductor.

Abstract: A floated button and a vehicle screen assembly are provided. The vehicle screen assembly includes a display and the floated button. The floated button includes an adapter board electrically connect to a controller and a power source of the vehicle screen assembly through signal cables; a fixed base includes a receiving space, the adapter board is received in the receiving space, and the fixed base is connected to a rotation motor holder of the vehicle screen assembly; a button supporter arranged on the fixed base; and a button module arranged on the button supporter and floated on a side of the display. The button module includes a plurality of buttons arranged along a broadside extending direction. The buttons are electrically connected to the adapter board, and each of the buttons is configured to transmit corresponding button data to the controller through the adapter board when the button is triggered.

Abstract: A rotary device with automatic reset function for precise stopping and holding of a viewable rotating element without free play or slop remaining includes a conversion assembly, and a power assembly with output element and sensing assembly. The conversion assembly comprises a light-shielding structure with the light-shielding structure on the extension part. The power assembly with output element can drive the extension part to rotate synchronously with the light-shielding structure. The detachable sensing assembly overlaps the rotation path of the light-shielding structure, and the conversion assembly rotates at different angles so that the light-shielding structure and the sensing assembly will match at certain angles, so as to trigger a sensing signal and call up the output element. A display screen includes the rotary device with automatic reset function and a display assembly.

Abstract: A two-parts shell or housing assembly not requiring a pressure jig for assembly includes an upper shell and a lower shell. The upper shell includes a first surface and a second surface. Hooks are disposed on edges of the second surface. The lower shell includes an inner bottom surface and a first blocking surface. A stepped portion is formed at the connection portion between the inner bottom surface and the first blocking surface. The stepped portion includes a first side and a second side. The upper shell is disposed on the second side. The first side defines slots corresponding to the hooks. A receiving space is formed by the hook, the stepped portion, and the second side for receiving adhesive. An electronic device is also provided.

Abstract: A method for detecting defects in product applied in a computer device inputs an image of a product under test to an automatic encoder to obtain a reconstructed image, and the image is segmented into N image blocks and the reconstructed image is segmented into N image blocks. The computer device associates each of the N testing blocks with one reconstructed blocks according to positions of the N testing blocks in the image and positions of the N reconstructed blocks in the reconstructed image. The computer device further calculates mean square errors between each of the N testing blocks and each of the N reconstructed blocks, and associates each mean square error with each of the N testing blocks, whether the product has defects being determined based on the mean square errors corresponding to each of the N testing blocks.

Abstract: A method for detecting defects in multi-scale images and a computing device applying the method acquires a to-be-detected image and converts the to-be-detected image into a plurality of target images of preset sizes. Feature extraction is performed on each target image by using a pre-trained encoder to obtain a latent vector, the latent vector of each target image is inputted into a decoder corresponding to the encoder to obtain a reconstructed image and then into a pre-trained Gaussian mixture model to obtain an estimated probability. Reconstruction error is calculated according to each target image and the corresponding reconstructed image. A total error is calculated according to the reconstruction error of each target image and the corresponding estimated probability, and a detection result is determined according to the total error of each target image and a corresponding preset threshold, thereby improving an accuracy of defect detection.

Abstract: A method of detecting defects revealed in images of products obtains sample image training data. An underlying feature dimension of an autoencoder is selected and a score is obtained. By comparing the score with a standard score, an optimal underlying feature dimension is confirmed. A test image is inputted into the autoencoder with the optimal underlying feature dimension to obtain a reconstruction image. A reconstruction error between the test image and the reconstruction image is computed. By comparing the reconstruction error with the predefined threshold a result of analysis of the test image is outputted. An image defect detection apparatus and a computer readable storage medium applying the method are also provided.

Abstract: A scalp monitoring method applied to a smart hair dryer is provided. The method includes obtaining a plurality of head photos by photographing a head of a user using a photographing device. A head region corresponding to each of the plurality of head photos is identified. Once related parameters of each head region is identified based on the head photos corresponding to the each head region, a condition of a scalp of the each head region is analyzed based on the related parameters of the each head region and historical related parameters corresponding to the each head region.

Abstract: A block operation method, an electronic device, and a storage medium are provided. A plurality of clusters are calculated according to computing power and rewards of each blockchain node, a node computing power and reward carried by each broadcast block being determined when a blockchain node receives a broadcast block. A target cluster to which a blockchain node belongs is determined, and a target difficulty weighting is determined, corresponding to the target cluster. A target difficulty of the target blockchain node is determined according to the node power and reward and the target difficulty weighting. A block is generated after the blockchain calculations are processed according to the target difficulty, the method improves decentralization of the blockchain.

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 processing data, applied in an electronic device, obtains dimensions of a target data set by dimensionality reduction processing, and creates a search index based on the target data set. A denominator d of a noise ratio is set. An unlabeled data point P is selected from the target data set, and neighbors of the data point P are searched for according to the search index to obtain a neighbor data set. A number of data in the neighbor data set of a type which is different from type of the data point P is calculated, the calculated number is set to be a numerator c of the noise ratio. The noise ratio A=c/d is calculated, and if greater than a preset noise ratio, the data point P is labeled as noise data. The present disclosure shortens the time of processing training data.

Abstract: A support assembly and a chassis structure for positioning and support of large and heavy computer cards. The chassis structure includes a housing, a main board in the housing, and a graphics card slot on the main board. The housing includes a positioning frame on the main board and the support assembly on the frame. The support assembly is detachable. The support assembly includes positioning base plate, positioning member, and adjusting member. The positioning member is on a base which is detachable from the positioning frame. The adjusting member is moveable. The positioning member includes a first support portion, the adjusting member includes a second support portion. Depending on card size, either the first support portion or the second support portion abuts against an end side of a graphics card away from the graphics card slot, to position and firmly hold in place graphics cards of different sizes.

Abstract: A waterproof connecting structure includes a first housing, a second housing employs a waterproof adhesive layer between the two. The first housing includes a first mounting member with a first groove, the first groove includes a first recessed area. The second housing includes a second recessed area and a protrusion of a certain shape. The second recessed area surrounds an edge of the second housing, the protrusion is disposed close to the second recessed area, and penetrates into the first recessed area. The waterproof adhesive layer is disposed between the first housing and the second housing, and is accommodated in the first groove and the second recessed area. An electronic device including the waterproof connecting structure is also provided.

Abstract: A system and method for controlling multi-party communication during video conferences communicatively connects with at least two participating terminals, each manned by one participant in the conference. A voice acquisition module acquires speech and video at each participating terminal. A recognition module acquires information as to an action state of a participant in the video and audio of what participant is saying, and determines whether the action of participant includes speaking movements and whether the audio includes what is being said by the participant. A control module can control the volume of other participating terminals and adjust the volumes of microphones of simultaneous speakers, to be louder, fainter, or muted, depending on assigned priority of each participant, for orderly control of conferences and as an overall recording.