Search results for: vector error correction model.

Authors: Ali Dulla

Abstract:

This paper introduces a new dataset (and the methodology used to generate it) based on a wide range of historical Arabic documents containing clean data simple and homogeneous-page layouts. The experiments are implemented on printed and handwritten documents obtained respectively from some important libraries such as Qatar Digital Library, the British Library and the Library of Congress. We have gathered and commented on 150 archival document images from different locations and time periods. It is based on different documents from the 17th-19th century. The dataset comprises differing page layouts and degradations that challenge text line segmentation methods. Ground truth is produced using the Aletheia tool by PRImA and stored in an XML representation, in the PAGE (Page Analysis and Ground truth Elements) format. The dataset presented will be easily available to researchers world-wide for research into the obstacles facing various historical Arabic documents such as geometric correction of historical Arabic documents.

Keywords: Dataset production, ground truth production, historical documents, arbitrary warping, geometric correction.

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Authors: Oluseye Adeniyi Adeleke, Mohd Fadzli Salleh

Abstract:

Cooperative communication provides transmit diversity, even when, due to size constraints, mobile units cannot accommodate multiple antennas. A versatile cooperation method called coded cooperation has been developed, in which cooperation is implemented through channel coding with a view to controlling the errors inherent in wireless communication. In this work we evaluate the performance of coded cooperation in flat Rayleigh fading environment using a concept known as the pair wise error probability (PEP). We derive the PEP for a flat fading scenario in coded cooperation and then compare with the signal-to-noise ratio of the users in the network. Results show that an increase in the SNR leads to a decrease in the PEP. We also carried out simulations to validate the result.

Keywords: Channel state information, coded cooperation, cooperative systems, pairwise-error-probability, Reed-Solomon codes.

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Authors: Mesut Gündüz, Ferruh Yildiz, Ayşe Onat

Abstract:

In Geographic Information System, one of the sources of obtaining needed geographic data is digitizing analog maps and evaluation of aerial and satellite photos. In this study, a method will be discussed which can be used to extract vectorial features and creating vectorized drawing files for aerial photos. At the same time a software developed for these purpose. Converting from raster to vector is also known as vectorization and it is the most important step when creating vectorized drawing files. In the developed algorithm, first of all preprocessing on the aerial photo is done. These are; converting to grayscale if necessary, reducing noise, applying some filters and determining the edge of the objects etc. After these steps, every pixel which constitutes the photo are followed from upper left to right bottom by examining its neighborhood relationship and one pixel wide lines or polylines obtained. The obtained lines have to be erased for preventing confusion while continuing vectorization because if not erased they can be perceived as new line, but if erased it can cause discontinuity in vector drawing so the image converted from 2 bit to 8 bit and the detected pixels are expressed as a different bit. In conclusion, the aerial photo can be converted to vector form which includes lines and polylines and can be opened in any CAD application.

Keywords: Vectorization, Aerial Photos, Vectorized DrawingFile.

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Authors: Neha Khanduja

Abstract:

This paper presents a comparative analysis of continuously stirred tank reactor (CSTR) control based on adaptive control and optimal tuning of PID control based on particle swarm optimization. In the design of adaptive control, Model reference adaptive control (MRAC) scheme is used, in which the adaptation law have been developed by MIT rule & Lyapunov’s rule. In PSO control parameters of PID controller is tuned by using the concept of particle swarm optimization to get optimized operating point for minimum integral square error (ISE) condition. The results show the adjustment of PID parameters converting into the optimal operating point and the good control response can be obtained by the PSO technique.

Keywords: Model reference adaptive control (MRAC), optimal control, particle swarm optimization (PSO).

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Authors: Yukiko Sasaki Alam

Abstract:

This research aims at finding out the causes that led to wrong lexical selections in machine translation (MT) rather than categorizing lexical errors, which has been a main practice in error analysis. By manually examining and analyzing lexical errors outputted by a MT system, it suggests what knowledge would help the system reduce lexical errors.

Keywords: Error analysis, causes of errors, machine translation, outputs evaluation.

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

Abstract:

In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces  high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: Activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss.

