The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: “useful” (sharp enough) and “useless” (worn out). The case “useless” indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the “useful” state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state (“useful” versus “useless”) can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system.
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The left heart chamber's contractibility is an important part of heart diagnostics. Ultrasonographic pictures are very often used as the imaging method, as they are widely available, inexpensive and non-invasive. However, ultrasonographic pictures are very unclear, blurred and noisy, and thus very difficult for automatic analysis. To obtain a quick and useful analysis of ventricle performance, a special mathematical model has been created. The model can be used in contour detection, visualization of the heart's motion and even in automatic surface analysis. We hope that in the future such programs could be incorporated into a general medical expert system.
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