The defect detection in glass materials by using discrete wavelet packet transform and artificial neural network

In this study, a method based on impact tests was designed in order to determine undamaged and broken glasses. By means of using an impact pendulum, impact was applied on glasses and the generated sounds were transferred to the computer using a microphone. The sound signals were decomposed into 128 components by using Discrete Wavelet Packet Transform (DWPT) at the seventh level. 16 of the 128 components that characterized the properties of undamaged and broken glasses were chosen as inputs for the designed Artificial Neural Network (ANN). The designed ANN model was tested with real-time simulation, and it was observed that the proposed method could determine undamaged and broken glasses with high precision. This method, which is based on analyzing the sounds generated after the impact, can detect defects that the conventional visual methods can detect; however, it can also be used as supplement to these methods

Determining damages in ceramic plates by using discrete wavelet packet transform and support vector machine

In this study, an analysis was conducted by using discrete wavelet packet transform (DWPT) and support vector machine (SVM) methods to determine undamaged and cracked plates. The pendulum was used to land equal impacts on plates in this experimental study. Sounds, which emerge from plates as a result of the impacts applied to undamaged and cracked plates, are sound signals used in the analysis and DWPT of these sound signals were obtained with 128 decompositions for feature extraction. The first four components, reflecting the characteristics of undamaged and cracked plates within these 128 components, were selected for enhancing the performance of the classifier and energy values were used as feature vectors. In the study, the SVM model was created by selecting appropriate C and γ parameters for the classifier. Undamaged and cracked plates were seen to be successfully identified by an analysis of the training and testing phases. Undamaged and cracked statuses of the plates that are undamaged and have the analysis had identified different cracks. The biggest advantage of this analysis method used is that it is high-precision, is relatively low in cost regarding experimental equipment and requires hardware

Can Tc-99m labeled erythrocyte scintigraphy be an alternative non-invasive method to endometriosis diagnosis?

Background: Endometriosis is defined as the implantation of endometrial gland and stroma ectopically outside the uterus. Clinically, it is a hormone dependent benign disease accompanied by pelvic pain and infertility. The aim of this study was to demonstrate the activated implants with 99m-Tc labeled erythrocyte scintigraphy (99mTc-RBCs) in patients with recurrent endometriosis and compare the results with pelvic MRI results.Methods: Patients who were diagnosed histopathologically as endometriosis either with operation and / or therapeutic laparascopy or laparotomy and, were included to present study. Thirty patients, who were diagnosed as recurrence by clinical, and laboratory terms and 10 healthy volunteer (control group) patients were included in the study. Between the second and fifth days of menstruation when the endometriotic lesions were highly activated, radionuclide imaging was performed by 99mTc-RBCs and compared with pelvic MRI findings.Results: In 27 patients out of 30 patients (90%) pathological accumulation of radioactivity foci with 99mTc-RBCs were present. The focal pathological accumulation was significant in 26 patients and moderate in 1 patient. In 22 patients (81.5%) the increased radioactivity accumulation in radionuclide images was concordant with MRI images. Regarding the MRI as reference, the sensitivity of 99mTc-RBCs was determined as 96%, specificity 29%, positive predicitive value 81% and negative predictive value was 66%.Conclusions: Imaging of endometriosis regions with 99m-Tc-RBCs can be an alternative diagnostic procedure for the patients with recurrent endometriosis

The defect detection in glass materials by using discrete wavelet packet transform and artificial neural network

In this study, a method based on impact tests was designed in order to determine undamaged and broken glasses. By means of using an impact pendulum, impact was applied on glasses and the generated sounds were transferred to the computer using a microphone. The sound signals were decomposed into 128 components by using Discrete Wavelet Packet Transform (DWPT) at the seventh level. 16 of the 128 components that characterized the properties of undamaged and broken glasses were chosen as inputs for the designed Artificial Neural Network (ANN). The designed ANN model was tested with real-time simulation, and it was observed that the proposed method could determine undamaged and broken glasses with high precision. This method, which is based on analyzing the sounds generated after the impact, can detect defects that the conventional visual methods can detect; however, it can also be used as supplement to these methods

Open strategy and open foresight : conceptual position and directions for research

We look at how the relatively grounded concept 'open innovation' has led to the development of open strategy and open foresight and focus on the latter twos' interaction, building a conceptual case and contributing to strategic management literature. Current literature focuses on innovation, we focus on SMEs strategy making. We conclude this paper highlighting that foresight practices have always advocated of being 'open' for collecting insights which is similar to what is advocated by open strategy, thus presenting an opportunity to explore both the conceptual and normative effects of combining open strategy and open foresight literature

Determination of optimum operation cases in electric arc welding machine using neural network

With arc welding machines, welding is only performed at optimum operating points. Determination of optimum operating points is important so as for welding machines which will be produced in future to be developed in a manner to operate in such parts. In this study, an Artificial Neutral Networks method was used in order to determine the optimum operating points of Electric Arc welding machine. For this purpose, a measurement system used to get the current measurements during the welding operation. A welding process includes some stages like initial case; transient case and operation case respectively. So as to use ANN model, a data set was established via time series. ANN is trained with 90% of data set and tested with 10% thereof At the end of the test, a prediction of 97.49% was made according to the regression value. And according to the MSE value, it was understood that a successful prediction was made with an error of 0.00353075 values