Abstract: Systems and methods are provided for self-calibrating a health state of a hardware resource using a Siamese network based on a plurality of feature variables. The feature variables may include hardware failure data, performance degradation data, and power consumption data. The hardware failure data is based on machine operation records and warranty logs. The performance degradation data is based on hourly performance data and a number of client requests for performing functions. The power consumption data uses power telemetry and a processor (e.g., CPU) usage. The present disclosure uses a Siamese network with a plurality of trained neural networks in parallel to determine a correlation between incident data and reference data (e.g., representing a hardware resource in a healthy state). Use of the Siamese network enables self-calibrating a health status of servers in a cloud system without imposing stress tests or complex computations to classify the respective servers.

Abstract: A method for stability analysis of a combustion chamber of a gas turbine engine based on image sequence analysis belongs to the field of fault prediction and health management of aeroengine. Firstly, flow field data inside a combustion chamber of a gas turbine engine is acquired. Secondly, flow field images of the combustion chamber are preprocessed to respectively obtain a discrimination model data set and a prediction model data set. Then, a 3DWaveNet model is constructed as a generation network of a prediction model. A discrimination network of the module is constructed. The generation network and the discrimination network are combined to form the prediction model. Finally, a discrimination model is constructed according to the discrimination model data set; the training set in the discrimination model data set is used for training, and the test set is used for assessment.

Abstract: A hybrid light measurement method for measuring a three-dimensional profile comprises: Step 1 using a MEMS laser projector to realize projection of a laser stripe and a fringe pattern: and Step 2 performing matching to obtain a parallax, and incorporating a binocular stereo vision to perform reconstruction. The present disclosure employs MEMS projection technology to realize a hybrid measurement system capable of performing laser stripe measurement and fringe pattern measurement, thereby realizing quick measurement of three-dimensional profiles of objects having different surface properties. The present disclosure enables laser stripe measurement and fringe pattern measurement to be performed by the same measurement system without needing any additional hardware equipment. The projection device used in the present disclosure enables projection of multiple laser stripes, thereby increasing measurement precision and speed.

Abstract: A hybrid light measurement method for measuring a three-dimensional profile comprises: Step 1 using a MEMS laser projector to realize projection of a laser stripe and a fringe pattern: and Step 2 performing matching to obtain a parallax, and incorporating a binocular stereo vision to perform reconstruction. The present disclosure employs MEMS projection technology to realize a hybrid measurement system capable of performing laser stripe measurement and fringe pattern measurement, thereby realizing quick measurement of three-dimensional profiles of objects having different surface properties. The present disclosure enables laser stripe measurement and fringe pattern measurement to be performed by the same measurement system without needing any additional hardware equipment. The projection device used in the present disclosure enables projection of multiple laser stripes, thereby increasing measurement precision and speed.

Abstract: Aspects of the subject matter described herein relate to workflows. In aspects, an activity of a workflow may request data and provide a logical handle. In response, distributed workflow components use the logical handle to determine whether the requested data is available locally or remotely. If the data is available remotely, the components may obtain the data for the requesting entity. In the process of obtaining the data, the data may be serialized and deserialized. After the data is available locally, it is provided to the requesting entity.

Abstract: A computer may function as a broker that brokers execution of portions of a workflow. The broker computer may have a processor and memory configured to receive the workflow via a network. The workflow may have a corresponding SLA document that has rules governing how the workflow is to be executed. The broker computer may identify discretely executable sub-workflows of the workflow. The broker computer may also obtain information describing computing characteristics of each of a plurality of service providers (e.g., computation clusters, cloud services, etc.) connected with the broker computer via the network. The broker computer may select a set of the service providers by determining whether their respective computing characteristics satisfy the SLA. The broker computer may pass the discretely executable sub-workflows to the selected set of service providers. The workflow is thus executed, in distributed federated fashion, transparently to the user submitting the workflow.

Abstract: Aspects of the subject matter described herein relate to workflows. In aspects, an activity of a workflow may request data and provide a logical handle. In response, distributed workflow components use the logical handle to determine whether the requested data is available locally or remotely. If the data is available remotely, the components may obtain the data for the requesting entity. In the process of obtaining the data, the data may be serialized and deserialized. After the data is available locally, it is provided to the requesting entity.