35results 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.

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 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.

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 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 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

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.

This application discloses a method and device for evaluating server performance levels, relating to the field of server technology. The method includes: performing performance testing on an accelerator card to obtain an accelerator card performance evaluation value; performing multi-type communication tests on multiple interconnect types corresponding to each device to obtain communication performance values; performing memory performance tests on the video memory unit to obtain memory performance values; testing the inference process and expert partitioning performance of the model deployed on the server to obtain a first separation fitness value and a second separation fitness value for the server; determining the server's total evaluation value based on the accelerator card performance evaluation value, communication performance value, memory performance value, first separation fitness value, second separation fitness value, and corresponding target weight values; and determining the server's target performance level from multiple performance levels based on the total evaluation value, thereby improving the accuracy of server performance level evaluation.

A processor system includes: one or more processors; and one or more memory resources. The memory resource is configured to store a diagnostic program configured to diagnose an electronic system including a mobile body or equipment, and a plurality of knowledge graphs for use in the diagnostic program and including at least a first knowledge graph and a second knowledge graph created based on domain knowledge different from that of the first knowledge graph. The processor is configured to execute the diagnostic program to (1) evaluate a semantic similarity between a first knowledge item included in the first knowledge graph and a second knowledge item included in the second knowledge graph, and (2) generate, when the similarity satisfies a predetermined condition, an integrated graph in which the first knowledge graph and the second knowledge graph are integrated.

A test and measurement system comprises one or more test and measurement devices, at least one of which is connected to a device under test (DUT), one or more memories, a generative artificial intelligence model (generative AI model) connected to the one or more test and measurement devices and the one or more memories, and one or more processors to provide an artificial intelligence assistant (AI assistant) as an interface to the generative AI model, to provide a user interface that allows a user to enter a prompt, to use the AI ​​assistant to translate the prompt into one or more queries for the generative AI model, and to send commands to the test and measurement device connected to the DUT to perform one or more tests on the DUT.To take the results from one or more tests and convert them into user-interpretable results, and to provide the user with the results from the input prompt in the user interface.

A failure predictor of a drive apparatus in a storage system is detected more accurately. A control apparatus 1 for a storage system S stores a learning model(s) 132M for evaluating response performance of a drive apparatus 3 with respect to execution of a command relating to input and output by the control apparatus 1. The control apparatus 1 acquires operation information of the drive apparatus 3 and inputs specified information regarding commands, which is included in the operation information, to the learning model 132M. The control apparatus 1 judges a failure predictor of the drive apparatus 3 on the basis of output relating to the response performance by the learning model 132M in response to the input of the specified information.

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.

An exemplary system includes a first memory containing a primary memory, a second memory containing a secondary memory, and a control system that anticipates the path taken through the test program during the planned execution of the test program and causes test data associated with the test program to be stored in the first or second memory based on the anticipated path.

The invention relates to a method for reporting fault states relating to components (11) of a passenger transport system, such as an elevator, an escalator, or a moving walkway, in a machine-supported manner. The method has the following steps: - detecting identity information (35) and / or fault information, which describes the identity or a fault state of a component (11) being examined, using a reporting device (5); - automatically transmitting the identity information (35) and / or the fault information to an analysis device (7); - automatically selecting a selection of possible causes (37) of the fault from a list (33) on the basis of the identity information (35) and / or the fault information and transmitting corresponding selection information to the reporting device (5); - automatically outputting the selection information on possible causes (37) of the fault using the reporting device (5) and receiving sub-selection information, which describes a sub-selection selected by a reporter from the selection of possible causes (37) of the fault; and - automatically transmitting the sub-selection information to the analysis device (7). Possible causes of the fault states can thus be reported and collected in a simple and standardized manner. If specific possible causes of faults arise particularly frequently, a monitoring device (9) can be informed so that developers can optionally further develop the pertinent components (11) in a targeted manner.

A method for configuring a test system may include receiving, with a switch, a request including an identification or configuration request of a first test asset of the test system for testing. The method may also include accessing a current configuration and current test asset utilization of the test system, evaluating the current configuration based on the current test asset utilization and the request, determining a test configuration based on the evaluation and an implementation matrix for the test system, and implementing the switch according to the test configuration. Implementing the switch may include the switch connecting the test assets according to the test configuration such that configuration according to the first test or configuration request for the first test asset is performed non-exclusively with respect to capabilities of the test system implemented to test other test assets.

A test and measurement system includes: one or more test and measurement instruments, at least one of which is connected to a device under test (DUT); one or more memories; a generative artificial intelligence (AI) model connected to the one or more test and measurement instruments and the one or more memories; and one or more processors to: provide an artificial intelligence (AI) assistant as an interface for a generative AI model; presenting a user interface, the user interface allowing a user to enter a prompt; translating the hint into one or more queries for a generative AI model using an AI assistant; sending a command to a test and measurement instrument connected to the DUT to perform one or more tests on the DUT; obtaining results from the one or more tests and converting them into user interpretable results; and providing a result to the user according to the prompt at the user interface.

A system for verifying a system-on-chip (SoC) including a plurality of intellectual property cores (IPs) includes a library configured to store a plurality of sequence codes corresponding to the plurality of IPs and a plurality of task codes for driving the SoC, and processing circuitry configured to, generate at least one test scenario by combining one or more of the sequence codes and one or more of the task codes stored in the library, generate metadata based on the at least one test scenario, the metadata having a specific format, and generate universal verification methodology (UVM) test code based on the metadata.

Embodiments of the present invention relate to an apparatus and method for checking a process and an electronic control apparatus. An apparatus for checking a process can include a conversion controller configured to control conversion into at least one process monitoring message by inputting a specific value into a virtual area included in at least one message corresponding to a preset operation when the preset operation is performed in serial communication, and a check controller configured to control a check process based on the process monitoring message.

A test and measurement system includes one or more test and measurement instruments at least one of which connects to a device under test (DUT), one or more memories, a generative artificial intelligence (AI) model connected to the one or more test and measurement instruments, and the one or more memories, and one or more processors to provide an artificial intelligence (AI) assistant as an interface to the generative AI model, present a user interface that allows a user to enter a prompt, use the AI assistant to translate the prompt into one or more queries for the generative AI model, send commands to the test and measurement instrument connected to the DUT to perform one or more tests on the DUT, take results from the one or more tests and convert them to user-interpretable results, and provide the user with results from the prompt at the user interface.