Abstract: Provided are a three-phase power conversion circuit for a household electrical appliance, a household electrical appliance, and a power-on control method for a household electrical appliance. The three-phase power conversion circuit includes a three-level active PFC unit, at least two power-on units, and a control unit. The three-phase power, subsequent to being converted by the three-level active PFC unit, obtains a first DC power and a second DC power to supply power to corresponding loads. The power-on unit is arranged at input terminals of the three-level active PFC unit to buffer a device in the three-level active PFC unit when the three-level active PFC unit is powered on. A first controllable switch module and a second controllable switch module in the power-on unit and the three-level active PFC unit are controlled by the control unit to complete buffering.

Abstract: Provided are a method for controlling a full bus load to discharge in a household electrical appliance and related apparatuses. In the method, in response to determining that a half bus load is turned on and a full bus load is turned off, a voltage difference between a first DC power and a second DC power is determined to obtain a voltage difference. The first DC power and the second DC power are obtained by converting three-phase power through a three-level active PFC unit. Subsequent to the voltage difference between the first DC power and the second DC power being determined, a full bus load is controlled to be turned on or off based on the voltage difference to stabilize a half bus voltage in a predetermined voltage interval.

Abstract: The present disclosure provides methods and apparatuses for multi-screen management. In some implementations, a plurality of connection requests may be received from a plurality of screen devices respectively. A screen host unit corresponding to each screen device may be created. A connection of each screen host unit to a corresponding screen device may be established. Screen display data may be transmitted, by at least one screen host unit, to at least one corresponding screen device through at least one corresponding connection respectively. Moreover, in some implementations, a connection request may be transmitted to a screen management hub. A connection to a corresponding screen host unit created by the screen management hub may be established. Screen display data may be received from the screen host unit through the connection. The screen display data may be displayed.

Abstract: The present disclosure provides methods and apparatuses for multi-screen management. In some implementations, a plurality of connection requests may be received from a plurality of screen devices respectively. A screen host unit corresponding to each screen device may be created. A connection of each screen host unit to a corresponding screen device may be established. Screen display data may be transmitted, by at least one screen host unit, to at least one corresponding screen device through at least one corresponding connection respectively. Moreover, in some implementations, a connection request may be transmitted to a screen management hub. A connection to a corresponding screen host unit created by the screen management hub may be established. Screen display data may be received from the screen host unit through the connection. The screen display data may be displayed.

Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Abstract: The present disclosure provides methods and apparatuses for multi-screen management. In some implementations, a plurality of connection requests may be received from a plurality of screen devices respectively. A screen host unit corresponding to each screen device may be created. A connection of each screen host unit to a corresponding screen device may be established. Screen display data may be transmitted, by at least one screen host unit, to at least one corresponding screen device through at least one corresponding connection respectively. Moreover, in some implementations, a connection request may be transmitted to a screen management hub. A connection to a corresponding screen host unit created by the screen management hub may be established. Screen display data may be received from the screen host unit through the connection. The screen display data may be displayed.

Abstract: A method for fabricating a gate-all-around (GAA) structure, including: etching a superlattice laminate to form active regions; performing selective epitaxy growth of a silicon germanium (SiGe) layer to form a SiGe-wrapped Si nanosheet stacked structure, where the SiGe layer and the SiGe/Silicon (Si) periodic superlattice laminate have the same germanium (Ge) content; after silicon oxide is backfilled and chemical mechanical polishing (CMP) is performed on the active regions, performing an amorphous-silicon dummy-gate process on a top of the active regions; removing dummy gate, and selectively etching the SiGe layer; and forming hole-trench structures connected with trenches of the dummy gate around the Si nanosheet stacked structure.

Abstract: A method for fabricating a vertical channel nanowire transistor with asymmetric stress distribution includes: (A) growing epitaxially a single-crystal material on a substrate; forming a laminate of a bottom source-drain material and a channel material; and generating a vertical uniaxial stress in the lightly-doped channel layer; (B) forming an inter-device isolation in an active layer; (C) forming a vertical channel by patterning; (D) depositing a dielectric layer to form a bottom gate isolation; (E) depositing a dummy gate layer followed by patterning to form a dummy gate pattern; (F) depositing a dielectric layer to form a top gate isolation; (G) patterning the top gate isolation; and forming a top source-drain by epitaxy growth; (H) removing a dummy gate; and forming a gate oxide layer and a metal gate; and (I) forming metal contact at individual ends of the device.

Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Abstract: Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.

Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Abstract: The present disclosure provides a method and apparatus for recognizing a text, a device and a storage medium, and relates to the field of deep learning technology. A specific implementation comprises: receiving a target image; performing a text detection on the target image using a pre-trained lightweight text detection network, to obtain a text detection box; and recognizing a text in the text detection box using a pre-trained lightweight text recognition network, to obtain a text recognition result.

Abstract: The present disclosure provides a method and apparatus for training an image recognition model, and a method and apparatus for recognizing an image, and relates to the field of artificial intelligence, and particularly to the fields of deep learning and computer vision. A specific implementation comprises: acquiring a tagged sample set, an untagged sample set and a knowledge distillation network; and performing following training steps: selecting an input sample from the tagged sample set and the untagged sample set, and accumulating a number of iterations; inputting respectively the input sample into a student network and a teacher network of the knowledge distillation network to train the student network and the teacher network; and selecting an image recognition model from the student network and the teacher network, if a training completion condition is satisfied.

Abstract: There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.

Abstract: The present disclosure provides a method and apparatus of processing an image, a device and a medium, which relates to a field of artificial intelligence, and in particular to a field of deep learning and image processing. The method includes: determining a background image of the image, wherein the background image describes a background relative to characters in the image; determining a property of characters corresponding to a selected character section of the image; replacing the selected character section with a corresponding section in the background image, so as to obtain an adjusted image; and combining acquired target characters with the adjusted image based on the property.

Abstract: The present invention provides a chitooligosaccharide-N-geraniol derivative and a preparation method and application thereof. The degree of substitution of a chitooligosaccharide-N-geraniol derivative is from 0.26 to 0.283. The method includes dissolving oligosaccharide in dimethyl sulfoxide, dissolving geranyl bromide in dimethylformamide, and then mixing, performing a water bath reaction and adding acetone in after the water bath reaction, performing a centrifugation and collecting precipitates, performing Soxhlet extraction and vacuum drying to obtain the chitooligosaccharide-N-geraniol derivative. The present invention has the advantages of easy preparation method, low cost, simple purification method and stable properties. The chitooligosaccharide derivative has good water solubility, antibacterial activity against Staphylococcus aureus, Escherichia coli and other bacteria, and has good application prospect in the fields of medicine, food, cosmetics, agriculture, etc.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for generating communicable linkages that collectively provide a cloud-based dynamic credential selection and implementation system. The system generates a user profile containing user preferences, historic trends, and user resources based on information collected from a user and user associated accounts. The dynamic credential selection and implementation system is triggered by the user signaling the system of a proposed transaction. The system requests updated resource characteristics from resource managers. The user profile, resource characteristics and transaction characteristics are evaluated and ranked by the dynamic credential selection and implementation system before completion of the transaction, which in some embodiments requires user approval of the selected resource.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for generating communicable linkages that collectively provide a cloud-based dynamic credential selection and implementation system. The system generates a user profile containing user preferences, historic trends, and user resources based on information collected from a user and user associated accounts. The dynamic credential selection and implementation system is triggered by the user signaling the system of a proposed transaction. The system requests updated resource characteristics from resource managers. The user profile, resource characteristics and transaction characteristics are evaluated and ranked by the dynamic credential selection and implementation system before completion of the transaction, which in some embodiments requires user approval of the selected resource.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for generating communicable linkages that collectively provide a cloud-based dynamic credential selection and implementation system. The system generates a user profile containing user preferences, historic trends, and user resources based on information collected from a user and user associated accounts. The dynamic credential selection and implementation system is triggered by the user signaling the system of a proposed transaction. The system requests updated resource characteristics from resource managers. The user profile, resource characteristics and transaction characteristics are evaluated and ranked by the dynamic credential selection and implementation system before completion of the transaction, which in some embodiments requires user approval of the selected resource.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for generating communicable linkages that collectively provide a cloud-based dynamic credential selection and implementation system. The system generates a user profile containing user preferences, historic trends, and user resources based on information collected from a user and user associated accounts. The dynamic credential selection and implementation system is triggered by the user signaling the system of a proposed transaction. The system requests updated resource characteristics from resource managers. The user profile, resource characteristics and transaction characteristics are evaluated and ranked by the dynamic credential selection and implementation system before completion of the transaction, which in some embodiments requires user approval of the selected resource.