56results about "Detecting faulty hardware using expert systems" patented technology

A method, system, and computer-readable storage media for anomaly detection on an Operational Technology (OT) network are disclosed. Input packets are obtained. Each of the input packets may represent a history of baseline packets. Based on the input packets, a foundation model is trained to generate corresponding recreated packets. The trained foundation model is optimized by minimizing a difference between the input packets and the corresponding generated recreated packets. Further, an incoming data packet is received on the OT network. Based upon the incoming data packet, a recreated data packet is generated using the trained and optimized foundation model. A difference between the recreated data packet and the incoming data packet is calculated. If the difference is above a predetermined threshold, a security protocol is triggered to a security system.

A method and device for predicting errors in a computing system are disclosed. The error prediction method includes: receiving log data generated by the computing system during operation of the computing system; tokenizing the log data into tokens; inputting the tokens to a discriminator model which generates scores of the respective tokens, each score corresponding to a probability that the corresponding token is an anomaly token; determining an anomaly score based on the scores; and determining a likelihood of future occurrence of an error in the computing system based on the anomaly score.

A method of operating a server is provided that includes providing, with the server, one or more services relating to network access control and management of a network, predicting a network configuration failure associated with the network with a failure prediction model, and generating a network configuration recommendation based on the predicted network configuration failure to avoid the predicted network configuration failure. The failure prediction model can be a machine-learning based network configuration failure prediction model that is trained on past network configuration failure events. Operated in this way, erroneous network configuration issues can be automatically identified and addressed in a timely fashion.

Components used during a video conference are managed during the video conference. A processing system associated with the video conference executes diagnostic software to test functionality of one or more components arranged in a physical space while the video conference is in progress, without interfering with operation of the video conference. Based on execution of the diagnostic software, the processing system determines a configuration of a component and compares the configuration to at least one parameter associated with the component to detect that the component is in a limited condition for use during the video conference. In response to detecting the limited condition, the processing system initiates, while the video conference is in progress, a response configured to change the limited condition of the component so as to increase functionality of the component during the video conference.

A bridging component is implemented as a computing unit that includes a first communications interface, a second communications interface, and a processor. The computing unit can receive, from one or more external systems that are communicatively coupled to the computing unit, test programs for testing DUTs that are communicatively coupled to the computing unit. The computing unit can also transmit commands for executing the test programs to the DUTs, can receive test results from the DUTs, and can transmit test results to the appropriate external system(s). Furthermore, the computing unit can determine, for each set of one or more of the DUTs, a respective type of DUTs in the set, associate a respective test program with the set, and issue commands for executing the respective test program to the DUT(s) in the set using an operating system that is compatible with the respective type of DUT(s) in the set.

The present application relates to memory subsystem analysis based on threshold distributions. A system is disclosed, comprising: a memory component having a plurality of memory cells capable of being in a plurality of states, each state in the plurality of states corresponding to a value stored by the memory cell; and a processing device operatively coupled to the memory component to perform operations comprising: obtaining a plurality of threshold voltage distributions for the plurality of memory cells, wherein each of the plurality of distributions corresponds to one of the plurality of states; classifying each of the plurality of distributions into one of a plurality of classifications; generating a vector comprising a plurality of components, wherein each of the plurality of components represents the class of a corresponding one of the plurality of distributions; and processing the generated vector using a classifier to determine a likelihood that the memory component will fail within a target time period.

A computer-implemented method for determining a signature embedding vector for information technology events. The method including: receiving a current data object representing an occurrence of an information technology event associated with a configurable item; obtaining a graph database of logical associations of the configurable item; applying a clustering algorithm to the graph database to determine a subset of graphs; extracting associated data of the subset of graphs; applying an embedding model to the extracted associated data to determine an embedding vector for each subset of graphs; aggregating the embedding vector for each subset of graphs to determine a signature embedding vector; and utilizing the signature embedding vector to perform further analysis of the current data object.

A vehicle data verification apparatus includes a database (DB) and a processor connected with the DB. The processor is configured to: import at least one vehicle data set; identify whether the at least one vehicle data set is stored in the DB; verify pieces of vehicle data in the at least one vehicle data set upon identifying that the at least one vehicle data set is stored in the DB; and export a result of the verification of the pieces of vehicle data.

Techniques for collaboratively executing runbook sessions are disclosed. A system provides for collaborative completion of runbook operations by delegating runbook operations or by transferring ownership of a runbook from one user to another. A system identifies a first set of runbook operations capable of being completed by a first user. The system identifies a second set of runbook operations that are not capable of being completed by the first user. The system identifies a second user capable of completing the second set of runbook operations. Upon completion of the first set of runbook operations by the first user, the system determines whether to delegate the second set of runbook operations to the second user or to transfer ownership of the runbook session to the second user. The system determines that the runbook session is completed based on the combined results collaboratively generated by the initial user and the alternate user.

A processing system may execute diagnostic software to test the functionality of one or more components arranged in a physical space for use during a video conference. Functionality of the diagnostic software may include detecting that the component is in a limited condition for use during the video conference. The processing system may initiate a response, based on an output of the diagnostic software, to change the condition of the component to increase the functionality of the component.

