Ning Xia resumes & CV records
Resumes
Ning Xia, 5620 Jarman Pl, Bridgewater, NJ 08807
Ning Xia Phones & Addresses
20 Jarman Pl, Bridgewater, NJ 08807
Saint Cloud, FL
Edison, NJ
Highland Park, NJ
Raritan, NJ
Somerset, NJ
Social networks
Ning Xia
Linkedin
Mentions for Ning Xia
Publications & IP owners
Us Patents
Time Series Retrieval For Analyzing And Correcting System Status
US Patent:
2019024, Aug 8, 2019
Filed:
Jan 11, 2019
Appl. No.:
16/245740
Inventors:
- Princeton NJ, US
Ning Xia - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
Ning Xia - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
International Classification:
G06F 11/34
G06F 11/07
G06F 11/28
G06F 16/2457
G06N 3/02
G06N 7/08
G06F 11/07
G06F 11/28
G06F 16/2457
G06N 3/02
G06N 7/08
Abstract:
Methods and systems for detecting and correcting anomalous behavior include generating a joint binary embedding of each of a set of historical time series sequences. A joint binary embedding of a recent time series sequence is generated. A ranked list of the plurality of historical time series sequences is generated according to respective similarities of each historical time series sequence to the recent time series sequence based on the respective joint binary embeddings of each. Anomalous behavior of a system associated with the recent time series sequence is determined according to a label of a top-ranked historical time series sequence in the ranked list. A corrective action is performed to correct the anomalous behavior.
Data2Data: Deep Learning For Time Series Representation And Retrieval
US Patent:
2019003, Jan 31, 2019
Filed:
May 29, 2018
Appl. No.:
15/991205
Inventors:
- Princeton NJ, US
Ning Xia - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
Ning Xia - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
International Classification:
G06F 17/30
G06F 17/11
G06F 15/18
G06F 17/11
G06F 15/18
Abstract:
A computer-implemented method for employing deep learning for time series representation and retrieval is presented. The method includes retrieving multivariate time series segments from a plurality of sensors, storing the multivariate time series segments in a multivariate time series database constructed by a sliding window over a raw time series of data, applying an input attention based recurrent neural network to extract real value features and corresponding hash codes, executing similarity measurements by an objective function, given a query, obtaining a relevant time series segment from the multivariate time series segments retrieved from the plurality of sensors, and generating an output including a visual representation of the relevant time series segment on a user interface.
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