Search results for: rough kernel

Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Tiee-Jian Wu, Chih-Yuan Hsu

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Mode estimation is an important task, because it has applications to data from a wide variety of sources. We propose a semi-parametric approach to estimate the mode of an unknown continuous multivariate density function. Our approach is based on a weighted average of a parametric density estimate using the Box-Cox transform and a non-parametric kernel density estimate. Our semi-parametric mode estimate improves both the parametric- and non-parametric- mode estimates. Specifically, our mode estimate solves the non-consistency problem of parametric mode estimates (at large sample sizes) and reduces the variability of non-parametric mode estimates (at small sample sizes). The performance of our method at practical sample sizes is demonstrated by simulation examples and two real examples from the fields of climatology and image recognition.

Keywords: Box-Cox transform, density estimation, mode seeking, semiparametric method

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Authors: Ahmed A. D. Sarhan, S. R. Besharaty, Javad Akbaria, M. Hamdi

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The capability of CNC gantry milling machines in manufacturing long components has caused the expanded use of such machines. On the other hand, the machines’ gantry rigidity can reduce under severe loads or vibration during operation. Indeed, the quality of machining is dependent on the machine’s dynamic behavior throughout the operating process. For this reason, this type of machines has always been used prudently and are non efficient. Therefore, they can usually be employed for rough machining and may not produce adequate surface finishing. In this paper, a CNC gantry milling machine with the potential to produce good surface finish has been designed and analyzed. The lowest natural frequency of this machine is 202 Hz at all motion amplitudes with a full range of suitable frequency responses. Meanwhile, the maximum deformation under dead loads for the gantry machine is 0.565µm, indicating that this machine tool is capable of producing higher product quality.

Keywords: frequency response, finite element, gantry machine, gantry design, static and dynamic analysis

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Mardiati Zain, Jurnida Rahman, Khasrad, Erpomen

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The aim of this experiment was to study the use of palm oil by products (oil palm fronds (OPF), palm oil sludge (POS) and palm kernel cake (PKC)), that supplemented with growth factor rumen microbes (Sapindus rarak and Sacharomyces cerevisiae) on digestibility and fermentation in vitro. Oil Palm Fronds was previously treated with 3% urea. The treatments consist of 50% OPF+ 30% POS+ 20% PKC as a control diet (A), B = A + 4% Sapindus rarak, C = A + 0.5 % Sacharomyces cerevisiae and D = A + 4% Sapindus rarak + 0.5% Sacharomyces cerevisiae. Digestibility of DM, OM, ADF, NDF, cellulose and rumen parameters (NH3 and VFA) of all treatments were significantly different (P < 0.05). Fermentation and digestibility treatment A were significantly lower than treatments B, C, and D. The result indicated that supplementation Sapindus rarak and S. cerevisiae were able to improve fermentability and digestibility of palm oil by product.

Keywords: palm oil by product, Sapindus rarak, Sacharomyces rerevisiae, fermentability, OPF ammoniated

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Authors: Abayomi O. Adetuyi, Jamiu M. Jabar, Samuel O. Afolabi

