Search results for: coupled Markov random field (MRF)

Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Viliam Makis, Farnoosh Naderkhani, Leila Jafari

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In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example.

Keywords: Bayesian control chart, semi-Markov decision process, quality control, partially observable process

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Authors: Abdulaziz Almustafa

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Considering the critical role permanence has on predictions related to the contextual interference effect on laboratory and field research, this study sought to determine whether the paradigm of the effect depends on the complexity of the skill during the acquisition and transfer phases. The purpose of the present study was to investigate the effects of contextual interference CI by extending previous laboratory and field research with adult athletes through the acquisition and transfer phases. Male (n=60) athletes age 18-22 years-old, were chosen randomly from Eastern Province Clubs. They were assigned to complete blocked, random, or serial practices. Analysis of variance with repeated measures MANOVA indicated that, the results did not support the notion of CI. There were no significant differences in acquisition phase between blocked, serial and random practice groups. During the transfer phase, there were no major differences between the practice groups. Apparently, due to the task complexity, participants were probably confused and not able to use the advantages of contextual interference. This is another contradictory result to contextual interference effects in acquisition and transfer phases in sport settings. One major factor that can influence the effect of contextual interference is task characteristics as the nature of level of difficulty in sport-related skill.

Keywords: contextual interference, acquisition, transfer, task difficulty

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Authors: Tomoaki Hashimoto

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Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization.

Keywords: optimal control, stochastic systems, random dither, quantization

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Authors: M. Fadlallah, G. Ghibaudo, C. G. Theodorou

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The dynamic variation in memory devices such as the Static Random Access Memory can give errors in read or write operations. In this paper, the effect of low-frequency and random telegraph noise on the dynamic variation of one SRAM cell is detailed. The effect on circuit noise, speed, and length of time of processing is examined, using the Supply Read Retention Voltage and the Read Static Noise Margin. New test run methods are also developed. The obtained results simulation shows the importance of noise caused by dynamic variation, and the impact of Random Telegraph noise on SRAM variability is examined by evaluating the statistical distributions of Random Telegraph noise amplitude in the pull-up, pull-down. The threshold voltage mismatch between neighboring cell transistors due to intrinsic fluctuations typically contributes to larger reductions in static noise margin. Also the contribution of each of the SRAM transistor to total dynamic variation has been identified.

Keywords: low-frequency noise, random telegraph noise, dynamic variation, SRRV

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Authors: Minh Quang Le, Dane Taylor

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Convection is a well-studied topic in fluid dynamics, yet it is less understood in the context of networks flows. Here, we incorporate techniques from topological data analysis (namely, persistent homology) to automate the detection and characterization of convective/cyclic/chiral flows over networks, particularly those that arise for irreversible Markov chains (MCs). As two applications, we study convection cycles arising under the PageRank algorithm, and we investigate chiral edges flows for a stochastic model of a bi-monomer's configuration dynamics. Our experiments highlight how system parameters---e.g., the teleportation rate for PageRank and the transition rates of external and internal state changes for a monomer---can act as homology regularizers of convection, which we summarize with persistence barcodes and homological bifurcation diagrams. Our approach establishes a new connection between the study of convection cycles and homology, the branch of mathematics that formally studies cycles, which has diverse potential applications throughout the sciences and engineering.

Keywords: homology, persistent homolgy, markov chains, convection cycles, filtration

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Authors: Weisi Guo

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It is well established that there may be running correlations in sequential exam marks due to students sitting in the order of course registration patterns. As such, a random and non-sequential sampling of exam marks is a standard recommended practice. Here, the paper examines a large number of exam data stretching several years across different modules to see the degree to which it is true. Using the real mark distribution as a generative process, it was found that random simulated data had no more sequential randomness than the real data. That is to say, the running correlations that one often observes are statistically identical to chance. Digging deeper, it was found that some high running correlations have students that indeed share a common course history and make similar mistakes. However, at the statistical scale of a module question, the combined effect is statistically similar to the random shuffling of papers. As such, there may not be the need to take random samples for marks, but it still remains good practice to mark papers in a random sequence to reduce the repetitive marking bias and errors.

