Search results for: material models

Authors: R. Meier, M. Pander

Abstract:

In accordance with the industry 4.0 concept, manufacturing process steps as well as the materials themselves are going to be more and more digitalized within the next years. The “digital twin” representing the simulated and measured dataset of the (semi-finished) product can be used to control and optimize the individual processing steps and help to reduce costs and expenditure of time in product development, manufacturing, and recycling. In the present work, two material characterization methods based on Lamb waves were evaluated and compared. For demonstration purpose, both methods were shown at a standard industrial product - copper ribbons, often used in photovoltaic modules as well as in high-current microelectronic devices. By numerical approximation of the Rayleigh-Lamb dispersion model on measured phase velocities second order elastic constants (Young’s modulus, Poisson’s ratio) were determined. Furthermore, the effective third order elastic constants were evaluated by applying elastic, “non-destructive”, mechanical stress on the samples. In this way, small microstructural variations due to mechanical preconditioning could be detected for the first time. Both methods were compared with respect to precision and inline application capabilities. Microstructure of the samples was systematically varied by mechanical loading and annealing. Changes in the elastic ultrasound transport properties were correlated with results from microstructural analysis and mechanical testing. In summary, monitoring the elastic material properties of plate-like structures using Lamb waves is valuable for inline and non-destructive material characterization and manufacturing process control. Second order elastic constants analysis is robust over wide environmental and sample conditions, whereas the effective third order elastic constants highly increase the sensitivity with respect to small microstructural changes. Both Lamb wave based characterization methods are fitting perfectly into the industry 4.0 concept.

Keywords: Lamb waves, industry 4.0, process control, elasticity, acoustoelasticity.

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Authors: S. Wongkoon, M. Pollar, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

This study aimed at developing a forecasting model on the number of Dengue Haemorrhagic Fever (DHF) incidence in Northern Thailand using time series analysis. We developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models on the data collected between 2003-2006 and then validated the models using the data collected between January-September 2007. The results showed that the regressive forecast curves were consistent with the pattern of actual values. The most suitable model was the SARIMA(2,0,1)(0,2,0)12 model with a Akaike Information Criterion (AIC) of 12.2931 and a Mean Absolute Percent Error (MAPE) of 8.91713. The SARIMA(2,0,1)(0,2,0)12 model fitting was adequate for the data with the Portmanteau statistic Q20 = 8.98644 ( x20,95= 27.5871, P>0.05). This indicated that there was no significant autocorrelation between residuals at different lag times in the SARIMA(2,0,1)(0,2,0)12 model.

Keywords: Dengue, SARIMA, Time Series Analysis, Northern Thailand.

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Authors: M. Goodarzi, P. Lashgari

Abstract:

A major part of the flow field involves no complicated turbulent behavior in many turbulent flows. In this research work, in order to reduce required memory and CPU time, the flow field was decomposed into several blocks, each block including its special turbulence. A two dimensional backward facing step was considered here. Four combinations of the Prandtl mixing length and standard k- E models were implemented as well. Computer memory and CPU time consumption in addition to numerical convergence and accuracy of the obtained results were mainly investigated. Observations showed that, a suitable combination of turbulence models in different blocks led to the results with the same accuracy as the high order turbulence model for all of the blocks, in addition to the reductions in memory and CPU time consumption.

Keywords: Computer memory, CPU time, Multi-block method, Turbulence modeling.

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Authors: S. Jakšić, B. Žmuk

Abstract:

The paper investigates the key factors of export dynamics for a set of Central and Southeast European (CSEE) countries in the context of current economic and financial crisis. In order to model the export dynamics a Global Vector Auto Regressive (GVAR) model is defined. As opposed to models which model each country separately, the GVAR combines all country models in a global model which enables obtaining important information on spillover effects in the context of globalisation and rising international linkages. The results of the study indicate that for most of the CSEE countries, exports are mainly driven by domestic shocks, both in the short run and in the long run. This study is the first application of the GVAR model to studying the export dynamics in the CSEE countries and therefore the results of the study present an important empirical contribution.

Keywords: Export, GFEVD, Global VAR, International trade, weak exogeneity.