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Vegetable fibres are classes of fibres of low density, biodegradable and non-abrasive that are largely abundant fibre materials with specific properties and mostly found/ obtained in plants on earth surface. They are classified into three categories, depending on the part of the plant from which they are gotten from namely: fruit, Blast and Leaf fibre. Ever since four/five millennium B.C, attention has been focussing on the commonest and highly utilized cotton fibre obtained from the fruit of cotton plants (Gossypium spp), for the production of cotton fabric used in every home today. The present study, therefore, focused on the ability of three underutilized vegetable (fruit) fibres namely: coir fiber (Eleas coniferus), palm kernel fiber and empty fruit bunch fiber (Elias guinensis) through chemical modifications for better composite value addition performance to polyurethane form and dye adsorption. These fibres were sourced from their parents’ plants, identified and cleansed with 2% hot detergent solution 1:100, rinsed in distilled water and oven-dried to constant weight, before been chemically modified through alkali bleaching, mercerization and acetylation. The alkali bleaching involves treating 0.5g of each fiber material with 100 mL of 2% H2O2 in 25 % NaOH solution with refluxing for 2 h. While that of mercerization and acetylation involves the use of 5% sodium hydroxide NaOH solution for 2 h and 10% acetic acid- acetic anhydride 1:1 (v/v) (CH3COOH) / (CH3CO)2O solution with conc. H2SO4 as catalyst for 1 h, respectively on the fibres. All were subsequently washed thoroughly with distilled water and oven dried at 105 0C for 1 h. These modified fibres were incorporated as composite into polyurethane form and used in dye adsorption study of indigo. The first two treatments led to fiber weight reduction, while the acidified acetic anhydride treatment gave the fibers weight increment. All the treated fibers were found to be of less hydrophilic nature, better mechanical properties, higher thermal stabilities as well as better adsorption surfaces/capacities than the untreated ones. These were confirmed by gravimetric analysis, Instron Universal Testing Machine, Thermogravimetric Analyser and the Scanning Electron Microscope (SEM) respectively. The fiber morphology of the modified fibers showed smoother surfaces than unmodified fibres.The empty fruit bunch fibre and the coconut coir fibre are better than the palm kernel fibres as reinforcers for composites or as adsorbents for waste-water treatment. Acetylation and alkaline bleaching treatment improve the potentials of the fibres more than mercerization treatment. Conclusively, vegetable fibres, especially empty fruit bunch fibre and the coconut coir fibre, which are cheap, abundant and underutilized, can replace the very costly powdered activated carbon in wastewater treatment and as reinforcer in foam.

Keywords: chemical modification, industrial application, value addition, vegetable fibre

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Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: S. Gao, S. Srirattayawong

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In this study, a computational fluid dynamics (CFD) model has been developed for studying the effect of surface roughness profile on the EHL problem. The cylinders contact geometry, meshing and calculation of the conservation of mass and momentum equations are carried out by using the commercial software packages ICEMCFD and ANSYS Fluent. The user defined functions (UDFs) for density, viscosity and elastic deformation of the cylinders as the functions of pressure and temperature have been defined for the CFD model. Three different surface roughness profiles are created and incorporated into the CFD model. It is found that the developed CFD model can predict the characteristics of fluid flow and heat transfer in the EHL problem, including the leading parameters such as the pressure distribution, minimal film thickness, viscosity, and density changes. The obtained results show that the pressure profile at the center of the contact area directly relates to the roughness amplitude. The rough surface with kurtosis value over 3 influences the fluctuated shape of pressure distribution higher than other cases.

Keywords: CFD, EHL, kurtosis, surface roughness

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Authors: Kevin Fernagut, Olivier Flauzac, Erick M. G. Robledo, Florent Nolot

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The adoption of modern lightweight virtualization often comes with new threats and network vulnerabilities. This paper seeks to assess this with a different approach studying the behavior of a testbed built with tools such as Kernel-Based Virtual Machine (KVM), Linux Containers (LXC) and Docker, by performing stress tests within a platform where students experiment simultaneously with cyber-attacks, and thus observe the impact on the campus network and also find the best solution for cyber-security learning. Interesting outcomes can be found in the literature comparing these technologies. It is, however, difficult to find results of the effects on the global network where experiments are carried out. Our work shows that other physical hosts and the faculty network were impacted while performing these trials. The problems found are discussed, as well as security solutions and the adoption of new network policies.

Keywords: containerization, containers, cybersecurity, cyberattacks, isolation, performance, virtualization, virtual machines

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Authors: M. Kharazmi, M. Zakeri, M. Packirisamy, J. Faraji

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Micro/Nanorobotics or manipulation of nanoparticles by Atomic Force Microscopic (AFM) is one of the most important solutions for controlling the movement of atoms, particles and micro/nano metrics components and assembling of them to design micro/nano-meter tools. Accurate modelling of manipulation requires identification of forces and mechanical knowledge in the Nanoscale which are different from macro world. Due to the importance of the adhesion forces and the interaction of surfaces at the nanoscale several friction models were presented. In this research, friction and normal forces that are applied on the AFM by using of the dynamic bending-torsion model of AFM are obtained based on Hurtado-Kim friction model (HK), Johnson-Kendall-Robert contact model (JKR) and Greenwood-Williamson roughness model (GW). Finally, the effect of standard deviation of asperities height on the normal load, friction force and friction coefficient are studied.