A computer-implemented method for determining a signature embedding vector for information technology events. The method including: receiving a current data object representing an occurrence of an information technology event associated with a configurable item; obtaining a graph database of logical associations of the configurable item; applying a clustering algorithm to the graph database to determine a subset of graphs; extracting associated data of the subset of graphs; applying an embedding model to the extracted associated data to determine an embedding vector for each subset of graphs; aggregating the embedding vector for each subset of graphs to determine a signature embedding vector; and utilizing the signature embedding vector to perform further analysis of the current data object.

Methods, system, and non-transitory processor-readable storage medium for an Out of Memory test baseline system are provided herein. An example method includes executing a plurality of test cases on a system. A test score calculation module calculates a test case score for each of the executed test cases in a subset of the plurality of test cases. An Out of Memory (OOM) test baseline configuration system trains a machine learning system, using the subset test scores, to predict a baseline test score for an unexecuted test case. A test case score prediction module predicts the baseline test score for the unexecuted test case. A test case configuration tuning module tunes the unexecuted test case to determine a baseline configuration for the unexecuted test case, to identify OOM issues when the unexecuted test case is executed on a test system.

An example system includes a first memory that includes primary storage; a second memory that includes secondary storage; and a control system for predicting paths through a test program that will be taken during a planned execution of the test program, and for causing test data associated with the test program to be stored in the first memory or the second memory based on the paths predicted.

A test device according to an embodiment includes a first substrate including a data conversion module having a first connector for receiving data acquired from an external equipment, an FPGA (Field Programmable Gate Array) for converting the received data, and a second connector for outputting the converted data; a second substrate including a test module including a third connector connected to the second connector of the first substrate and receiving the converted data, a test unit for performing a test operation using the data received through the third connector, a communication unit for transmitting test result information according to a test operation result of the test unit to an outside, and a control unit for controlling the test unit and the communication unit; and a housing for accommodating the first substrate and the second substrate.

A neural processing unit (NPU) is capable of testing a component of the NPU in a running system, i.e., during runtime. The NPU includes a plurality of functional components, each of which includes an electronic circuit; at least one wrapper connected to at least one of the functional components; and an in-system component tester (ICT). The ICT performs a selection of one of the at least one functional component, in an idle state, as a component under test (CUT) and performs a test, via the at least one wrapper, of the selected functional component. The ICT may monitor states of the plurality of the functional components via the at least one wrapper, stop the test based on a detection of a collision due to an access to the selected functional component, and return a connection of the selected functional component to the at least one wrapper according to the stop.

An example system includes a first memory that includes primary storage; a second memory that includes secondary storage; and a control system for predicting paths through a test program that will be taken during a planned execution of the test program, and for causing test data associated with the test program to be stored in the first memory or the second memory based on the paths predicted.

A processing system may execute diagnostic software to test the functionality of one or more components arranged in a physical space for use during a video conference. Functionality of the diagnostic software may include detecting that the component is in a limited condition for use during the video conference. The processing system may initiate a response, based on an output of the diagnostic software, to change the condition of the component to increase the functionality of the component.

The invention discloses a server performance grade evaluation method and device, and relates to the technical field of servers, and the method comprises the steps: carrying out the performance test of an accelerator card, and obtaining an accelerator card performance evaluation value; performing multi-class communication test on the plurality of interconnection types corresponding to each device to obtain a communication performance value; performing a video memory performance test on the video memory unit to obtain a video memory performance value; testing the reasoning process and expert partition performance of the model deployed on the server to obtain a first separation adaptive value and a second separation adaptive value of the server; determining a total evaluation value of the server according to the accelerator card performance evaluation value, the communication performance value, the video memory performance value, the first separation adaptive value, the second separation adaptive value and the corresponding target weight values; and according to the total evaluation value, determining the target performance grade of the server from the plurality of performance grades, so that the accuracy of server performance grade evaluation is improved.

A method, computer program product, and computing system for generating a respective diagnostic history associated with each physical storage device of a plurality of physical storage devices. A subset of diagnostic history associated with a class of failing physical storage devices is identified from the diagnostic history associated with each physical storage device. The subset of diagnostic history associated with the class of failing physical storage devices is enhanced. A physical storage device failure event for a target physical storage device is forecast using a machine learning model and the enhanced subset of diagnostic history associated with the class of failing physical storage devices.

Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.

A method of operating a network is provided that includes receiving a query, using a first language model to determine an intent or purpose of the query, using a second language model to extract a named entity from the query, and obtaining search results by searching for the extracted named entity on a named entity list corresponding to a particular tenant. The method can further include generating a response based on the search results. The query can be a natural language query, and the first language model can be a natural language model. The second language model for extracting the named entity can be a network-related language model that is trained on network records associated with a plurality of tenants. The network records associated with the plurality of tenants can be stored on a multi-tenant database.

