High-fidelity modeling approaches for the analysis of reinforced structures using one-, two- and three-dimensional elements

The present paper proposes a method for analyzing reinforced thin-walled structures based on high-order one-, two- and three-dimensional finite elements (FE). Refined finite elements are developed in the domain of the Carrera unified formulation (CUF). The node-dependent kinematic approach (NDK), which allows to connect in an easy manner elements with incompatible kinematics, has been used to connect elements with different dimensions without the need of ad hoc connection techniques. The formulation ensures the continuity of the displacement at the interface preventing the onset of singularities that lead to inaccurate results when beam, plate and solid elements have to be coupled to solve complex structures. The effectiveness of the present method has been confirmed by comparing the results with those from literature and with those obtained using commercial finite element codes. Static and free-vibration analyses of reinforced panels have been carried out to demonstrate the capabilities of the present models. The results show that the limits of classical structural models can be easily overcome using the present approach, and at the same time, a quasi three-dimensional solution can be obtained with a large computational cost saving

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

An Integral Evaluation of the Financial State of the Regional Enterprises

The subject matter of the article is the development of theoretical positions and methodical approaches to the integral evaluation of the financial state of the region’s metallurgical enterprises. The purpose is to show the possibility of dividing the integral evaluation into separate elements for using this tool to build individual models based on the forecasting of the various coordinates of the financial position of enterprise. The hypothesis of the study is based on the objective need to improve the integral evaluation of the financial position of enterprises. This involves the modernization of existing theoretical and methodological approaches to the increase of the quality of analysis by eliminating certain shortcomings of discriminant models in order to clarify the algorithm of constructing the integral index. The methodological bases of systemic approach and mathematical modelling in economics are applied: the methods of financial analysis, grouping, abstraction, comparison which give the possibility of determining the financial indicators needed to build the predictive models of financial state; the methods of correlation and regression analysis, which allow to improve the integral value and to build the mathematical forecasting models. With the purpose of improving the integral evaluation of the financial condition of enterprise, the geometric interpretation is used, which involves the dividing of the integral indicator on the individual elements. The special feature of the proposed methodological approach consists in the implementation rules for the certain procedures of the evaluation of financial position and generalization of the analysis results. The proposed approach can be used by financial analysts to elaborate the strategic plans of company development and structure optimization of financial resources. This research allows to define the quantitative influence of separate parameters on the general assessment of the financial position for the purpose of its forecasting, which is understood as the system of the evidence-based probabilistic assumptions of the basic and alternative structural changes of the enterprise’s assets and liabilities

A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers

Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems

VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

The Genetic Algorithm is an area in the field of Artificial Intelligence that is founded on the principles of biological evolution. Visualization techniques help in understanding the searching behaviour of Genetic Algorithm. lt also makes possible the user interactions during the searching process. It is noted that active user intervention increases the acceleration of Genetic Algorithm towards an optimal solution. In proposed research work, the user is aided by a visualization based on the representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. The user participates in the search by proposing a new individual. This is difTerent from existing Interactive Genetic Algorithm in which selection and evaluation of solutions is done by the users. A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. This visual tool enables the display of the evolution of gene values from generation to generation to observing and analysing the behaviour of the search space with user interactions. Individuals for the next generation are selected by using the objective function. Hence, a novel humanmachine interaction is developed in the proposed approach. The efficiency of the proposed approach is evaluated by two benchmark functions. The analysis and comparison of VIGA-20 is based on convergence test against the results obtained from the Simple Genetic Algorithm. This comparison is based on the same parameters except for the interactions of the user. The application of proposed approach is the modelling the branching structures by deriving a rule from best solution of VIGA-20. The comparison of results is based on the different user's perceptions, their involvement in the VIGA-20 and the difference of the fitness convergence as compared to Simple Genetic Algorithm

A Study on Change Detection in Hyperspectral Image

Change detection is the procedure of obtaining changes between two Hyperspectral pictures of same topographical zone taken at two unique times. It conveys the essential and important change data of a scene. Due to a breakthrough in Hyperspectral remote sensing Hyperspectral remote sensors can capable of producing narrow spectral resolution images. These high resolution spectral and spatial hyperspectral images can find small variations in images. This work describes an efficient algorithm for detecting changes in Hyperspectral images by using spectral signatures of Hyperspectral images. The objective is developing of a proficient algorithm that can show even small variations in Hyperspectral images. It reviews Hierarchical method for finding changes in Hyperspectral images by comparing spectral homogeneity between spectral change vectors. For any scenery locating and also exploration regarding adjust delivers treasured data regarding achievable changes. Hyperspectral satellite detectors get effectiveness throughout gathering data with large spectral rings. These types of detectors typically deal with spatially and also spectrally high definition graphics and this can be used by adjust discovery. This particular function is actually elaborated and also applied your adjust discovery procedure by simply controlling Hyperspectral graphics. The main aim with this thesis is actually studying and also constructing of Hyperspectral adjust discovery algorithms This kind of analysed approach is really applied to assess Hyperspectral picture image resolution files along with the approach analysed in this particular thesis is really change breakthrough making use of Hierarchical method of spectral change vectors and also making use of principal ingredient examination and also k-means clustering. This particular document offers applying and also verify of trends Hyperspectral image