Search results for: spatial features

Authors: Jonathan Galvan-Colin, Ariel Valladares, Renela Valladares, Alexander Valladares

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Both porous materials and bulk metallic glasses have been studied due to their potential applications and their exceptional physical and chemical properties. However, each material presents certain drawbacks which have been thought to be overcome by generating bulk metallic glass foams (BMGF). Although some experimental reports have been performed on multicomponent BMGF, still no ab initio works have been published, as far as we know. We present an approach based on the expanding lattice (EL) method to generate binary amorphous nanoporous Cu64Zr36. Starting from two different configurations: a 108-atom crystalline cubic supercell (cCu64Zr36) and a 108-atom amorphous supercell (aCu64Zr36), both with an initial density of 8.06 g/cm3, we applied EL method to halve the density and to get 50% of porosity. After the lattice expansion the supercells were subject to ab initio molecular dynamics for 500 steps at constant room temperature. Then, the samples were geometry-optimized and characterized with the pair and radial distribution functions, bond-angle distributions and a coordination number analysis. We found that pores appeared along specific spatial directions different from one to another and that they differed in size and form as well, which we think is related to the initial structure. Due to the lack of experimental counterparts our results should be considered predictive and further studies are needed in order to handle a larger number of atoms and its implication on pore topology.

Keywords: ab initio molecular dynamics, bulk mettalic glass, porous alloy

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Authors: Abeer Samy, Oliver C. Saavedra Valeriano, Abdelazim Negm

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The Blue Nile Basin is the most important tributary of the Nile River. Egypt and Sudan are almost dependent on water originated from the Blue Nile. This multi-dependency creates conflicts among the three countries Egypt, Sudan, and Ethiopia making the management of these conflicts as an international issue. Good assessment of the water resources of the Blue Nile is an important to help in managing such conflicts. Hydrological models are good tool for such assessment. This paper presents a critical review of the nature and variability of the climate and hydrology of the Blue Nile Basin as a first step of using hydrological modeling to assess the water resources of the Blue Nile. Many several attempts are done to develop basin-scale hydrological modeling on the Blue Nile. Lumped and semi distributed models used averages of meteorological inputs and watershed characteristics in hydrological simulation, to analyze runoff for flood control and water resource management. Distributed models include the temporal and spatial variability of catchment conditions and meteorological inputs to allow better representation of the hydrological process. The main challenge of all used models was to assess the water resources of the basin is the shortage of the data needed for models calibration and validation. It is recommended to use distributed model for their higher accuracy to cope with the great variability and complexity of the Blue Nile basin and to collect sufficient data to have more sophisticated and accurate hydrological modeling.

Keywords: Blue Nile Basin, climate change, hydrological modeling, watershed

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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: Ersin Bozkurt

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The aim of the study is to develop materials for understanding image formations through virtual objects in geometric optics. The images in physics course books are formed by using real objects. This results in mistakes in the features of images because of generalizations which leads to conceptual misunderstandings in learning. In this study it was intended to identify pre-service physics teachers misunderstandings arising from false generalizations. Focused group interview was used as a qualitative method. The findings of the study show that students have several misconceptions such as "the image in a plain mirror is always virtual". However a real image can be formed in a plain mirror. To explain a virtual object's image formation in a more understandable way an overhead projector and episcope and their design was illustrated. The illustrations are original and several computer simulations will be suggested.

Keywords: computer simulations, geometric optics, physics education, students' misconceptions in physics

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Authors: Khalliyeva Gulnoz Iskandarovna, Mannonova Feruzabonu Sherali Qizi

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A scientific field called comparative literature or literary comparative studies compares two or more literary phenomena. One of the most important scientific fields nowadays, when global social, cultural, and literary relations are growing daily, is comparative literature. Any comparative investigation reveals shared and unique characteristics of literary phenomena, which provide the cornerstone for the creation of overarching theoretical principles that apply to all literature. Comparative analysis consists of objects, and they are their constituents. For researchers, it is enough to know this. Comparative analysis, in addition to the above-mentioned actions, also focuses on comparing the components of the objects of analysis with each other. The purpose of this article is to investigate comparative analysis in literature and to identify similarities and differences between comparable objects. Students, teachers, and researchers should be able to describe comparative research techniques and their fundamental ideas when studying this topic. They should also have a basic understanding of comparative literature and their summary.

