Abstract: In some embodiments, a method for tuning a disaster recovery preparation system for a storage system includes determining whether the disaster recovery preparation system has not met a recovery point objective; and in response to determining that the recovery point objective is not met: determining a state of the storage system; applying a machine learning model to determine a change to one or more of a plurality of adjustable parameters of the disaster recovery preparation system based on the state of the storage system and the recovery point objective that is not met; adjusting the one or more of the plurality of adjustable parameters based on the determined change to the one or more parameters; after adjusting the one or more parameters, monitoring one or more metrics of the disaster recovery preparation system or the storage system; and updating the machine learning model based on the monitoring.

Abstract: This application discloses a site model updating method and system in the field of artificial intelligence. The method includes: obtaining a surveillance image; determining, based on the obtained surveillance image, a change type of a device that changes and a change amount corresponding to the change type; calculating a pose and camera parameter of the surveillance image based on the surveillance image and a site model; determining, based on the pose and camera parameter of the surveillance image, a pose of the device that changes; and updating the site model based on the pose of the device that changes, the change type, and the change amount corresponding to the change type.

Abstract: A sampling device and an online surveying and mapping system has a ground control unit for transmitting a control command downhole and a downhole unit. The downhole unit includes a housing; a downhole control assembly for receiving the control command from the ground control unit to control sampling and online surveying and mapping operations; a probe assembly configured to draw a sample of a formation; a power system for driving the probe assembly to draw samples; a tank assembly for storing qualified formation samples; a contamination-rate evaluating assembly for determining whether a contamination rate of the sample of the formation is qualified and discharging unqualified sample of the formation while delivering qualified sample of the formation to the tank assembly; and a fluid-parameter measuring assembly for measuring parameters of the sample of the formation in the tank assembly and transmitting measured data to the ground control unit.

Abstract: One or more techniques and/or computing devices are provided for managing an arbitrary set of storage items using a granset. For example, a storage controller may host a plurality of storage items and/or logical unit numbers (LUNs). A subset of the storage items are grouped into a consistency group. A granset is created for tracking, managing, and/or providing access to the storage items within the consistency group. For example, the granset comprises application programming interfaces (APIs) and/or properties used to provide certain levels of access to the storage items (e.g., read access, write access, no access), redirect operations to access either data of an active file system or to a snapshot, fence certain operations (e.g., rename and delete operations), and/or other properties that apply to each storage item within the consistency group. Thus, the granset provides a persistent on-disk layout used to manage an arbitrary set of storage items.

Abstract: A technique provides network efficient data failover by explicitly protecting one or more common snapshot references at sites of a multi-site data replication environment to improve granularity of control of recovery point objectives (RPO) for data across the sites. A common snapshot reference or recovery point (RP) ensures that, in the event of failure to a site, data designated for failover may be quickly protected by replicating only small incremental changes to the RP so as to maintain RPO requirements across the sites. Illustratively, the technique enhances and extends a disaster recovery (DR) application programming interface (API) protocol through an extension that defines and applies a tag to the RP, wherein the tag enables protection and/or preservation of the RP by ensuring that the sites honor the tag applied to the RP. The tag essentially functions as an advisory lock for the RP that is shared among the sites to prevent deletion of the RP at the sites throughout the duration of the lock.

Abstract: A site and storage tier aware technique replicates data as one or more recovery points (RPs) from a primary site to a secondary site in a multi-site data replication environment. A storage tier aware reference resolver determines (i) an amount of RP data transfer associated with the replication and (ii) location information associated with a cloud storage tier storing the RP data in an object store. The storage tier aware reference resolution aspect provides two additional factors to consider when retrieving data of a reference RP from cloud storage: (iii) the time (duration) needed to retrieve the data and (iv) the cost (financial expense) needed to retrieve the data. In addition, a site aware reference resolution aspect of the technique determines an optimal RP to use as the reference RP and involves consideration of (v) which RPs have been replicated from the primary site to the secondary site and (vi) which RPs have been retained for storage at the sites.

Abstract: Provided herein is a power distribution system comprising a feedback circuit including a transistor in series with a relay, the feedback circuit regulating regulate a main power path including a main power supply connected in series with an electric power converter. The power distribution system further comprises OR-ing controllers that regulate the main power path and a backup power path including a low-voltage battery. The power distribution system further comprises terminals through which power from the main power path or the backup power path is transmitted to respective components corresponding to channels. The power distribution system further includes a microcontroller that acquires data in each of the channels and control operations associated with each of the channels based on the acquired data.

Abstract: One or more techniques and/or computing devices are provided for managing an arbitrary set of storage items using a granset. For example, a storage controller may host a plurality of storage items and/or logical unit numbers (LUNs). A subset of the storage items are grouped into a consistency group. A granset is created for tracking, managing, and/or providing access to the storage items within the consistency group. For example, the granset comprises application programming interfaces (APIs) and/or properties used to provide certain levels of access to the storage items (e.g., read access, write access, no access), redirect operations to access either data of an active file system or to a snapshot, fence certain operations (e.g., rename and delete operations), and/or other properties that apply to each storage item within the consistency group. Thus, the granset provides a persistent on-disk layout used to manage an arbitrary set of storage items.

Abstract: A defect detection method and device based on nonlinear system identification are provided. The defect detection method includes: performing, by a modal force hammer, hammer excitation on a test specimen according to a test schedule to generate an excitation signal and a response signal; acquiring, by a laser vibrometer, the excitation signal and the response signal; performing parameter calculation on the excitation signal and the response signal to obtain model parameters; constructing an initial Hammerstein model based on the model parameters; optimizing the initial Hammerstein model to obtain a Hammerstein model of the test specimen; adjusting the Hammerstein model of the test specimen by using a cross-validation method; perform defect determination based on the Hammerstein model of the test specimen adjusted and a pre-constructed template specimen model to obtain a determination result, and determining whether the test specimen is defective based on the determination result.

Abstract: An exemplary system and method provide nuclear reactors with optimized detector placement. The exemplary system and method include a nuclear reactor model, Markov decision process, and reward function, where reinforcement learning can be used to iteratively generate placements of detectors to candidate positions within the nuclear reactor.

Abstract: A communication method for a local area network (LAN), is applicable to a first device, includes: obtaining at least one second device connected to the first device by a wired communication mode, the first device configured to provide a wireless access hotspot to the at least one second device; sending initial hotspot information of the wireless access hotspot to the at least one second device by the wired communication mode, the initial hotspot information being configured to provide the at least one second device with authentication information for the at least one second device to access the wireless access hotspot; and establishing, by the first device and the at least one second device, a first wireless LAN connection according to the initial hotspot information.

Abstract: In some embodiments, a method for tuning a disaster recovery preparation system for a storage system includes determining whether the disaster recovery preparation system has not met a recovery point objective; and in response to determining that the recovery point objective is not met: determining a state of the storage system; applying a machine learning model to determine a change to one or more of a plurality of adjustable parameters of the disaster recovery preparation system based on the state of the storage system and the recovery point objective that is not met; adjusting the one or more of the plurality of adjustable parameters based on the determined change to the one or more parameters; after adjusting the one or more parameters, monitoring one or more metrics of the disaster recovery preparation system or the storage system; and updating the machine learning model based on the monitoring.