Mesterséges intelligencia módszerek alkalmazása a folyamatmodellezésben = Artificial intelligence methods in process modelling

Mesterséges intelligencia folyamatmérnöki alkalmazásai területén folytattunk kutatást az alábbi résztémákban: 1. Folyamatrendszerek számítógéppel segített modellezése Formális módszereket dolgoztunk ki folyamatrendszerek modelljeinek felállítására, ellenőrzésére és egyszerűsítésére. A folyamatrendszereket jellemző teljesítmény- és méret-indexek definiálásával feltételeket fogalmaztunk meg minimális modellek meghatározására. 2. Folyamatmodell ontológiák létrehozása Vizsgálatokat folytattunk folyamatrendszerek meghibásodásainak és az ezeket kiváltó berendezésekhez, emberekhez és eljárásokhoz kapcsolódó tényezők közötti összefüggések leírására, amelyeket formálisan ontológiák segítségével fogalmaztuk meg. 3. Predikció-alapú diagnosztika többléptékű (multi-scale) modellek alkalmazásával Predikció-alapuló intelligens diagnosztikai rendszert dolgoztunk ki, amely egységes környezetben kezeli a mérnöki modelleket és a heurisztikus információkat. A többléptékű modell alkalmazásával lehetőség nyílt automatikus fókuszálásra a hibadetektálási és veszteség?megelőzési feladatokban. 4. Multi-ágens alapú diagnosztikai rendszer fejlesztése Kidolgoztunk egy hibadetektálási, predikció-alapú diagnosztikai és veszteség-megőrzési feladatokat együttesen megvalósító multi-ágens alapú diagnosztikai rendszert. A folyamat-specifikus és a diagnosztikai ismereteket moduláris ontológiák formájában reprezentáltuk, ezeket integráltuk a kifejlesztett multi-ágens rendszerbe. | Artificial intelligence methods in process systems engineering has been investigated as follows: 1. Computer aided process modelling Formal methods are developed for construction, investigation and simplification of process models. Based on the notion of performance and size indices conditions are proposed for developing minimal models. 2. Generating ontology for process models The abnormal conditions of process system and their relation to the plant, people and procedures has been investigated and formalised with ontology. 3. Prediction-based diagnosis using multi-scale models A prototype prediction-based intelligent diagnostic system is developed that is capable integrating process models and operation experiences. The multi-scale model approach supports focusing in case of fault detection and loss prevention. 4. Multi-agent based diagnostic system A multi-agent diagnostic system is developed using of combination of diagnostic methods from heterogeneous knowledge sources. The process specific and diagnostic information are represented as ontology and they are integrated to the multi-agent system

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

Process hazard analysis, hazard identification and scenario definition: are the conventional tools sufficient, or should and can we do much better?

Hazard identification is the first and most crucial step in any risk assessment. Since the late 1960s it has been done in a systematic manner using hazard and operability studies (HAZOP) and failure mode and effect analysis (FMEA). In the area of process safety these methods have been successful in that they have gained global recognition. There still remain numerous and significant challenges when using these methodologies. These relate to the quality of human imagination in eliciting failure events and subsequent causal pathways, the breadth and depth of outcomes, application across operational modes, the repetitive nature of the methods and the substantial effort expended in performing this important step within risk management practice. The present article summarizes the attempts and actual successes that have been made over the last 30 years to deal with many of these challenges. It analyzes what should be done in the case of a full systems approach and describes promising developments in that direction. It shows two examples of how applying experience and historical data with Bayesian network, HAZOP and FMEA can help in addressing issues in operational risk management

Prediction-based diagnosis and loss prevention using qualitative multi-scale models

A prototype prediction based intelligent diagnostic system that is capable of integrating qualitative and quantitative process models and operation experience in the form of HAZOP result tables is proposed in this paper. The knowledge base of the system is organized in a hierarchical way following the hierarchy levels of the multi-scale model of the process system. This supports the focusing of the fault detection and loss prevention and thus decomposes the otherwise computationally hard problem. The system is illustrated on the example of a commercial fertilizer granulator drum

Prediction-based diagnosis and loss prevention using qualitative multi-scale models

A prototype prediction based intelligent diagnostic system that is capable of integrating qualitative and quantitative process models and operational experience in the form of HAZOP result tables is proposed in this paper. The diagnostic system utilizes Gensym's real time G2 expert system software. Its diagnostic "cause-effect" rules and possible actions (suggestions) are extracted from the results of standard HAZOP analysis. The knowledge base of the system is organized in a hierarchical way following the hierarchy levels of a multi-scale model of the process system. This supports focusing used by fault detection and loss prevention and thus decomposes the otherwise computationally hard problem. Prediction by simplified dynamic models are used to reduce ambiguity in case of multiple possible causes and/or multiple possible mitigating actions. The system is illustrated on the example of a commercial fertilizer granulator circuit using a simulation test bed. (C) 2006 Elsevier Inc. All rights reserved