Search results for: thermal cycling machine

Authors: H. Afsari, H. Shokouhmand

Abstract:

When stack is placed in a thermoacoustic resonator in a cryocooler, one extremity of the stack heats up while the other cools down due to the thermoacoustic effect. In the present, with expression a formula by linear theory, will see this temperature difference depends on what factors. The computed temperature difference is compared to the one predicted by the formula. These discrepancies can not be attributed to non-linear effects, rather they exist because of thermal effects. Two correction factors are introduced for close up results among linear theory and computed and use these correction factors to modified linear theory. In fact, this formula, is optimized by GA (Genetic Algorithm). Finally, results are shown at different Mach numbers and stack location in resonator.

Keywords: heat transfer, thermoacoustic cryocooler, stack, resonator, mach number, genetic algorithm

Procedia PDF Downloads 372

Authors: D. Pradeep Mitra, Prafulla

Abstract:

Looking to the word propellant-less system it makes us to believe that it is an impossible one, but this paper demonstrates the use of microwaves to create a system which makes impossible to be possible, it means a propellant-less propulsion system using microwaves. In these thrusters, microwaves are radiated into a sealed parabolic cavity through a waveguide, which act on the surface of the cavity and follow the axis of the thrusters to produce thrust. The advantages of these thrusters are: (1) Producing thrust without propellant; without erosion, wear, and thermal stress from the hot exhaust gas; and at the same time increasing quality. (2) If the microwave output power is stable, the performance of thrusters is not affected by its working environment. This paper is demonstrated from general maxwell equations. These equations are used to create the mathematical model of the thrusters. These mathematical model helps us to calculate the Q factor and calculate the approximate thrust which would be generated in the system.

Keywords: propellant less, microwaves, parabolic wave guide, propulsion system

Procedia PDF Downloads 375

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 115

Authors: Hassane Ouazzani Chahdi, Omar Helli, Bourzgui Nour Eddine, Hassan Maher, Ali Soltani

Abstract:

The real-time knowledge of the NO, NO₂ concentration at high temperature, would allow manufacturers of automobiles to meet the upcoming stringent EURO7 anti-pollution measures for diesel engines. Knowledge of the concentration of each of these species will also enable engines to run leaner (i.e., more fuel efficient) while still meeting the anti-pollution requirements. Our proposed technology is promising in the field of automotive sensors. It consists of nanostructured semiconductors based on gallium nitride and zirconia dioxide. The development of new technologies for selective detection of NO and NO₂ gas species would be a critical enabler of superior depollution. The current response was well correlated to the NO concentration in the range of 0–2000 ppm, 0-2500 ppm NO₂, and 0-300 ppm NH₃ at a temperature of 600.

Keywords: NOₓ sensors, HEMT transistor, anti-pollution, gallium nitride, gas sensor

Procedia PDF Downloads 237

Authors: Pallab Bhattacharya, C. K. Das

Abstract:

Development of microwave absorbing material is a growing field of research in both the commercial and defense sector, and also to enrich the field of stealth technology. The recent work is attentive to the preparation of nanocomposite based on acid modified MWCNT, hexagonal shaped magnetic M-type hexaferrite (CuFe10Al2O19) and polyaniline. CuFe10Al2O19 was prepared by a facile chemical co-precipitation method. An in-situ approach was employed for the coating of polyaniline on MWCNT/CuFe10Al2O19 nanocomposite. The final fabrication of this nanocomposite for microwave measurements was done suitably in the matrix of thermoplastic polyurethane with 10% filler content. The nanocomposites showed the maximum reflection loss of -60.2 dB (in X-band) at the thickness of 2.5 mm with a broad absorption range in contrast to the pristine MWCNT and CuFe10Al2O19. Addition of PANI improves the microwave absorption property of the nanocomposites. The thermal stability of the prepared nanocomposites is also very high.

Keywords: magnetic materials, microwave absorption, MWCNT, nanocomposites

Procedia PDF Downloads 290

Authors: Murat Sarı, Emin Gundogar, Mumtaz Ipek

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Nowadays, in a furniture sector, competition of market share (portion) and production variety and changeability enforce the firm to reengineer operations on manufacturing line to increase the productivity. In this study, spring mattress manufacturing line of the furniture manufacturing firm is analyzed analytically. It’s intended to search and find the bottlenecks of production to balance the semi-finished material flow. There are four base points required to investigate in bottleneck elimination process. These are bottlenecks of Method, Material, Machine and Man (work force) resources, respectively. Mentioned bottlenecks are investigated and varied scenarios are created for recruitment of manufacturing system. Probable near optimal alternatives are determined by system models built in Arena simulation software.