Keywords: data analysis, empirical study, exams, marking

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Authors: Khushalini N. Ulman, Samane Maroufi, Veena H. Sahajwalla

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Globally, electronic waste (e-waste) continues to grow at an alarming rate. Several technologies have been developed to recover valuable materials from e-waste, however, their efficiency can be increased with a better knowledge of the e-waste components. Random access memory cards (RAMs) are considered as high value scrap for the e-waste recyclers. Despite their high precious metal content, RAMs are still recycled in a conventional manner resulting in huge loss of resources. Our research work highlights the precious metal rich components of a RAM. Inductively coupled plasma (ICP) analysis of RAMs of six different generations have been carried out and the trends in their metal content have been investigated. Over the past decade, the copper content of RAMs has halved and their tin content has increased by 70 %. The stricter environmental laws have facilitated ~96 % drop in the lead content of RAMs. To comprehend the fundamentals of thermal transformation of RAMs, our research provides their detailed kinetic study. This can assist the e-waste recyclers in optimising their metal recovery processes. Thus, understanding the chemical and thermal behaviour of RAMs can open new avenues for efficient e-waste recycling.

Keywords: electronic waste, kinetic study, recycling, thermal transformation

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Authors: Majid Karimabadi, Ahmad Farzaneh Kore, Behnam Azadegan

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The evolution of magnetized quark gluon plasma (QGP) in the framework of magneto- hydrodynamics is the focus of our study. We are investigating the temporal and spatial evolution of QGP using a second order viscous hydrodynamic framework. The fluid is considered to be magnetized and subjected to the influence of a magnetic field that is generated during the early stages of relativistic heavy ion collisions. We assume boost invariance along the beam line, which is represented by the z coordinate, and fluid expansion in the x direction. Additionally, we assume that the magnetic field is perpendicular to the reaction plane, which corresponds to the y direction. The fluid is considered to have infinite electrical conductivity. To analyze this system, we solve the coupled Maxwell and conservation equations. By doing so, we are able to determine the time and space dependence of the energy density, velocity, and magnetic field in the transverse plane of the viscous magnetized hot plasma. Furthermore, we obtain the spectrum of hadrons and compare it with experimental data.

Keywords: QGP, magnetohydrodynamics, hadrons, conversation

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Authors: Ramanpreet Kaur, Upasana Sharma

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The purpose of this paper is to address the challenges facing the water supply for the Machine Learning Database (MLDB) system at the piston foundry plant. In the MLDB system, one main unit, i.e., robotic, is connected by two sub-units. The functioning of the system depends on the robotic and water supply. Lack of water supply causes system failure. The system operates at full capacity with the help of two sub-units. If one sub-unit fails, the system runs at a low capacity. Reliability modeling is performed using semi-Markov processes and regenerative point techniques. Several system effects such as mean time to system failure, availability at full capacity, availability at reduced capacity, busy period for repair and expected number of visits have been achieved. Benefits have been analyzed. The graphical study is designed for a specific case using programming in C++ and MS Excel.

Keywords: MLDB system, robotic, semi-Markov process, regenerative point technique

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Authors: Khenfer-Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

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this paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a man-machine interface with a voice recognition system that allows the operator to tele-operate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands spoken in two languages: French and Arabic. The recognition rate obtained is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equal to 30 db, the Arabic speech recognition rate is 69% and 80% for French speech recognition rate. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: voice command, HMM, TIMIT, noise, HTK, Arabic, speech recognition

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Authors: Mahdi Alkaeed, Adeel Ehsan

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Enterprise information systems (EIS) act as the backbone of organizations that belong to different domains. These systems not only play a major role in the efficient usage of resources and time but also throw light on the future roadmap for the enterprise. In today's rapidly expanding world of business and technology, enterprise systems from various heterogenous environments have to exchange information at some point, be it within the same organization or between different organizations. This reality strengthens the importance of interoperability between these systems, which is one of the key enablers of systems collaboration. Both information technology infrastructure and business processes have to be aligned with each other to achieve this effect. This will be difficult to attain if traditional tightly coupled architecture is used. Instead, a more loosely coupled service-oriented architecture has to be used. That would enable an effective interoperability level between different EIS. This paper discusses and presents the current work that has been done in the field of EIS interoperability. Along the way, it also discusses the challenges, solutions to tackle those challenges presented in the studied literature, and limitations, if any.

Keywords: enterprise systems interoperability, collaboration and integration, service-based architecture, open system architecture

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Authors: Suparman

Abstract:

Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

Keywords: regression, piecewise, Bayesian, reversible Jump MCMC

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Authors: J. V. Gonzalez Fernandez, T. Mozume, S. Gozu, A. Lastras Martinez, L. F. Lastras Martinez, J. Ortega Gallegos, R. E. Balderas Navarro

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We report on a theoretical-experimental study of photoreflectance anisotropy (PRA) spectroscopy of coupled double quantum wells. By probing the in-plane interfacial optical anisotropies, we demonstrate that PRA spectroscopy has the capacity to detect and distinguish layers with quantum dimensions. In order to account for the experimental PRA spectra, we have used a theoretical model at k=0 based on a linear electro-optic effect through a piezoelectric shear strain.

