Application of artificial neural networks for short term wind speed forecasting in Mardin, Turkey

Artificial neural network models were used for short term wind speed forecasting in the Mardin area, located in the Southeast Anatolia region of Turkey. Using data that was obtained from the State Meteorological Service and that encompassed a ten year period, short term wind speed forecasting for the Mardin area was performed. A number of different ANN models were developed in this study. The model with 60 neurons is the most successful model for short term wind speed forecasting. The mean squared error and approximation values for training of this model were 0.378088 and 0.970490, respectively. The ANN models developed in the study have produced satisfactory results. The most successful among those models constitutes a model that can be used by the Mardin Electric Utility Control Centre

A proposal for visually handicapped students to use electrical control laboratory

In this paper a technical solution is presented for blind or visually impaired students to acquire abilities of experimental work in laboratory conditions enabling them to participate in the experiments jointly with the healthy students. For this purpose a special apparatus has been designed, which possesses deciding and declaring properties to aid the visually impaired persons in the laboratory environment. An approach based on artificial neural network was implemented. Motor sounds generated during experiments were used for training the ANN model. The results demonstrate that the designed ANN model produces highly reliable estimates used in the operation of the apparatus

Application of Artificial Neural Networks for Defect Detection in Ceramic Materials

In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items