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Authors: Refaat M Mohamed, Ayman El-Baz, Aly A. Farag

Abstract:

This paper presents a new approach for the prob-ability density function estimation using the Support Vector Ma-chines (SVM) and the Expectation Maximization (EM) algorithms.In the proposed approach, an advanced algorithm for the SVM den-sity estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the para-meters of the kernel function which is used by the SVM algorithm,the proposed approach uses the EM algorithm for an automatic optimization of the kernel. Experimental evaluation using simulated data set shows encouraging results.

Keywords: Density Estimation, SVM, Learning Algorithms, Parameters Estimation.

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

Abstract:

In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r²) and the cross-validated coefficient (q²) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, Triazoles.

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

Abstract:

Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: Accelerometer, AdaBoost, GPS, Mode Prediction, Support vector Machine.

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Authors: Reza Lotfalian, Sudarshan Martins, Peter Radziszewski

Abstract:

The effect of reliability on life-cycle cost, including initial and maintenance cost of a system is studied. The failure probability of a component is used to calculate the average maintenance cost during the operation cycle of the component. The standard deviation of the life-cycle cost is also calculated as an error measure for the average life-cycle cost. As a numerical example, the model is used to study the average life-cycle cost of an electric motor.

Keywords: Initial Cost, Life-cycle cost, Maintenance Cost, Reliability.

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Authors: Nora Taghavi, Ali Sadr

Abstract:

System identification is the process of creating models of dynamic process from input- output signals. The aim of system identification can be identified as “ to find a model with adjustable parameters and then to adjust them so that the predicted output matches the measured output". This paper presents a method of modeling and simulating with system identification to achieve the maximum fitness for transformation function. First by using optimized KLM equivalent circuit for PVDF piezoelectric transducer and assuming different inputs including: sinuside, step and sum of sinusides, get the outputs, then by using system identification toolbox in MATLAB, we estimate the transformation function from inputs and outputs resulted in last program. Then compare the fitness of transformation function resulted from using ARX,OE(Output- Error) and BJ(Box-Jenkins) models in system identification toolbox and primary transformation function form KLM equivalent circuit.

Keywords: PVDF modeling, ARX, BJ(Box-Jenkins), OE(Output-Error), System Identification.

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Authors: B. Sathyanandhi, P. M. Balasubramaniam

Abstract:

This paper presents Buck-Boost converter topology to maintain the input power factor by using the power factor stage control and regulation stage control. Suppose, if we are using the RL load the power factor will be reduced due to the presence of total harmonic distortion in the current wave. To improve the power factor the current waveform should follow the fundamental component of the voltage waveform. These can be achieved by using the high -frequency power converter. Based on the resonant circuit the converter is able to perform the function of Buck, Boost, and buck-boost converter. Here ,we have used Buck-Boost converter, because, the buck-boost converter has more advantages than the boost converter. Here the switching action of the power converter can  take place by using the external zero comparator PFC stage control. The power converter consisting of the resonant  circuit which is used to control the output voltage gain of the converter. The power converter is operated at a very high switching frequency in the range of 400KHz in order to overcome the switching losses of the power converter. Due to  presence of high switching frequency, the power factor will improve. Therefore, the total harmonics distortion present in the current waveform has also reduced. These results has generated in the form of simulation by using MATLAB/SIMULINK software.  Similar to the Buck and Boost converters, the operation of the Buck-Boost has best understood, in terms of the inductor's "reluctance" for allowing rapid change in current, which also reduces the Total Harmonic Distortion (THD) in the input current waveform, which can improve the input Power factor, based on the type of load used.

Keywords: Buck-boost converter, High switching frequency, Power factor correction, power factor correction stage Regulation stage, Total harmonic distortion (THD).

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Authors: E. Giovanis

Abstract:

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer of the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model. Moreover this technique can be used in Autoregressive-Moving Average models, with and without exogenous inputs, as also the training process with genetics algorithms optimization can be replaced by the error back-propagation algorithm.

Keywords: Autoregressive model, Feed-Forward neuralnetworks, Genetic Algorithms, Gross Domestic Product

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Authors: K. P. Oyeduntan, K. Oshinubi

Abstract:

Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the sparkplug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria.

Keywords: Economy, GDP, maritime transport, port, regression.

