FI114749B - Mekanism för att detektera avvikelser och förfarande för att undervisa den - Google Patents

Mekanism för att detektera avvikelser och förfarande för att undervisa den Download PDF

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Publication number
FI114749B
FI114749B FI20001997A FI20001997A FI114749B FI 114749 B FI114749 B FI 114749B FI 20001997 A FI20001997 A FI 20001997A FI 20001997 A FI20001997 A FI 20001997A FI 114749 B FI114749 B FI 114749B
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Prior art keywords
time
input
period
behavior
indicators
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FI20001997A
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English (en)
Finnish (fi)
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FI20001997A (sv
FI20001997A0 (sv
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Antti Sorvari
Albert Hoeglund
Kimmo Haetoenen
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Nokia Corp
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Application filed by Nokia Corp filed Critical Nokia Corp
Publication of FI20001997A0 publication Critical patent/FI20001997A0/sv
Priority to FI20001997A priority Critical patent/FI114749B/sv
Priority to EP01947345A priority patent/EP1325588A1/en
Priority to US10/363,745 priority patent/US7519860B2/en
Priority to PCT/EP2001/006405 priority patent/WO2002021774A1/en
Priority to AU2001269056A priority patent/AU2001269056A1/en
Priority to AU2001287759A priority patent/AU2001287759A1/en
Priority to PCT/FI2001/000783 priority patent/WO2002021242A1/en
Priority to CA2421928A priority patent/CA2421928C/en
Priority to CNB018154476A priority patent/CN1196984C/zh
Priority to JP2002524792A priority patent/JP4436042B2/ja
Priority to EP01967371A priority patent/EP1334417A1/en
Publication of FI20001997A publication Critical patent/FI20001997A/sv
Priority to US10/383,224 priority patent/US7613668B2/en
Application granted granted Critical
Publication of FI114749B publication Critical patent/FI114749B/sv

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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
    • G06N3/08—Learning methods
    • 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
    • G06N20/00—Machine learning

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
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  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medical Informatics (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
  • Alarm Systems (AREA)
  • Telephonic Communication Services (AREA)
  • Debugging And Monitoring (AREA)
  • General Factory Administration (AREA)

Claims (10)

1. Förfarande för inläming av en detekteringsmekanism för avvikel-ser i ett system, som omfattar observerbara element (302), varav ätminstone ett har ett periodiskt tidsberoende beteende, vilken detekteringsmekanism för 5 avvikelser omfattar en datorbaserad inlärningsmekanism (314) med en indata-rymd för definiering av indata bestäende av indatakomponenter (11,12); i vilket förfarande: - indikatorer (304), som indikerar de observerbara elementens (302) beteende, samlas och de samlade indikatorerna ordnas sä att indikatorerna för 10 respektive observerbara element adresseras tili samma indatakomponent; - inlärningsmekanismen (314) inlärs sä att dess indata omfattar indatakomponenter, som baserar sig pä de samlade indikatorerna (304); - punkter (14) som approximerar indata placeras i indatarymden; kännetecknat av att: 15. i ätminstone en indatakomponent (11, 12) inkluderas en tidspre- sentation (420-424); - väri tidspresentationen (420-424) är periodisk, kontinuerlig och en-tydig inom perioden av ätminstone ett element med ett periodiskt tidsberoende beteende. 20
2. Förfarande enligt patentkrav 1, kännetecknat av att inlär ningsmekanismen är eller den omfattar en självordnande karta. I
· : ' ’ 3. Förfarande enligt patentkrav 1 eller 2, kännetecknat av att v : tidspresentationen har en första period och ätminstone en andra period, som är en multipel av den första perioden. i 25
4. Förfarande enligt nägot av de föregäende patentkraven, k ä n - n e t e c k n a t av att indatakomponenterna skalas sä att var och ens varians ;" ‘; är densamma, företrädesvis ett.
5. Förfarande enligt nägot av de föregäende patentkraven, kännetecknat av att tidspresentationen har en variabel resolution sä att en ’ ' , _ 30 bit motsvarar olika tidsenheter beroende pä ändringar i det tidsberoende bete- ;* endet.
• ’· 6. Arrangemang för detektering av avvikelser i ett system, som om- :'fattar observerbara element (302), varav ätminstone ett har ett periodiskt tids-\ beroende beteende, *. 35 vilket arrangemang omfattar: - en datorbaserad inlärningsmekanism (314) med en indatarymd för 114749 definiering av indata bestäende av indatakomponenter (11,12); - medel för att samla indikatorer (304), som indikerar de observer-bara elementens (302) beteende och ordna de samlade indikatorerna sä att indikatorerna för respektive observerbara element adresseras tili samma inda- 5 takomponent; - medel för att inlära inlämingsmekanismen (314) sa att dess indata omfattar indatakomponenter, som baserar sig pä de samlade indikatorerna (304); - medel för att placera punkter (14) som approximerar indata i inda- 10 tarymden; kännetecknat av att arrangemanget omfattar: - ätminstone en indatakomponent (11,12), som omfattar en tidspre-sentation (420-424); - väri tidspresentationen (420-424) är periodisk, kontinuerlig och en-15 tydig inom perioden av nämnda ätminstone ett element med ett periodiskt tids- beroende beteende.
7. Arrangemang enligt patentkrav 6, k ä n n e t e c k n a t av att in-lärningsmekanismen är eller den omfattar en självordnande karta.
8. Arrangemang enligt patentkrav 6 eller7, kännetecknat av 20 att tidspresentationen har en första period och ätminstone en andra period, som är en multipel av den första perioden.
• ’ ., 9. Arrangemang enligt nägot av patentkraven 6-8, känneteck- . ; . n a t av att det har hopsatts tili ett nätelement.
10. Med dator avläsbart minnesmedel, som omfattar ett dataprog-t' 25 ram, k ä n n etec k n a t av att utförandet av programmet i en dator fär denna att utföra stegen i förfarandet enligt patentkrav 1. • > * » I I * - · ‘ · * · t » ‘ * » *
FI20001997A 2000-09-11 2000-09-11 Mekanism för att detektera avvikelser och förfarande för att undervisa den FI114749B (sv)

