Abstract: Embodiments of the present disclosure provide a video processing method and device. The method includes: receiving an edit request for a target video; and presenting an edit page of the target video in response to the edit request, wherein a video track of the target video is presented on the edit page, an edit prompt identifier is presented on the video track, and the edit prompt identifier is configured to prompt a modification to the target video.

Abstract: A fuse and a manufacturing method thereof, the fuse including: a tube body; a fuse element disposed inside the tube body; two inner caps respectively disposed at two ends of the tube body, the inner caps being electrically connected to the fuse element; and two flexible terminals sleeving over the inner caps and electrically connected to the inner caps, the flexible terminals being capable of deforming under an external force. In the present disclosure, when the flexible terminals are connected to external cables, the flexible terminals are capable of deforming to conform to the external cables, thereby improving a reliability of connection between the external cables and the fuse, reducing impedance between the external cables and the fuse, and preventing abnormal disconnection of the fuse.

Abstract: A fuse and a manufacturing method thereof, the fuse including: a tube body; a fuse element disposed inside the tube body; two inner caps respectively disposed on two ends of the tube body, where two ends of the fuse element respectively welded to the inner caps respectively disposed on the two ends of the tube body; and two external terminals welded to the inner caps. The present disclosure provides the fuse that allows the external terminals and the inner caps to be welded into one piece, thereby reducing impedance caused by an interference-fit assembly method in the prior art, and reducing power consumption of the fuse while ensuring an electrical protection capability.

Abstract: An automated checkout system accesses an image of an item inside a shopping cart and a location of the shopping cart within a store. The automated checkout system identifies a set of candidate items located within a threshold distance of the location of the shopping cart based on an item map. The item map describes a location of each item within the store and the location of each candidate item corresponds to a location of the candidate item on the item map. The automated checkout system inputs visual features of the item extracted from the image to a machine-learning model to identify the item by determining a similarity score between the item and each candidate item of the set of candidate items. After identifying the item, the automated checkout system displays a list comprising the item and additional items within the shopping cart to a user.

Abstract: The embodiments of the disclosure provide a method, apparatus, electronic device, and storage medium for displaying a video. The method includes: receiving a trigger operation for viewing a video detection interface; in response to the trigger operation, displaying the video detection interface, wherein a video playback area and a playback progress bar of the video are displayed in the video detection interface, at least one progress identification is presented on the playback progress bar, and the progress identification is used for indicating video detection information corresponding to a playback progress.

Abstract: An automated checkout system uses a shopping cart that is automatically charged when stacked into another shopping cart. Each shopping cart has a front charging connector and a rear charging connector. When a first shopping cart is stacked into a second shopping cart, the front charging connector of the first shopping cart connects with the rear charging connector of the second shopping cart. Electrical power can flow to the first shopping cart via the second shopping cart to charge a battery of the first shopping cart. The second shopping cart may be similarly stacked into a third shopping cart, wherein the second shopping cart receives electrical power from the third shopping cart. The second shopping cart may use this electrical power to charge its own battery or may provide some or all of the electrical power to the first shopping cart to charge the first shopping cart's battery.

Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

Abstract: Disclosed herein relates to a self-checkout anti-theft vehicle system, comprising: a self-checkout vehicle having a plurality of sensors and components implemented thereon, the self-checkout vehicle being used by shoppers for storing selected merchandises in a retail environment; and a centralized computing device. The centralized computing device is configured to: obtain information related to each merchandise selected and placed into the self-checkout vehicle by a shopper by exchanging data with the plurality of sensors and components via a first communication network, identify each merchandise via a second, different communication network based at least upon the information obtained from the plurality of sensors and components, and process payment information of each merchandise.

Abstract: Disclosed herein relates to a system, comprising: at least one load receiver mounted on a shopping cart or basket and configured to receive an item placed into the shopping cart or basket for a weighing operation; a plurality of sensors configured to detect a plurality of parameters relating to the weighing operation of the item including at least one of: a relative angle between a force sensing axis of the at least one load receiver and a direction of gravity, a motion of the shopping cart or basket, and an ambient temperature surrounding the shopping cart or basket and the at least one load receiver; and a processor configured to determine an actual weight of the item based on at least a portion of the plurality of parameters.