Keywords: atomic force microscopy, contact model, friction coefficient, Greenwood-Williamson model

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Authors: Sumeet Kumar, C. V. Krishnamurthy, Krishnan Balasubramaniam

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Normal spectral emissivity of Al 6061 alloys with different surface finishes was experimentally measured at 833°K. Four different samples were prepared by polishing the surfaces of the alloy by 80, 220, 600 grit sizes of SiC abrasive papers and diamond paste. The samples were heated in air for 6 h at 833°K, and the emissivity was measured during the process from pyrometers operating at wavelengths of 3.9, 5.14 and 7.8 μm. The results indicated that the emissivity was increasing with heating time and the rate of increase was rapid during the initial stage of heating in comparison with the later stage. This appears to be because of the parabolic rate law followed by the process of oxidation. Further, it is found that the increase in emissivity with heating time was higher for rough surfaces than that for polished surfaces. Both the results were analyzed at all the three wavelengths, and qualitatively similar results were obtained for all of them. In this way emissivity of the alloy can be increased by roughening the surfaces and heating it at high temperature until the surfaces are oxidized.

Keywords: aluminum alloy, high temperature, normal spectral emissivity, surface roughness

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Hesham A. Elkaranshawy, Amr Abdelrazek, Hosam Ezzat

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This paper analyzes a single point rough collision in three dimensional rigid-multibody systems. A set of nonlinear different equations describing the progress and outcome of the impact are obtained. Specifically in case of the tangential, referred to as sliding, component of impact velocity is of great importance. Numerical methods are used to solve this problem. In this work, all these possible sliding behaviors during impact are identified, conditions leading to each behavior are specified, and an appropriate numerical procedure is suggested. A case of a four-degrees-of-freedom spatial robot that collides with its environment is investigated. The phase portrait of the tangential velocity, which presents the flow trajectories for different initial conditions, is calculated. Using the coefficient of friction as a control parameter, few phase portraits are drawn, each for a specific value of this coefficient. In addition, the bifurcation associated with the variation of this coefficient will be investigated.

Keywords: friction impact, three-dimensional rigid multibody systems, sliding velocity, nonlinear ordinary differential equations, phase portrait

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Sorayya Malek, Mogeeb Mosleh, Sharifah M. Syed

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In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, water temperature, and conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.

Keywords: dissolved oxygen, water quality, predication DO, support vector machine

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Authors: Azadeh Malekan, Shahin Rouhani

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Superlubricity is a phenomenon where two surfaces in contact show negligible friction;this may be because the asperities of the two surfaces do not interlock. Two rough surfaces, when pressed against each other, can get into a formation where the summits of asperities of one surface lock into the valleys of the other surface. The amount of interlock depends on the geometry of the two surfaces. We suggest the friction force may then be proportional to the amount of interlock; this explains Superlubricity as the situation where there is little interlock. Then the friction force will be directly proportional to the normal force as it is related to the work necessary to lift the upper surface in order to clear the interlock. To investigate this model, we simulate the contact of two surfaces. In order to validate our model, we first investigate Amontons‘ law. Assuming that asperities retain deformations in the time scale while the top asperity moves across the lattice spacing Amonton’s law is observed. Structural superlubricity is examined by the hypothesis that surfaces are very rigid and there is no deformation in asperities. This may happen at small normal forces. When two identical surfaces come into contact, rotating the top surface we observe a peak in friction force near the angle of orientation where the two surfaces can interlock.

