Search results for: 2D discrete wavelet transform (DWT-2D)

Authors: James Adewale, Joshua Sunday

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In this paper, we developed a linear multistep method, which is implemented in predictor corrector-method. The corrector is developed by method of collocation and interpretation of power series approximate solutions at some selected grid points, to give a continuous linear multistep method, which is evaluated at some selected grid points to give a discrete linear multistep method. The predictors were also developed by method of collocation and interpolation of power series approximate solution, to give a continuous linear multistep method. The continuous linear multistep method is then solved for the independent solution to give a continuous block formula, which is evaluated at some selected grid point to give discrete block method. Basic properties of the corrector were investigated and found to be zero stable, consistent and convergent. The efficiency of the method was tested on some linear, non-learn, oscillatory and stiff problems of first order, initial value problems of ordinary differential equations. The results were found to be better in terms of computer time and error bound when compared with the existing methods.

Keywords: predictor, corrector, collocation, interpolation, approximate solution, independent solution, zero stable, consistent, convergent

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Authors: Louis N. Y. Wong

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En-echelon fractures are commonly found in rocks, which appear as a special set of regularly oriented and spaced fractures. By using both experimental and numerical approaches, this study investigates the interaction among them, and how this interaction finally contributes to the development of a shear rupture (fault), especially in brittle natural rocks. Firstly, uniaxial compression tests are conducted on marble specimens containing en-echelon flaws. The latter is cut by using the water abrasive jet into the rock specimens. The fracturing processes of these specimens leading to the formation of a fault are observed in detail by the use of a high speed camera. The influences of the flaw geometry on the production of tensile cracks and shear cracks, which in turn dictate the coalescence patterns of the entire set of en-echelon flaws are comprehensively studied. Secondly, a numerical study based on a recently developed contact model, flat-joint contact model using the discrete element method (DEM) is carried out to model the present laboratory experiments. The numerical results provide a quantitative assessment of the interaction of en-echelon flaws. Particularly, the evolution of the stress field, as well as the characteristics of new crack initiation, propagation and coalescence associated with the generation of an eventual shear rupture are studied in detail. The numerical results are found to agree well with the experimental results obtained in both microscopic and macroscopic observations.

Keywords: discrete element method, en-echelon flaws, fault, marble

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Authors: A. B. Disu, M. S. Dada

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This study was conducted to investigate two dimensional heat transfer of a free convective-radiative MHD (Magneto-hydrodynamics) flow with temperature dependent viscosity and heat source of a viscous incompressible fluid in a porous medium between two vertical wavy walls. The fluid viscosity is assumed to vary as an exponential function of temperature. The flow is assumed to consist of a mean part and a perturbed part. The perturbed quantities were expressed in terms of complex exponential series of plane wave equation. The resultant differential equations were solved by Differential Transform Method (DTM). The numerical computations were presented graphically to show the salient features of the fluid flow and heat transfer characteristics. The skin friction and Nusselt number were also analyzed for various governing parameters.

Keywords: differential transform method, MHD free convection, porous medium, two dimensional radiation, two wavy walls

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Authors: Stojan Kravanja, Andrej Ivanič, Tomaž Žula

