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1. (WO2019066104) PROCESS CONTROL METHOD AND SYSTEM WHICH USE HISTORY DATA-BASED NEURAL NETWORK LEARNING
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Pub. No.: WO/2019/066104 International Application No.: PCT/KR2017/010922
Publication Date: 04.04.2019 International Filing Date: 29.09.2017
IPC:
G05B 13/02 (2006.01) ,G05B 13/04 (2006.01) ,G05B 11/36 (2006.01) ,G05B 23/02 (2006.01) ,G05B 19/418 (2006.01)
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13
Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02
electric
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13
Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02
electric
04
involving the use of models or simulators
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
11
Automatic controllers
01
electric
36
with provision for obtaining particular characteristics, e.g. proportional, integral, differential
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23
Testing or monitoring of control systems or parts thereof
02
Electric testing or monitoring
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
19
Programme-control systems
02
electric
418
Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
Applicants:
전자부품연구원 KOREA ELECTRONICS TECHNOLOGY INSTITUTE [KR/KR]; 경기도 성남시 분당구 새나리로 25 25, Saenari-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 13509, KR
Inventors:
지영민 JI, Young Min; KR
유준재 YOO, June Jae; KR
Agent:
남충우 NAM, Choong Woo; KR
Priority Data:
10-2017-012684229.09.2017KR
Title (EN) PROCESS CONTROL METHOD AND SYSTEM WHICH USE HISTORY DATA-BASED NEURAL NETWORK LEARNING
(FR) PROCÉDÉ ET SYSTÈME DE COMMANDE DE TRAITEMENT FAISANT APPEL À UN APPRENTISSAGE DE RÉSEAU NEURONAL BASÉ SUR DES DONNÉES D'HISTORIQUE
(KO) 히스토리 데이터 기반 뉴럴 네트워크 학습을 통한 공정 제어 방법 및 시스템
Abstract:
(EN) Provided are a process control method and system which use history data-based neural network learning. A process control method according to an embodiment of the present invention comprises: learning a control algorithm by using a past process variable and a past variable characteristic as an input and a past process control variable as an output; and using the learned control algorithm so as to receive a current process variable and a current variable characteristic, thereby outputting a current process control variable, wherein the variable characteristic is characteristic data extracted from the process variable. Therefore, automatic control can be performed by utilizing an artificial intelligence technology without stopping a process or an equipment operation.
(FR) L'invention concerne un procédé et un système de commande de traitement faisant appel à un apprentissage de réseau neuronal basé sur des données d'historique. Un procédé de commande de traitement selon un mode de réalisation de la présente invention consiste à : apprendre un algorithme de commande à l'aide d'une variable de traitement passée et d'une caractéristique de variable passée en tant qu'entrée et d'une variable de commande de traitement passée en tant que sortie ; et utiliser l'algorithme de commande appris de façon à recevoir une variable de traitement courante et une caractéristique de variable courante, délivrant ainsi une variable de commande de traitement courante, la caractéristique de variable étant des données caractéristiques extraites de la variable de traitement. Par conséquent, une commande automatique peut être effectuée au moyen d'une technologie d'intelligence artificielle sans arrêter un traitement ou une opération d'équipement.
(KO) 히스토리 데이터 기반 뉴럴 네트워크 학습을 통한 공정 제어 방법 및 시스템이 제공된다. 본 발명의 실시예에 따른 공정 제어 방법은, 과거의 공정 변수와 과거의 변수 특성을 입력으로 과거의 공정 제어 변수를 출력으로 제어 알고리즘을 학습시키고, 학습된 제어 알고리즘을 이용하여 현재의 공정 변수와 현재의 변수 특성을 입력받아 현재의 공정 제어 변수를 출력하며, 변수 특성은 공정 변수로부터 추출한 특성 데이터이다. 이에 의해, 공정이나 설비 운영을 멈추지 않고도 인공지능 기술을 활용하여 자동제어를 수행할 수 있다.
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Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: Korean (KO)
Filing Language: Korean (KO)