Keywords: object, natural, social, spiritual, epistemological, logical, methodological, methodological, axiological tasks, stages of comparison, environment, internal features, and typical situations

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Authors: Endalew Fufa Kufi

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One of the major concerns in educational provision and success determination is access to available opportunities. In that, girls’ access to education has been a point of concern, and more emphasis has come to be at the forefront regarding success. Researches have mostly been held on extremes such as equal access and success, but only a few works deal with process issues related to home and school interplay, issues of progress from lower to higher levels, and spatial conditions related to girls’ education. Hence, this survey assessed experiences in three countries of Africa: Ethiopia, Ghana, and Botswana regarding girls’ education in policy and practice as related to contextual matters in girls’ education. Contextual discourse analysis of qualitative design was used to materialize the study. From each country, five research works held 2010 onwards were purposively selected through criterion-sampling. On the policy aspect, workable documents were looked into. The findings denoted that educational access was of more stretch and generic nature, and the narration was dominated by institutional expectations, not identifying which group should benefit what. The researches largely dealt with either subject-specific dealings or access alone at large. Success studies, by far, dealt with a comparison of girls with boys rather than determinant-related projections. Moreover, the cultural representation of girls’ education had a very minimal part in both policy and researches. From that, it could be found that in-depth scrutiny on the individual, institutional, and leadership determinants of girls’ education would be necessary.

Keywords: determinants, girls, education, feminism

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Authors: Maryam Hasheminasab, Ahmed Cheldavi, Ahmed Kishk

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Frequency-diverse arrays (FDAs) have garnered significant attention from researchers due to their ability to combine frequency diversity with the inherent spatial diversity of an array. The introduction of frequency diversity in FDAs enables the generation of auto-scanning patterns that are range-dependent, which can have advantageous applications in communication and radar systems. However, the main challenge in implementing FDAs lies in determining the technique for distributing frequencies among the array elements. One approach to address this challenge is by utilizing coupled oscillators, which are a technique commonly employed in active microwave theory. Nevertheless, the limited stability range of coupled oscillators poses another obstacle to effectively utilizing this technique. In this paper, we explore the possibility of employing a coupled oscillator array in the mode lock state (MLS) for implementing frequency distribution in FDAs. Additionally, we propose and simulate the use of a digital phase-locked loop (DPLL) as a backup technique to stabilize the oscillators. Through simulations, we validate the functionality of this technique. This technique holds great promise for advancing the implementation of phased arrays and overcoming current scan rate and phase shifter limitations, especially in millimeter wave frequencies.

Keywords: angle-changing rate, auto scanning beam, pull-in range, hold-in range, locking range, mode locked state, frequency locked state

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Authors: Huiwu Yu, Xiangyou Li, Xiaoyan Zeng

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Porous spheres have been widely used in many fields due to their attractive features. In this work, an approach for fabricating porous spheres of nanoparticles was presented, in which the nanoparticles were welded together to form micro spheres by simply irradiating the nanoparticles in liquid medium by a picosecond laser. As an example, anatase titanium dioxide was chosen as a typical material on account of its metastability. The structure and morphologies of the products were characterised by X-ray diffraction (XRD), scanning electron microscope (SEM), Raman, and high-resolution transmission electron microscopy (HRTEM), respectively. The results showed that, anatase titanium dioxide micro spheres (2-10 μm) with macroporous (10-100 nm) were prepared from nano-anatase titanium dioxide nanoparticles (10-100 nm). The formation process of polycrystalline anatase titanium dioxide microspheres was investigated with different liquid mediums and the input laser fluences. Thus, this facile laser irradiation approach might provide a way for the fabrication of porous microspheres without phase-transition.

