Abstract: The present disclosure relates to the field of EV-DWPT (electric vehicle dynamic wireless power transfer), and specifically discloses a double solenoid EV-DWPT system and a parameter optimization method thereof. The system is provided with a magnetic coupling mechanism, comprising a transmitting structure and a receiving structure. The transmitting structure includes a plurality of double solenoid transmitting rails arranged equidistantly along a road direction. Each double solenoid transmitting rail includes a square tubular magnetic core perpendicular to the road surface, and transmitting solenoids wound spirally using one and the same Litz wire wound in opposite directions.

Abstract: A variable displacement swash plate type compressor control method and a variable displacement swash plate type compressor controlled thereby may include a first determination step of determining whether liquid refrigerant exists within the variable displacement swash plate type compressor; a first operation step of applying power to the field coil assembly and of not applying current to the electronic control valve, when it is determined in the first determination step S1 that the liquid refrigerant exists; a second determination step of comparing an elapsed period of time of operating in the first operation step with a predetermined reference time; and a second operation step of maintaining the power applied in the first operation step to the field coil assembly and applying current to the electronic control valve when the elapsed period of time is greater than or equal to the reference time.

Abstract: Methods and systems for managing data processing systems are disclosed. A data processing system may include hardware and/or software components. The operation of the data processing system may depend on the operation of these components. To manage the operation of the data processing system, a system may include a data processing system manager. The data processing system manager may obtain logs for components of the data processing system reflecting the historical operation of these components and use the log to predict the future operation of the data processing system, identify similar operation of other data processing systems, and/or for other purposes. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system becoming impaired.

Abstract: This disclosure provides systems, methods, and media for creating a data graph database from various unstructured and unstructured data items for use by various services. The method comprises the operations of identifying unstructured data items in data subjects; recognizing regions of interest (ROIs) in the unstructured data items; and extracting the ROIs from the unstructured data items. The method further comprises encoding the extracted ROIs into ROI vectors; creating a data graph to represent the data subjects, the data items, and the ROI vectors; and storing the data graph into a graph database. The various embodiments can manage data items of different data formats together rather than separately, thus creating a data management system for managing data across data formats. The data management system can also store structured data items into the graph database, thus complementing the existing ETL procedure for structured data items.

Abstract: The present disclosure provides a polysilicon resistor, a method for manufacturing the same, and a successive approximation register analog-to-digital converter.

Abstract: An efficient cut blasting method for medium-length holes in deep high-stress rock roadway is disclosed. The method may comprise steps of carrying out a crustal stress blasting test on the free face of the in-situ rock roadway to be excavated, and obtaining a distribution state of cracks under a synergistic action of crustal stress, explosion stress waves and clamping force of surrounding rock of a rock mass in the stratum where the in-situ rock roadway to be excavated is located; arranging a cutting hole net on the free face of the rock roadway to be excavated according to a distribution state of the cracks; performing cut blasting based on the cutting hole net.

Abstract: File annotation is described. An example method includes: processing files to be annotated by using an annotation model to determine a first performance of the annotation model, the first performance being associated with the confidence of a model annotation result generated by the annotation model; if the first performance is lower than a predetermined threshold, determining a group of target files from the files based at least on the confidence of the model annotation result; acquiring truth-value annotation information of the group of target files for retraining the annotation model; and if a second performance of the retrained annotation model is higher than or equal to the predetermined threshold, determining annotation information for at least some of the files by using the retrained annotation model. Based on this approach, automatic annotation of files can be realized with less truth-value annotation information, thereby reducing annotation costs.

Abstract: An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.

Abstract: Methods and systems for managing data processing systems based on indications of anomalous behaviors in logs are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system that reflect the historical and/or current operation of these components. The logs may be used to identify the operational state of the data processing system, improving the context of the log information. Based on the identified state, inference models may be implemented to predict future infrastructure issues by detecting anomalous behaviors of the data processing system (e.g., behaviors that are unusual for the given state) recorded in the logs. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system becoming impaired.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for collecting training data. The method for collecting training data provided in embodiments of the present disclosure includes: scanning a plurality of data sources to acquire information relating to a plurality of training data to be collected, and creating a collection list based on the information, the collection list including at least a plurality of identifiers of the plurality of training data and a plurality of storage locations of the plurality of training data in the plurality of data sources. The method further includes: collecting the plurality of training data from the plurality of data sources based at least on the collection list.

Abstract: This disclosure provides systems, methods, and media for creating a data graph database from various unstructured and unstructured data items for use by various services. The method comprises the operations of identifying unstructured data items in data subjects; recognizing regions of interest (ROIs) in the unstructured data items; and extracting the ROIs from the unstructured data items. The method further comprises encoding the extracted ROIs into ROI vectors; creating a data graph to represent the data subjects, the data items, and the ROI vectors; and storing the data graph into a graph database. The various embodiments can manage data items of different data formats together rather than separately, thus creating a data management system for managing data across data formats. The data management system can also store structured data items into the graph database, thus complementing the existing ETL procedure for structured data items.