Keywords: friction, amonton`s law, superlubricity, contact model

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Authors: Md Abdullah Al Faruque, Ram Balachandar

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The present study was carried out to understand the extent of effect of roughness and Reynolds number in open channel flow (OCF). To this extent, four different types of bed surface conditions consisting smooth, distributed roughness, continuous roughness, natural sand bed and two different Reynolds number for each bed surfaces were adopted in this study. Particular attention was given on mean velocity, turbulence intensity, Reynolds shear stress, correlation, higher order moments and quadrant analysis. Further, the extent of influence of roughness and Reynolds number in the depth-wise direction also studied. Increasing Reynolds shear stress near rough beds are noticed due to arrays of discrete roughness elements and flow over these elements generating a series of wakes which contributes to the generation of significantly higher Reynolds shear stress.

Keywords: bed roughness, ejection and sweep, open channel flow, Reynolds shear stress, turbulent boundary layer, velocity triple product

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Authors: Zikiou Nadia, Lahdir Mourad, Ameur Soltane

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In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement.

Keywords: image compression, 2D discrete wavelet transform (DWT-2D), support vector regression (SVR), SVM Kernels, run-length, arithmetic coding

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Authors: U.V. Suryawanshi, S.S. Chowhan, U.V Kulkarni

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Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper portraits performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI has been developed. The objective of this paper is to perform a segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with a discussion on the trend of future research in brain segmentation and changing norms in IFCM for better results.

Keywords: image segmentation, preprocessing, MRI, FCM, KFCM, SFCM, IFCM

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Authors: Anjela Koblischka-Veneva, Michael R. Koblischka

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To increase the flux pinning performance of bulk YBa2Cu3O7-δ (YBCO or Y-123) superconductors, it is common to employ secondary phase particles, either Y2BaCuO5 (Y-211) particles created during the growth of the samples or additionally added (nano)particles of various types, embedded in the superconducting Y-123 matrix. As the crystallographic parameters of all the particles indicate a misfit to Y-123, there will be residual strain within the Y-123 matrix around such particles. With a dedicated analysis of electron backscatter diffraction (EBSD) data obtained on various bulk, Y-123 superconductor samples, the strain distribution around such embedded secondary phase particles can be revealed. The results obtained are presented in form of Kernel Average Misorientation (KAM) mappings. Around large Y-211 particles, the strain can be so large that YBCO subgrains are formed. Therefore, it is essential to properly control the particle size as well as their distribution within the bulk sample to obtain the best performance. The impact of the strain distribution on the flux pinning properties is discussed.

Keywords: Bulk superconductors, EBSD, Strain, YBa2Cu3Oy

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Authors: Francky Fouedjio

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Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Shiuh-Jer Huang, Su-Shean Chen, Sheam-Chyun Lin

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Two-wheel inverted pendulum robot (TWIPR) is designed with two-hub DC motors for human riding and motion control evaluation. In order to measure the tilt angle and angular velocity of the inverted pendulum robot, accelerometer and gyroscope sensors are chosen. The mobile robot’s moving position and velocity were estimated based on DC motor built in hall sensors. The control kernel of this electric mobile robot is designed with embedded Arduino Nano microprocessor. A handle bar was designed to work as steering mechanism. The intelligent model-free fuzzy sliding mode control (FSMC) was employed as the main control algorithm for this mobile robot motion monitoring with different control purpose adjustment. The intelligent controllers were designed for balance control, and moving speed control purposes of this robot under different operation conditions and the control performance were evaluated based on experimental results.

Keywords: balance control, speed control, intelligent controller, two wheel inverted pendulum

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Authors: Nabi Ullah

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The energy crisis of the current society has attracted research attention for alternative energy sources. Methanol oxidation is the source of energy but needs efficient electrocatalysts like Pt. However, their practical ability is hindered due to cost and poisoning effects. In this regard, an efficient catalyst is required for methanol oxidation. Herein, high temperature, pressure, and diethylenetryamine (DETA) as reaction medium/structure directing agent during the solvothermal method are used for nanobelt Cu₃Se₂/Cu₁.₈Se (mostly hexagonal appearance) formation. The electrocatalyst shows optimized methanol electrooxidation reaction (MOR) response in 1 M KOH and 0.5 M methanol at a scan rate of 50 mV/s and delivers a current density of 7.12 mA/mg at a potential of 0.65 V (vs Ag/AgCl). The catalyst exhibits high electrochemical active surface area (ECSA) (0.088 mF/cm²) and low Rct with good stability for 3600 s, which favors its high MOR performance. This high response is due to its 2D hexagonal nanobelt morphology, which provides a large surface area for reaction. The space among nanobelts reduces diffusion kinetics, and the rough/irregular edge increases the reaction site to improve the methanol oxidation reaction overall.