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This contribution focuses on structural optimization in civil engineering using mixed integer non-linear programming (MINLP). MINLP is characterized as a versatile method that can handle both continuous and discrete optimization variables simultaneously. Continuous variables are used to optimize parameters such as dimensions, stresses, masses, or costs, while discrete variables represent binary decisions to determine the presence or absence of structural elements within a structure while also calculating discrete materials and standard sections. The optimization process is divided into three main steps. First, a mechanical superstructure with a variety of different topology-, material- and dimensional alternatives. Next, a MINLP model is formulated to encapsulate the optimization problem. Finally, an optimal solution is searched in the direction of the defined objective function while respecting the structural constraints. The economic or mass objective function of the material and labor costs of a structure is subjected to the constraints known from structural analysis. These constraints include equations for the calculation of internal forces and deflections, as well as equations for the dimensioning of structural components (in accordance with the Eurocode standards). Given the complex, non-convex and highly non-linear nature of optimization problems in civil engineering, the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm is applied. This algorithm alternately solves subproblems of non-linear programming (NLP) and main problems of mixed-integer linear programming (MILP), in this way gradually refines the solution space up to the optimal solution. The NLP corresponds to the continuous optimization of parameters (with fixed topology, discrete materials and standard dimensions, all determined in the previous MILP), while the MILP involves a global approximation to the superstructure of alternatives, where a new topology, materials, standard dimensions are determined. The optimization of a convex problem is stopped when the MILP solution becomes better than the best NLP solution. Otherwise, it is terminated when the NLP solution can no longer be improved. While the OA/ER algorithm, like all other algorithms, does not guarantee global optimality due to the presence of non-convex functions, various modifications, including convexity tests, are implemented in OA/ER to mitigate these difficulties. The effectiveness of the proposed MINLP approach is demonstrated by its application to various structural optimization tasks, such as mass optimization of steel buildings, cost optimization of timber halls, composite floor systems, etc. Special optimization models have been developed for the optimization of these structures. The MINLP optimizations, facilitated by the user-friendly software package MIPSYN, provide insights into a mass or cost-optimal solutions, optimal structural topologies, optimal material and standard cross-section choices, confirming MINLP as a valuable method for the optimization of structures in civil engineering.

Keywords: MINLP, mixed-integer non-linear programming, optimization, structures

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Authors: Tamas Garamhegyi, Jozsef Kovacs, Rita Pongracz, Peter Tanos, Balazs Trasy, Norbert Magyar, Istvan G. Hatvani

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Like most environmental processes, shallow groundwater fluctuation under natural circumstances also behaves periodically. With the statistical tools at hand, it can easily be determined if a period exists in the data or not. Thus, the question may be raised: Does the estimated average period time characterize the whole time period, or not? This is especially important in the case of such complex phenomena as shallow groundwater fluctuation, driven by numerous factors. Because of the continuous changes in the oscillating components of shallow groundwater time series, the most appropriate method should be used to investigate its periodicity, this is wavelet spectrum analysis. The aims of the research were to investigate the periodic behavior of the shallow groundwater time series of an agriculturally important and drought sensitive region in Central-Eastern Europe and its relationship to the European pressure action centers. During the research ~216 shallow groundwater observation wells located in the eastern part of the Great Hungarian Plain with a temporal coverage of 50 years were scanned for periodicity. By taking the full-time interval as 100%, the presence of any period could be determined in percentages. With the complex hydrogeological/meteorological model developed in this study, non-periodic time intervals were found in the shallow groundwater levels. On the local scale, this phenomenon linked to drought conditions, and on a regional scale linked to the maxima of the regional air pressures in the Gulf of Genoa. The study documented an important link between shallow groundwater levels and climate variables/indices facilitating the necessary adaptation strategies on national and/or regional scales, which have to take into account the predictions of drought-related climatic conditions.

Keywords: climate change, drought, groundwater periodicity, wavelet spectrum and coherence analyses

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Authors: Qianqian He, Naian Liu, Xiaodong Xie, Linhe Zhang, Yang Zhang, Weidong Yan

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In the wild, the vegetation layer with exceptionally complex fuel composition and heterogeneous spatial distribution strongly affects the rate of fire spread (ROS) and fire intensity. Clarifying the influence of fuel bed structure on fire spread behavior is of great significance to wildland fire management and prediction. The packing ratio is one of the key physical parameters describing the property of the fuel bed. There is a threshold value of the packing ratio for ROS, but little is known about the controlling mechanism. In this study, to address this deficiency, a series of fire spread experiments were performed across a discrete fuel bed composed of some regularly arranged laser-cut cardboards, with constant wind speed and different packing ratios (0.0125-0.0375). The experiment aims to explore the relative importance of the internal and surface heat transfer with packing ratio. The dependence of the measured ROS on the packing ratio was almost consistent with the previous researches. The data of the radiative and total heat fluxes show that the internal heat transfer and surface heat transfer are both enhanced with increasing packing ratio (referred to as ‘Stage 1’). The trend agrees well with the variation of the flame length. The results extracted from the video show that the flame length markedly increases with increasing packing ratio in Stage 1. Combustion intensity is suggested to be increased, which, in turn, enhances the heat radiation. The heat flux data shows that the surface heat transfer appears to be more important than the internal heat transfer (fuel preheating inside the fuel bed) in Stage 1. On the contrary, the internal heat transfer dominates the fuel preheating mechanism when the packing ratio further increases (referred to as ‘Stage 2’) because the surface heat flux keeps almost stable with the packing ratio in Stage 2. As for the heat convection, the flow velocity was measured using Pitot tubes both inside and on the upper surface of the fuel bed during the fire spread. Based on the gas velocity distribution ahead of the flame front, it is found that the airflow inside the fuel bed is restricted in Stage 2, which can reduce the internal heat convection in theory. However, the analysis indicates not the influence of inside flow on convection and combustion, but the decreased internal radiation of per unit fuel is responsible for the decrease of ROS.

