MX368054B - Sistema y procedimiento de deteccion de anomalias. - Google Patents

Sistema y procedimiento de deteccion de anomalias.

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Publication number
MX368054B
MX368054B MX2016002227A MX2016002227A MX368054B MX 368054 B MX368054 B MX 368054B MX 2016002227 A MX2016002227 A MX 2016002227A MX 2016002227 A MX2016002227 A MX 2016002227A MX 368054 B MX368054 B MX 368054B
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MX
Mexico
Prior art keywords
error vectors
detection system
series data
hardware processor
time
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MX2016002227A
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English (en)
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MX2016002227A (es
Inventor
Puneet Agarwal
Gautam Shroff
Malhotra Pankaj
Vig Lovekesh
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Tata Consultancy Services Ltd
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Publication of MX2016002227A publication Critical patent/MX2016002227A/es
Publication of MX368054B publication Critical patent/MX368054B/es

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2237—Vectors, bitmaps or matrices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G06F16/2365—Ensuring data consistency and integrity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • G06F2218/16—Classification; Matching by matching signal segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Algebra (AREA)
  • Operations Research (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Security & Cryptography (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Debugging And Monitoring (AREA)

Abstract

Se proporciona un sistema y un procedimiento de detección de anomalías. Comprendiendo el sistema: un procesador de hardware; y una memoria que almacena instrucciones para configurar el procesador de hardware, en el que el procesador de hardware recibe unos primeros datos de series temporales que comprenden un primer conjunto de puntos, y unos segundos datos de series temporales que comprenden un segundo conjunto de puntos, calcula un primer conjunto de vectores de error para cada punto del primer conjunto, y un segundo conjunto de vectores de error para cada punto del segundo conjunto, comprendiendo cada conjunto de vectores de error uno o más errores de predicción; estima parámetros en base al primer conjunto de vectores de error; aplica (o usa) los parámetros en el segundo conjunto de vectores de error; y detecta una anomalía en los segundos datos de series temporales cuando los parámetros se aplican en el segundo conjunto de vectores de error.
MX2016002227A 2015-04-10 2016-02-19 Sistema y procedimiento de deteccion de anomalias. MX368054B (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IN1516MU2015 2015-04-10

Publications (2)

Publication Number Publication Date
MX2016002227A MX2016002227A (es) 2017-01-05
MX368054B true MX368054B (es) 2019-09-18

Family

ID=55398189

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2016002227A MX368054B (es) 2015-04-10 2016-02-19 Sistema y procedimiento de deteccion de anomalias.

Country Status (7)

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US (1) US10223403B2 (es)
EP (1) EP3139313B1 (es)
JP (1) JP6313730B2 (es)
AU (2) AU2016201088A1 (es)
BR (1) BR102016003934B1 (es)
CA (1) CA2921054C (es)
MX (1) MX368054B (es)

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AU2018203321B2 (en) 2019-06-06
JP2016201088A (ja) 2016-12-01
US10223403B2 (en) 2019-03-05
AU2018203321A1 (en) 2018-05-31
AU2016201088A1 (en) 2016-10-27
EP3139313A2 (en) 2017-03-08
BR102016003934A2 (pt) 2016-10-11
MX2016002227A (es) 2017-01-05
EP3139313B1 (en) 2021-07-21
BR102016003934B1 (pt) 2022-09-06
US20160299938A1 (en) 2016-10-13
EP3139313A3 (en) 2017-04-05
CA2921054C (en) 2020-07-07
JP6313730B2 (ja) 2018-04-18
CA2921054A1 (en) 2016-10-10

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