A flight deck system for an aircraft includes a display device for providing a graphical interface for displaying flight-related information including a plurality of windows to an operator. The display device is configured for displaying the plurality of windows within a plurality of regions. The plurality of regions can each have a predefined shape and orientation on the display screen according to a regular grid layout. A touch interface is coordinated with the display device for receiving touch information from the operator and allowing the operator to interact with the graphical interface. A processor is communicatively coupled with the touch interface device and operatively coupled with the display device. The processor can be configured to dynamically recreate a selected window of flight-related information within one or more of the plurality of regions corresponding to an operator-selected icon. In such embodiments, the operator can operate the graphical interface through direct touch.

Disclosed herein are systems and method for determining a boot status of a failover server. In an exemplary aspect, a method may receive a failover test request for a failover server that provides disaster recovery for a production server, wherein the failover test request queries a successful boot status of the failover server. The method may determine whether a login into the failover server can be performed to execute the failover test request. In response to determining that the login cannot be performed, the method may retrieve server metrics for a failover server from a metric store and may determine a probability of the successful boot status based on both the retrieved server metrics and historic server metrics. In response to determining that the probability is greater than a threshold probability, the method may mark a recovery point of the failover server as validated.

Disclosed is a system comprising a memory component having a plurality of memory cells capable of being in a plurality of states, each state of the plurality of states corresponding to a value stored by the memory cell, and a processing device, operatively coupled with the memory component, to perform operations comprising: obtaining, for the plurality of memory cells, a plurality of distributions of threshold voltages, wherein each of the plurality of distributions corresponds to one of the plurality of states, classifying each of the plurality of distributions among one of a plurality of classes, generating a vector comprising a plurality of components, wherein each of the plurality of components represents the class of a respective one of the plurality of distributions, and processing, using a classifier, the generated vector to determine a likelihood that the memory component will fail within a target period of time.

Methods and systems for networked systems are provided. A reinforcement learning (RL) agent is deployed during runtime of a networked system having at least a first component and a second component. The RL agent detects a first degradation signal in response to an error associated with the first component and a second degradation signal from the second component, the second degradation signal generated in response to the error. The RL agent identifies from a learned data structure an action for fixing degradation, at both the first component and the second component; and continues to update the learned data structure, upon successful and unsuccessful attempts to fix degradation associated with the first component and the second component.

A method for testing a technical system. The method includes: tests are carried out with the aid of a simulation of the system, the tests are evaluated with respect to a fulfillment measure of a quantitative requirement on the system and an error measure of the simulation, on the basis of the fulfillment measure and error measure, a classification of the tests as either reliable or unreliable is carried out, and a test database is improved on the basis of the classification.

In accordance with an embodiment, described herein are systems and methods for supporting a declarative non code self-learning advisory framework in an orchestration based application integration. The systems and methods can provide an advisory framework as a component of an integration platform which can allow declaratively defined recommendations, guidance, warnings etc. to be shown to the consumer of the platform on occurrence of certain events. The advisory framework can provide benefits such as: 1) allowing any entity to declaratively define/modify the rules and advices which will immediately get reflected across the customer fleet without dependency on product's release cadence; 2) where such updates to declaratively defined rules and advices does not involve any code changes to do the product; 3) comprises a structure which is generic and not component specific; and 4) can have self-learning capabilities from the generated product metrics.

A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

A signal analysis method of analyzing a performance of a device under test is described. A digitized input signal is obtained, wherein the digitized input signal is associated with the device under test. At least one characteristic quantity is determined via an artificial intelligence circuit. The artificial intelligence circuit includes at least one computing parameter. The at least one characteristic quantity is determined based on the digitized input signal and based on the at least one computing parameter. The at least one characteristic quantity is indicative of at least one performance property of the device under test. Further, a test system for analyzing a performance of a device under test as well as a computer program or program product are described.

A system for condition monitoring and fault diagnosis includes a data collection function that acquires time histories of selected variables for one or more of the components, a pre-processing function that calculates specified characteristics of the time histories, an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components, and a reasoning function for determining the condition of the one or more components from the one or more hypotheses.

A method for detecting repairable boards requiring repair amongst many boards which may or may not require repair applies a board detection model based on training features of many sample repairable boards. The method obtains repair-relevant information of all the sample repairable boards, extracts predetermined features from the repair-relevant information, and analyzes the predetermined features to obtain the training features. The board detection model is established and trained based on the training features, and receives repair-relevant information of each repairable board to obtain a result of detection repairable board according to the board detection model. A device for detecting repairable boards is also provided.