Keywords: discrete fuel bed, fire spread, packing ratio, wildfire

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Authors: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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Authors: J. Ranjbarn, A. Alibeigloo

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In this paper, we present an analytical method for analysis of nano-scale spherical shell subjected to thermo-mechanical shocks based on nonlocal elasticity theory. Thermo-mechanical properties of nano shpere is assumed to be temperature dependent. Governing partial differential equation of motion is solved analytically by using Laplace transform for time domain and power series for spacial domain. The results in Laplace domain is transferred to time domain by employing the fast inverse Laplace transform (FLIT) method. Accuracy of present approach is assessed by comparing the the numerical results with the results of published work in literature. Furtheremore, the effects of non-local parameter and wall thickness on the dynamic characteristics of the nano-sphere are studied.

Keywords: nano-scale spherical shell, nonlocal elasticity theory, thermomechanical shock, dynamic response

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Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: agricultural mobile robot, image processing, path recognition, hough transform

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Cemil Turan, Mohammad Shukri Salman

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The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms.

Keywords: adaptive filtering, sparse system identification, TD-LMS algorithm, VSSLMS algorithm

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Authors: Raj Banerjee, Y. M. Parulekar, Aniruddha Sengupta, J. Chattopadhyay

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This study proposes a Discrete Element Method (DEM) simulation using a commercial software PFC 2D (2019) for quantitatively simulating the monotonic and cyclic behaviour of sand using irregular shapes of sand grains. A preliminary analysis of the number of particles for optimal Representative Element Volume (REV) simulation of dimension 35mm x 35mm x 70mm using the scaled Grain Size Distribution (GSD) of sand is carried out. Subsequently, the effect of particle shape on the performance of sand during monotonic and cyclic bi-axial tests is assessed using numerical simulation. The validation of the numerical simulation for one case is carried out using the test results from the literature. Further numerical studies are performed in which the particles in REV are simulated by mixing round discs with irregular clumps (100% round disc, 75% round disc 25% irregular clump, 50% round disc 50% irregular clump, 25% round disc 75% irregular clump, 100% irregular clump) in different proportions using Dry Deposition (DD) method. The macro response for monotonic loading shows that irregular sand has a higher strength than round particles and that the Mohr-Coulomb failure envelope depends on the shape of the grains. During cyclic loading, it is observed that the liquefaction resistance curve (Cyclic Stress Ratio (CSR)-Number of cycles (N)) of sand is dependent on the combination of particle shapes with different proportions.

Keywords: biaxial test, particle shape, monotonic, cyclic

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Authors: Yasser Mahmoudi, Nader Karimi

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The present work investigates numerically the effect of thermal radiation from the solid phase on the rate of heat transfer inside a porous medium. Forced convection heat transfer process within a pipe filled with a porous media is considered. The Darcy-Brinkman-Forchheimer model is utilized to represent the fluid transport within the porous medium. A local thermal non-equilibrium (LTNE), two-equation model is used to represent the energy transport for the solid and fluid phases. The radiative heat transfer equation is solved by discrete ordinate method (DOM) to compute the radiative heat flux in the porous medium. Two primary approaches (models A and B) are used to represent the boundary conditions for constant wall heat flux. The effects of radiative heat transfer on the Nusselt numbers of the two phases are examined by comparing the results obtained by the application of models A and B. The fluid Nusselt numbers calculated by the application of models A and B show that the Nusselt number obtained by model A for the radiative case is higher than those predicted for the non-radiative case. However, for model B the fluid Nusselt numbers obtained for the radiative and non-radiative cases are similar.

