Gears are used to transmit power and motion in mechanical, electrical and chemical process industries. Influenced by vibration, torque, temperature, lubrication & specific film thickness, the gear teeth contacts may experience change leading to unexpected failures such as wear, scutting, pitting and micro-pitting on teeth surface. In order to avoid these damages, continuous monitoring is essential using knowledge based systems, Generic capability of artificial neural network (ANN) is exploited to formulate prediction and classification based on heuristic models of condition of lubricating oil in spur gears. Based on the loading conditions such as vibration, temperature and torque, the algorithm predicts film thickness to classify oil conditions as elastohydrodynamic (EHD), mixed wear and severe wear that helps in detecting faults in gear operation. and Obsahuje seznam literatury
Our aim was to analyze the correlation of early postoperative cortisol levels in patients after transsphenoidal pituitary adenoma surgery compared to the standard dose ACTH test and Insulin tolerance test (ITT) several months later. We retrospectively reviewed data from 94 patients operated for pituitary adenoma in years 2009-2012. The comparison of day 7 (median) postoperative basal cortisol levels and 3.6 months (median) after pituitary adenoma surgery stimulation test - standard dose 250 μg 1-24ACTH test in 83 patients or ITT in 11 patients were performed. All 16 patients with early postoperative cortisol levels >500 nmol/l proved a sufficient response in the stimulation tests. At basal cortisol levels of 370-500 nmol/l the sufficient response was found in 96 % (27/28) of patients. In the postoperative basal cortisol levels 200-370 nmol/l we found a preserved corticotroph axis later on in 88 % (28/32) of cases. Patients with basal cortisol levels 100-200 nmol/l had a maintained corticotroph axis function in 8/11 cases - 73 %. All patients with an early postoperative basal cortisol level above 500 nmol/l proved in the stimulation tests a preserved corticotroph axis function. The interval 370-500 nmol/l showed a minimal risk of postoperative adrenal insufficiency., V. Hána Jr., J. Ježková, M. Kosák, M. Kršek, J. Marek, D. Netuka, M. Hil, V. Hána., and Obsahuje bibliografii
The paper presents the possibility of application of frontal neural rietworks, genetic and eiigenic algorithrns in predicting gross domestic product development by designing a prediction model whose accuracy is superior to the model ušed in practice [1]. The learning process is implemented by means of a newly designed algorithm based on the EuSANE algorithm [2].
This paper addresses the problem of stock market data prediction. It discusses the abilities of neural networks to learn and to forecast price quotations as well as proposes a neural approach to the future stock price prediction and detection of high increases or high decreases in stock prices. In order to validate the approach, a large number of experiments were performed on real-life data from the Warsaw Stock Exchange.
The final microstructure and resulting mechanical properties in the linepipe steels are predominantly determined by austenite decomposition during cooling after thermomechanical and welding processes. The paper presents some results of the research connected with the development of a new approach based on the artificial neural network to predicting the martensite fraction of the phase constituents occurring in five microalloyed steels after continuous cooling. The independent variables in the model are chemical compositions, niobium condition, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For the purpose of constructing these models, 104 different experimental data were gathered from the literature. According to the input parameters in feedforward backpropagation algorithm, the constructed networks were trained, validated and tested. In this model, the training and testing results in the artificial neural network have shown a strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.
Safety monitoring and stability analysis of high slopes are important for high dam construction in mountainous regions or precipitous gorges. Slope stability estimation is an engineering problem that involves several parameters. To address these problems, a hybrid model based on the combination of Genetic algorithm (GA) and Back-propagation Artificial Neural Network (BP-ANN) is proposed in this study to improve the forecasting performance. GA was employed in selecting the best BP-ANN parameters to enhance the forecasting accuracy. Several important parameters, including the slope geological conditions, location of instruments, space and time conditions before and after measuring, were used as the input parameters, while the slope displacement was the output parameter. The results shown that the GA-BP model is a powerful computational tool that can be used to predict the slope stability.
Uniaxial compressive strength (UCS) represents one of the key mechanical properties used to characterize rocks along with the other important properties of porosity and density. While several studies have proved the accuracy of artificial intelligence in modeling UCS, some authors believe that the use of artificial intelligence is not practical in predicting. The present paper highlights the ability of an artificial neural network (ANN) as an accurate and revolutionary method with regression models, as a conventional statistical analysis, to predict UCS within carbonate rocks and mortar. Thus, ANN and multiple linear regressions (MLR) were applied to estimate the UCS values of the tested samples. For experimentation we carried out ultrasonic measurements on cubic samples before testing uniaxial compressive strength perpendicularly to the stress direction. The models were performed to correlate effective porosity, density and ultrasonic velocity to the UCS measurements. The resulting models would allow the prediction of carbonate rocks and mortar’s UCS values usually determined by laborious experiments. Although the results demonstrate the usefulness of the MLP method as a simple, practical and economical model, the ANN model is more accurate.
Ankylosing spondylarthritis (AS) is associated falsely increased lumbar spine bone mineral density (BMD). New tool for discrimination of subjects at fracture risk is needed. Vertebral fracture (VF) prediction of routine methods for osteoporosis assessment, BMD and trabecular bone score (TBS), in patients with AS. Cross-sectional study of all AS patients regularly followed at the rheumatology outpatient clinics of two centers. All subjects undergone BMD measurement at lumbar spine (LS), total hip (TH) and femoral neck (FN) using Hologic® Horizon device. TBS at L1-4 in all subjects by TBS InSight® software were assessed. Vertebral fracture assessment (VFA) was performed using the lateral spine imaging IVA™ and graded using Genant semi-quantitative approach. 119 AS subjects (90 males/29 females), mean age 47.6 years were included in the study. In 20 patients 34 VFs were detected, from whom 7 patients had multiple fractures. Subjects with VF were older and had lower FN BMD, TBS in comparison to non-VF subjects. No differences in LS BMD, FN BMD or BASDAI between groups were observed. Among patients with VF only 3 had T-score less than -2.5 but 7 has TBS less than 1.23 which means highly degraded microarchitecture. AS patients with VF have lower TBS and FN BMD in comparison to non-VF subjects. In addition, TBS was able to detect 20 % more VFs than BMD. Therefore, TBS seems promising in VF discrimination among patients with AS., Zdenko Killinger, Martin Kužma, Soňa Tomková, Kristína Brázdilová, Peter Jackuliak, Juraj Payer., and Obsahuje bibliografii
The basic principle of the experimental-and-computational technique for predicting the subsonic flutter stability of axial compressor blading are stated. The methodology is described for the experimental determination of non-stationary aerodynamic forces and moments acting on blades during their in-flow vibrations; the calculation of the dynamic stability of a blade assembly against flutter; the aerodynamic rig design and peculiar features of its components to perform testing of airfoil cascades. The results of testing of the developed experimental-and-computational complex are presented. and Obsahuje seznam literatury
In this article a predictive model and a novel methodology of processing the data measured in the physical model of an optical telecommunications infrastructure is presented. The task is motivated by practical use of the results of experiments in the environment of the telecommunications network. We present an original predictive model and methodology, reflecting the specifics of examined infrastructure. The probabilistic prediction of the occurrence of emergencies is calculated via cluster analysis techniques used in Bayesian approach in the n-dimensional data space. The predictive model is experimentally verified on real data. Results of experiments are interpreted for practical use in real environment of the telecommunications infrastructure.