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Authors: B. Varaprasad, C. Srinivasa Rao

Abstract:

In the manufacturing sector, hard turning has emerged as vital machining process for cutting hardened steels. Besides many advantages of hard turning operation, one has to implement to achieve close tolerances in terms of surface finish, high product quality, reduced machining time, low operating cost and environmentally friendly characteristics. In the present study, three-dimensional CAE (Computer Aided Engineering) based simulation of  hard turning by using commercial software DEFORM 3D has been compared to experimental results of  stresses, temperatures and tool forces in machining of AISI D3 steel using mixed Ceramic inserts (CC6050). In the present analysis, orthogonal cutting models are proposed, considering several processing parameters such as cutting speed, feed, and depth of cut. An exhaustive friction modeling at the tool-work interfaces is carried out. Work material flow around the cutting edge is carefully modeled with adaptive re-meshing simulation capability. In process simulations, feed rate and cutting speed are constant (i.e.,. 0.075 mm/rev and 155 m/min), and analysis is focused on stresses, forces, and temperatures during machining. Close agreement is observed between CAE simulation and experimental values.

Keywords: Hard-turning, computer-aided engineering, computational machining, finite element method.

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Authors: Lee P. Leon, Raymond Charles

Abstract:

This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: Aggregate angularity, asphalt concrete, permanent deformation, rutting prediction.

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Authors: Belkacem Selma, Boumediene Selma, Samira Chouraqui, Hanifi Missoum, Tourkia Guerzou

Abstract:

Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. Earthquake prediction to prevent the loss of human lives and even property damage is an important factor; that, is why it is crucial to develop techniques for predicting this natural disaster. This study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 104 J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines have been analyzed. The results obtained show that the ANN is able to predict earthquake parameters with  high accuracy; the classification accuracy through neural networks is more than 94%, and the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: Earthquake prediction, artificial intelligence, AI, Artificial Neural Network, ANN, seismic bumps.

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Authors: Manish Kumar

Abstract:

Reinforced cement concrete is getting extensively used for construction of different type of structures for the last one century. During this period, we have constructed many structures like buildings, bridges, industrial structures, pavement, water tanks etc. using this construction material. These structures have been created with huge investment of resources. It is essential to maintain those structures in functional condition. Since deterioration in RCC Structures is a common and natural phenomenon it is required to have a detailed plan, methodology for structural repair and rehabilitation shall be in place for dealing such issues. It is important to know exact reason of distress, type of distress and correct method of repair concrete structures. The different methods of repair are described in paper according to distress category which can be refereed for repair. Major finding of the study is that to protect our structure we need to have maintenance frequency and correct material to be chosen for repair. Also workmanship during repair needs to be taken utmost care for quality repair.

Keywords: Deterioration, functional condition, reinforced cement concrete, resources.

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Authors: M. D. Nayeri, M. M. Mashhadi, K. Mohajeri

Abstract:

Defining strategic position of the organizations within the industry environment is one of the basic and most important phases of strategic planning to which extent that one of the fundamental schools of strategic planning is the strategic positioning school. In today-s knowledge-based economy and dynamic environment, it is essential for universities as the centers of education, knowledge creation and knowledge worker evolvement. Till now, variant models with different approaches to strategic positioning are deployed in defining the strategic position within the various industries. Balanced Scorecard as one of the powerful models for strategic positioning, analyzes all aspects of the organization evenly. In this paper with the consideration of BSC strength in strategic evaluation, it is used for analyzing the environmental position of the best-s Iranian Business Schools. The results could be used in developing strategic plans for these schools as well as other Iranian Management and Business Schools.

Keywords: Strategic planning, Strategic position, Balancedscorecard, Higher education institutions.

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Authors: Ahmed Hossam_ ElDin, Ahmed Farag, Ibrahim Madi., Hanaa Karawia

Abstract:

In our modern society electricity is vital to our health, safety, comfort and well-being. While our daily use of electricity is often taken for granted, public concern has arisen about potential adverse health effects from electric and magnetic – electromagnetic – fields (EMFs) produced by our use of electricity. This paper aims to compare between the measured magnetic field values and the simulated models for the indoor medium to low voltage (MV/LV) distribution substations. To calculate the magnetic flux density in the substations, interactive software SUBCALC is used which is based on closed form solution of the Biot-Savart law with 3D conductor model. The comparison between the measured values and the simulated models was acceptable. However there were some discrepancies, as expected, may be due to the current variation during measurements.

Keywords: Distribution substation, magnetic field, measurement, simulation.