Keywords: bottleneck search, buffer stock, furniture sector, simulation

Procedia PDF Downloads 353

Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

Procedia PDF Downloads 218

Authors: Srinivas Nerella

Abstract:

A series of new heterodentate N, O-donor ligands derived from condensing 3-amino Coumarins with hydroxy benzaldehydes and acetophenones were used to afford new mononuclear Mn(II), Co(II), Ni(II), Cu(II), Zn(II) and Pd(II) coordination compounds. All the complexes were characterized by IR, 1H-NMR, 13C-NMR, Mass, ESR, Electronic spectra, Conductance, Magnetic and Thermal studies. The ligands show hexa coordination in Mn(II), Co(II), Ni(II), and Pd(II) complexes resulting octahedral geometries, while the ligands in Zn(II) and Cu(II) complexes show tetra coordination resulting tetrahedral and square planar geometries respectively. These mononuclear complexes were investigated as catalysts in the hydrothiolation of aromatic and aliphatic alkynes with thiols. These metal complexes were acted as versatile catalysts and gave good yields.

Keywords: schiff bases, divalent metal complexes of schiff bases, Catalytic activity, hydrothiolation

Procedia PDF Downloads 411

Authors: Mwafak Shakoor

Abstract:

The purpose of this study was to reduce patient waiting times, improve system throughput and improve resources utilization in radiology department. A discrete event simulation model was developed using Arena simulation software to investigate different alternatives to improve the overall system delivery based on adding resource scenarios due to the linkage between patient waiting times and resource availability. The study revealed that there is no addition investment need to procure additional scanner but hospital management deploy managerial tactics to enhance machine utilization and reduce the long waiting time in the department.

Keywords: discrete event simulation, radiology department, arena, waiting time, healthcare modeling, computed tomography

Procedia PDF Downloads 586

Authors: Janaina Camile Pasqual Lofhagen, David Savarese, Veronika Vazhnik

Abstract:

The built environment is considered as a key element in transitioning to clean energy, needed to create resilient buildings and cities, enhance their adaptability to changes, and pursue energy saving. For such energy transition, this paper presents biogas as one of the sustainable sources of energy, as it is produced from organic materials often available in both urban and rural areas and can be converted into electrical and thermal energy, or into vehicular energies fuel. The resilience benefits of this fuel is being a localized alternative energy, and also provides tangible benefits for water, air, and soil quality. Through bibliographic and empirical research, this study analyzed the biogas potential and applications in Brazil and in the U.S. The results indicated that biogas emits 85% less CO2 to the atmosphere compared to diesel and could supply 40% of domestic electricity demand and 70% of diesel consumption in Brazil, with a similar scenario for the U.S.

Keywords: resilience, sustainability, built environment, energy transition, biogas.

Procedia PDF Downloads 87

Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

Procedia PDF Downloads 124

Authors: Irlana C. do Mar, Thayna A. Ferreira, Dayane S. Rezende, Bruno A. M. Figueira, José M. R. Mercury

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This work presents a low-cost Mn starting material to synthesis manganese oxide octahedral molecular sieve with Mg²⁺ in the tunnel (Mg-OMS-1), based on the Mn residues from Carajás Mineral Province (Amazon, Brazil). After hydrothermal and cation exchange procedures, the Mn residues transformed to a single phase, Mg-OMS-1. The raw material and the synthesis processes were analyzed by means of X-ray diffraction (XRD), Scanning electron microscopy (SEM) and Infrared spectroscopy (FTIR). The tunnel structure was synthesized hydrothermally at 180 °C for three days without impurities. According to the XRD analysis, the formation of crystalline Mg-OMS-1 was identified through reflections at 9.8º, 12º and 18º (2θ), as well as a thermal stability around 300 ºC. The SEM analysis indicated that the final product presents good crystallinity with a homogeneous size. In addition, an intense and diagnostic FTIR band was identified at 515 cm⁻¹ related to the MnO₆ octahedral stretching vibrations.

