The present invention relates to an automatic prediction system for a server failure, which monitors the status of a single server connected to a network and providing web, DB and network services, and predicts and warns a server failure of a target system by using the collected status data of the single server. The automatic prediction system for a server failure comprises: a data collection module to collect status information of a server and service of a target system; a model generation and optimization module to generate a CNN-based failure prediction model by using the collected data and to optimize model parameters and hyper-parameter values; and a prediction module to perform online failure prediction by using the optimized CNN-based failure prediction model.

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 method for repairing a memory element in a memory device by an electronic device includes configuring a memory element as a graph with a vertex and an edge, a node associated with the memory element being encoded with information related to a fault, determining, from the graph, a repair policy using a probability distribution over one or more of a faulty line and a non-faulty line as predicted by a graph neural network (GNN) based on a final node feature value from message passing stages of the GNN, and determining a value of a state using a probability of the memory element being repaired from a particular state based on a global mean of all the final node feature values predicted by the GNN.

A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

The present disclosure relates to system(s) and method(s) for assisting a user to resolve a hardware issue and a software issue. The system identifies, a target cluster, associated with a new ticket received from the user, from the set of clusters. Further, the system recommends one or more runbook scripts, from a runbook repository, associated with the new ticket. The system further identifies a new runbook script, corresponding to the new ticket, from a set of external repositories. Further, the system executes at least one of the one or more runbook scripts or the new runbook script, associated with the new ticket. The system further generates a document based on the execution of the one or more runbook scripts or the new runbook script, thereby assisting the user to resolve a target issue.

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.

A device trains a classification model with defect classifier training data to generate a trained classification model and processes information indicating priorities and rework efforts for defects, with a Pareto analysis model, to select a set of classes for the defects. The device calculates defect scores for the set of the classes and selects a particular class, from the set of the classes, based on the defect scores. The device processes a historical data set for the particular class to identify a root cause corrective action (RCCA) recommendation and processes information indicating a defect associated with the particular class, with the trained classification model, to generate a predicted RCCA recommendation for the defect. The device processes the predicted RCCA recommendation and the RCCA recommendation, with a linear regression model, to determine an effectiveness score for the predicted RCCA recommendation and retrains the classification model based on the effectiveness score.

Memory devices, and methods of operating similar memory devices, include an array of memory cells comprising a plurality of access lines each configured for biasing control gates of a respective plurality of memory cells of the array of memory cells, wherein the respective plurality of memory cells for one access line of the plurality of access lines is mutually exclusive from the respective plurality of memory cells for each remaining access line of the plurality of access lines, and a controller having a plurality of selectively-enabled operating modes and configured to selectively operate the memory device using two or more concurrently enabled operating modes of the plurality of selectively-enabled operating modes for access of the array of memory cells, with each of the enabled operating modes of the two of more concurrently enabled operating modes utilizing an assigned respective portion of the array of memory cells.

Methods, apparatus, systems and articles of manufacture are disclosed that provide an apparatus to monitor watermark encoder operation, the apparatus comprising: a data collector to collect one or more types of heartbeat data from a watermark encoder, the heartbeat data including time varying data, the one or more types of the heartbeat data defined by a software development kit (SDK); a machine learning engine to process the heartbeat data to predict whether the watermark encoder is associated with respective ones of a plurality of failure modes; and an alert generator to, in response to the machine learning engine predicting the watermark encoder is associated with a first one of the failure modes: generate an alert indicating the at least one of the one or more components to be remedied according to the first one of the failure modes; and transmit the alert to a watermark encoder management agent.

A method of the present disclosure includes: plating a plurality of substrates using a substrate holder; determining a total number of substrates that have been plated using the substrate holder until a failure occurs in the substrate holder; determining a first processable number and a second processable number; generating a first data set constituted by a combination of first condition data and the first processable number, the first condition data representing a state of a component of the substrate holder; generating a second data set constituted by a combination of second condition data and the second processable number, the second condition data representing a state of a component of the substrate holder; and optimizing a parameter of a prediction model constituted by a neural network using training data including the first data set and the second data set.