138results about "Error avoidance" patented technology

A system and method for assessing, broadcasting and remediating a health of a cloud computing infrastructure. A system includes a health assessment platform interfaced with components of the cloud computing infrastructure, and configured to perform an evaluation loop of the cloud computing infrastructure to produce a health assessment, the evaluation loop assessing one or more of a drift between desired and actual states, resource utilization, cost or spend controls, policy compliance, and planning or refactoring of the components of the cloud computing infrastructure. The health assessment platform is further configured to report the health assessment via notifications to a user interface associated with the cloud computing infrastructure. The system further includes a remediation engine that is interfaced with the user interface, the remediation engine configured to provide a user of the user interface with remediation recommendations and optimizations based on the reported health assessment.

A system and method for detecting code intent of code objects in a computing environment for assessing cybersecurity risk is presented. The method includes detecting a plurality of code objects in a computing environment; statically analyzing each code object of the plurality of code objects to determine a plurality of potential intents, each potential intent corresponding to an action; generating a cybersecurity signal in the computing environment based on a plurality of event records; detecting in the cybersecurity signal a first event corresponding to a potential intent; determining that the potential intent is a code intent based on the detection; detecting in the cybersecurity signal a second event which does not correspond to any potential intent; and initiating a remediation action in the computing environment based on the detected second event.

Methods and articles of manufacture for hosting a safety critical application on an uncontrolled data processing device are provided. Various checks and combinations of checks including installation, functional, host integrity, coexistence, interoperability, power management, and environment checks are performed at various times to determine if the safety critical application operates properly on the device. The operation of the SCA on the UDPD may be controlled accordingly.

A system and method for fail-operational mission continuity in a multi-agent autonomous system. Each autonomous agent includes an onboard Prognostic Health Management (PHM) module that monitors health using sensor data. Upon detecting an incipient fault, the PHM module calculates a prognostic Remaining Useful Life (RUL). This RUL is transformed into a quantitative Operational Risk Cost (Ω) and communicated to a multi-agent control system. The control system's dynamic task allocation algorithm uses the Ω values as key inputs in a multi-objective optimization process. This enables the system to proactively and autonomously re-allocate a task from a degrading agent to a healthy agent before a failure occurs. The degrading agent is simultaneously commanded to perform a safe contingency maneuver. This integration of real-time prognostics and multi-agent control creates a resilient, self-healing system capable of completing missions despite hardware degradation.

A system and method for anomaly prediction in a plurality of applications to maintain operability of the system. Real-time data from the plurality of applications can be received, and for each desired variable in the data an entropy that can indicate an anomaly in the respective data. The data can be divided into batches of fixes size and a plurality of machine learning (ML) models applied to each of the batches to determine whether the data contains an anomaly or is about to contain an anomaly. The plurality of ML models can be trained based on historical data from the plurality of applications. A large language model (LLM) can be applied to the output of the plurality of ML models to evaluate performance / fine tune the plurality of ML models, and an alert can be transmitted to a system that automatically executes script(s) to take action to avoid the anomaly.

This disclosure describes techniques for intelligently detecting overheating devices in a network or data center and taking actions to address such overheating devices. This disclosure also describes evaluating heat dissipation information associated with components of devices in a network, making predictions about network disruptions based on the evaluation of the heat dissipation information, and taking actions to address, mitigate, or prevent such network disruptions. In one example, this disclosure describes a method that includes collecting, by a computing system, information about thermal metrics for a plurality of network devices; identifying, by the computing system and based on the information about the thermal metrics, a specific network device that changes temperature quickly; and taking action, by the computing system, to address effects of overheating associated with the specific network device.