Keywords: coupled double quantum well (CDQW), linear electro-optic (LEO) effect, photoreflectance anisotropy (PRA), piezoelectric shear strain

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Authors: Diep Thanh Tung, Joachim Aurbacher

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This study aims to measure the level of on-farm diversification in Vietnam. The empirical results of the research carried out reflect regional differences in terms of on-farm diversification and its determinants. Households in the northern regions have adapted to the fragmented and small-sized parcels of land held by diversifying their on-farm activities. In contrast, the Mekong delta region in the south of Vietnam is characterized by larger agricultural parcels and a specialization in rice production. Land use fragmentation, as reflected by a large number of plots in a given area, is one of the most important reasons for the high levels of on-farm diversification seen, while the higher share of non-farm income in total income is the reason of lower levels of on-farm diversification. Households have reacted to natural and economic shocks by diversifying their on-farm activities. The non-stationary Markov chain model used here shows various diversification scenarios and trends. In most cases, on-farm diversification generally tends to reduce over the next few years.

Keywords: diversification, simpson index, fixed effects, non-stationary markov chain

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Authors: Abdelatif Gadoum, Djilali Benyoucef, Mouloudj Hadj, Alla Eddine Toubal Maamar, Mohamed Habib Allah Lahoual

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Hydrogen occurs naturally in the form of chemical compounds, most often in water and hydrocarbons. The main objective of this study is 2D modeling of hydrogen production in inductively coupled plasma in methane at low pressure. In the present model, we include the motions and the collisions of both neutral and charged particles by considering 19 species (i.e in total ; neutrals, radicals, ions, and electrons), and more than 120 reactions (electron impact with methane, neutral-neutral, neutral-ions and surface reactions). The results show that the rate conversion of methane reach 90% and the hydrogen production is about 30%.

Keywords: hydrogen production, inductively coupled plasma, fluid model, methane plasma

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Authors: Y. Wang, S. Zhang, X. Chen

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The longitudinal compressive strength of fibre reinforced plastic (FRP) possess a large stochastic variability, which limits efficient application of composite structures. This study aims to address how the random fibre packing affects the uncertainty of FRP compressive strength. An novel approach is proposed to generate random fibre packing status by a combination of Latin hypercube sampling and random sequential expansion. 3D nonlinear finite element model is built which incorporates both the matrix plasticity and fibre geometrical instability. The matrix is modeled by isotropic ideal elasto-plastic solid elements, and the fibres are modeled by linear-elastic rebar elements. Composite with a series of different nominal fibre volume fractions are studied. Premature fibre waviness at different magnitude and direction is introduced in the finite element model. Compressive tests on uni-directional CFRP (carbon fibre reinforced plastic) are conducted following the ASTM D6641. By a comparison of 3D FE models and compressive tests, it is clearly shown that the stochastic variation of compressive strength is partly caused by the random fibre packing, and normal or lognormal distribution tends to be a good fit the probabilistic compressive strength. Furthermore, it is also observed that different random fibre packing could trigger two different fibre micro-buckling modes while subjected to longitudinal compression: out-of-plane buckling and twisted buckling. The out-of-plane buckling mode results much larger compressive strength, and this is the major reason why the random fibre packing results a large uncertainty in the FRP compressive strength. This study would contribute to new approaches to the quality control of FRP considering higher compressive strength or lower uncertainty.

Keywords: compressive strength, FRP, micro-buckling, random fibre packing

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Authors: Azher Jameel, Ghulam Ashraf Harmain

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In the recent years, the enriched techniques like the extended finite element method, the element free Galerkin method, and the Coupled finite element-element free Galerkin method have found wide application in modeling different types of discontinuities produced by cracks, contact surfaces, and bi-material interfaces. The extended finite element method faces severe mesh distortion issues while modeling large deformation problems. The element free Galerkin method does not have mesh distortion issues, but it is computationally more demanding than the finite element method. The coupled FE-EFGM proves to be an efficient numerical tool for modeling large deformation problems as it exploits the advantages of both FEM and EFGM. The present paper employs the coupled FE-EFGM to model large elastoplastic deformations in bi-material engineering components. The large deformation occurring in the domain has been modeled by using the total Lagrangian approach. The non-linear elastoplastic behavior of the material has been represented by the Ramberg-Osgood model. The elastic predictor-plastic corrector algorithms are used for the evaluation stresses during large deformation. Finally, several numerical problems are solved by the coupled FE-EFGM to illustrate its applicability, efficiency and accuracy in modeling large elastoplastic deformations in bi-material samples. The results obtained by the proposed technique are compared with the results obtained by XFEM and EFGM. A remarkable agreement was observed between the results obtained by the three techniques.