Keywords: titanium dioxide, porous microspheres, picosecond laser, nano-welding

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Authors: Onifade Yemi Sikiru, Vwoke Eruya

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An investigation has been made of the groundwater potentials of Erusu-Arigidi, Ondo State, Nigeria and using an electrical resistivity survey. This study was motivated to determine the electrical resistivity parameters of the area. This work aims to use the electrical resistivity method to explore the groundwater potentials of the study area. A total of ten vertical electrical soundings (VES) were conducted with a maximum electrode spacing of 150 m. The data was acquired using ABEM SAS 1000 Terrameter and processed using WINRESIST. The interpreted and analyzed results reveal four to six geoelectric layers. The VES curves obtained were QH, H, AAA, HKH, and HA. Findings from the study revealed that the geoelectric layer ranges from 3 to 5 layers. From the result, the Dar Zarrouk parameters longitudinal conductance (S) and transverse resistance (Tr), average longitudinal resistance (), transverse resistivity (), coefficient of anisotropy (λ), and reflection coefficient ranges from 0.22 to 1.45mhos, 67.12 to 4262.91 Ω/m², 8.81 to 76.12 Ω-m, 12.0 to 243.5 Ωm², 1.01 to 1.78, and 0.72 to 0.99 respectively. Deduction from S suggested that groundwater tends to be slightly vulnerable to surface contamination. Further findings from Dar Zarrouk parameters revealed that southwest parts of the study area tend to have high groundwater potential when compared to other parts of the study area. While hydraulic conductivity and transmissivity range from 0.003 to 0.051m/day, and 11.16 to 158.30m²/day, results obtained from H and T revealed northwest parts of the study area are considered to be aquiferous when compared to other parts of the research area.

Keywords: variation, isoresistivity, hydraulic conductivity, groundwater

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Authors: Zhehao Zhang

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Walking has become an indispensable and sustainable way of travel for college students in their daily lives; campus street is an important carrier for students to walk and take part in a variety of activities, improving the walkability of campus streets plays an important role in optimizing the quality of campus space environment, promoting the campus walking system and inducing multiple walking behaviors. The purpose of this paper is to explore the effect of campus layout, facility distribution, and location site selection on the walkability of campus streets, and assess the street design qualities from the elements of imageability, enclosure, complexity, transparency, and human scale, and further examines the relationship between street-level urban design perceptual qualities and walkability and its effect on walking behavior in the campus. Taking Tianjin University as the research object, this paper uses the optimized walk score method based on walking frequency, variety, and distance to evaluate the walkability of streets from a macro perspective and measures the urban design qualities in terms of the calculation of street physical environment characteristics, as well as uses behavior annotation and street image data to establish temporal and spatial behavior database to analyze walking activity from the microscopic view. In addition, based on the conclusions, the improvement and design strategy will be presented from the aspects of the built walking environment, street vitality, and walking behavior.

Keywords: walkability, streetscapes, pedestrian activity, walk score

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Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

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Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Laura Josabeth Oros-Avilés, Josefina León-León

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In recent decades, the concern of the monetary authorities has increased because of the instability of the financial sector caused by the crash of speculative bubbles. In fact, the crash of "housing bubble" in U.S. (2007-2008) led the latest global crisis. The aim of paper is to analyze the features and causes of the financial and banking crisis from an historical view. In particular, in this research, a comparative study of some analytical approaches about economic and financial history is discussed. In addition, the role of monetary policy of central banks in managing financial crises, from its origins to today, is analyzed. According to the studied approaches, two types of factors that cause the financial instability were identified: subjective and objectives. In the research, these factors are deeply discussed, in order to noting the agreements and disagreement between the authors. Specially, it is worth noting that all of them recognized that the credit boom and the financial deregulation are the main causes of financial crises.

Keywords: asset prices, banking crises, financial bubble, financial instability, monetary policy

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Authors: Yakup Sahin, Naim Suleyman Ting, Ismail Aksoy

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In this paper, a single phase soft switched Zero Voltage Transition and Zero Current Transition (ZVT-ZCT) Power Factor Correction (PFC) boost converter is proposed. In the proposed PFC converter, the main switch turns on with ZVT and turns off with ZCT without any additional voltage or current stresses. Auxiliary switch turns on and off with zero current switching (ZCS). Also, the main diode turns on with zero voltage switching (ZVS) and turns off with ZCS. The proposed converter has features like low cost, simple control and structure. The output current and voltage are controlled by the proposed PFC converter in wide line and load range. The theoretical analysis of converter is clarified and the operating steps are given in detail. The simulation results of converter are obtained for 500 W and 100 kHz. It is observed that the semiconductor devices operate with soft switching (SS) perfectly. So, the switching power losses are minimum. Also, the proposed converter has 0.99 power factor with sinusoidal current shape.