Keywords: energy application, electrocatalysis, MOR, nanobelt

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Authors: R. Sabre, W. Horrigue, J. C. Simon

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This paper is interested in two difficulties encountered in practice when observing a continuous time process. The first is that we cannot observe a process over a time interval; we only take discrete observations. The second is the process frequently observed with a constant additive error. It is important to give an estimator of the spectral density of such a process taking into account the additive observation error and the choice of the discrete observation times. In this work, we propose an estimator based on the spectral smoothing of the periodogram by the polynomial Jackson kernel reducing the additive error. In order to solve the aliasing phenomenon, this estimator is constructed from observations taken at well-chosen times so as to reduce the estimator to the field where the spectral density is not zero. We show that the proposed estimator is asymptotically unbiased and consistent. Thus we obtain an estimate solving the two difficulties concerning the choice of the instants of observations of a continuous time process and the observations affected by a constant error.

Keywords: spectral density, stable processes, aliasing, periodogram

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Authors: Nauman A. Siddiqui

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High performance requirement of aircrafts and marines industry demands to cater major industrial problems like wetting, high-speed efficiency, and corrosion resistance. These problems can be resolved by producing the hydrophobic surfaces on the metal substrate. By anodization process, the surface of AA 7075 has been modified and achieved a rough surface with a porous aluminum oxide (Al2O3) structure at nano-level. This surface modification process reduces the surface contact energy and increases the liquid contact angle which ultimately enhances the anti-icing properties. Later the Silane and Polyurethane (PU) coatings on the anodized surface have produced a contact angle of 130°. The results showed a good water repellency and self-cleaning properties. Using SEM analysis, micrographs revealed the round nano-porous oxide structure on the substrate. Therefore this technique can help in increasing the speed efficiency by reducing the friction with the outer interaction and can also be declared as a green technique since it is user-friendly.

Keywords: AA 7075, hydrophobicity, silanes, polyurethane, anodization

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Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

Abstract:

In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Shashank Bhatnagar, W. Wilfred Godfrey

Abstract:

We develop in this paper, an approach to find the trendy topic, which not only considers the user-topic interaction but also considers the community, in which user belongs. This method modifies the previous approach of user-topic interaction to user-community-topic interaction with better speed-up in the range of [1.1-3]. We assume that trend detection in a social network is dependent on two things. The one is, broadcast of messages in social network governed by self-exciting point process, namely called Hawkes process and the second is, Community Rank. The influencer node links to others in the community and decides the community rank based on its PageRank and the number of users links to that community. The community rank decides the influence of one community over the other. Hence, the Hawkes process with the kernel of user-community-topic decides the trendy topic disseminated into the social network.

Keywords: community detection, community rank, Hawkes process, influencer node, pagerank, trend detection

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Authors: A. Chateauneuf, M. Mostoufi, D. Vyncke

Abstract:

Monte Carlo (MC) simulation is a technique that provides approximate solutions to a broad range of mathematical problems. A drawback of the method is its high computational cost, especially in a high-dimensional setting, such as estimating the Tail Value-at-Risk for large portfolios or pricing basket options and Asian options. For these types of problems, one can construct an upper bound in the convex order by replacing the copula by the comonotonic copula. This comonotonic upper bound can be computed very quickly, but it gives only a rough approximation. In this paper we introduce the Comonotonic Monte Carlo (CoMC) simulation, by using the comonotonic approximation as a control variate. The CoMC is of broad applicability and numerical results show a remarkable speed improvement. We illustrate the method for estimating Tail Value-at-Risk and pricing basket options and Asian options when the logreturns follow a Black-Scholes model or a variance gamma model.

Keywords: control variate Monte Carlo, comonotonicity, option pricing, scientific computing

Procedia PDF Downloads 484