Search results for: Numerical weather prediction

Authors: Wei Jun-xia, Yuan Guang-wei, Yang Shu-lin, Shen Wei-dong

Abstract:

In order to make conventional implicit algorithm to be applicable in large scale parallel computers , an interface prediction and correction of discontinuous finite element method is presented to solve time-dependent neutron transport equations under 2-D cylindrical geometry. Domain decomposition is adopted in the computational domain.The numerical experiments show that our parallel algorithm with explicit prediction and implicit correction has good precision, parallelism and simplicity. Especially, it can reach perfect speedup even on hundreds of processors for large-scale problems.

Keywords: Transport Equation, Discontinuous Finite Element, Domain Decomposition, Interface Prediction And Correction

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Authors: Marco Actis Grande, Somlak Wannarumon

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This paper proposes the numerical simulation of the investment casting of gold jewelry. It aims to study the behavior of fluid flow during mould filling and solidification and to optimize the process parameters, which lead to predict and control casting defects such as gas porosity and shrinkage porosity. A finite difference method, computer simulation software FLOW-3D was used to simulate the jewelry casting process. The simplified model was designed for both numerical simulation and real casting production. A set of sensor acquisitions were allocated on the different positions of the wax tree of the model to detect filling times, while a set of thermocouples were allocated to detect the temperature during casting and cooling. Those detected data were applied to validate the results of the numerical simulation to the results of the real casting. The resulting comparisons signify that the numerical simulation can be used as an effective tool in investment-casting-process optimization and casting-defect prediction.

Keywords: Computer fluid dynamic, Investment casting, Jewelry, Mould filling, Simulation.

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Authors: Leonardo Juan Ramírez López, Javier Oswaldo Rodriguez Velasquez

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We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.

Keywords: Entropy, mathematical, prediction, cardiac, holter, attractor.

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Authors: Kefaya Qaddoum

Abstract:

The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: Neural network, conformal prediction, cancer classification, regression.

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Authors: Fan Ye

Abstract:

Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: Low visibility, RWIS, traffic safety, visibility.

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Authors: M. H. Alhajeri, Jasem Alrajhi, Mohsen Alardhi, Saleh Alhajeri

Abstract:

A numerical prediction of flow in a tube bank is reported. The flow regimes considered cover a wide range of Reynolds numbers, which range from 380 to 99000 and which are equivalent to a range of inlet velocities from very low (0.072 m/s) to very high (60 m/s). In this study, calculations were made using the standard k-e model with standard wall function. The drag coefficient, skin friction drag, pressure drag, and pressure distribution around a tube were investigated. As the velocity increased, the drag coefficient decreased until the velocity exceeded 45 m/s, after which it increased. Furthermore, the pressure drag and skin friction drag depend on the velocity.

Keywords: Numerical, Fluid, Flow, Turbine, Cooling, Blade.

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Authors: Tien-Shun Chan, Yung-Chung Chang, Cheng-Yu Chu, Wen-Hui Chen, Yuan-Lin Chen, Shun-Chong Wang, Chang-Chun Wang

Abstract:

98% of the energy needed in Taiwan has been imported. The prices of petroleum and electricity have been increasing. In addition, facility capacity, amount of electricity generation, amount of electricity consumption and number of Taiwan Power Company customers have continued to increase. For these reasons energy conservation has become an important topic. In the past linear regression was used to establish the power consumption models for chillers. In this study, grey prediction is used to evaluate the power consumption of a chiller so as to lower the total power consumption at peak-load (so that the relevant power providers do not need to keep on increasing their power generation capacity and facility capacity). In grey prediction, only several numerical values (at least four numerical values) are needed to establish the power consumption models for chillers. If PLR, the temperatures of supply chilled-water and return chilled-water, and the temperatures of supply cooling-water and return cooling-water are taken into consideration, quite accurate results (with the accuracy close to 99% for short-term predictions) may be obtained. Through such methods, we can predict whether the power consumption at peak-load will exceed the contract power capacity signed by the corresponding entity and Taiwan Power Company. If the power consumption at peak-load exceeds the power demand, the temperature of the supply chilled-water may be adjusted so as to reduce the PLR and hence lower the power consumption.

Keywords: Gery system theory, grey prediction, chller.

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Authors: Lee P. Leon, Raymond Charles

Abstract:

This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: Aggregate angularity, asphalt concrete, permanent deformation, rutting prediction.