Keywords: power factor correction, zero-voltage transition, zero-current transition, soft switching

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Authors: Marie-Therese Claes, Anett Hermann

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With the advent of the web and information technology since the end of the 20ᵗʰ century, digitalization has revolutionized our everyday life, from shopping and dating to education and transportation. The world of work is one of the areas that has been highly transformed by changing the time and spatial limits of the work. The expansion of the internet, wireless, and easily portable devices such as laptop computers and mobile phones has enabled us to work almost from any place at any time. As a result, telework, which started in the 1950s and elevated in the 1970s, steeply raised to a new level in 21ˢᵗ century. Telework consists of various forms of work done from outside the traditional workplace by using information technologies. The social distancing and lockdown measures that have been taken to reduce the spread of the virus in many countries worldwide resulted in an increasing number of teleworkers and made “working from home’’ synonymous with telework. Post-COVID-19, the number of teleworkers is still higher than before the pandemic period, and the interest in expanding teleworking has been growing too. Notwithstanding the advantages ushered by telework, it also has a number of drawbacks that negatively affect organizations and employees. The intention of this piece of work is not to indicate a causational relationship between telework and discrimination. Our aim is to indicate some unintended and/or unnoticed deleterious effects of telework in reinforcing discrimination and to instigate discussion on how to mitigate the effects. To do so, this insight indicates how telework reinforces traditional gender roles and how organizational culture towards telework and its access to employees at different levels of the organizational hierarchy opens the room for discrimination.

Keywords: mobile work, discrimination, gender roles, organizational culture

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Authors: M. Dinesh, V. Kenny Mark, Dharni Vasudhevan Venkatesan, B. Santhosh Kumar, R. Sree Radesh, V. R. Sanal Kumar

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Comprehensive numerical studies have been carried out to examine the best aerodynamic performance of subsonic aircraft at different winglet cant angles using a validated 3D k-ω SST model. In the parametric analytical studies, NACA series of airfoils are selected. Basic design of the winglet is selected from the literature and flow features of the entire wing including the winglet tip effects have been examined with different cant angles varying from 150 to 600 at different angles of attack up to 140. We have observed, among the cases considered in this study that a case with 150 cant angle the aerodynamics performance of the subsonic aircraft during takeoff was found better up to an angle of attack of 2.80 and further its performance got diminished at higher angles of attack. Analyses further revealed that increasing the winglet cant angle from 150 to 600 at higher angles of attack could negate the performance deterioration and additionally it could enhance the peak CL/CD on the order of 3.5%. The investigated concept of variable-cant-angle winglets appears to be a promising alternative for improving the aerodynamic efficiency of aircraft.

Keywords: aerodynamic efficiency, cant angle, drag reduction, flexible winglets

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Sasitharan Ambicapathy, J. Vignesh, P. Sivaraj, Godfrey Derek Sams, K. Sabarinath, V. R. Sanal Kumar

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The external flow simulation of a flying car at take off phase is a daunting task owing to the fact that the prediction of the transient unsteady flow features during its deployment phase is very complex. In this paper 3D numerical simulations of external flow of Ferrari F430 proposed flying car with different NACA 9618 rectangular wings have been carried. Additionally, the aerodynamics characteristics have been generated for optimizing its geometry for achieving the minimum take off velocity with better overall performance in both road and air. The three-dimensional standard k-omega turbulence model has been used for capturing the intrinsic flow physics during the take off phase. In the numerical study, a fully implicit finite volume scheme of the compressible, Reynolds-Averaged, Navier-Stokes equations is employed. Through the detailed parametric analytical studies we have conjectured that Ferrari F430 flying car facilitated with high wings having three different deployment histories during the take off phase is the best choice for accomplishing its better performance for the commercial applications.