Embodiments of this disclosure relate to methods, electronic devices, and computer program products for managing storage systems. The method includes determining whether the storage device failure will no longer occur based on reference values ​​of attribute parameters in a first set of the attribute parameters and current values ​​of attribute parameters in a second set of the attribute parameters, if it is determined that the storage device will fail based on current values ​​of multiple attribute parameters in the storage system. The method further includes determining the cause of the failure based on the attribute parameters in the first set if it is determined that the storage device failure will no longer occur. Embodiments of this disclosure can analyze the cause of storage device failure, thereby avoiding the erroneous identification of internally faulty storage devices as internally faulty ones.

The present invention provides a device and method for predicting the occurrence of failures in underground conduit networks. [Solution] The device includes a reading module configured to read connection data, the connection data including data indicating one or more pipe segments and data indicating one or more valves, where the end of each pipe segment is connected to the end of another pipe segment or a valve; a clustering module configured to automatically generate one or more clusters from the connection data, where each cluster includes pipe segments that form a connection network between valves; a cluster failure likelihood calculation module configured to calculate the failure likelihood of each cluster by summing the failure likelihoods of the pipe segments included in each cluster; and a display module configured to display the one or more clusters and the failure likelihood of each cluster on a display.

A system and related method, including a first processor core, a second processor core and a control circuitry to receive requests to update existing firmware of the first processor core to a new firmware. The control circuitry loads existing firmware onto the second processor core and migrates data associated with the existing firmware on the first processor core to the second processor core. The control circuitry stops the transaction execution by the first processor core while causing the second processor core to execute transactions in place of the first processor core. The control circuitry restarts the first processor core, replaces the existing firmware on the first processor core with the new firmware, and migrates data of the existing firmware on the second processor core to the first processor core. The control circuitry stops the transaction execution of the second processor core and causes the first processor core to execute transactions.

Embodiments of the present application disclose a wild pointer processing method, system, device and storage medium. According to the type information of each memory object, the technical solution provided by the embodiments of the present application determines the memory object of a specified type as a target memory object; then in the case of releasing the target memory object, a target zombie object is created, the instruction set architecture pointer of the target memory object is modified, and the instruction set architecture pointer is pointed to the target zombie object; and then the target zombie object is saved to the application memory, so that when the calling party uses the instruction set architecture pointer to call the target memory object, the calling party is intercepted based on the target zombie object through a message forwarding mechanism, and the calling logic is processed. By using the above technical means, the wild pointer processing of the online running environment of the application program can be realized, the application program can be prevented from crashing, the running effect of the application program is optimized, and the user experience is improved.

This application relates to the computer field, and provides a data restoration method and apparatus for a storage device, to implement data restoration without losing data after an attack by ransomware, and reduce performance overheads and storage space overheads. The method includes: A storage device stores data in append write mode; determines first data in response to inaccessibility to target data, where the first data is data marked as invalid, and is data associated with the target data; and restores the first data.

The invention relates to a method for calibrating a physical unit (12) and a system (10) for calibrating and adjusting a physical unit. The physical unit is arranged in the real process environment (14). A virtual component (26) disposed in a virtual process environment (24) is provided. The virtual process environment is formed to correspond to the real process environment such that the physical units arranged in the real process environment are represented in a manner corresponding to the virtual components (26) in the virtual process environment. Measurements are performed with a physical unit to obtain true measurements. The measurement is simulated with a virtual component to obtain a virtual measurement value. The offset is determined by a verification unit (30) based on a comparison of the real measurement and the virtual measurement. The real measurement value corresponds to the virtual measurement value. The physical unit is calibrated by the verification unit using at least the offset such that the compensation data based on the offset takes into account future measurements for the physical unit.

The present invention relates to a system (108) and a method (600) for processing alarm data. The method (600) includes step of retrieving, historic alarm data from one or more data sources (110). The method (600) further includes step of analysing, using an Artificial Intelligence / Machine Learning (AI / ML) model (220) at least one of, patterns, trends and behaviour of one or more anomalies based on the retrieved historic alarm data. Further, receiving, real time alarm data from the one or more data sources (110). The method (600) further includes step of categorizing, one or more alarms based on the received real time alarm data using at least one of, the AI / ML model (220). Thereafter the method (600) further includes step of predicting, using the AI / ML model (220), one or more future alarms representing the one or more anomalies based on the categorization of the one or more alarms.

