Vmware, Inc.

International Business Machines Corporation

Chinese Academy of Sciences

The Fourth Paradigm (beijing) Tech Co Ltd

Rui Cao

Donghai Han

Ziyou Wang

Mengdie Song

Zhe Wang

Jianjun Shen

Yong Zhao

Pei Jian Liu

Qing Bo Wang

Ying Zhou

Wenfeng Liu

Danting Liu

Xing Jin

Yibo Wang

Le He

Ran Gu

Yuqiang Chen

Yuanfei Luo

Weiwei Tu

Guoen Cao

A training method and system of a neural network model including a three-level model, and a prediction method and system are provided. The training method includes: acquiring a training data record; generating features of a training sample based on attribute information of the training data record, and using a label of the training data record as a label of the training sample; training the neural network model using a set of the training samples, learning an interaction representation between corresponding input items respectively by a plurality of intermediate models comprised in a second-level model of the neural network model, learning a prediction result at least based on the interaction representations output by the second-level model by a third-level model of the neural network model, and adjusting the neural network model at least based on a difference between the prediction result and the label.

Training method and system of neural network model and prediction method and system

Example methods and systems for intent-based network virtualization design are disclosed. One example may comprise: obtaining configuration information and traffic information associated with multiple virtualized computing instances, processing the configuration information and traffic information to identify network connectivity intents and mapping the network connectivity intents to a logical network topology template. Based on a switching intent, a first group may be assigned to a logical network domain and the logical network topology template modified to include a logical switching element. Based on a routing intent, the logical network topology template may be modified to include a logical routing element. A logical network may be configured based on the modified logical network topology template to satisfy the switching intent and routing intent.

Intent-based network virtualization design

The method of some embodiments allocates a secondary network interface for a pod, which has a primary network interface, in a container network operating on an underlying logical network. The method receives an ND that designates a network segment. The method receives the pod, wherein the pod includes an identifier of the ND. The method then creates a secondary network interface for the pod and connects the secondary network interface to the network segment. In some embodiments, the pods include multiple ND identifiers that each identify a network segment. The method of such embodiments creates multiple secondary network interfaces and attaches the multiple network segments to the multiple secondary network interfaces.

Automatic configuring of VLAN and overlay logical switches for container secondary interfaces

Example methods and systems for intent-based network virtualization design are disclosed. One example may comprise: obtaining configuration information and traffic information associated with multiple virtualized computing instances, processing the configuration information and traffic information to identify network connectivity intents and mapping the network connectivity intents to a logical network topology template. Based on a first switching intent, a first group may be assigned to a first logical network domain and the logical network topology template configured to include a first logical switching element. Based on a second switching intent, a second group may be assigned to a second logical network domain and the logical network topology template configured to include a second logical switching element. Based on a routing intent, the logical network topology template may be configured to include a logical routing element.

This invention proposes a three-phase CLLC bidirectional DC-DC converter, which includes a high-voltage side voltage dividing capacitor module, a three-phase half-bridge series module, a three-phase half-bridge parallel module, a three-phase primary/secondary side resonant module, a three-phase isolation transformer and a low-voltage side capacitor module. The high-voltage side voltage dividing capacitor module includes three voltage dividing capacitors. The three-phase half-bridge series/parallel module includes three bridge arms connected in series/parallel. Each bridge arm includes two switches connected in series. The three-phase primary/secondary side resonant module includes a, b, c three-phase primary/secondary side resonant tank. The three-phase isolation transformer includes three single-phase transformers a, b, and c. The voltage of the high-voltage side is divided through the voltage dividing capacitors to reduce the voltage stress of the switches, and the switching loss is diminished via the high-frequency soft switching technology.

Three-phase CLLC bidirectional DC-DC converter and a method for controlling the same

A technique for updating configurations of a target system in a computer. The method comprises booting, based on Preboot Execution Environment technology, a micro-system for updating configurations of a target system before booting the target system, the micro-system performing the steps of: (a) acquiring target configuration packages via a network from a server; (b) extracting target configuration data from said target configuration packages; and (c) updating the configurations of said target system by using said target configuration data, wherein said micro-system is independent of said target system.

Method and micro-system for updating configurations of target system in computer

Growing community of inventors

Beijing, China

Rui Cao