In this paper, we present different papers discussing soft computing methods applied in development, optimization and control of intelligent building systems. We present the approaches used, their advantages in solving control problems, analyze and modelling of human behavior in order to implement energy saving control solutions besides accounting for user comfort.
The University of Oradea is working on developing a research base in material sciences with capabilities of vacuum deposition. The laboratory will be equipped with two deposition system along with with other research equipment, mainly an atomic force microscope and a Nano indenter for material characterization. This paper presents the continuity of our efforts to install two vacuum deposition systems that will be part of the SMARTMAT Laboratory at the University of Oradea. As steps were made in cleaning the equipment, we now focus on obtaining high vacuum. In this process, a constant monitoring solution of the pressure inside the chamber is required, and the steps taken to realize it are presented in this paper. The main focus of the paper is the data acquisition system implemented for monitoring several parameters of which vacuum is the most important. The system will be based on two Pfeiffer Gauges for vacuum and a National Instruments PXI system.
Production systems must be flexible and endowed with techniques and tools allowing an automatic recovery of errors. And so, the subject of error recovery in flexible manufacturing system is always an open issue. The objective of this work consists in proposing a new type of software framework for error troubleshooting in a flexible manufacturing system that is perceived as an Intelligent Space (iSpace). Our framework system is designed to solve the failures in the functioning of the FMS and to generate self-training from previous experience.
This paper introduces a new type of software framework for error troubleshooting in flexible manufacturing systems using a frame marker device. Our framework system meant to resolve the failures originated from the human-machine interface and to generate self-training from previous experience.
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