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Authors: Jun Sung Park, Hyo Jung Song

Abstract:

H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression standards such as MPEG-2, but computational complexity is increased significantly. In this paper, we propose selective mode decision schemes for fast intra prediction mode selection. The objective is to reduce the computational complexity of the H.264/AVC encoder without significant rate-distortion performance degradation. In our proposed schemes, the intra prediction complexity is reduced by limiting the luma and chroma prediction modes using the directional information of the 16×16 prediction mode. Experimental results are presented to show that the proposed schemes reduce the complexity by up to 78% maintaining the similar PSNR quality with about 1.46% bit rate increase in average.

Keywords: Video encoding, H.264, Intra prediction.

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Authors: Francesco Caputo, Giuseppe Lamanna, Alessandro Soprano

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This work is focused on the numerical prediction of the fracture resistance of a flat stiffened panel made of the aluminium alloy 2024 T3 under a monotonic traction condition. The performed numerical simulations have been based on the micromechanical Gurson-Tvergaard (GT) model for ductile damage. The applicability of the GT model to this kind of structural problems has been studied and assessed by comparing numerical results, obtained by using the WARP 3D finite element code, with experimental data available in literature. In the sequel a home-made procedure is presented, which aims to increase the residual strength of a cracked stiffened aluminum panel and which is based on the stochastic design improvement (SDI) technique; a whole application example is then given to illustrate the said technique.

Keywords: Residual strength, R-Curve, Gurson model, SDI.

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Authors: Dursun Aydin

Abstract:

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression.

Keywords: Kernel regression, Nonparametric models, Prediction, Smoothing spline.

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Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: Fault prediction, Neural network, GM (1.5), Genetic algorithm, GBPGA.

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Authors: Sherif. S. Nafee, Ameer. K. Al-Ramady, Salem. A. Shaheen

Abstract:

The analytical prediction of the decay heat results from the fast neutron fission of actinides was initiated under a project, 10-MAT1134-3, funded by king Abdulaziz City of Science and Technology (KASCT), Long-Term Comprehensive National Plan for Science, Technology and Innovations, managed by a team from King Abdulaziz University (KAU), Saudi Arabia, and supervised by Argonne National Laboratory (ANL) has collaborated with KAU's team to assist in the computational analysis. In this paper, the numerical solution of coupled linear differential equations that describe the decays and buildups of minor fission product MFA, has been used to predict the total decay heat and its components from the fast neutron fission of 235U and 239Pu. The reliability of the present approach is illustrated via systematic comparisons with the measurements reported by the University of Tokyo, in YAYOI reactor.

Keywords: Decay heat, fast neutron fission, and Numerical Solution of Linear Differential Equations.

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Authors: A. R. Salimi, M. Esmaeili, B. Salehi

Abstract:

The development and extension of large cities induced a need for shallow tunnel in soft ground of building areas. Estimation of ground settlement caused by the tunnel excavation is important engineering point. In this paper, prediction of surface subsidence caused by tunneling in one section of seventh line of Tehran subway is considered. On the basis of studied geotechnical conditions of the region, tunnel with the length of 26.9km has been excavated applying a mechanized method using an EPB-TBM with a diameter of 9.14m. In this regard, settlement is estimated utilizing both analytical and numerical finite element method. The numerical method shows that the value of settlement in this section is 5cm. Besides, the analytical consequences (Bobet and Loganathan-Polous) are 5.29 and 12.36cm, respectively. According to results of this study, due tosaturation of this section, there are good agreement between Bobet and numerical methods. Therefore, tunneling processes in this section needs a special consolidation measurement and support system before the passage of tunnel boring machine.

Keywords: TBM, Subsidence, Numerical Method, Analytical Method.

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Authors: Muhammad Sameer Ahmed, Piotr Remlein, Tansal Gucluoglu

Abstract:

The technique called as Generalized frequency division multiplexing (GFDM) used in the free space optical channel can be a good option for implementation free space optical communication systems. This technique has several strengths e.g. good spectral efficiency, low peak-to-average power ratio (PAPR), adaptability and low co-channel interference. In this paper, the impact of weather conditions such as haze, rain and fog on GFDM over the gamma-gamma channel model is discussed. A Trade off between link distance and system performance under intense weather conditions is also analysed. The symbol error probability (SEP) of GFDM over the gamma-gamma turbulence channel is derived and verified with the computer simulations.

Keywords: Free space optics, generalized frequency division multiplexing, weather conditions, gamma gamma distribution.