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Authors: V. Uma Rani, K. Soma Sundaram

Abstract:

The major challenge faced by wireless sensor networks is security. Because of dynamic and collaborative nature of sensor networks the connected sensor devices makes the network unusable. To solve this issue, a trust model is required to find malicious, selfish and compromised insiders by evaluating trust worthiness sensors from the network. It supports the decision making processes in wireless sensor networks such as pre key-distribution, cluster head selection, data aggregation, routing and self reconfiguration of sensor nodes. This paper discussed the kinds of trust model, trust metrics used to address attacks by monitoring certain behavior of network. It describes the major design issues and their countermeasures of building trust model. It also discusses existing trust models used in various decision making process of wireless sensor networks.

Keywords: Attacks, Security, Trust, Trust model, Wireless sensor network.

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: Agricultural object detection, Deep learning, machine vision, YOLO family.

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Authors: Nebojsa B. Raicevic, Teodoros S. Prokic, Vladan Golubovic

Abstract:

Recently, bianisotropic media again received increasing importance in electromagnetic theory because of advances in material science which enable the manufacturing of complex bianisotropic materials. By using Maxwell's equations and corresponding boundary conditions, the electromagnetic field distribution in bianisotropic solenoid coils is determined and the influence of the bianisotropic behaviour of coil to the impedance and Q-factor is considered. Bianisotropic media are the largest class of linear media which is able to describe the macroscopic material properties of artificial dielectrics, artificial magnetics, artificial chiral materials, left-handed materials, metamaterials, and other composite materials. Several special cases of coils, filled with complex substance, have been analyzed. Results obtained by using the analytical approach are compared with values calculated by numerical methods, especially by our new hybrid EEM/BEM method and FEM.

Keywords: Bianisotropic media, impedance and Q-factor, Maxwell`s equations, hybrid EEM/BEM method.

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Authors: M. Jalali Azizpour, H. Mohammadi majd

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In this paper, the wear of high speed steel hobs during hobbing has been studied. The wear mechanisms are strongly influenced by the choice of cutting speed. At moderate and high cutting speeds three major wear mechanisms were identified: abrasion, mild adhesive and severe adhesive. The microstructure and wear behavior of two high speed steel grades (M2 and ASP30) has been compared. In contrast, a variation in chemical composition or microstructure of HSS tool material generally did not change the dominant wear mechanism. However, the tool material properties determine the resistance against the operating wear mechanism and consequently the tool life. The metallographic analysis and wear measurement at the tip of hob teeth included scanning electron microscopy and stereoscope microscopy. Roughness profilometery is used for measuring the gear surface roughness.

Keywords: abrasion, adhesion, cutting speed, hobbing, wear mechanism

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Authors: M.Davami, M.Zadshakoyan

Abstract:

Production of hard-to-cut materials with uncoated carbide cutting tools in turning, not only cause tool life reduction but also, impairs the product surface roughness. In this paper, influence of hot machining method were studied and presented in two cases. Case1-Workpiece surface roughness quality with constant cutting parameter and 300 ºC initial workpiece surface temperature. Case 2- Tool temperature variation when cutting with two speeds 78.5 (m/min) and 51 (m/min). The workpiece material and tool used in this study were AISI 1060 steel (45HRC) and uncoated carbide TNNM 120408-SP10(SANDVIK Coromant) respectively. A gas flam heating source was used to preheating of the workpiece surface up to 300 ºC, causing reduction of yield stress about 15%. Results obtained experimentally, show that the method used can considerably improved surface quality of the workpiece.

Keywords: Hard-to-cut material, Hot machining, Surfaceroughness, Tool Temperature

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Authors: M. Vatani, A. R. Rahimi, F. Brazandeh, A. Sanati nezhad

Abstract:

Although the STL (stereo lithography) file format is widely used as a de facto industry standard in the rapid prototyping industry due to its simplicity and ability to tessellation of almost all surfaces, but there are always some defects and shortcoming in their usage, which many of them are difficult to correct manually. In processing the complex models, size of the file and its defects grow extremely, therefore, correcting STL files become difficult. In this paper through optimizing the exiting algorithms, size of the files and memory usage of computers to process them will be reduced. In spite of type and extent of the errors in STL files, the tail-to-head searching method and analysis of the nearest distance between tails and heads techniques were used. As a result STL models sliced rapidly, and fully closed contours produced effectively and errorless.

Keywords: Layer manufacturing, STL files, slicing algorithm, nearest distance analysis.

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

Abstract:

A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian Automotive Sector, Stock Market Decisions, Equity Portfolio Analysis, Decision Tree Classifiers, Statistical Data Analysis.