The invention relates to an open world communication device (16) in communication with an avionics system (12) of an aircraft, comprising application components (22-1,..., 22-N). The device (16) further comprises an interface component (24) comprising a communication module (31) able to intercept each request sent by the application components (22-1,..., 22-N) and a clone (33) of the avionics system (12) able to test each request intercepted by the communication module (31) to determine a status of this request between a qualified status and a non-qualified status. The communication module (31) is only able to send to the avionics system (12) requests having a qualified status.

The invention discloses a large-scale scientific computing workflow collaborative management and fault-tolerant system based on multiple clusters, and the system comprises a workflow and resource modeling module which obtains task and resource feature data; the entropy regular optimal transmission scheduling module is used for calculating a transportation plan result; the GMPC coding structure construction module is used for introducing cluster dimensions into a GMPC model and constructing a topological anisotropic multi-element coding structure; the parameter linkage control module is used for adaptively adjusting a redundancy rate and a cluster dimension index; the multi-cluster distributed encoding and computing module is used for executing encoding and parallel computing; the cross-cluster fault-tolerant recovery and reconstruction module is used for executing GMPC interpolation recovery and dynamically adjusting parameters when the performance is insufficient; the global result integration and feedback updating module is used for integrating reconstruction results and updating task execution states; and the operation monitoring and data management module is used for acquiring system operation information and uniformly managing data. According to the invention, the multi-cluster collaborative scheduling and fault-tolerant efficiency is improved.

Aspects of the disclosure are directed to power state management and diagnostics. In accordance with one aspect, the disclosure includes a neural network (NN) module configured to process a plurality of vectorized channel samples and to generate a plurality of power supply fault predictions; and a vectorization module coupled to the NN module, the vectorization module configured to vectorize a plurality of windowed channel samples and to generate the plurality of vectorized channel samples.

A method and an apparatus for handling a memory failure is provided. The method includes: in response to detecting a failure occurring in memory of a host machine, acquiring (101) a failure parameter of the memory, determining (102) a crash probability of the host machine based on the failure parameter, and transferring (103) all virtual machines on the host machine to a target host machine when the crash probability is greater than or equal to a first predetermined threshold. A crash probability of the target host machine is less than a second predetermined threshold. The second predetermined threshold is less than the first predetermined threshold. Further, an electronic device and storage medium are provided.

To provide data management, reduction, and sampling schemes for a storage device failure.SOLUTION: A method for training a machine learning model includes the steps of: segmenting a dataset of a database into one or more datasets on the basis of time period windows by a processor; allocating one or more weights to the one or more datasets on the basis of the time period windows of the one or more datasets by the processor; generating a training dataset from the one or more datasets on the basis of the one or more weights by the processor; and training the machine learning model using the training dataset by the processor.SELECTED DRAWING: Figure 5

In some implementations, a memory device may cache a subset of one or more block family error avoidance (BFEA) lookup tables associated with a block family associated with host data in a first memory location. The block family may be based on at least one of a time window during which the host data was written or a temperature window at which the host data was written. The memory device may receive a read command associated with host data and determine, based on the block family and the subset of the one or more BFEA tables, a threshold voltage offset associated with the host data. The memory device may compute a modified threshold voltage by applying the threshold voltage offset to a base read level voltage associated with the host data. The memory device may read, using the modified threshold voltage, the host data from the first memory location.

A data processing method, an apparatus, a chip, and a computer device are disclosed, and relate to the field of computers. The method includes: based on a state of a cache line in a memory changing from a non-modified state to a modified state, starting a timer of the cache line; and writing the cache line back to a next-level memory when the timer of the cache line expires. In this way, because the cache line in the modified state is monitored based on the timer of the cache line, the cache line does not remain in the modified state for a long time. When the timer of the cache line expires, the cache line is written back to the next-level memory, and data of the cache line is backed up. This effectively alleviates a risk, of data failure of the cache line in the modified state, caused by bitflip, so that reliability of the cache line in the modified state is improved.