Keywords: porous media, local thermal non-equilibrium, forced convection heat transfer, thermal radiation, Discrete Ordinate Method (DOM)

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Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

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Authors: Lei Bi, Yunpeng Jiao, Chunjiang Liu, Jianhua Chen, Wei Ge

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Two energy paths are proposed from thermodynamic viewpoints. Energy consumption means total power input to the specific system, and it can be decomposed into energy retention and energy dissipation. Energy retention is the variation of accumulated mechanical energy in the system, and energy dissipation is the energy converted to heat by irreversible processes. Based on the Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) framework, different energy terms are quantified from the specific flow elements of fluid cells and particles as well as their interactions with the wall. Direct energy consumption statistics are carried out for both cold and hot flow in gas-solid fluidization systems. To clarify the statistic method, it is necessary to identify which system is studied: the particle-fluid system or the particle sub-system. For the cold flow, the total energy consumption of the particle sub-system can predict the onset of bubbling and turbulent fluidization, while the trends of local energy consumption can reflect the dynamic evolution of mesoscale structures. For the hot flow, different heat transfer mechanisms are analyzed, and the original solver is modified to reproduce the experimental results. The influence of the heat transfer mechanisms and heat source on energy consumption is also investigated. The proposed statistic method has proven to be energy-conservative and easy to conduct, and it is hopeful to be applied to other multiphase flow systems.

Keywords: energy consumption statistic, gas-solid fluidization, CFD-DEM, regime transition, heat transfer mechanism

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Authors: Nuo Duan, Yi Pik Cheng

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This paper presents the data of a series of two-dimensional Discrete Element Method (DEM) simulations of a large-diameter rigid monopile subjected to cyclic loading under a high gravitational force. At present, monopile foundations are widely used to support the tall and heavy wind turbines, which are also subjected to significant from wind and wave actions. A safe design must address issues such as rotations and changes in soil stiffness subject to these loadings conditions. Design guidance on the issue is limited, so are the availability of laboratory and field test data. The interpretation of these results in sand, such as the relation between loading and displacement, relies mainly on empirical correlations to pile properties. Regarding numerical models, most data from Finite Element Method (FEM) can be found. They are not comprehensive, and most of the FEM results are sensitive to input parameters. The micro scale behaviour could change the mechanism of the soil-structure interaction. A DEM model was used in this paper to study the cyclic lateral loads behaviour. A non-dimensional framework is presented and applied to interpret the simulation results. The DEM data compares well with various set of published experimental centrifuge model test data in terms of lateral deflection. The accumulated permanent pile lateral displacements induced by the cyclic lateral loads were found to be dependent on the characteristics of the applied cyclic load, such as the extent of the loading magnitudes and directions.

Keywords: cyclic loading, DEM, numerical modelling, sands

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Authors: Ajay Krishnamurthy, Mariyappan Mahesh Kumar, Sellamuthu Periyar Selvam

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Fruits are considered as functional foods due to the presence of various bioactive compounds available such as polyphenols, which are beneficial to health when consumed as part of our diet. The primary objective of this study was to analyze the various functional groups present in ethanolic extracts of Hylocereus undatus and Garcinia indica and also measure their antibacterial and antioxidant potential respectively thereby affirming its nutraceutical potential. To fulfill our objective, a Fourier - transform Infrared Spectroscopy (FTIR) was conducted for functional group analysis, Total Phenolic Content and DPPH free radical scavenging activity for measuring it anti-oxidant potential and agar-well diffusion assay for antibacterial potential. On careful observation and analysis of the spectrum it was found that both the fruit extracts contain similar compounds viz. Phenols, Alkanes, Alkenes, Aldehydes, Ketones, Carboxylic Acid and Amines. Total phenolic content of H.undatus and G.indica was estimated to be (26.85 ± 1.84 mg GAE/100g) and (32.84 ± 1.63 mg GAE/100g) respectively which corresponds to an inhibition of 84% and 81% respectively. H.undatus shows an inhibition of (3.4 ± 2.1mm) in gram-positive and (4.2 ± 2.24mm) in gram-negative organism on the other hand G.indica shows (2.1 ± 0.98mm) in gram-positive and (3.1 ± 1.44mm) in gram negative. The presence of such diverse compounds in the fruits helps us to understand the necessity for the inclusion of fruits in our daily diet and also helps the pharmaceutical industry in realizing the importance of exotic fruits as a potential nutraceutical.