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Authors: Kibaek Kim, Dongjin Jung, Jinik Jang, Jechang Jeong

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In this paper, we proposed a method to reduce quantization error. In order to reduce quantization error, low pass filtering is applied on neighboring samples of current block in H.264/AVC. However, it has a weak point that low pass filtering is performed regardless of prediction direction. Since it doesn-t consider prediction direction, it may not reduce quantization error effectively. Proposed method considers prediction direction for low pass filtering and uses a threshold condition for reducing flag bit. We compare our experimental result with conventional method in H.264/AVC and we can achieve the average bit-rate reduction of 1.534% by applying the proposed method. Bit-rate reduction between 0.580% and 3.567% are shown for experimental results.

Keywords: Coding efficiency, H.264/AVC, Intra prediction, Low pass filter

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Authors: A. I. Dounis, P. Tiropanis, D. Tseles, G. Nikolaou, G. P. Syrcos

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The prediction of meteorological parameters at a meteorological station is an interesting and open problem. A firstorder linear dynamic model GM(1,1) is the main component of the grey system theory. The grey model requires only a few previous data points in order to make a real-time forecast. In this paper, we consider the daily average ambient temperature as a time series and the grey model GM(1,1) applied to local prediction (short-term prediction) of the temperature. In the same case study we use a fuzzy predictive model for global prediction. We conclude the paper with a comparison between local and global prediction schemes.

Keywords: Fuzzy predictive model, grey model, local andglobal prediction, meteorological forecasting, time series.

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Authors: Maryam Jamela Ismail, Rosdiazli Ibrahim, Idris Ismail

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In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.

Keywords: Energy Prediction, Multilayer Feedforward, Levenberg-Marquardt, Root Mean Square Error (RMSE)

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Authors: Raza Abdulla Saeed

Abstract:

In this study, the three-dimensional cavitating turbulent flow in a complete Francis turbine is simulated using mixture model for cavity/liquid two-phase flows. Numerical analysis is carried out using ANSYS CFX software release 12, and standard k-ε turbulence model is adopted for this analysis. The computational fluid domain consist of spiral casing, stay vanes, guide vanes, runner and draft tube. The computational domain is discretized with a threedimensional mesh system of unstructured tetrahedron mesh. The finite volume method (FVM) is used to solve the governing equations of the mixture model. Results of cavitation on the runner’s blades under three different boundary conditions are presented and discussed. From the numerical results it has been found that the numerical method was successfully applied to simulate the cavitating two-phase turbulent flow through a Francis turbine, and also cavitation is clearly predicted in the form of water vapor formation inside the turbine. By comparison the numerical prediction results with a real runner; it’s shown that the region of higher volume fraction obtained by simulation is consistent with the region of runner cavitation damage.

Keywords: Computational Fluid Dynamics, Hydraulic Francis Turbine, Numerical Simulation, Two-Phase Mixture Cavitation Model.

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Authors: Kai-Ti Hsu, Hui-Ling Huang, Chun-Wei Tung, Yi-Hsiung Chen, Shinn-Ying Ho

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Bioinformatics methods for predicting the T cell coreceptor usage from the array of membrane protein of HIV-1 are investigated. In this study, we aim to propose an effective prediction method for dealing with the three-class classification problem of CXCR4 (X4), CCR5 (R5) and CCR5/CXCR4 (R5X4). We made efforts in investigating the coreceptor prediction problem as follows: 1) proposing a feature set of informative physicochemical properties which is cooperated with SVM to achieve high prediction test accuracy of 81.48%, compared with the existing method with accuracy of 70.00%; 2) establishing a large up-to-date data set by increasing the size from 159 to 1225 sequences to verify the proposed prediction method where the mean test accuracy is 88.59%, and 3) analyzing the set of 14 informative physicochemical properties to further understand the characteristics of HIV-1coreceptors.

Keywords: Coreceptor, genetic algorithm, HIV-1, SVM, physicochemical properties, prediction.

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Authors: Hadi Farhadian, Homayoon Katibeh

Abstract:

Groundwater inflow to the tunnels is one of the most important problems in tunneling operation. The objective of this study is the investigation of model dimension effects on tunnel inflow assessment in discontinuous rock masses using numerical modeling. In the numerical simulation, the model dimension has an important role in prediction of water inflow rate. When the model dimension is very small, due to low distance to the tunnel border, the model boundary conditions affect the estimated amount of groundwater flow into the tunnel and results show a very high inflow to tunnel. Hence, in this study, the two-dimensional universal distinct element code (UDEC) used and the impact of different model parameters, such as tunnel radius, joint spacing, horizontal and vertical model domain extent has been evaluated. Results show that the model domain extent is a function of the most significant parameters, which are tunnel radius and joint spacing.

Keywords: Water inflow, Tunnel, Discontinues rock, Numerical simulation.