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Authors: Quy Dang Nguyen, Reza Nakhaie Jazar

Abstract:

The paper investigates vibration dynamics of tire-road separation for a quarter-car model; this separation model is developed to be close to the real situation considering the tire is able to separate from the ground plane. A set of piecewise linear mathematical models is developed and matches the in-contact and no-contact states to be considered as mother models for further investigations. The bound dynamics are numerically simulated in the time response and phase portraits. The separation analysis may determine which values of suspension parameters can delay and avoid the no-contact phenomenon, which results in improving ride comfort and eliminating the potentially dangerous oscillation. Finally, model verification is carried out in the MSC-ADAMS environment.

Keywords: Quarter-car vibrations, tire-road separation, separation analysis, separation dynamics, ride comfort, ADAMS validation.

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Authors: Aissam Belghiat, Mustapha Bourahla

Abstract:

Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.

Keywords: ATOM3, MDA, Ontology, OWL, UML

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Authors: Z. Heirany, M. Ghaemian

Abstract:

Behavior of dams against the seismic loads has been studied by many researchers. Most of them proposed new numerical methods to investigate the dam safety. In this paper, to study the effect of nonlinear parameters of concrete in gravity dams, a twodimensional approach was used including the finite element method, staggered method and smeared crack approach. Effective parameters in the models are physical properties of concrete such as modulus of elasticity, tensile strength and specific fracture energy. Two different models were used in foundation (mass-less and massed) in order to determine the seismic response of concrete gravity dams. Results show that when the nonlinear analysis includes the dam- foundation interaction, the foundation-s mass, flexibility and radiation damping are important in gravity dam-s response.

Keywords: Numerical methods; concrete gravity dams; finiteelement method; boundary condition

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Authors: C. Pislaru, A. Shebani

Abstract:

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: System identification, Nonlinear system, Neural networks, RBF neural network.

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Authors: Hamad K. Al-Khalid, Ayman M. Alaskari, Samy E. Oraby

Abstract:

Hardness of the widely used structural steel is of vital importance since it may help in the determination of many mechanical properties of a material under loading situations. In order to obtain reliable information for design, properties homogeneity should be validated. In the current study the hardness variation over the different diameters of the same AISI 4140 bar is investigated. Measurements were taken on the two faces of the stock at equally spaced eight sectors and fifteen layers. Statistical and graphical analysis are performed to asses the distribution of hardness measurements over the specified area. Hardness measurements showed some degree of dispersion with about ± 10% of its nominal value provided by manufacturer. Hardness value is found to have a slight decrease trend as the diameter is reduced. However, an opposite behavior is noticed regarding the sequence of the sector indicating a nonuniform distribution over the same area either on the same face or considering the corresponding sector on the other face (cross section) of the same material bar.

Keywords: Hardness; Hardness variation; AISI 4140 steel; Bardiameter; Statistical Analysis.

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Authors: Nhon Do, Tam Pham

Abstract:

Knowledge bases are basic components of expert systems or intelligent computational programs. Knowledge bases provide knowledge, events that serve deduction activity, computation and control. Therefore, researching and developing of models for knowledge representation play an important role in computer science, especially in Artificial Intelligence Science and intelligent educational software. In this paper, the extensive deduction computational model is proposed to design knowledge bases whose attributes are able to be real values or functional values. The system can also solve problems based on knowledge bases. Moreover, the models and algorithms are applied to produce the educational software for solving alternating current problems or solving set of equations automatically.

Keywords: Educational software, artificial intelligence, knowledge base systems, knowledge representation.

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Authors: S. H. Yahaya, J. M. Ali, T.A. Abdullah

Abstract:

The exploration of this paper will focus on the Cshaped transition curve. This curve is designed by using the concept of circle to circle where one circle lies inside other. The degree of smoothness employed is curvature continuity. The function used in designing the C-curve is Bézier-like cubic function. This function has a low degree, flexible for the interactive design of curves and surfaces and has a shape parameter. The shape parameter is used to control the C-shape curve. Once the C-shaped curve design is completed, this curve will be applied to design spur gear tooth. After the tooth design procedure is finished, the design will be analyzed by using Finite Element Analysis (FEA). This analysis is used to find out the applicability of the tooth design and the gear material that chosen. In this research, Cast Iron 4.5 % Carbon, ASTM A-48 is selected as a gear material.

