Computational Intelligence Methods in Measurement Systems
Fast, reliable and accurate measurements are the precondition for any serious experimental scientific research or industrial application. The common characteristic of the contemporary measurement system is its increasing complexity. The measuring instruments and the measurement systems are becoming the components of a global information system and offer not only simple measurements but also have embedded a high level of complexity in the analysis and interpretation of measurement results. Using distributed object computing the components of the measurement system can be deployed anywhere on the network and shared by a large number of applications. The artificial intelligence methods offer the new approaches in the design of smart measurement systems.
The goal of this project is to investigate the possibilities of development and application of the artificial intelligence methods and machine learning techniques in measurement systems ranging from the development of simple models to the synthesis of sophisticated measurement procedures and smart instruments capable not only to execute simple commands but to perform rather complex tasks. A smart instrument is expected to have integrated domain-specific knowledge and learning ability that will enable it to adapt to peculiar operating conditions and to maintain high accuracy and reliability of measurements. The research will be focused on algorithms and methods for reducing the processing time and the complexity of measurement procedures and for increasing the accuracy, flexibility and the reliability of both embedded and distributed measurement systems.
By reducing the complexity of measurement algorithms and calculation procedures while preserving a high measurement accuracy we expect to shorten their calculation time thus enabling its efficient application in real-time measurements. We hope to discover accurate and reliable models of complex measurement methods and to develop adaptive instruments and the advanced measurement procedures of distributed measurement system. The proposed research is expected to solve some specific real-time flow measurement problems. We expect the cooperation with research laboratories and industry.
Ivan Ivek, Ph.D. student