Keywords: Mn residues , Octahedral Molecular Sieve, Synthesis, Characterization

Procedia PDF Downloads 186

Authors: Jaewook Ha, Jin-beak Kim, Seongmin Kim

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In order to solve global warming and climate change issues, increased efforts have been devoted towards clean and sustainable energy sources as well as new energy generating devices. Nanogenerator is a device that converts mechanical/thermal energy as produced by small-scale physical change into electricity. Here we propose that nature-leaf surface could be used for preparation of a triboelectric nanogenerator. The nature-leaf surface consists of polydimethylsiloxane microscale pillars and polytetrafluoroethylene nanoparticles. Interaction between the nature-leaf surface and water was studied and the electrical outputs from the motion of single water drop were measured. A 40-μL water drop can generate a peak voltage of 1 V and a peak current of 0.7 μA. This nanogenerator might be used to drive electric devices in the outdoor environments in a sustainable manner.

Keywords: hierarchical surface, lotus-leaf, electrical generator, waterdrop

Procedia PDF Downloads 285

Authors: Hicham Salhi

Abstract:

This research project focuses on the numerical investigation of the mixed convection of Hybrid nanofluids in a round bottom flask commonly used in organic chemistry synthesis. The aim of this study is to improve the thermal properties of the reaction medium and enhance the rate of chemical reactions by using hybrid nanofluids. The flat bottom wall of the flask is maintained at a constant high temperature, while the top, left, and right walls are kept at a low temperature. The nanofluids used in this study contain suspended Cu and Al2O3 nanoparticles in pure water. The governing equations are solved numerically using the finite-volume approach and the Boussinesq approximation. The effects of the volume fraction of nanoparticles (φ) ranging from 0% to 5%, the Rayleigh number from 103 to 106, and the type of nanofluid (Cu and Al2O3) on the flow streamlines, isotherm distribution, and Nusselt number are examined in the simulation. The results indicate that the addition of Cu and Al2O3 nanoparticles increases the mean Nusselt number, which improves heat transfer and significantly alters the flow pattern. Moreover, the mean Nusselt number increases with increasing Rayleigh number and volume fraction, with Cu- Al2O3 hybrid nanofluid producing the best results. This research project focuses on the numerical investigation of the mixed convection of Hybrid nanofluids in a round bottom flask commonly used in organic chemistry synthesis. The aim of this study is to improve the thermal properties of the reaction medium and enhance the rate of chemical reactions by using hybrid nanofluids. The flat bottom wall of the flask is maintained at a constant high temperature, while the top, left, and right walls are kept at a low temperature. The nanofluids used in this study contain suspended Cu and Al2O3 nanoparticles in pure water. The governing equations are solved numerically using the finite-volume approach and the Boussinesq approximation. The effects of the volume fraction of nanoparticles (φ) ranging from 0% to 5%, the Rayleigh number from 103 to 106, and the type of nanofluid (Cu and Al2O3) on the flow streamlines, isotherm distribution, and Nusselt number are examined in the simulation. The results indicate that the addition of Cu and Al2O3 nanoparticles increases the mean Nusselt number, which improves heat transfer and significantly alters the flow pattern. Moreover, the mean Nusselt number increases with increasing Rayleigh number and volume fraction, with Cu- Al2O3 hybrid nanofluid producing the best results.

Keywords: bottom flask, mixed convection, hybrid nanofluids, numerical simulation

Procedia PDF Downloads 76

Authors: Rohana Abdullah, Md Nizam Abd Rahman, Seri Rahayu Kamat

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Operator efficiency aspect is gaining importance in ensuring optimized usage of resources especially in the semi-automated manufacturing environment. This paper addresses a case study done to solve operator efficiency and line balancing issue at a semiconductor assembly and test manufacturing. A Man-to-Machine (M2M) work study technique is used to study operator current utilization and determine the optimum allocation of the operators to the machines. Critical factors such as operator activity, activity frequency and operator competency level are considered to gain insight on the parameters that affects the operator utilization. Equipment standard time and overall equipment efficiency (OEE) information are also gathered and analyzed to achieve a balanced and optimized production.

Keywords: operator efficiency, optimized production, line balancing, industrial and manufacturing engineering

Procedia PDF Downloads 723

Authors: Abdellah Boualouch, Ahmed Essadki, Tamou Nasser, Ali Boukhriss, Abdellatif Frigui

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This paper presents a power control for a Doubly Fed Induction Generator (DFIG) using in Wind Energy Conversion System (WECS) connected to the grid. The proposed control strategy employs two nonlinear controllers, Backstipping (BSC) and sliding-mode controller (SMC) scheme to directly calculate the required rotor control voltage so as to eliminate the instantaneous errors of active and reactive powers. In this paper the advantages of BSC and SMC are presented, the performance and robustness of this two controller’s strategy are compared between them. First, we present a model of wind turbine and DFIG machine, then a synthesis of the controllers and their application in the DFIG power control. Simulation results on a 1.5MW grid-connected DFIG system are provided by MATLAB/Simulink.