Keywords: Bézier-like cubic function, Curvature continuity, Cshapedtransition curve, Spur gear tooth.

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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Authors: L. Ramirez, E. Guillén, J. Sánchez

Abstract:

Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.

Keywords: Analytics, telemedicine, internet of things, cloud computing.

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Authors: Kai Häussermann, Christoph Hubig, Paul Levi, Frank Leymann, Oliver Siemoneit, Matthias Wieland, Oliver Zweigle

Abstract:

Using spatial models as a shared common basis of information about the environment for different kinds of contextaware systems has been a heavily researched topic in the last years. Thereby the research focused on how to create, to update, and to merge spatial models so as to enable highly dynamic, consistent and coherent spatial models at large scale. In this paper however, we want to concentrate on how context-aware applications could use this information so as to adapt their behavior according to the situation they are in. The main idea is to provide the spatial model infrastructure with a situation recognition component based on generic situation templates. A situation template is – as part of a much larger situation template library – an abstract, machinereadable description of a certain basic situation type, which could be used by different applications to evaluate their situation. In this paper, different theoretical and practical issues – technical, ethical and philosophical ones – are discussed important for understanding and developing situation dependent systems based on situation templates. A basic system design is presented which allows for the reasoning with uncertain data using an improved version of a learning algorithm for the automatic adaption of situation templates. Finally, for supporting the development of adaptive applications, we present a new situation-aware adaptation concept based on workflows.

Keywords: context-awareness, ethics, facilitation of system use through workflows, situation recognition and learning based on situation templates and situation ontology's, theory of situationaware systems

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Authors: María-Dolores Cubiles-de-la-Vega, Rafael Pino-Mejías, Esther-Lydia Silva-Ramírez

Abstract:

A dissimilarity measure between the empiric characteristic functions of the subsamples associated to the different classes in a multivariate data set is proposed. This measure can be efficiently computed, and it depends on all the cases of each class. It may be used to find groups of similar classes, which could be joined for further analysis, or it could be employed to perform an agglomerative hierarchical cluster analysis of the set of classes. The final tree can serve to build a family of binary classification models, offering an alternative approach to the multi-class SVM problem. We have tested this dendrogram based SVM approach with the oneagainst- one SVM approach over four publicly available data sets, three of them being microarray data. Both performances have been found equivalent, but the first solution requires a smaller number of binary SVM models.

Keywords: Cluster Analysis, Empiric Characteristic Function, Multi-class SVM, R.

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma

Abstract:

Deep cold rolling (DCR) and low plasticity burnishing (LPB) process are cold working processes, which easily produce a smooth and work-hardened surface by plastic deformation of surface irregularities. The present study focuses on the surface roughness and surface hardness aspects of AISI 4140 work material, using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in order to identify the predominant factors amongst the selected parameters. They were then categorized in order of significance followed by setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. In the present work, the influence of main process parameters (force, feed rate, number of tool passes/overruns, initial roughness of the work piece, ball material, ball diameter and lubricant used) on the surface roughness and the hardness of AISI 4140 steel were studied for both LPB and DCR process and the results are compared. It was observed that by using LPB process surface hardness has been improved by 167% and in DCR process surface hardness has been improved by 442%. It was also found that the force, ball diameter, number of tool passes and initial roughness of the workpiece are the most pronounced parameters, which has a significant effect on the work piece-s surface during deep cold rolling and low plasticity burnishing process.

Keywords: Deep cold rolling, burnishing, surface roughness, surface hardness, design of experiments, AISI4140 steel.

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Authors: I. Goual, M. S. Goual, M. K. Gueddouda, Taïbi Saïd, Abou-Bekr Nabil, A. Ferhat

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The aim of the paper is to study the hydro-mechanical behavior of a tuff and calcareous sand mixture. A first experimental phase was carried out in order to find the optimal mixture. This showed that the material composed of 80% tuff and 20% calcareous sand provides the maximum mechanical strength. The second experimental phase concerns the study of the drying-wetting behavior of the optimal mixture was carried out on slurry samples and compacted samples at the MPO. Experimental results let to deduce the parameters necessary for the prediction of the hydro-mechanical behavior of pavement formulated from tuff and calcareous sand mixtures, related to moisture. This optimal mixture satisfies the regulation rules and hence constitutes a good local eco-material, abundantly available, for the conception of pavements.

Keywords: Tuff, sandy calcareous, road engineering, hydro mechanical behaviour, suction.

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