Abstract: Provided is a process platform, relating to the field of semiconductor technologies and specifically. The process platform includes a conveying bin, an accommodating bin, and reaction chambers. The conveying bin is internally provided with a first conveying part having grabbing mechanisms. The accommodating bin is communicated with the conveying bin and used for loading a wafer. The reaction chambers are each communicated with the conveying bin, at least one wafer carrying table is arranged in each reaction chamber, and liftable ejector pins are arranged on a top surface of the wafer carrying table.

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: 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: 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.