Keywords: DPPH, fourier-transform infrared spectroscopy (FTIR), Hylocereus undatus, Garcinia indica

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Authors: Zeinab Yazdanfar, Dilan Robert, Daniel Lester, S. Setunge

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Scour has been identified as the most common threat to bridge stability worldwide. Traditionally, scour around bridge piers is calculated using the empirical approaches that have considerable limitations and are difficult to generalize. The multi-physic nature of scouring which involves turbulent flow, soil mechanics and solid-fluid interactions cannot be captured by simple empirical equations developed based on limited laboratory data. These limitations can be overcome by direct numerical modeling of coupled hydro-mechanical scour process that provides a robust prediction of bridge scour and valuable insights into the scour process. Several numerical models have been proposed in the literature for bridge scour estimation including Eulerian flow models and coupled Euler-Lagrange models incorporating an empirical sediment transport description. However, the contact forces between particles and the flow-particle interaction haven’t been taken into consideration. Incorporating collisional and frictional forces between soil particles as well as the effect of flow-driven forces on particles will facilitate accurate modeling of the complex nature of scour. In this study, a coupled Computational Fluid Dynamics and Discrete Element Model (CFD-DEM) has been developed to simulate the scour process that directly models the hydro-mechanical interactions between the sediment particles and the flowing water. This approach obviates the need for an empirical description as the fundamental fluid-particle, and particle-particle interactions are fully resolved. The sediment bed is simulated as a dense pack of particles and the frictional and collisional forces between particles are calculated, whilst the turbulent fluid flow is modeled using a Reynolds Averaged Navier Stocks (RANS) approach. The CFD-DEM model is validated against experimental data in order to assess the reliability of the CFD-DEM model. The modeling results reveal the criticality of particle impact on the assessment of scour depth which, to the authors’ best knowledge, hasn’t been considered in previous studies. The results of this study open new perspectives to the scour depth and time assessment which is the key to manage the failure risk of bridge infrastructures.

Keywords: bridge scour, discrete element method, CFD-DEM model, multi-phase model

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Authors: Mojtaba Zabihi, Stephen Munro, Jonathan Little, Ri Li, Joshua Brinkerhoff, Sina Kheirkhah

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Occupational Safety and Health Administration (OSHA) identified dental workers at the highest risk of contracting COVID-19. This is because aerosol-generating procedures (AGP) during dental practices generate aerosols ( < 5µm) and droplets. These particles travel at varying speeds, in varying directions, and for varying durations. If these particles bear infectious viruses, their spreading causes airborne transmission of the virus in the dental room, exposing dentists, hygienists, dental assistants, and even other dental clinic clients to the infection risk. Computational fluid dynamics (CFD) simulation of two-phase flows based on a discrete phase model (DPM) is carried out to study the spreading of aerosol and droplets in a dental room. The simulation includes momentum, heat, and mass transfers between the particles and the airflow. Two simulations are conducted and compared. One simulation focuses on the effects of room ventilation in winter and summer on the particles' travel. The other simulation focuses on the control of aerosol and droplets' spreading. A suction collector is added near the source of aerosol and droplets, creating a flow sink in order to remove the particles. The effects of the suction flow on the aerosol and droplet travel are studied. The suction flow can remove aerosols and also reduce the spreading of droplets.

Keywords: aerosols, computational fluid dynamics, COVID-19, dental, discrete phase model, droplets, two-phase flow

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Authors: Carmel Sofer, Dan Vilenchik, Ron Dotsch, Galia Avidan

