http://ktisis.cut.ac.cy/handle/10488/32 Search the Channel search http://ktisis.cut.ac.cy/simple-search http://ktisis.cut.ac.cy/handle/10488/8146 Title: Fallback options for airgap sensor fault of an electromagnetic suspension system<br/><br/>Authors: Michail, Konstantinos; Zolotas, Argyrios C.; Goodall, Roger M.<br/><br/>Abstract: The paper presents a method to recover the performance of an electromagnetic suspension under faulty airgap sensor. The proposed control scheme is a combination of classical control loops, a Kalman Estimator and analytical redundancy (for the airgap signal). In this way redundant airgap sensors are not essential for reliable operation of this system. When the airgap sensor fails the required signal is recovered using a combination of a Kalman estimator and analytical redundancy. The performance of the suspension is optimised using genetic algorithms and some preliminary robustness issues to load and operating airgap variations are discussed. Simulations on a realistic model of such type of suspension illustrate the efficacy of the proposed sensor tolerant control method. http://ktisis.cut.ac.cy/handle/10488/8112 Title: Sensor fault detection with low computational cost : a proposed neural network-based control scheme<br/><br/>Authors: Michail, Konstantinos; Deliparaschos, Kyriakos M<br/><br/>Abstract: The paper describes a low computational power method for detecting sensor faults. A typical fault detection unit for multiple sensor fault detection with modelbased approaches, requires a bank of estimators. The estimators can be either observer or artificial intelligence based. The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as ‘i-FD’. In contrast with the bank-estimators approach the proposed i-FD unit is using only one estimator for multiple sensor fault detection. The efficacy of the scheme is tested on an Electro-Magnetic Suspension (EMS) system and compared with a bank of Kalman estimators in simulation environment. http://ktisis.cut.ac.cy/handle/10488/7837 Title: Optimizing Statistical Classification Accuracy of Satellite Remotely Sensed Imagery for supporting fast flood Hydrological Analysis<br/><br/>Authors: Alexakis, Dimitrios; Agapiou, Athos; Hadjimitsis, Diofantos G.; Retalis, Adrianos<br/><br/>Abstract: The aim of this study is to improve classification results of multispectralsatellite imagery for supporting flood risk assessment analysis ina catchment area in Cyprus. For this purpose, precipitation and groundspectroradiometric data have been collected and analyzed with innovativestatistical analysis methods. Samples of regolith and constructionmaterial were in situ collected and examined in the spectroscopy laboratoryfor their spectral response under consecutive different conditions ofhumidity. Moreover, reflectance values were extracted from the sametargets using Landsat TM/ETM+ images, for drought and humid timeperiods, using archived meteorological data. The comparison of theresults showed that spectral responses for all the specimens were lesscorrelated in cases of substantial humidity, both in laboratory and satelliteimages. These results were validated with the application of differentclassification algorithms (ISODATA, maximum likelihood, object based,maximum entropy) to satellite images acquired during time period whenprecipitation phenomena had been recorded. http://ktisis.cut.ac.cy/handle/10488/7787 Title: Monitoring urban land cover using satellite remote sensing techniques and field spectroradiometric measurements: case study of “Yialias” catchment area in Cyprus<br/><br/>Authors: Alexakis, Dimitrios; Hadjimitsis, Diofantos G.; Agapiou, Athos; Themistocleous, Kyriacos; Retalis, Adrianos<br/><br/>Abstract: This study highlights the need for digital mapping of urban sprawl phenomenon incatchment areas with the use of both satellite and ground remote-sensing techniques. The YialiasRiver, located in the central part of Cyprus, was selected as a case study area. In this catchment,devastating flash flood events took place in both 2003 and 2009, with catastrophic results. Initially,ground spectroradiometric measurements were applied to investigate the spectral similarityof different classes such as those of “urban fabric” and “marl/chalk origin formations” within thewatershed. Temporal land cover changes were analyzed by using multitemporal satellite imagery(ASTER) and by incorporating both pixel- and object-oriented classification techniques. Tocreate effective land use and land cover maps, a classification model was proposed based onspectral, texture, and shape characteristics. The pixel-based classification results were comparedand evaluated with the object-based classification products. The optimum classification productswere imported to geographical information systems and FRAGSTATS software and were used tovisually and statistically detect landscape identifying trends based on spatial landscape metrics.The final results indicated considerable urban expansion within the study area during the last10 years.