Priority Applications (12)

Application Number Priority Date Filing Date Title
FI20001997A FI114749B (sv) 2000-09-11 2000-09-11 Mekanism för att detektera avvikelser och förfarande för att undervisa den
EP01947345A EP1325588A1 (en) 2000-09-11 2001-06-06 System, device and method for automatic anomaly detection
US10/363,745 US7519860B2 (en) 2000-09-11 2001-06-06 System, device and method for automatic anomaly detection
PCT/EP2001/006405 WO2002021774A1 (en) 2000-09-11 2001-06-06 System, device and method for automatic anomaly detection
AU2001269056A AU2001269056A1 (en) 2000-09-11 2001-06-06 System, device and method for automatic anomaly detection
PCT/FI2001/000783 WO2002021242A1 (en) 2000-09-11 2001-09-10 Anomaly detection system and a method of teaching it
AU2001287759A AU2001287759A1 (en) 2000-09-11 2001-09-10 Anomaly detection system and a method of teaching it
CA2421928A CA2421928C (en) 2000-09-11 2001-09-10 Anomaly detection system and a method of teaching it
CNB018154476A CN1196984C (zh) 2000-09-11 2001-09-10 异常检测系统及其教导方法
JP2002524792A JP4436042B2 (ja) 2000-09-11 2001-09-10 異常検出システム及びそれを教授する方法
EP01967371A EP1334417A1 (en) 2000-09-11 2001-09-10 Anomaly detection system and a method of teaching it
US10/383,224 US7613668B2 (en) 2000-09-11 2003-03-07 Anomaly detection system and a method of teaching it

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FI20001997A FI114749B (sv) 2000-09-11 2000-09-11 Mekanism för att detektera avvikelser och förfarande för att undervisa den
FI20001997 2000-09-11

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FI20001997A0 FI20001997A0 (sv) 2000-09-11
FI20001997A FI20001997A (sv) 2002-03-12
FI114749B true FI114749B (sv) 2004-12-15

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US (2) US7519860B2 (sv)
EP (2) EP1325588A1 (sv)
JP (1) JP4436042B2 (sv)
CN (1) CN1196984C (sv)
AU (2) AU2001269056A1 (sv)
CA (1) CA2421928C (sv)
FI (1) FI114749B (sv)
WO (2) WO2002021774A1 (sv)

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EP1334417A1 (en) 2003-08-13
CA2421928C (en) 2011-11-15
JP2004508631A (ja) 2004-03-18
WO2002021242A1 (en) 2002-03-14
US7519860B2 (en) 2009-04-14
AU2001269056A1 (en) 2002-03-22
CA2421928A1 (en) 2002-03-14
FI20001997A (sv) 2002-03-12
JP4436042B2 (ja) 2010-03-24
US20040039968A1 (en) 2004-02-26
WO2002021774A1 (en) 2002-03-14
US20030225520A1 (en) 2003-12-04
CN1455890A (zh) 2003-11-12
US7613668B2 (en) 2009-11-03
CN1196984C (zh) 2005-04-13
EP1325588A1 (en) 2003-07-09
AU2001287759A1 (en) 2002-03-22
FI20001997A0 (sv) 2000-09-11

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