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Studies investigating emotional facial expressions typically reveal consensus among observes regarding the meaning of basic expressions, whose number ranges between 6 to 15 emotional states. Given this limited number of discrete expressions, how is it that the human vocabulary of emotional states is so rich? The present study argues that perceivers use sequences of these discrete expressions as the basis for a much richer vocabulary of emotional states. Such mechanisms, in which a relatively small number of basic components is expanded to a much larger number of possible combinations of meanings, exist in other human communications modalities, such as spoken language and music. In these modalities, letters and notes, which serve as basic components of spoken language and music respectively, are temporally linked, resulting in the richness of expressions. In the current study, in each trial participants were presented with sequences of two images containing facial expression in different combinations sampled out of the eight static basic expressions (total 64; 8X8). In each trial, using single word participants were required to judge the 'state of mind' portrayed by the person whose face was presented. Utilizing word embedding methods (Global Vectors for Word Representation), employed in the field of Natural Language Processing, and relying on machine learning computational methods, it was found that the perceived meanings of the sequences of facial expressions were a weighted average of the single expressions comprising them, resulting in 22 new emotional states, in addition to the eight, classic basic expressions. An interaction between the first and the second expression in each sequence indicated that every single facial expression modulated the effect of the other facial expression thus leading to a different interpretation ascribed to the sequence as a whole. These findings suggest that the vocabulary of emotional states conveyed by facial expressions is not restricted to the (small) number of discrete facial expressions. Rather, the vocabulary is rich, as it results from combinations of these expressions. In addition, present research suggests that using word embedding in social perception studies, can be a powerful, accurate and efficient tool, to capture explicit and implicit perceptions and intentions. Acknowledgment: The study was supported by a grant from the Ministry of Defense in Israel to GA and CS. CS is also supported by the ABC initiative in Ben-Gurion University of the Negev.

Keywords: Glove, face perception, facial expression perception. , facial expression production, machine learning, word embedding, word2vec

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Authors: Fethi Soltani, Adel Almarashi, Idir Mechai

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Tikhonov regularization and reproducing kernels are the most popular approaches to solve ill-posed problems in computational mathematics and applications. And the Fourier multiplier operators are an essential tool to extend some known linear transforms in Euclidean Fourier analysis, as: Weierstrass transform, Poisson integral, Hilbert transform, Riesz transforms, Bochner-Riesz mean operators, partial Fourier integral, Riesz potential, Bessel potential, etc. Using the theory of reproducing kernels, we construct a simple and efficient representations for some class of Fourier multiplier operators Tm on the Paley-Wiener space Hh. In addition, we give an error estimate formula for the approximation and obtain some convergence results as the parameters and the independent variables approaches zero. Furthermore, using numerical quadrature integration rules to compute single and multiple integrals, we give numerical examples and we write explicitly the extremal function and the corresponding Fourier multiplier operators.

Keywords: fourier multiplier operators, Gauss-Kronrod method of integration, Paley-Wiener space, Tikhonov regularization

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Authors: Seyfullah Madakbas, Hatice Birtane, Memet Vezir Kahraman

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In this study, we aimed to synthesize and characterize polyhedral oligomeric silsesquioxanes containing polyimide nanocomposite. Polyimide nanocomposites widely have been used in membranes in fuel cell, solar cell, gas filtration, sensors, aerospace components, printed circuit boards. Firstly, polyamic acid was synthesized and characterized by Fourier Transform Infrared. Then, polyhedral oligomeric silsesquioxanes containing polyimide nanocomposite was prepared with thermal imidization method. The obtained polyimide nanocomposite was characterized by Fourier Transform Infrared, Scanning Electron Microscope, Thermal Gravimetric Analysis and Differential Scanning Calorimetry. Thermal stability of polyimide nanocomposite was evaluated by thermal gravimetric analysis and differential scanning calorimetry. Surface morphology of composite samples was investigated by scanning electron microscope. The obtained results prove that successfully prepared polyhedral oligomeric silsesquioxanes are containing polyimide nanocomposite. The obtained nanocomposite can be used in many industries such as electronics, automotive, aerospace, etc.

