The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.
Cortisol is secreted by the central hypothalamo-pituitary-adrenal axis and affects many target organs and tissues, particularly in response to stressor demands and infection. Recent data reporting cortisol synthesis in hair follicles have shown the existence of a parallel “peripheral” HPA-axis. However, although there is evidence from in vitro studies and single-observation comparisons between groups that cortisol from hair follicles reflects endocrine changes associated with stressor demands, there are no reports to date of repeated measurements of in vivo cortisol responsivity in hair to transitory stressors. This issue was investigated with three males who underwent 1 min cold pressor test (CP). Cortisol response in hair to stressor demand appears to be (a) swift but transitory, (b) localized to the site of the demand and (c) independent of central HPA-axis activity., C. F. Sharpley, K. G. Kauter, J. R. McFarlane., and Obsahuje seznam literatury
This paper considers the data-based identification of industrial robots using an instrumental variable method that uses off-line estimation of the joint velocities and acceleration signals based only on the measurement of the joint positions. The usual approach to this problem relies on a `tailor-made' prefiltering procedure for estimating the derivatives that depends on good prior knowledge of the system's bandwidth. The paper describes an alternative Integrated Random Walk SMoothing (IRWSM) method that is more robust to deficiencies in such a priori knowledge and exploits an optimal recursive algorithm based on a simple integrated random walk model and a Kalman filter with associated fixed interval smoothing. The resultant IDIM-IV instrumental variable method, using this approach to signal generation, is evaluated by its application to an industrial robot arm and comparison with previously proposed methods.
This paper deals with the identification of BIFs and associated sulphide mineralisation. An integrated approach, including the use of Landsat ETM-plus and Cartosat DEM data, GIS analysis, and geological data, is adopted for this purpose in the Nagavi area of Gadag Schist Belt (GSB), India. This integrated approach has enabled in identifying BIFs and structures. Band-7 of the ETM-plus sensor of Landsat-7 is used to identify BIFs and Band-5 for lineaments and shear zones. As a result of this study, the presence of gold mineralisation in sheared zones is noticed. BIFs are the economically prominent litho-units in the GSB hosting high-grade iron ore deposits along with sulphide mineralised shear zones. The strata bound ore is hosted primarily by BIF, consisting of chlorite, alternating chert and magnetite, sulphides and carbonate bands of a millimetre to centimetre scale.
Profitability of Turkish banking sector gained importance after national and international financial crisis happened in the last decade, which revealed the need to make a research on profitability and the factors determining profitability. In recent years, new techniques of soft computing (SC) like genetic algorithms (GAs), fuzzy logic (FL) and especially artificial neural networks (ANNs) have been applied into the financial domain to solve the domain issues because of their successful applications in nonlinear multivariate situations. An adaptive system was needed due to the fact that insufficient use of application software programs for SC and the fact that single software is only applicable for specific model. Furthermore, even though ANNs have been applied to many areas; little attention has been paid to estimation of bank profitability with ANNs. This article is intended to analyze and estimate the profitability of deposit banks in Turkey with an adaptive software model of ANNs which have not been previously applied for this context, comprehensively. The results from the software model, which processes the factors affecting profitability, indicate that all of the variables used have significant impacts in varying proportions on profitability and that obtained estimations achieved the targeted and acceptable performance of success. This software model is expected to provide easiness on estimating bank profitability, since giving successful estimations and not being affected by user differences. Additionally, it is aimed to construct a software model for being used in different fields of study and financial domain.
We give a short introduction to a method for the data-sparse approximation of matrices resulting from the discretisation of non-local operators occurring in boundary integral methods or as the inverses of partial differential operators. The result of the approximation will be the so-called hierarchical matrices (or short H-matrices). These matrices form a subset of the set of all matrices and have a data-sparse representation. The essential operations for these matrices (matrix-vector and matrixmatrix multiplication, addition and inversion) can be performed in, up to logarithmic factors, optimal complexity.