A hard disk status testing apparatus, including: a voltage reading module, a controller, an analogue switch, and multiple hard disk slot units. Each hard disk slot unit includes: a hard disk status testing module and a hard disk status indication module. The hard disk status testing modules and the hard disk status indication modules among different hard disk slot units are time-multiplexed, achieving independent control of the hard disk status testing modules of different hard disk slot units and independent control of the hard disk status indication modules of different hard disk slot units. A hard disk status testing method that effectively solves the problem of an increase in the number of pins needed for hard disk status testing and status indication, thus effectively decreasing hardware costs and increasing the utilization rate of pin resources.

A polling method for server sensors, a polling system for server sensors, and a computer-readable memory medium. The polling method includes: when a BMC is activated, acquiring attribute information of sensors (S101); classifying the sensors according to polling cycles in the attribute information (S102); adding the sensors with the same polling cycle into a same preset data structure (S103); and polling the sensors in the preset data structure using a thread (S104). According to the method, the problem of low polling efficiency caused by polling sensors with different cycles using a single thread may be solved, and each sensor may be polled independently while minimizing influences between polling of the sensors.

Provided is a computer capable of reducing a diagnosis load. For each predetermined diagnosis target node among a plurality of nodes in a neural network, a determination processing unit calculates an expected output value expected as a calculation result of a node calculation process corresponding to the predetermined diagnosis target node, which is obtained when the node calculation process is executed using a predetermined input value. For each diagnosis target node, a generation processing unit generates as a diagnosis program a program for comparing the calculation result of the node calculation process corresponding to the diagnosis target node, which is obtained when the node calculation process is executed by an NN calculation processor using the input value, with the expected output value.

A receiver includes the following: a signal receiving circuit, including a first MOS transistor and a second MOS transistor, where a gate of the first MOS transistor is configured to receive a reference signal and a gate of the second MOS transistor is configured to receive a data signal, and the signal receiving circuit is configured to output a comparison signal, the comparison signal being configured to represent a magnitude relationship between a voltage value of the reference signal and a voltage value of the data signal; and an adjusting circuit, including a third MOS transistor, where a source of the third MOS transistor is connected to a source of the first MOS transistor, a drain of the third MOS transistor is connected to a drain of the first MOS transistor, and a gate of the third MOS transistor is configured to receive an adjusting signal.

Systems, methods, and computer programming products for predicting, preventing and remediating failures of batch jobs being executed and/or queued for processing at future scheduled time. Batch job parameters, messages and system logs are stored in knowledge bases and/or inputted into AI models for analysis. Using predictive analytics and/or machine learning, batch job failures are predicted before the failures occur. Mappings of processes used by each batch job, historical data from previous batch jobs and data identifying the success or failure thereof, builds an archive that can be refined over time through active learning feedback and AI modeling to predictively recommend actions that have historically prevented or remediated failures from occurring. Recommended actions are reported to the system administrator or automatically applied. As job failures occur over time, mappings of the current system log to logs for the unsuccessful batch jobs help the root cause analysis becomes simpler and more automated.

Disclosed are a method and system for testing a wearable device. The method includes: performing an angle acquisition process for at least two times, and calculating an optical imaging parameter value of a target virtual image on the basis of angle variation values acquired in the at least two angle acquisition processes. With the method and system according to the present disclosure, the finally calculated optical imaging parameter value is more objective and more accurate than that acquired by means of the human eyes.

An Information Handling System (IHS) includes multiple hardware devices, and a baseboard Management Controller (BMC) in communication with the plurality of hardware devices. The BMC includes executable instructions for causing the one hardware device to be inhibited from functioning with the IHS when at least one of the hardware devices is powered on, and performing an authentication procedure with that hardware device. After that hardware device has been successfully authenticated, the instructions then enable the one hardware device to function with the IHS.

A test device includes a test plate and an adapter box. The rest plate includes a test port, and a first positioning portion disposed on the test plate. The adapter box includes a box body configured to detachably mount Solid State Drives to be tested, and a second positioning portion disposed on the box body and configured to cooperate with the first positioning portion to cause the Solid State Drives to be tested to form a communication connection with the test port.

In various examples, permanent faults in hardware component(s) and/or connections to the hardware component(s) of a computing platform may be predicted before they occur using in-system testing. As a result of this prediction, one or more remedial actions may be determined to enhance the safety of the computing platform (e.g., an autonomous vehicle). A degradation rate of a performance characteristic associated with the hardware component may be determined, detected, and/or computed by monitoring values of performance characteristics over time using fault testing.

A virtual device acquires a transaction history between a legacy computing device and a linked device; obtains a first request provided from the legacy computing device based on the transaction history and a first response received from the linked device in response to the first request; receives a second request corresponding to the first request from a new computing device and determines a second response to the second request; and provides test information for the new computing device based on a comparison of the first response and the second response.