Keywords: polyimide, nanocomposite, polyhedral oligomeric silsesquioxanes

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Authors: Yu-Ching Cheng, Yi-Kai Juan, Daniel Castro

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Energy consumption of buildings has been an increasing concern for researchers and practitioners in the last decade. Sustainable building renovation can reduce energy consumption and carbon dioxide emissions; meanwhile, it also can extend existing buildings useful life and facilitate environmental sustainability while providing social and economic benefits to the society. School buildings are different from other designed spaces as they are more crowded and host the largest portion of daily activities and occupants. Strategies that focus on reducing energy use but also improve the students’ learning environment becomes a significant subject in sustainable school buildings development. A decision model is developed in this study to solve complicated and large-scale combinational, discrete and determinate problems such as school renovation projects. The task of this model is to automatically search for the most cost-effective (lower cost and higher quality) renovation strategies. In this study, the search process of optimal school building renovation solutions is by nature a large-scale zero-one programming determinate problem. A* is suitable for solving deterministic problems due to its stable and effective search process, and genetic algorithms (GA) provides opportunities to acquire global optimal solutions in a short time via its indeterminate search process based on probability. These two algorithms are combined in this study to consider trade-offs between renovation cost and improved quality, this decision model is able to evaluate current school environmental conditions and suggest an optimal scheme of sustainable school buildings renovation strategies. Through adoption of this decision model, school managers can overcome existing limitations and transform school buildings into spaces more beneficial to students and friendly to the environment.

Keywords: decision model, school buildings, sustainable renovation, genetic algorithm, A* search algorithm

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Authors: A. Idawaty, O. Mohamed, A. Z. Zuriati

Abstract:

This paper presents the development of an event based Discrete Event Simulation (DES) for a recovery algorithm known Backward Recovery Global Preemptive Utility Accrual Scheduling (BR_GPUAS). This algorithm implements the Backward Recovery (BR) mechanism as a fault recovery solution under the existing Time/Utility Function/ Utility Accrual (TUF/UA) scheduling domain for multiprocessor environment. The BR mechanism attempts to take the faulty tasks back to its initial safe state and then proceeds to re-execute the affected section of the faulty tasks to enable recovery. Considering that faults may occur in the components of any system; a fault tolerance system that can nullify the erroneous effect is necessary to be developed. Current TUF/UA scheduling algorithm uses the abortion recovery mechanism and it simply aborts the erroneous task as their fault recovery solution. None of the existing algorithm in TUF/UA scheduling domain in multiprocessor scheduling environment have considered the transient fault and implement the BR mechanism as a fault recovery mechanism to nullify the erroneous effect and solve the recovery problem in this domain. The developed BR_GPUAS simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. Simulation results revealed that BR_GPUAS algorithm can saved almost 20-30% of the accumulated utilities making it reliable and efficient for the real-time application in the multiprocessor scheduling environment.

Keywords: real-time system (RTS), time utility function/ utility accrual (TUF/UA) scheduling, backward recovery mechanism, multiprocessor, discrete event simulation (DES)

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Authors: Hamidullah Binol, Abdullah Bal

Abstract:

Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

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Authors: Annabelle Nelson

Abstract:

This literature review documents indigenous storytelling as it functions to help humans face adversity and find emotional strength by aligning with nature. Archetypes in stories can transform the inner world from a Jungian perspective. Joseph Campbell’s hero-heroine cycle depicts the structure of stories to include a call to adventure, tests, helpers, and a return as the transformed person can help him or herself and even help their communities. By showcasing certain character traits, such as bravery or perseverance or humility, stories give maps for humans to face adversity. The main characters or archetypes in stories, as Carl Jung posited, provide a vehicle that can open consciousness if a listener identifies with the character. As documented in the review, this has many benefits. First, it can open consciousness to the collective unconscious for insight and intuitive clarity, as well as healing and release emotional trauma. The resultant spacious quality of consciousness allows the spiritual self to present insights to conscious awareness. Research in applied youth development programs demonstrates the utility of storytelling to prompt healthy choices and transform difficult life experience into success.

Keywords: archetypes, learning, storytelling, transformation

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Authors: Kamand Bagherian, Nariman Niknejad

Abstract:

A vibration isolation technology for precise position control of a rotary system powered by two permanent magnet DC (PMDC) motors is proposed, where this system is mounted on an oscillatory frame. To achieve vibration isolation for this system, active damping and disturbance rejection (ADDR) technology is presented which introduces a cooperation of a main and an auxiliary PMDC, controlled by discrete-time sliding mode control (DTSMC) based schemes. The controller of the main actuator tracks a desired position and the auxiliary actuator simultaneously isolates the induced vibration, as its controller follows a torque trend. To determine this torque trend, a combination of two algorithms is introduced by the ADDR technology. The first torque-trend producing algorithm rejects the disturbance by counteracting the perturbation, estimated using a model-based observer. The second torque trend applies active variable damping to minimize the oscillation of the output shaft. In this practice, the presented technology is implemented on a rotary system with a pendulum attached, mounted on a linear actuator simulating an oscillation-transmitting structure. In addition, the obtained results illustrate the functionality of the proposed technology.