Keywords: XFEM, EFGM, coupled FE-EFGM, level sets, large deformation

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Authors: Vikas Kumar

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The present study is carried out to investigate the magneto-viscous effects on incompressible ferrofluid flow over a porous rotating disc with suction or injection on the surface of the disc subjected to a magnetic field. The flow under consideration is axi-symmetric steady ferrofluid flow of electrically non-conducting fluid. Karman’s transformation is used to convert the governing boundary layer equations involved in the problem to a system of non linear coupled differential equations. The solution of this system is obtained by using power series approximation. The flow characteristics i.e. radial, tangential, axial velocities and boundary layer displacement thickness are calculated for various values of MFD (magnetic field dependent) viscosity and for different values of suction injection parameter. Besides this, skin friction coefficients are also calculated on the surface of the disk. Thus, the obtained results are presented numerically and graphically in the paper.

Keywords: axi-symmetric, ferrofluid, magnetic field, porous rotating disk

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: availability modeling, Markov process, milling system, rice milling plant

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Authors: H. Kazemi Esfeh, V. Akbari, M. Ehdaei, T. N. G. Borhani, A. Shamiri, M. Najafi

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In an industrial fluidized bed polymerization reactor, particle size distribution (PSD) plays a significant role in the reactor efficiency evaluation. The computational fluid dynamic (CFD) models coupled with population balance equation (CFD-PBM) have been extensively employed to investigate the flow behavior in the poly-disperse multiphase fluidized bed reactors (FBRs) utilizing ANSYS Fluent code. In this study, an existing CFD-PBM/ DQMOM coupled modeling framework has been used to highlight its potential to analyze the industrial-scale gas phase polymerization reactor. The predicted results reveal an acceptable agreement with the observed industrial data in terms of pressure drop and bed height. The simulated results also indicate that the higher particle growth rate can be achieved for bigger particles. Hence, the 2D CFD-PBM/DQMOM coupled model can be used as a reliable tool for analyzing and improving the design and operation of the gas phase polymerization FBRs.

Keywords: computational fluid dynamics, population balance equation, fluidized bed polymerization reactor, direct quadrature method of moments

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Authors: Rattanan Tippayaphalapholgul, Yasothorn Sapsathiarn

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Composites of piezoelectric materials are widely use in practical applications such as nondestructive testing devices, smart adaptive structures and medical devices. A thorough understanding of coupled electro-elastic response and properties of piezocomposite are crucial for the development and design of piezoelectric composite materials used in advanced applications. The micromechanics analysis is employed in this paper to determine the response and engineering properties of the piezocomposite. A mechanical imperfect interface bonding between piezoelectric inclusion and polymer matrix is taken into consideration in the analysis. The micromechanics analysis is based on the Boundary Element Method (BEM) together with the periodic micro-field micromechanics theory. A selected set of numerical results is presented to investigate the influence of volume ratio and interface bonding condition on effective piezocomposite material coefficients and portray basic features of coupled electroelastic response within the domain of piezocomposite unit cell.

Keywords: effective engineering properties, electroelastic response, imperfect interface, piezocomposite

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Authors: Yacine Arioua

Abstract:

In this paper, we investigate the existence and uniqueness of solutions for a coupled system of nonlinear Caputo-type Katugampola fractional differential equations with integral boundary conditions. Based upon a contraction mapping principle, Schauders fixed point theorems, some new existence and uniqueness results of solutions for the given problems are obtained. For application, some examples are given to illustrate the usefulness of our main results.

Keywords: fractional differential equations, coupled system, Caputo-Katugampola derivative, fixed point theorems, existence, uniqueness

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Authors: Yiqi Wu, Simon Tait, Andrew Nichols

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Management of UK railway drainage assets is challenging due to the large amounts of historical assets with long asset life cycles. A major concern for asset managers is to maintain the required performance economically and efficiently while complying with the relevant regulation and legislation. As the majority of the drainage assets are buried underground and are often difficult or costly to examine, it is important for asset managers to understand and model the degradation process in order to foresee the upcoming reduction in asset performance and conduct proactive maintenance accordingly. In this research, a Markov chain approach is used to model the deterioration process of rail drainage assets. The study is based on historical condition scores and characteristics of drainage assets across the whole railway network in England, Scotland, and Wales. The model is used to examine the effect of various characteristics on the probabilities of degradation, for example, the regional difference in probabilities of degradation, and how material and shape can influence the deterioration process for chambers, channels, and pipes.