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Authors: Yves Lacroix, Van Van Than, Didier Leandri, Pierre Liardet

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The western Tombolo of the Giens peninsula in southern France, known as Almanarre beach, is subject to coastal erosion. We are trying to use computer simulation in order to propose solutions to stop this erosion. Our aim was first to determine the main factors for this erosion and successfully apply a coupled hydrosedimentological numerical model based on observations and measurements that have been performed on the site for decades. We have gathered all available information and data about waves, winds, currents, tides, bathymetry, coastal line, and sediments concerning the site. These have been divided into two sets: one devoted to calibrating a numerical model using Mike 21 software, the other to serve as a reference in order to numerically compare the present situation to what it could be if we implemented different types of underwater constructions. This paper presents the first part of the study: selecting and melting different sources into a coherent data basis, identifying the main erosion factors, and calibrating the coupled software model against the selected reference period. Our results bring calibration of the numerical model with good fitting coefficients. They also show that the winter South-Western storm events conjugated to depressive weather conditions constitute a major factor of erosion, mainly due to wave impact in the northern part of the Almanarre beach. Together, current and wind impact is shown negligible.

Keywords: Almanarre beach, coastal erosion, hydro-sedimentological, numerical model.

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Authors: N. Z. A. Hamid, M. S. M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly Ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: Chaotic approach, phase space, Cao method, local linear approximation method.

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Authors: Insung Jung, lockjo Koo, Gi-Nam Wang

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The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.

Keywords: Neural network, U-healthcare, prediction, timeseries, computer aided prediction.

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Authors: Sreeyuth Lal, Yohei Sato, Bojan Niceno

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With the long-term objective of Critical Heat Flux (CHF) prediction, a Direct Numerical Simulation (DNS) framework for simulation of subcooled and saturated nucleate pool boiling is developed. One case of saturated, and three cases of subcooled boiling at different subcooling levels are simulated. Grid refinement study is also reported. Both boiling and condensation phenomena can be computed simultaneously in the proposed numerical framework. Computed bubble detachment diameters of the saturated nucleate pool boiling cases agree well with the experiment. The flow structures around the growing bubble are presented and the accompanying physics is described. The relation between heat flux evolution from the heated wall and the bubble growth is studied, along with investigations of temperature distribution and flow field evolutions.

Keywords: CFD, interface tracking method, phase change model, subcooled nucleate pool boiling.

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

Abstract:

Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: Early Warning System, Knowledge Management, Topic Modeling, Market Prediction.

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Authors: Manpreet Singh, Parvinder Singh Sandhu, Reet Kamal Kaur

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Protein structure determination and prediction has been a focal research subject in the field of bioinformatics due to the importance of protein structure in understanding the biological and chemical activities of organisms. The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. However, prediction accuracies of these methods rarely exceed 70%.

Keywords: Protein, Secondary Structure, Prediction, DNA, RNA.

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Authors: Mohsen Zahraei

Abstract:

In this paper, the notion of rank−k numerical range of rectangular complex matrix polynomials are introduced. Some algebraic and geometrical properties are investigated. Moreover, for Є > 0, the notion of Birkhoff-James approximate orthogonality sets for Є−higher rank numerical ranges of rectangular matrix polynomials is also introduced and studied. The proposed definitions yield a natural generalization of the standard higher rank numerical ranges.

Keywords: Rank−k numerical range, isometry, numerical range, rectangular matrix polynomials.

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Authors: Hy Dang, Bo Mei

Abstract:

The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: Classification, machine learning, time representation, stock prediction.

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Authors: Hong Son Hoang, Remy Baraille

Abstract:

This paper addresses the problem of how one can improve the performance of a non-optimal filter. First the theoretical question on dynamical representation for a given time correlated random process is studied. It will be demonstrated that for a wide class of random processes, having a canonical form, there exists a dynamical system equivalent in the sense that its output has the same covariance function. It is shown that the dynamical approach is more effective for simulating and estimating a Markov and non- Markovian random processes, computationally is less demanding, especially with increasing of the dimension of simulated processes. Numerical examples and estimation problems in low dimensional systems are given to illustrate the advantages of the approach. A very useful application of the proposed approach is shown for the problem of state estimation in very high dimensional systems. Here a modified filter for data assimilation in an oceanic numerical model is presented which is proved to be very efficient due to introducing a simple Markovian structure for the output prediction error process and adaptive tuning some parameters of the Markov equation.

Keywords: Statistical simulation, canonical form, dynamical system, Markov and non-Markovian processes, data assimilation.

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