A method and device for predicting errors in a computing system are disclosed. The error prediction method includes: receiving log data generated by the computing system during operation of the computing system; tokenizing the log data into tokens; inputting the tokens to a discriminator model which generates scores of the respective tokens, each score corresponding to a probability that the corresponding token is an anomaly token; determining an anomaly score based on the scores; and determining a likelihood of future occurrence of an error in the computing system based on the anomaly score.

To provide a dynamic hierarchical connection method formed in each measurement window of automobile bus tool software.SOLUTION: Setting an upstream data stream input port and a downstream data stream output port for each measurement window, the upstream data stream input port of each measurement window being connected to a downstream data stream output port or a data source of a measurement window other than the measurement window to which the upstream data stream input port belongs to form a hierarchical connection, and each measurement window and other measurement windows being dynamic hierarchical connections.SELECTED DRAWING: Figure 1

To provide a method and a system of implementing a measurement data filter, specifically in automotive bus tool software, belonging to a technical field of data filtering.SOLUTION: A method of implementing a measurement data filter in automotive bus tool software, includes: setting an upstream data flow input port, a downstream data flow output port and a filtering function for each measurement window; connecting the upstream data flow input port of each measurement window to the downstream data flow output port of one measurement window other than the measurement window itself or a data source to form a hierarchical connection; receiving, by each layer of measurement window, data from an upper-layer measurement window via its upstream data flow input port and filtering the received data and then transporting the data to a lower-layer measurement window via its downstream data flow output port.SELECTED DRAWING: Figure 1

Systems and methods of testing portable access devices are described. One method includes configuring, by an access device comprising a reader device, an access application on the access device for a first interaction scenario based on a first configuration command received from a reader controller. The method also includes conducting, by the access device, the first interaction scenario between the access device and a test tool; The method also includes receiving, by the access device, first interaction data from the test tool; The method also includes processing, by the access device, the first interaction data, thereby generating first test output data; The method also includes providing, by the access device, the first test output data to the reader controller, which sends the test output data to the test tool, wherein the first test output data is compared to first expected output data to determine if the access device is capable of conducting the second interaction scenario; The method also includes configuring, by the access device, the access application on the access device for a second interaction scenario based on a second configuration command received from the reader controller; The method also includes conducting, by the access device, the second interaction scenario between the access device and the test tool receiving, by the access device, second interaction data from the test tool; The method also includes processing, by the access device, the second interaction data, thereby generating second test output data; The method also includes providing, by the access device, the second test output data to the reader controller, which sends the second test output data to the test tool, wherein the second test output data is compared to second expected output data to determine if the access device is capable of conducting the second interaction scenario, The method also comprises the reader controller sending a first signal to the reader device to turn off the reader device after the first interaction scenario is completed, and the reader controller sending a second signal to the reader device to turn on the reader device to start the second interaction scenario.

This invention discloses a device testing method, apparatus, electronic device, and storage medium. The invention can acquire device status information; retrieve puncture simulation scenarios from a puncture library based on the device status information; simulate punctures according to the puncture simulation scenarios to perform puncture scenario testing on the device; and inspect the device that has completed the puncture scenario testing to obtain the test results of the device's online capability. This invention automates device testing, accurately completing various puncture scenario simulation tests. While ensuring sufficiently large and refined test coverage, it improves testing efficiency. Furthermore, when device failure occurs later, the device testing method of this invention can locate the corresponding fault point based on the corresponding puncture simulation scenario. This invention not only achieves automated testing, reducing labor costs, but also improves testing efficiency and reduces repair costs after device failure.

An autonomous operational health intelligence system for mission-critical SAP cloud workloads and method thereof is disclosed. The invention relates to a device-based structure configured to continuously monitor, analyze, predict, and regulate operational conditions of enterprise workloads deployed across distributed cloud infrastructure environments. The system comprises at least one processing unit coupled with a non-transitory memory unit, a telemetry acquisition unit for receiving real-time operational data from application execution systems, database systems, and infrastructure resources, a data harmonization processor for transforming heterogeneous telemetry signals into structured operational datasets, an analytical processing unit for deriving workload health indicators, a predictive assessment processor for generating forward-looking stability indicators based on historical and real-time execution patterns, a control determination unit for identifying deviations and producing corrective directives, and a response execution unit configured to transmit operational control signals to remote computing elements.

Automated portable device testing methods and systems are described. One method includes receiving, by a reader controller, a configuration file including configuration data, and providing, by the reader controller, a configuration command associated with the configuration data to an access application on an access device. The method also includes configuring the access application on the access device for an interaction context based on the configuration command, and initiating the interaction context between the access device and a testing tool. The method also includes receiving, by the access device, interaction data from the testing tool, and processing, by the access device, the interaction data, thereby generating testing output data. The method also includes providing, by the access device, the testing output data to the reader controller, and sending, by the reader controller, the testing output data to the testing tool.

The invention concerns a method of detecting abnormal behavior of a computer system comprising an application layer, a software layer and a hardware layer; the method being carried out by a processing unit and comprising the steps of: - collecting metrics relating to at least one of application layer, software layer and hardware layer, - normalizing every metric into a range of an image coding, - creating an image from at least all normalized metrics, and - applying a data science model such as a deep learning model on the created image to detect abnormal behavior.