Keywords: active damping, discrete-time nonlinear controller, disturbance tracking algorithm, oscillation transmitting support, position control, stability robustness, vibration isolation

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Authors: Mijoh A. Gbededo, Kapila Liyanage, Ilias Oraifige

Abstract:

Decision making for sustainable manufacturing design and management requires critical considerations due to the complexity and partly conflicting issues of economic, social and environmental factors. Although there are tools capable of assessing the combination of one or two of the sustainability factors, the frameworks have not adequately integrated all the three factors. Case study and review of existing simulation applications also shows the approach lacks integration of the sustainability factors. In this paper we discussed the development of a simulation based framework for support of a holistic assessment of sustainable manufacturing design and management. To achieve this, a strategic approach is introduced to investigate the strengths and weaknesses of the existing decision supporting tools. Investigation reveals that Discrete Event Simulation (DES) can serve as a rock base for other Life Cycle Analysis frameworks. Simio-DES application optimizes systems for both economic and competitive advantage, Granta CES EduPack and SimaPro collate data for Material Flow Analysis and environmental Life Cycle Assessment, while social and stakeholders’ analysis is supported by Analytical Hierarchy Process, a Multi-Criteria Decision Analysis method. Such a common and integrated framework creates a platform for companies to build a computer simulation model of a real system and assess the impact of alternative solutions before implementing a chosen solution.

Keywords: discrete event simulation, life cycle sustainability analysis, manufacturing, sustainability

Procedia PDF Downloads 279

Authors: Julia Cristina Bonaldo, Christophe L. Martin, Severine Romero Baivier, Stephane Mazerat

Abstract:

Alumina refractories are commonly used in steel and foundry industries. These refractories are prepared through a powder metallurgy route. They are a mixture of hard alumina particles and graphite platelets embedded into a soft carbonic matrix (binder). The powder can be cold pressed isostatically or uniaxially, depending on the application. The compact is then fired to obtain the final product. The quality of the product is governed by the microstructure of the composite and by the process parameters. The compaction behavior and the mechanical properties of the fired product depend greatly on the amount of each phase, on their morphology and on the initial microstructure. In order to better understand the link between these parameters and the macroscopic behavior, we use the Discrete Element Method (DEM) to simulate the compaction process and the fracture behavior of the fired composite. These simulations are coupled with well-designed experiments. Four mixes with various amounts of Al₂O₃ and binder were tested both experimentally and numerically. In DEM, each particle is modelled and the interactions between particles are taken into account through appropriate contact or bonding laws. Here, we model a bimodal mixture of large Al₂O₃ and small Al₂O₃ covered with a soft binder. This composite is itself mixed with graphite platelets. X-ray tomography images are used to analyze the morphologies of the different components. Large Al₂O₃ particles and graphite platelets are modelled in DEM as sets of particles bonded together. The binder is modelled as a soft shell that covers both large and small Al₂O₃ particles. When two particles with binder indent each other, they first interact through this soft shell. Once a critical indentation is reached (towards the end of compaction), hard Al₂O₃ - Al₂O₃ contacts appear. In accordance with experimental data, DEM simulations show that the amount of Al₂O₃ and the amount of binder play a major role for the compaction behavior. The graphite platelets bend and break during the compaction, also contributing to the macroscopic stress. Firing step is modeled in DEM by ascribing bonds to particles which contact each other after compaction. The fracture behavior of the compacted mixture is also simulated and compared with experimental data. Both diametrical tests (Brazilian tests) and triaxial tests are carried out. Again, the link between the amount of Al₂O₃ particles and the fracture behavior is investigated. The methodology described here can be generalized to other particulate materials that are used in the ceramic industry.

Keywords: cold compaction, composites, discrete element method, refractory materials, x-ray tomography

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