Keywords: backstipping, DFIG, power control, sliding-mode, WESC

Procedia PDF Downloads 589

Authors: Muhammad Omer Aijaz, Sajjad Haider, Fahad S. Al Mubddal, Yousef Al-Zeghayer, Waheed A. Al Masry

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The present study is focused on the preparation of chitosan-based blend and Semi-Interpenetrating Polymer Network (SEMI-IPN) with polyacrylonitrile (PAN). Blend Chitosan/Polyacrylonitrile (PAN) hydrogel films were prepared by solution blending and casting technique. Chitosan in the blend was cross-linked with epichlorohydrin (ECH) to prepare SEMI-IPN. The developed Chitosan/PAN blend and SEMI-IPN hydrogels were characterized with SEM, FTIR, TGA, and DSC. The result showed good miscibility between chitosan and PAN, crosslinking of chitosan in the blend, and improved thermal properties for SEMI-IPN. The swelling of the different blended and SEMI-IPN hydrogels samples were examined at room temperature. Blend (C80/P20) sample showed highest swelling (2400%) and fair degree of stability (28%) whereas SEMI-IPN hydrogel exhibited relatively low degree of swelling (244%) and high degree of aqueous stability (85.5%).

Keywords: polymer hydrogels, chitosan, SEMI-IPN, polyacrylonitrile, epichlorohydrin

Procedia PDF Downloads 364

Authors: Maxim A. Kadatskiy, Konstantin V. Khishchenko

Abstract:

Iron alloys are widespread components in various types of structural materials which are exposed to intensive thermal and mechanical loads. Various quantum-statistical cell models with the approximation of self-consistent field can be used for the prediction of the behavior of these materials under extreme conditions. The application of these models is even more valid, the higher the temperature and the density of matter. Results of Hugoniot calculation for iron alloys in the framework of three quantum-statistical (the Thomas–Fermi, the Thomas–Fermi with quantum and exchange corrections and the Hartree–Fock–Slater) models are presented. Results of quantum-statistical calculations are compared with results from other reliable models and available experimental data. It is revealed a good agreement between results of calculation and experimental data for terra pascal pressures. Advantages and disadvantages of this approach are shown.

Keywords: alloy, Hugoniot, iron, terapascal pressure

Procedia PDF Downloads 335

Authors: Ayalew Yimam Ali

Abstract:

The Y-shaped microchannel is used to mix both miscible or immiscible fluids with different viscosities. However, mixing at the entrance of the Y-junction microchannel can be a difficult mixing phenomena due to micro-scale laminar flow aspects with the two miscible high-viscosity water-glycerol fluids. One of the most promising methods to improve mixing performance and diffusion mass transfer in laminar flow phenomena is acoustic streaming (AS), which is a time-averaged, second-order steady streaming that can produce rolling motion in the microchannel by oscillating a low-frequency range acoustic transducer and inducing an acoustic wave in the flow field. The developed 3D trapezoidal, triangular structure spine used in this study was created using sophisticated CNC machine cutting tools used to create microchannel mold with a 3D trapezoidal triangular structure spine alone the Y-junction longitudinal mixing region. In order to create the molds for the 3D trapezoidal structure with the 3D sharp edge tip angles of 30° and 0.3mm trapezoidal triangular sharp edge tip depth from PMMA glass (Polymethylmethacrylate) with advanced CNC machine and the channel manufactured using PDMS (Polydimethylsiloxane) which is grown up longitudinally on top surface of the Y-junction microchannel using soft lithography nanofabrication strategies. Flow visualization of 3D rolling steady acoustic streaming and mixing enhancement with high-viscosity miscible fluids with different trapezoidal, triangular structure longitudinal length, channel width, high volume flow rate, oscillation frequency, and amplitude using micro-particle image velocimetry (μPIV) techniques were used to study the 3D acoustic streaming flow patterns and mixing enhancement. The streaming velocity fields and vorticity flow fields show 16 times more high vorticity maps than in the absence of acoustic streaming, and mixing performance has been evaluated at various amplitudes, flow rates, and frequencies using the grayscale value of pixel intensity with MATLAB software. Mixing experiments were performed using fluorescent green dye solution with de-ionized water in one inlet side of the channel, and the de-ionized water-glycerol mixture on the other inlet side of the Y-channel and degree of mixing was found to have greatly improved from 67.42% without acoustic streaming to 0.96.83% with acoustic streaming. The results show that the creation of a new 3D steady streaming rolling motion with a high volume flowrate around the entrance was enhanced by the formation of a new, three-dimensional, intense streaming rolling motion with a high-volume flowrate around the entrance junction mixing zone with the two miscible high-viscous fluids which are influenced by laminar flow fluid transport phenomena.

