The LED unit induced significantly higher temperature changes than did the HQTH. The temperature increase during orthodontic bonding was increased with long exposure time. A shorter light-tip tooth surface distance leads to greater increases in temperature.
Computer technology and software are widely used in every multi-discipline field. Geomatics engineering can be seen as a pioneer of these disciplines especially in photogrammetry and image processing. Photogrammetry is a method where geometric parameters of objects on digitally captured images are determined and make measurements on them. Capturing the digital images and photogrammetric processing include several fully defined stages, which allows to generate three-dimension or two-dimension digital models of the body as an end product. The aim of this study is to predict Holstein cows' live weight via artificial neural network whose body dimensions were determined with photogrammetry method. The body dimensions to be used in this study are obtained metric from analysis of cows' images captured by synchronized three-dimension camera environment from different aspects. Wither height, hip height, body length, hip width of cows determined with photogrammetry. Artificial neural network prediction model was developed by using these body measurements. Dataset is divided into two after preprocessing as training and testing dataset. Different structured artificial neural network models are generated and the artificial neural network model which has the best performance is determined. Then with this artificial neural network model live weight of animals is estimated by using measurements obtained from images. After comparison of estimated live weights and weights obtained from scale, correlation coefficient is found (R=0.995). The statistical analysis shows that both groups are meaningful and artificial neural network can be used in live weight prediction safely.
Artificial Neural Networks is the most used machine learning approach today. It is a very successful method in terms of accuracy and reliability. It is widely used in classification and estimation calculations. In order to achieve the desired performance a model created with ANN, a series of processes such as selection of network structure, learning algorithms, input and output values adjustment and transfer functions determination needs to be implemented in a sensitive manner. Multilayer Feedforward Backpropagation Network, which is used most frequently in supervised learning approaches, was considered in this study. The effect on the prediction performance of the developed model was investigated by using different statistical normalization methods on the data to be used in the network. For this purpose, 4-input 1-output artificial neural networks model were operated with wind-based data taken from Osmaniye Korkut Ata University measuring station. Wind speed, Wind Direction, Humidity and Density data are defined as input values while wind power was defined as output value. Input and output data are calculated with different normalization methods and more than one network models are designed with calculated values. As a result, the study showed that artificial neural networks model which is established by sigmoid normalization method has the best performance value.
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