Abstract: Embodiments of 3D memory devices and methods for forming the same are disclosed. In one example, a 3D memory device includes a multi-layer stacked structure, where the multi-layer stacked structure includes a plurality of alternately stacked conductive layers and dielectric layers. The 3D memory device further includes a semiconductor layer over the multi-layer stacked structure, and a plurality of channel structures penetrating into the multi-layer stacked structure and the semiconductor layer. A first end of each channel structure is located within the semiconductor layer, and the first ends of the channel structures are aligned with one another.

Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: The present disclosure discloses a handheld scanner and a scanning method for the handheld scanner. The handheld scanner includes a texture camera, a first black-and-white camera and a second black-and-white camera, where the first black-and-white camera and the second black-and-white camera are spaced apart from each other; and the handheld scanner further includes a laser projector, and the texture camera and the first black-and-white camera are respectively arranged at two sides of the laser projector. When a highly reflective or dark object is scanned, textures are obtained by the texture camera while laser scanning is performed; and after point cloud fusion is completed, texture images are screened and fused according to a shooting angle and the highlight degree of the image, so that the overall texture image of point cloud is obtained.

Abstract: The present disclosure provides a video generation method, an apparatus, a system, a device and a storage medium. The method is applied to a video generation system, and the video generation system includes a first client and at least one second client. Specifically, the method includes: firstly, the first client creates a first virtual room and sets a target capturing effect for the first virtual room; then, the second client acquires a first video added with the target capturing effect after receiving a notification message that a current user has successfully joined the first virtual room, and uploads the first video to the first virtual room. Further, the first client acquires a target video from the first virtual room and generates a target result video based on the target video, and the target video includes the first video.

Abstract: A method for preparing isophorone diisocyanate by (1) reacting isophorone with hydrogen cyanide in the presence of a catalyst to obtain isophorone nitrile; (2) reacting the isophorone nitrile obtained in step (1) with ammonia gas and hydrogen in the presence of a catalyst to obtain isophorone diamine; and (3) subjecting the isophorone diamine to a phosgenation reaction to obtain the isophorone diisocyanate, wherein the content of impurities containing a secondary amine group in the isophorone diamine that undergoes the phosgenation reaction in step (3) is ?0.5 wt. The method reduces the content of hydrolyzed chlorine in the isophorone diisocyanate product, improves the yellowing resistance of the product, and the harm due to presence of hydrolyzed chlorine in the product is reduced.

Abstract: This application relates to the artificial intelligence (AI) field, and specifically, to a voice quality enhancement method and a related device. The method includes: after a PNR mode is enabled, obtaining a noisy voice signal and target voice-related data, where the noisy-carrying voice signal includes a voice signal of a target user and an interfering noise signal, and the target voice-related data indicates a voice feature of the target user; and performing noise reduction on the noisy voice signal based on the target voice-related data by using a trained voice noise reduction model to obtain a noise-reduced voice signal of the target user, where the voice noise reduction model is implemented based on a neural network. In embodiments of this application, voice of a target person can be enhanced, and interference can be suppressed.

Abstract: The present application discloses a display device, the display device includes a frame area. The display device includes a backplate, a light guide plate, and an optical diaphragm. A receiving cavity is defined in the backplate, the light guide plate is located in the receiving cavity, the light guide plate includes at least one light guide plate extension part, the light guide plate extension part is located in the frame area, the optical diaphragm is arranged on the light guide plate, the optical diaphragm includes at least one diaphragm extension part, and the diaphragm extension part is located in the frame area. The diaphragm extension part is engaged together and connected with the light guide plate extension part.

Abstract: Embodiments relate to diagnosis and recovery of cloud based systems. From an incident ticket, a dynamic diagnostics graph is generated visualizing a hierarchy (ancestor, child) of diagnostic jobs investigating the functioning cloud system. By indicating and checking job statuses, child jobs dependent on a skipped or failed job can be skipped according to a dynamic pruning technique—thereby trimming an entire branch. And, by running separate groups of diagnostic jobs in parallel across different nodes, the diagnostic process can be finished rapidly and efficiently. A diagnostic report includes the dynamic diagnostics graph. For system recovery, the dynamic diagnostic graph is analyzed to automatically provide one or more appropriate Recommended Actions (RAs) resolving cloud system problem(s) revealed by diagnostic efforts. Those appropriate RAs may be provided by performing machine learning (e.g., referencing a neural network) with a model trained from historical cloud diagnostic and recovery activity.

Abstract: A voice processing method is provided, including: when a terminal records a video, if a current video frame includes a face and a current audio frame includes a voice, determining a target face in the current video frame; obtaining a target distance between the target face and the terminal; determining a target gain based on the target distance, where a larger target distance indicates a larger target gain; separating a voice signal from a voice signal of the current audio frame; and performing enhancement processing on the voice signal based on the target gain, to obtain a target voice signal. This implements adaptive enhancement of a human voice signal during video recording.