Keywords: micro fabrication, 3d acoustic streaming flow visualization, micro-particle image velocimetry, mixing enhancement

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Authors: Gałka Aleksandra, Jelińska Justyna, Masiak Albert, Walentukiewicz Krzysztof

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An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper.

Keywords: dynamic time warping, handwritten signature verification, feature-based recognition, online signature

Procedia PDF Downloads 164

Authors: Gianna Zou

Abstract:

Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

Procedia PDF Downloads 139

Authors: Baeza S. Roberto

Abstract:

The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

Procedia PDF Downloads 577

Authors: Zélia Maria Da Costa Ludwig, Maria José Valenzuela Bell, Geraldo Henriques Da Silva, Thales Alves Faraco, Victor Rocha Da Silva, Daniel Rotmeister Teixeira, Vírgilio De Carvalho Dos Anjos, Valdemir Ludwig

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Advances in telecommunications grow with the development of optical amplifiers based on rare earth ions. The focus has been concentrated in silicate glasses although their amplified spontaneous emission is limited to a few tens of nanometers (~ 40nm). Recently, phosphate glasses have received great attention due to their potential application in optical data transmission, detection, sensors and laser detector, waveguide and optical fibers, besides its excellent physical properties such as high thermal expansion coefficients and low melting temperature. Compared with the silica glasses, phosphate glasses provide different optical properties such as, large transmission window of infrared, and good density. Research on the improvement of physical and chemical durability of phosphate glass by addition of heavy metals oxides in P2O5 has been performed. The addition of Na2O further improves the solubility of rare earths, while increasing the Al2O3 links in the P2O5 tetrahedral results in increased durability and aqueous transition temperature and a decrease of the coefficient of thermal expansion. This work describes the structural and spectroscopic characterization of a phosphate glass matrix doped with different Er (Erbium) concentrations. The phosphate glasses containing Er3+ ions have been prepared by melt technique. A study of the optical absorption, luminescence and lifetime was conducted in order to characterize the infrared emission of Er3+ ions at 1540 nm, due to the radiative transition 4I13/2 → 4I15/2. Our results indicate that the present glass is a quite good matrix for Er3+ ions, and the quantum efficiency of the 1540 nm emission was high. A quenching mechanism for the mentioned luminescence was not observed up to 2,0 mol% of Er concentration. The Judd-Ofelt parameters, radiative lifetime and quantum efficiency have been determined in order to evaluate the potential of Er3+ ions in new phosphate glass. The parameters follow the trend as Ω2 > Ω4 > Ω6. It is well known that the parameter Ω2 is an indication of the dominant covalent nature and/or structural changes in the vicinity of the ion (short range effects), while Ω4 and Ω6 intensity parameters are long range parameters that can be related to the bulk properties such as viscosity and rigidity of the glass. From the PL measurements, no red or green upconversion was measured when pumping the samples with laser excitation at 980 nm. As future prospects: Synthesize this glass system with silver in order to determine the influence of silver nanoparticles on the Er3+ ions.

Keywords: phosphate glass, erbium, luminescence, glass system

Procedia PDF Downloads 504

Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

Procedia PDF Downloads 186

Authors: Shengzhu Cao, Hui Zhou, Gan Wu, Lanxi Wanhg, Kaifeng Zhang, Rui Wang, Hu Wang

Abstract:

The functional micro and nanostructures, which can endow material surface with unique properties such as super-absorptance, hydrophobic and drag reduction. Recently, femtosecond laser ablation has been demonstrated to be a promising technology for surface functional micro and nanostructures fabrication. In this paper, using femtosecond laser ablation processing technique, we fabricated functional micro and nanostructures on Ti and Al alloy surfaces, test results showed that processed surfaces have 82%~96% absorptance over a broad wavelength range from ultraviolet to infrared. The surface function properties, which determined by micro and nanostructures, could be modulated by variation laser parameters. These functional surfaces may find applications in such areas as photonics, plasmonics, spaceborne devices, thermal radiation sources, solar energy absorbers and biomedicine.