Keywords: aerodynamics of flying car, air taxi, negative lift, roadable airplane

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Authors: Gurleen Kaur

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Being tall-short, fat-thin, black-white, etc. is an inevitable part of how people perceive you. This association of people with your external appearance carves out an identity for you. This paper will look at the reasons why people relate a person to a particular categorization on the basis of his/her physical appearance. The paper delves into reasons for this categorization into groups: Subconscious grouping, personal gain, ease of relating to the group, and social acceptance. Development of certain unique physical features also leads to a person relating himself to a collective identity. Thus, this paper will support the fact that physical appearance plays a crucial role in categorization of people into groups and hence forming a group identity for them. This paper is divided into three parts. The first part will discuss what physical appearance is and how is it linked to our daily lives. The second part will talk about why it works i.e. why this factor of external appearance is important in formation of identity. The last part will talk about the factors which lead to categorization of identity because of physical appearance.

Keywords: group identity, physical appearance, subconscious grouping, collective identity

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Authors: Rowan Moore, Kai Scheffler, Eugen Damoc, Jennifer Sutton, Aaron Bailey, Stephane Houel, Simon Cubbon, Jonathan Josephs

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Purpose: MS analysis of monoclonal antibodies (mAbs) at the protein and peptide levels is critical during development and production of biopharmaceuticals. The compositions of current generation therapeutic proteins are often complex due to various modifications which may affect efficacy. Intact proteins analyzed by MS are detected in higher charge states that also provide more complexity in mass spectra. Protein analysis in native or native-like conditions with zero or minimal organic solvent and neutral or weakly acidic pH decreases charge state value resulting in mAb detection at higher m/z ranges with more spatial resolution. Methods: Three commercially available mAbs were used for all experiments. Intact proteins were desalted online using size exclusion chromatography (SEC) or reversed phase chromatography coupled on-line with a mass spectrometer. For streamlined use of the LC- MS platform we used a single SEC column and alternately selected specific mobile phases to perform separations in either denaturing or native-like conditions: buffer A (20 % ACN, 0.1 % FA) with Buffer B (100 mM ammonium acetate). For peptide analysis mAbs were proteolytically digested with and without prior reduction and alkylation. The mass spectrometer used for all experiments was a commercially available Thermo Scientific™ hybrid Quadrupole-Orbitrap™ mass spectrometer, equipped with the new BioPharma option which includes a new High Mass Range (HMR) mode that allows for improved high mass transmission and mass detection up to 8000 m/z. Results: We have analyzed the profiles of three mAbs under reducing and native conditions by direct infusion with offline desalting and with on-line desalting via size exclusion and reversed phase type columns. The presence of high salt under denaturing conditions was found to influence the observed charge state envelope and impact mass accuracy after spectral deconvolution. The significantly lower charge states observed under native conditions improves the spatial resolution of protein signals and has significant benefits for the analysis of antibody mixtures, e.g. lysine variants, degradants or sequence variants. This type of analysis requires the detection of masses beyond the standard mass range ranging up to 6000 m/z requiring the extended capabilities available in the new HMR mode. We have compared each antibody sample that was analyzed individually with mixtures in various relative concentrations. For this type of analysis, we observed that apparent native structures persist and ESI is benefited by the addition of low amounts of acetonitrile and formic acid in combination with the ammonium acetate-buffered mobile phase. For analyses on the peptide level we analyzed reduced/alkylated, and non-reduced proteolytic digests of the individual antibodies separated via reversed phase chromatography aiming to retrieve as much information as possible regarding sequence coverage, disulfide bridges, post-translational modifications such as various glycans, sequence variants, and their relative quantification. All data acquired were submitted to a single software package for analysis aiming to obtain a complete picture of the molecules analyzed. Here we demonstrate the capabilities of the mass spectrometer to fully characterize homogeneous and heterogeneous therapeutic proteins on one single platform. Conclusion: Full characterization of heterogeneous intact protein mixtures by improved mass separation on a quadrupole-Orbitrap™ mass spectrometer with extended capabilities has been demonstrated.