Abstract: A precise dose syringe for medical botulinum toxin is provided, which includes a syringe and a plunger handle. Multiple arc-shaped grooves are respectively provided on both sides of the plunger handle, and a scale line is processed on the front side of the plunger handle. A reminder mechanism and a quantitative mechanism are sleeved on the outer side of the plunger handle. The indicator arrow is located in front of the plunger handle, and the I-shaped block is connected to a thumb screw. The front side of the thumb screw is fixedly connected to a contact block, and the front side of the contact block is in contact with a back side of the plunger handle. The quantitative mechanism can make the injection dose of the syringe precise and controllable, while the reminder mechanism can remind doctors of the real-time injection amount during botulinum toxin injection.

Abstract: Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.

Abstract: An automated checkout system uses a shopping cart that is automatically charged when stacked into another shopping cart. Each shopping cart has a front charging connector and a rear charging connector. When a first shopping cart is stacked into a second shopping cart, the front charging connector of the first shopping cart connects with the rear charging connector of the second shopping cart. Electrical power can flow to the first shopping cart via the second shopping cart to charge a battery of the first shopping cart. The second shopping cart may be similarly stacked into a third shopping cart, wherein the second shopping cart receives electrical power from the third shopping cart. The second shopping cart may use this electrical power to charge its own battery or may provide some or all of the electrical power to the first shopping cart to charge the first shopping cart's battery.

Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

Abstract: A precise dose syringe for medical botulinum toxin is provided, which includes a syringe and a plunger handle. Multiple arc-shaped grooves are respectively provided on both sides of the plunger handle, and a scale line is processed on the front side of the plunger handle. A reminder mechanism and a quantitative mechanism are sleeved on the outer side of the plunger handle. The indicator arrow is located in front of the plunger handle, and the I-shaped block is connected to a thumb screw. The front side of the thumb screw is fixedly connected to a contact block, and the front side of the contact block is in contact with a back side of the plunger handle. The quantitative mechanism can make the injection dose of the syringe precise and controllable, while the reminder mechanism can remind doctors of the real-time injection amount during botulinum toxin injection.

Abstract: An online concierge system may use images received from shopping carts within retailers to determine the availability of items within those retailers. A shopping cart includes externally-facing cameras that automatically capture images of the area around the shopping cart as the shopping cart travels through a retailer. The online concierge system receives these images, which depict displays within the retailers from which a picker or a retailer patron can collect items. The online concierge system determines which items should be depicted in the images and which items are actually depicted in the images. The online concierge system identifies which items should be depicted, but are not depicted, and determines that these items are unavailable (e.g., out of stock) at that retailer. The online concierge system updates an availability database to indicate that these items are unavailable and may notify the retailer that the item is unavailable.

Abstract: Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.

Abstract: The present disclosure provides an integrated management and control system for a power plant including a power plant cloud edge vertical architecture system, which includes an inter-plant cloud platform, a plurality of intelligent control platforms, and a plurality of plant-side edge cloud platforms. Each intelligent control platform corresponds to one plant-side edge cloud platform and one power plant that are located in the corresponding power plant. The intelligent control platform is positively interconnected to the corresponding plant-side edge cloud platform via a forward isolation device, and the intelligent control platform is inversely interconnected to the corresponding plant-side edge cloud platform via a network switching device and a reverse isolation device. The plant-side edge cloud platform is bidirectionally interconnected to the inter-plant cloud platform over a private power network. The plant-side edge cloud platform is a reduced capacity private edge cloud system for a power plant.

Abstract: This application provides methods and an apparatuses, for example, for determining a transmit power. In an example method, a first terminal device receives first information, where the first information includes identification information of a first group and a preset first target power spectral density corresponding to the first group; and the first terminal device determines a target transmit power based on the first information. According to the method, a terminal device may determine a target transmit power based on a target power spectral density corresponding to a group to which the terminal device belongs.

Abstract: A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.