Keywords: surface functional, micro and nanostructures, femtosecond laser, ablation

Procedia PDF Downloads 361

Authors: Almagul Assainova , Dariya Abykenova, Liudmila Goncharenko, Sergey Sybachin, Saule Rakhimova, Abay Aman

Abstract:

The recognition of a handwritten Kazakh text is a relevant objective today for the digitization of materials. The study presents a model of a hybrid neural network for handwriting recognition, which includes a convolutional neural network and a multi-layer perceptron. Each network includes 1024 input neurons and 42 output neurons. The model is implemented in the program, written in the Python programming language using the EMNIST database, NumPy, Keras, and Tensorflow modules. The neural network training of such specific letters of the Kazakh alphabet as ә, ғ, қ, ң, ө, ұ, ү, h, і was conducted. The neural network model and the program created on its basis can be used in electronic document management systems to digitize the Kazakh text.

Keywords: handwriting recognition system, image recognition, Kazakh font, machine learning, neural networks

Procedia PDF Downloads 255

Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

Abstract:

We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

Procedia PDF Downloads 105

Authors: Somnath Bhattacharya, Kamal Sharma, Vaibhav Sonkar

Abstract:

Composite materials emerged in the middle of the 20th century as a promising class of engineering materials providing new prospects for modern technology. Recently, a new class of composite materials known as functionally graded materials (FGMs) has drawn considerable attention of the scientific community. In general, FGMs are defined as composite materials in which the composition or microstructure or both are locally varied so that a certain variation of the local material properties is achieved. This gradual change in composition and microstructure of material is suitable to get gradient of properties and performances. FGMs are synthesized in such a way that they possess continuous spatial variations in volume fractions of their constituents to yield a predetermined composition. These variations lead to the formation of a non-homogeneous macrostructure with continuously varying mechanical and / or thermal properties in one or more than one direction. Lightweight functionally graded composites with high strength to weight and stiffness to weight ratios have been used successfully in aircraft industry and other engineering applications like in electronics industry and in thermal barrier coatings. In the present work, elastic-plastic crack growth problems (using Ramberg-Osgood Model) in an FGM plate under cyclic load has been explored by extended finite element method. Both edge and centre crack problems have been solved by taking additionally holes, inclusions and minor cracks under plane stress conditions. Both soft and hard inclusions have been implemented in the problems. The validity of linear elastic fracture mechanics theory is limited to the brittle materials. A rectangular plate of functionally graded material of length 100 mm and height 200 mm with 100% copper-nickel alloy on left side and 100% ceramic (alumina) on right side is considered in the problem. Exponential gradation in property is imparted in x-direction. A uniform traction of 100 MPa is applied to the top edge of the rectangular domain along y direction. In some problems, domain contains major crack along with minor cracks or / and holes or / and inclusions. Major crack is located the centre of the left edge or the centre of the domain. The discontinuities, such as minor cracks, holes, and inclusions are added either singly or in combination with each other. On the basis of this study, it is found that effect of minor crack in the domain’s failure crack length is minimum whereas soft inclusions have moderate effect and the effect of holes have maximum effect. It is observed that the crack growth is more before the failure in each case when hard inclusions are present in place of soft inclusions.

Keywords: elastic-plastic, fatigue crack, functionally graded materials, extended finite element method (XFEM)

Procedia PDF Downloads 383

Authors: Domingo Planes Meseguer, Raffaele Danilo Esposito, Maria Del Pilar Dorado Rodriguez

Abstract:

Monthly quality control checks for a Radiation Therapy Linac may be performed is a simple and efficient way once they have been standardized and protocolized. On the other hand this checks, in spite of being imperatives, require a not negligible execution times in terms of machine time and operators time. Besides it must be taken into account the amount of disposable material which may be needed together with the use of commercial software for their performing. With the aim of optimizing and standardizing mechanical-geometric checks and multi leaves collimator checks, we decided to implement a protocol which makes use of the Electronic Portal Imaging Device (EPID) available on our Linacs. The user is step by step guided by the software during the whole procedure. Acquired images are automatically analyzed by our programs all of them written using only free software.

Keywords: quality control checks, linac, radiation oncology, medical physics, free software

Procedia PDF Downloads 195

Authors: Kanda Runapongsa Saikaew, Chaluemwut Noyunsan

Abstract:

Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.

Keywords: facebook, social media, credibility measurement, internet

Procedia PDF Downloads 351