Nec Corporation

Siemens Aktiengesellschaft

Kai Zhang

Haifeng Chen

Guofei Jiang

Wei Cheng

Zhengzhang Chen

Hui Joyce Zhang

Amit Chakraborty

Zhichun Li

LuAn Tang

Kenji Yoshihira

Jianwu Xu

Fabian Moerchen

Tan Yan

A computer-implemented method for simultaneous metric learning and variable selection in non-linear regression is presented. The computer-implemented method includes introducing a dataset and a target variable, creating a univariate neighborhood probability map for each reference point of the dataset, and determining a pairwise distance between each reference point and other points within the dataset. The computer-implemented method further includes computing a Hessian matrix of a quadratic programming (QP) problem, performing optimization of the QP problem, re-weighing data derived from the optimization of the QP problem, and performing non-linear regression on the re-weighed data.

Mining non-linear dependencies via a neighborhood mixture model

Systems and methods are provided for acquiring data from an input signal using multitask regression. The method includes: receiving the input signal, the input signal including data that includes a plurality of features; determining at least two computational tasks to analyze within the input signal; regularizing all of the at least two tasks using shared adaptive weights; performing a multitask regression on the input signal to create a solution path for all of the at least two tasks, wherein the multitask regression includes updating a model coefficient and a regularization weight together under an equality norm constraint until convergence is reached, and updating the model coefficient and regularization weight together under an updated equality norm constraint that has a greater l-penalty than the previous equality norm constraint until convergence is reached; selecting a sparse model from the solution path; constructing an image using the sparse model; and displaying the image.

Annealed sparsity via adaptive and dynamic shrinking

Systems and methods for managing components of physical systems, including decomposing raw time series by extracting an aging trend and a fluctuation term from the time series using an objective function of an optimization problem, the objective function minimizing reconstruction error and ensuring flatness of the fluctuation term over time. The optimization problem is transformed into a Quadratic Programming (QP) formulation including a monotonicity constraint and a non-negativity constraint, the constraints being merged together to reduce computational costs. An aging score and a confidence score are generated for the extracted aging trend to determine a severeness of aging for one or more components of the physical system, and the aging score and confidence score are fused to provide a fused ranking for the extracted aging trend for predicting future failures of the components.

Aging profiling engine for physical systems

A system and method are provided. The system includes a processor. The processor is configured to receive a plurality of events from network devices, the plurality of events including entities that are involved in the plurality of events. The processor is further configured to embed the entities into a common latent space based on co-occurrence of the entities in the plurality of events and model respective pairs of the entities for compatibility according to the embedding of the entities to form a pairwise interaction for the respective pairs of the entities. The processor is additionally configured to weigh the pairwise interaction of different ones of the respective pairs of the entities based on one or more compatibility criterion to generate a probability of an occurrence of an anomaly and alter the configuration of one or more of the network devices based on the probability of the occurrence of the anomaly.

Entity embedding-based anomaly detection for heterogeneous categorical events

A method is provided for root cause anomaly detection in an invariant network having a plurality of nodes that generate time series data. The method includes modeling anomaly propagation in the network. The method includes reconstructing broken invariant links in an invariant graph based on causal anomaly ranking vectors. Each broken invariant link involves a respective node pair formed from the plurality of nodes such that one of the nodes in the respective node pair has an anomaly. Each causal anomaly ranking vector is for indicating a respective node anomaly status for a given one of the plurality of nodes when paired. The method includes calculating a sparse penalty of the casual anomaly ranking vectors to obtain a set of time-dependent anomaly rankings. The method includes performing temporal smoothing of the set of rankings, and controlling an anomaly-initiating one of the plurality of nodes based on the set of rankings.

Ranking causal anomalies via temporal and dynamical analysis on vanishing correlations

An exemplary method for detecting one or more anomalies in a system includes building a temporal causality graph describing functional relationship among local components in normal period; applying the causality graph as a propagation template to predict a system status by iteratively applying current system event signatures; and detecting the one or more anomalies of the system by examining related patterns on the template causality graph that specifies normal system behaviors. The system can align event patterns on the causality graph to determine an anomaly score.

Anomaly fusion on temporal casualty graphs

Systems and methods are provided for optimizing system output in production systems, comprising. The method includes separating, by a processor, one or more initial input variables into a plurality of output variables, the output variables including environmental variables and system response variables. The method also includes building, using the processor, a nonparametric estimation that determines a relationship between one or more initial control variables and the system response variables, and estimating a global input-output mapping function, using the determined relationship, and a range of the environmental variables. The method further includes generating one or more optimal control variables from the initial control variables by maximizing the input-output mapping function and the range of the environmental variables. The method additionally includes incorporating one or more of the optimal control variables into a production system to increase production output of the production system.

Output efficiency optimization in production systems

In a support vector regression approach to forecasting power load in an electrical grid, a feature learning scheme weights each feature in the input data with its correlation with the predicted load, increasing the prediction accuracy. The kernel matrix for the input training data is computed such that features that align better with the target variable are given greater weight. The resulting load forecast may be used to compute commands sent to demand response modules.

Growing community of inventors

Monmouth Junction, NJ, United States of America

Kai Zhang