Keywords: disulfide bond analysis, intact analysis, native analysis, mass spectrometry, monoclonal antibodies, peptide mapping, post-translational modifications, sequence variants, size exclusion chromatography, therapeutic protein analysis, UHPLC

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Authors: Zaki Ahmad Khan, Jamshed Siddiqui, Abdus Samad

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The Scheduling and mapping of tasks on a set of processors is considered as a critical problem in parallel and distributed computing system. This paper deals with the problem of dynamic scheduling on a special type of multiprocessor architecture known as Linear Crossed Cube (LCQ) network. This proposed multiprocessor is a hybrid network which combines the features of both linear type of architectures as well as cube based architectures. Two standard dynamic scheduling schemes namely Minimum Distance Scheduling (MDS) and Two Round Scheduling (TRS) schemes are implemented on the LCQ network. Parallel tasks are mapped and the imbalance of load is evaluated on different set of processors in LCQ network. The simulations results are evaluated and effort is made by means of through analysis of the results to obtain the best solution for the given network in term of load imbalance left and execution time. The other performance matrices like speedup and efficiency are also evaluated with the given dynamic algorithms.

Keywords: dynamic algorithm, load imbalance, mapping, task scheduling

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Authors: Sundara Subramanian Karuppasamy, Che Hua Yang

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In this work, the laser ultrasound technique has been used for analyzing and imaging the inner defects in metal blocks. To detect the defects in blocks, traditionally the researchers used piezoelectric transducers for the generation and reception of ultrasonic signals. These transducers can be configured into the sparse and phased array. But these two configurations have their drawbacks including the requirement of many transducers, time-consuming calculations, limited bandwidth, and provide confined image resolution. Here, we focus on the non-contact method for generating and receiving the ultrasound to examine the inner defects in aluminum blocks. A Q-switched pulsed laser has been used for the generation and the reception is done by using Laser Doppler Vibrometer (LDV). Based on the Doppler effect, LDV provides a rapid and high spatial resolution way for sensing ultrasonic waves. From the LDV, a series of scanning points are selected which serves as the phased array elements. The side-drilled hole of 10 mm diameter with a depth of 25 mm has been introduced and the defect is interrogated by the linear array of scanning points obtained from the LDV. With the aid of the Synthetic Aperture Focusing Technique (SAFT) algorithm, based on the time-shifting principle the inspected images are generated from the A-scan data acquired from the 1-D linear phased array elements. Thus the defect can be precisely detected with good resolution.

Keywords: laser ultrasonics, linear phased array, nondestructive testing, synthetic aperture focusing technique, ultrasonic imaging

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Authors: Eduardo Remirez, Luis Moreno

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The rapid growth of the private satellite launchers sector is leading the space race. Hence, with the privatization of the sector, all the companies are racing for a more efficient and reliant way to set satellites in orbit. Having detected the current needs for power management in the launcher vehicle industry, the Modular Power Bus is proposed as a technology to revolutionize power management in current and future Launcher Vehicles. The MPBus Project is committed to develop a new power bus architecture combining ejectable batteries with the main bus through intelligent nodes. These nodes are able to communicate between them and a battery controller using an improved, data over DC line technology, expected to reduce the total weight in two main areas: improving the use of the batteries and reducing the total weight due to harness. This would result in less weight for each launch stage increasing the operational satellite payload and reducing cost. These features make the system suitable for a number of launchers.

Keywords: modular power bus, Launcher vehicles, ejectable batteries, intelligent nodes

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Betim Bahtiri, B. Arash, R. Rolfes

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Epoxy-based materials are frequently exposed to high-humidity environments in many engineering applications. As a result, their material properties would be degraded by water absorption. A full characterization of the material properties under hygrothermal conditions requires time- and cost-consuming experimental tests. To gain insights into the physics of diffusion mechanisms, atomistic simulations have been shown to be effective tools. Concerning the diffusion of water in polymers, spatial trajectories of water molecules are obtained from molecular dynamics (MD) simulations allowing the interpretation of diffusion pathways at the nanoscale in a polymer network. Conventional MD simulations of water diffusion in amorphous polymers lead to discrepancies at low temperatures due to the short timescales of the simulations. In the proposed model, this issue is solved by using a combined scheme of autonomous basin climbing (ABC) with kinetic Monte Carlo and reactive MD simulations to investigate the diffusivity of water molecules in epoxy resins across a wide range of temperatures. It is shown that the proposed simulation framework estimates kinetic properties of water diffusion in epoxy resins that are consistent with experimental observations and provide a predictive tool for investigating the diffusion of small molecules in other amorphous polymers.

