Abstract: The present disclosure relates to a system and a method for image reconstruction. The method may include obtaining a projection image of a subject acquired by an imaging device, the projection image including a first region with a normal exposure corresponding to a first portion of the subject and a second region with an overexposure corresponding to a second portion of the subject; using first pixel values of first pixels in the first region to correct second pixel values of second pixels in the second region; and reconstructing, based on the first pixel values of the first pixels in the first region and the corrected second pixel values of the second pixels in the second region, a target image of the subject.

Abstract: Various embodiments include methods for determining a resource configuration of a cloud service system using a system simulation tool with a model library. This may include: obtaining a model required for each service; obtaining a resource limit setting of each required model; creating a service workflow among the models to obtain a system simulation model, indicating the interaction workflow among the models and the sequence of model execution in a single service; using training data to train a service performance model of each service, wherein the training data comprises test data collected from the cloud service system in a testing environment, and the service performance model defines a resource consumption of a single request running on a service in the system simulation model; performing system simulation with a given resource configuration set and the resource limit settings, to obtain a system performance KPI; and determining a resource configuration with the best KPI.

Abstract: Provided in an embodiment of the present disclosure is a method for acquiring data of a data source associated with a production line, including: acquiring a semantic model, the semantic model including semantic relationships between respective semantic units and data source identifiers corresponding to one or more production lines; receiving production line identifiers and acquiring one or more semantic units; converting, based upon the semantic model, the one or more semantic units to data source identifiers corresponding to production lines indicated by the production line identifiers; and acquiring data of data sources indicated by the data source identifiers. Implementing the embodiments disclosed in the present disclosure simplifies a configuration file required for a virtual model of a production line, thereby greatly reducing configuration workload, and enhancing convenience of acquiring data of a data source.

Abstract: Various embodiments include methods for determining a resource configuration of a cloud service system using a system simulation tool with a model library. This may include: obtaining a model required for each service; obtaining a resource limit setting of each required model; creating a service workflow among the models to obtain a system simulation model, indicating the interaction workflow among the models and the sequence of model execution in a single service; using training data to train a service performance model of each service, wherein the training data comprises test data collected from the cloud service system in a testing environment, and the service performance model defines a resource consumption of a single request running on a service in the system simulation model; performing system simulation with a given resource configuration set and the resource limit settings, to obtain a system performance KPI; and determining a resource configuration with the best KPI.

Abstract: The present disclosure provides a system for imaging via an imaging device including a plurality of radiation sources. Each of at least a portion of the plurality of radiation sources may be configured with a beam stop array that is configured to block at least a portion of radiation beams emitted by the radiation source. For one of the at least a portion of the plurality of radiation sources, the system may determine, based on a scatter distribution, first image data, and second image data, third image data of a subject corresponding to each of the at least a portion of the plurality of radiation sources. For each of at least a portion of the plurality of radiation sources, the system may further determine, based on image data of the subject, target image data of the subject using a calibration model.

Abstract: Various embodiments include a knowledge base building method. The method may include: building a first model based on a connection relationship between a controller and a device; building a second model based on a correspondence between an I/O port of the controller and a memory of the controller; and building a knowledge base based on both the first model and the second model. The knowledge base comprises configuration information related to the device.

Abstract: A scanning apparatus (110), a medical image obtaining method, and a medical image obtaining system (100) are provided. The scanning apparatus (110) comprises a gantry, a controller, a C-shaped arm, a radiation source (112), and a detector (113). The radiation source (112) and the detector (113) are arranged at both ends of the C-shaped arm. The C-shaped arm is connected to the gantry. The controller is configured to control a motion of the gantry to drive the C-shaped arm to move so as to scan a target object.

Abstract: The present disclosure is related to a method for scattering correction of an image. The method may include obtaining an image of a subject and a reference image of air. The method may also include identifying an OOI from the image, the OOI including one or more pixels. For each pixel of the one or more pixels of the OOI, the method may also include determining an equivalent thickness of the OOI corresponding to the each pixel based on a pixel value of the each pixel and the reference image, and determining a scatter correction coefficient of the each pixel based at least in part on the equivalent thickness of the OOI corresponding to the each pixel. The method may further include correcting the pixel value of the each pixel using the corresponding scatter correction coefficient for each pixel of the one or more pixels of the OOI.

Abstract: A data analysis method, device and system are disclosed. In an embodiment, the method includes performing data analysis on the devices by an application by collecting at least one key performance indicator of the devices, estimating the performance of the application according to the result of the data analysis, generating an application upgrade requirement on the basis of the performance estimation result, and sending the upgrade requirement to an industrial cloud; receiving a context data requirement generated by the industrial cloud based upon of the upgrade requirement, collecting context data from the devices according to the context data requirement, and returning the context data to the industrial cloud; and downloading from the industrial cloud the application updated on the basis of the context data. The data analysis mechanism reduces the development process of custom and special applications by automatically upgrading to adapt to changes in field application conditions.

Abstract: A data analysis method, device and system are disclosed. In an embodiment, the method includes performing data analysis on the devices by an application by collecting at least one key performance indicator of the devices, estimating the performance of the application according to the result of the data analysis, generating an application upgrade requirement on the basis of the performance estimation result, and sending the upgrade requirement to an industrial cloud; receiving a context data requirement generated by the industrial cloud based upon of the upgrade requirement, collecting context data from the devices according to the context data requirement, and returning the context data to the industrial cloud; and downloading from the industrial cloud the application updated on the basis of the context data. The data analysis mechanism reduces the development process of custom and special applications by automatically upgrading to adapt to changes in field application conditions.

Abstract: Provided in an embodiment of the present disclosure is a method for acquiring data of a data source associated with a production line, including: acquiring a semantic model, the semantic model including semantic relationships between respective semantic units and data source identifiers corresponding to one or more production lines; receiving production line identifiers and acquiring one or more semantic units; converting, based upon the semantic model, the one or more semantic units to data source identifiers corresponding to production lines indicated by the production line identifiers; and acquiring data of data sources indicated by the data source identifiers. Implementing the embodiments disclosed in the present disclosure simplifies a configuration file required for a virtual model of a production line, thereby greatly reducing configuration workload, and enhancing convenience of acquiring data of a data source.

Abstract: The present disclosure is related to a method for scattering correction of an image. The method may include obtaining an image of a subject and a reference image of air. The method may also include identifying an OOI from the image, the OOI including one or more pixels. For each pixel of the one or more pixels of the OOI, the method may also include determining an equivalent thickness of the OOI corresponding to the each pixel based on a pixel value of the each pixel and the reference image, and determining a scatter correction coefficient of the each pixel based at least in part on the equivalent thickness of the OOI corresponding to the each pixel. The method may further include correcting the pixel value of the each pixel using the corresponding scatter correction coefficient for each pixel of the one or more pixels of the OOI.