A method of operating a blockchain system with decentralized computing nodes includes determining an elected clan representing a. subset of the plurality of the decentralized computing nodes that are capable of approving addition of the block to the blockchain with less than or equal to a threshold probability of failure and performing a block approval process which includes determining a vertex comprising a digest of the block, communicating the vertex to each computing node, communicating the block only to the nodes in the elected, clan, while preventing communication of the block to the nodes that are not members of the elected clan, and receiving one or more messages indicating receipt of the vertex and / or receipt of both the vertex and the block.

Apparatus and methods for flexible input / output signaling over a same signaling channel are described. A programmable interface circuit includes a signaling channel that can be adapted, prior to use or during operation, for transmission and / or reception of different types of analog and digital signals. The interface circuit can be used for communications between an isolating communication controller and components of a machine that use diverse signaling types.

The invention relates to a distributed file system, a method thereof and a non-transitory machine readable medium. The plurality of computing devices are communicatively coupled to each other via a network, and each of the plurality of computing devices is operably coupled to one or more of the plurality of storage devices. The plurality of faulty resilient strips are distributed over the plurality of storage devices such that each of the plurality of faulty resilient strips spans the plurality of storage devices. The graphics processing unit is operable to access the data file from the faulty resilient stripe while bypassing the kernel page cache. In addition, these data files may be accessed in parallel by a graphics processing unit.

Techniques for data intake that prevent corruption of data repositories with faulty data are disclosed. A data load may include individual values that are erroneous and individual values that are non-erroneous. A system uses a machine learning (ML) model trained to classify the data load, as a whole, as erroneous or non-erroneous. In a data intake process, the system applies the ML model to the data load. In response to determining that the data load is erroneous, the system prevents the storage of the data load within a target data repository.

An artificial-intelligence-assisted (AI-assisted) certification system includes an argumentation processor and an assurance case processor. The argumentation processor is configured to generate an argumentation pattern. The assurance case processor is configured to obtain the argumentation pattern from the argumentation processor, to automatically generate an assurance case based on one or more argumentation patterns, to determine evidence indicative of premises in the argumentation pattern, and to automatically assess the assurance case based on the evidence.

Systems and methods for assessing essential services fitness scores and responding thereto are disclosed. According to an embodiment, a method may include: receiving, by a computer program executed by an electronic device, data points from a plurality of computing assets; retrieving, by the computer program, a mapping of the plurality of computing assets to essential services; calculating, by the computer program, a fitness score for each essential service using the data points and the mapping; generating, by the computer program, a report comprising the fitness score for each of the essential services; and executing, by the computer program, a corrective action in response to one of the fitness scores being below a threshold.

An apparatus predicts a failure occurring in a pipe network, the apparatus comprising a reading module configured to read connection data, wherein the connection data comprises data indicating pipe segments and data valves, and wherein an end of each pipe segment is connected to an end of another pipe segment or a valve, a clustering module configured to automatically create data indicating clusters from the connection data, wherein each cluster comprises some of the at pipe segments which form a connection network between some of the valves, a cluster likelihood of failure (LOF) calculating module configured to calculate an LOF of each cluster by summing LOFs of the pipe segments included in the cluster, and a displaying module configured to display the data indicating the clusters and the LOF of each cluster on a display device.

Forecasting industrial asset failures is described. A system determines a data start value associated with an industrial asset at a data start time. The system determines an end-of-data value associated with the industrial asset at an end-of-data time. The system estimates a fault time when a trend of estimating from a data start value to a data end value will reach a fault limit value. The system determines a distance to the fault based on the fault limit value and the data end value. The system outputs a fault forecast for the industrial asset associated with a fault time and a distance from the fault.