Keywords: deterioration, degradation, markov models, probability, railway drainage

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Authors: H. Shokouhmand, M. Tajerian

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At this paper velocity profile modeling and heat transfer in the micro heat pipes by using electrohydrodynamic (EHD) field at the transient regime have been studied. In the transient flow, one dimensional and two phase fluid flow and heat transfer for micro heat pipes with square cross section, have been studied. At this model Coulomb and dielectrophoretic forces are considered. Coupled, non-linear equations governed on the model (continuity, momentum, and energy equations) have been solved simultaneously by numerical methods. Transient behavior of affecting parameters e.g. substrate temperature, velocity of coolant liquid, radius of curvature and coolant liquid pressure, has been verified. By obtaining and plotting the mentioned parameters, it has been shown that the EHD field enhances the heat transfer process. So, the time required to reach the steady state regime decreases from 16 seconds to 2.4 seconds after applying EHD field. Another result has been observed implicitly that by increasing the heat input the effect of EHD field became more significant. The numerical results of model predict the experimental results available in the literature successfully, and it has been observed there is a good agreement between them.

Keywords: micro heat pipe, transient modeling, electrohydrodynamics, capillary, meniscus

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Authors: Chungkuk Jin, Moo-Hyun Kim, HeonYong Kang

Abstract:

A surface riding (SR) wave energy converter (WEC) is designed and its feasibility and performance are numerically simulated by the author-developed floater-mooring-magnet-electromagnetics fully-coupled dynamic analysis computer program. The biggest advantage of the SR-WEC is that the performance is equally effective even in low sea states and its structural robustness is greatly improved by simply riding along the wave surface compared to other existing WECs. By the numerical simulations and actuator testing, it is clearly demonstrated that the concept works and through the optimization process, its efficiency can be improved.

Keywords: computer simulation, electromagnetics fully-coupled dynamics, floater-mooring-magnet, optimization, performance evaluation, surface riding, WEC

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Authors: Suparman

Abstract:

Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: piecewise regression, bayesian, reversible jump MCMC, segmentation

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Authors: Keltoum Bouherine, Olivier Leroy

Abstract:

The present work is dedicated to study a three–dimensional (3D) self-consistent fluid simulation of microwave discharges of argon plasma in PECVD reactor. The model solves the Maxwell’s equations, continuity equations for charged species and the electron energy balance equation, coupled with Poisson’s equation, and Navier-Stokes equations by finite element method, using COMSOL Multiphysics software. In this study, the simulations yield the profiles of plasma components as well as the charge densities and electron temperature, the electric field, the gas velocity, and gas temperature. The results show that the microwave plasma reactor is outside of local thermodynamic equilibrium.The present work is dedicated to study a three–dimensional (3D) self-consistent fluid simulation of microwave discharges of argon plasma in PECVD reactor. The model solves the Maxwell’s equations, continuity equations for charged species and the electron energy balance equation, coupled with Poisson’s equation, and Navier-Stokes equations by finite element method, using COMSOL Multiphysics software. In this study, the simulations yield the profiles of plasma components as well as the charge densities and electron temperature, the electric field, the gas velocity, and gas temperature. The results show that the microwave plasma reactor is outside of local thermodynamic equilibrium.

Keywords: electron density, electric field, microwave plasma reactor, gas velocity, non-equilibrium plasma

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Authors: Schadrack Mwizerwa

Abstract:

The characterization and prediction of landslides are crucial for assessing geological hazards and mitigating risks to infrastructure and communities. This research aims to develop a probabilistic framework for analyzing excavation-induced landslides, which is fundamental for assessing geological hazards and mitigating risks to infrastructure and communities. The study uses Hermite polynomial chaos, a non-stationary random process, to analyze the stability of a slope and characterize the failure probability of a real landslide induced by highway construction excavation. The correlation within the data is captured using the Karhunen-Loève (KL) expansion theory, and the finite element method is used to analyze the slope's stability. The research contributes to the field of landslide characterization by employing advanced random field approaches, providing valuable insights into the complex nature of landslide behavior and the effectiveness of advanced probabilistic models for risk assessment and management. The data collected from the Baiyuzui landslide, induced by highway construction, is used as an illustrative example. The findings highlight the importance of considering the probabilistic nature of landslides and provide valuable insights into the complex behavior of such hazards.

Keywords: Hermite polynomial chaos, Karhunen-Loeve, slope stability, probabilistic analysis

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Authors: Krishang Surapaneni

Abstract:

The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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