Keywords: epoxy resins, water diffusion, autonomous basin climbing, kinetic Monte Carlo, reactive molecular dynamics

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

Abstract:

The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 279

Authors: Saou Mohamed Amine

Abstract:

The construction industry has been identified as a user of substantial amount of materials and energy resources that has an enormous impact on environment. The energy efficient in refurbishment project is being considered as one of the approaches to achieve sustainability in construction industry. The increasing concern for environment has made building owners and designers to incorporate the energy efficiency features into their building projects. However, an overwhelming issue of existing non-energy efficient buildings which exceeds the number of new building could be ineffective if the buildings are not refurbished through the energy efficient measures. Thus, energy efficient in refurbishment project is being considered as one of the approaches to achieve sustainability that offers significant opportunities for reducing global energy consumption and greenhouse gas emissions. However, the quality of design team attributes and the characteristics of the refurbishment building projects have been argued to be the main factors that determine the energy efficiency performance of the building.

Keywords: construction industry, design team attributes, energy efficient performance, refurbishment projects characteristics

Procedia PDF Downloads 361

Authors: Jagdish Prasad Maurya, Sanjay Kumar Pandey

Abstract:

Inadequate understanding of the complex nature of flow features in tornado vortex is a major problem in modelling tornadoes. Tornadoes are violent atmospheric phenomenon that appear all over the world. Modelling tornadoes aim to reduce the loss of the human lives and material damage caused by the tornadoes. Dynamics of tornado is investigated by a numerical technique, the improved version of the projection method. In this paper, authors solve the problem for axisymmetric tornado vortex by the said method that uses a finite difference approach for getting an accurate and stable solution. The conclusions drawn are that large radial inflow velocity occurs near the ground that leads to increase the tangential velocity. The increased velocity phenomenon occurs close to the boundary and absolute maximum wind is obtained near the vortex core. The results validate previous numerical and theoretical models.

Keywords: computational fluid dynamics, mathematical model, Navier-Stokes equations, tornado

Procedia PDF Downloads 344

Authors: Ali Jahanfar, Brajesh Dubey, Bahram Gharabaghi, Saber Bayat Movahed

Abstract:

Ever increasing population growth of major urban centers and environmental challenges in siting new landfills have resulted in a growing trend in design of mega-landfills some with extraordinary heights and dangerously steep slopes. Landfill failure mobility risk analysis is one of the most uncertain types of dynamic rheology models due to very large inherent variabilities in the heterogeneous solid waste material shear strength properties. The waste flow of three historic dumpsite and two landfill failures were back-analyzed using run-out modeling with DAN-W model. The travel distances of the waste flow during landfill failures were calculated approach by taking into account variability in material shear strength properties. The probability distribution function for shear strength properties of the waste material were grouped into four major classed based on waste material compaction (landfills versus dumpsites) and composition (high versus low quantity) of high shear strength waste materials such as wood, metal, plastic, paper and cardboard in the waste. This paper presents a probabilistic method for estimation of the spatial extent of waste avalanches, after a potential landfill failure, to create maps of vulnerability scores to inform property owners and residents of the level of the risk.

Keywords: landfill failure, waste flow, Voellmy rheology, friction coefficient, waste compaction and type

Procedia PDF Downloads 282

Authors: Farrukh Javed, Krzysztof Podgórski

Abstract:

We propose a new model that accounts for the asymmetric response of volatility to positive ('good news') and negative ('bad news') shocks in economic time series the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of 'bad' and 'good' news processes given the past the property that is important for the statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data.

Keywords: heavy tails, volatility clustering, generalized asymmetric laplace distribution, leverage effect, conditional heteroskedasticity, asymmetric power volatility, GARCH models

Procedia PDF Downloads 377