Abstract: Methods and systems for managing data processing systems are disclosed. A data processing system may include hardware and/or software components. The operation of the data processing system may depend on the operation of these components. To manage the operation of the data processing system, a system may include a data processing system manager. The data processing system manager may obtain logs for components of the data processing system reflecting the historical operation of these components and use the log to predict the future operation of the data processing system, identify similar operation of other data processing systems, and/or for other purposes. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system becoming impaired.

Abstract: Methods and systems for managing deployments are disclosed. A deployment may include one or more devices. The devices may include hardware and/or software components. The operation of the deployment may depend on the operation of these devices and components. To manage the operation of the deployment, a system may include a deployment manager. The deployment manager may obtain logs for components of the deployment reflecting the historical operation of these components and use the log to predict the future operation of the deployment. Based on the predictions, the deployment manager may take proactive action to reduce the likelihood of the deployment becoming impaired.

Abstract: Methods, apparatus, and processor-readable storage media for artificial intelligence-based techniques for automated visual data searching using edge devices are provided herein. An example computer-implemented method includes obtaining visual data from one or more edge devices; generating at least one automated searching tool by processing at least a portion of the obtained data using one or more artificial intelligence techniques; deploying the at least one automated searching tool to at least a portion of the one or more edge devices; and performing one or more automated actions based at least in part on data received, from at least a portion of the one or more edge devices, in connection with operation of the at least one automated searching tool.

Abstract: Methods and systems for managing data processing systems are disclosed. A data processing system may include hardware and/or software components. The operation of the data processing system may depend on the operation of these components. To manage the operation of the data processing system, a system may include a data processing system manager. The data processing system manager may obtain logs for components of the data processing system reflecting the historical operation of these components and use the log to predict the future operation of the data processing system, actions that may be performed to address predicted undesirable operation of the data processing system, and/or for other purposes. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system operating in an undesirable manner.

Abstract: Facilitating outlier object detection in tiered storage systems is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise determining respective parameters associated with objects of a group of objects of a tiered storage system. The respective parameters can comprise at least one of a size, an access percentage, or a cost. The operations also can comprise using the respective parameters associated with the objects of the group of objects as inputs and performing data clustering on the group of objects, resulting in at least one data cluster. Further, the operations can comprise selecting at least one object from the group of objects as at least one outlier object within the tiered storage system based on the at least one data cluster.

Abstract: The present disclosure relates to a method, an electronic device, and a computer program product for monitoring a storage system. For example, a method of monitoring a storage system is provided. This method may include setting a quota type of a folder to be monitored in the storage system to a monitored type. This method may further include acquiring quota monitoring data of which the quota type is the monitored type from a quota monitoring report associated with the storage system. In addition, this method may further include generating storage information of the folder based on the quota monitoring data. In this way, the time spent on monitoring the storage system can be shortened, the system resources can be saved, and ultimately, the user experience can be improved.

Abstract: Embodiments of the present disclosure include a method, an electronic device, and a computer program product for training a failure analysis model. In a method for training a failure analysis model in an illustrative embodiment, at least one set of log files including multiple preprocessed log files is obtained, the at least one set of log files including a marked failure cause of a storage system, and preprocessed log files in the multiple preprocessed log files including one or more potential failure causes of the storage system and scores associated with the potential failure causes; a failure cause of the storage system is predicted according to a failure analysis model and based on the potential failure causes and the scores in the multiple preprocessed log files; and parameters of the failure analysis model are updated based on a probability that the predicted failure cause is the marked failure cause.

Abstract: Embodiments of the present disclosure include a method, an electronic device, and a computer program product for training a failure analysis model. In a method for training a failure analysis model in an illustrative embodiment, at least one set of log files including multiple preprocessed log files is obtained, the at least one set of log files including a marked failure cause of a storage system, and preprocessed log files in the multiple preprocessed log files including one or more potential failure causes of the storage system and scores associated with the potential failure causes; a failure cause of the storage system is predicted according to a failure analysis model and based on the potential failure causes and the scores in the multiple preprocessed log files; and parameters of the failure analysis model are updated based on a probability that the predicted failure cause is the marked failure cause.

Abstract: Methods and systems for managing deployments are disclosed. A deployment may include one or more devices. The devices may include hardware and/or software components. The operation of the deployment may depend on the operation of these devices and components. To manage the operation of the deployment, a system may include a deployment manager. The deployment manager may obtain logs for components of the deployment reflecting the historical operation of these components and use the log to predict the future operation of the deployment. Based on the predictions, the deployment manager may take proactive action to reduce the likelihood of the deployment becoming impaired.