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Authors: Hichem Sassi, Tawfik Najeh, Noureddine Liouane

Abstract:

Information of nodes’ locations is an important criterion for lots of applications in Wireless Sensor Networks. In the hop-based range-free localization methods, anchors transmit the localization messages counting a hop count value to the whole network. Each node receives this message and calculates its own distance with anchor in hops and then approximates its own position. However the estimative distances can provoke large error, and affect the localization precision. To solve the problem, this paper proposes an algorithm, which makes the unknown nodes fix the nearest anchor as a reference and select two other anchors which are the most accurate to achieve the estimated location. Compared to the DV-Hop algorithm, experiment results illustrate that proposed algorithm has less average localization error and is more effective.

Keywords: Wireless Sensors Networks, Localization problem, localization average error, DV–Hop Algorithm, MATLAB.

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Authors: Shuhe Shao

Abstract:

This article provides partial evaluation index and its standard of sports aerobics, including the following 12 indexes: health vitality, coordination, flexibility, accuracy, pace, endurance, elasticity, self-confidence, form, control, uniformity and musicality. The three-layer BP artificial neural network model including input layer, hidden layer and output layer is established. The result shows that the model can well reflect the non-linear relationship between the performance of 12 indexes and the overall performance. The predicted value of each sample is very close to the true value, with a relative error fluctuating around of 5%, and the network training is successful. It shows that BP network has high prediction accuracy and good generalization capacity if being applied in sports aerobics performance evaluation after effective training.

Keywords: BP neural network, sports aerobics, performance, evaluation.

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Authors: Ugur Akyol, Dinçer Akal, Ahmet Cihan, Kamil Kahveci

Abstract:

This study is concerned with the investigation of the suitability of several empirical and semi-empirical drying models available in the literature to define drying behavior of viscose yarn bobbins. For this purpose, firstly, experimental drying behaviour of viscose bobbins was determined on an experimental dryer setup which was designed and manufactured based on hot-air bobbin dryers used in textile industry. Afterwards, drying models considered were fitted to the experimentally obtained moisture ratios. Drying parameters were drying temperature and bobbin diameter. The fit was performed by selecting the values for constants in the models in such a way that these values make the sum of the squared differences between the experimental and the model results for moisture ratio minimum. Suitability of fitting was specified as comparing the correlation coefficient, standard error and mean square deviation. The results show that the most appropriate model in describing the drying curves of viscose bobbins is the Page model.

Keywords: Drying, moisture ratio, Page model, viscose

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Authors: Shih-Pin Chen, Shih-Syuan You

Abstract:

Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: Dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming.

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Authors: Hongyu Wei, Wenliang Chen, Lin Gao

Abstract:

Incremental forming is a complex forming process with continuously local cumulative deformation taking place during its process, and springback that forming quality affected by would occur. The springback evaluation method based on forming error compensation also was proposed, which it can be defined as the difference between theory and the actual amount of compensation along the measured direction. According to forming error compensation evaluation method, experiments was designed and implemented. And from the results that obtained it can be show, the magnitude of springback average (δE) of formed parts was very small, and the forming precision could be significantly improved by adopting compensation method. Based on double tensile stress state in the main deformation area, a hypothesis that there is little springback be arisen by bending behavior on the formed parts that was proposed.

Keywords: Sheet metal, incremental forming, springback, forming error compensation, geometric accuracy

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Authors: Gints Jekabsons

Abstract:

The approach of subset selection in polynomial regression model building assumes that the chosen fixed full set of predefined basis functions contains a subset that is sufficient to describe the target relation sufficiently well. However, in most cases the necessary set of basis functions is not known and needs to be guessed – a potentially non-trivial (and long) trial and error process. In our research we consider a potentially more efficient approach – Adaptive Basis Function Construction (ABFC). It lets the model building method itself construct the basis functions necessary for creating a model of arbitrary complexity with adequate predictive performance. However, there are two issues that to some extent plague the methods of both the subset selection and the ABFC, especially when working with relatively small data samples: the selection bias and the selection instability. We try to correct these issues by model post-evaluation using Cross-Validation and model ensembling. To evaluate the proposed method, we empirically compare it to ABFC methods without ensembling, to a widely used method of subset selection, as well as to some other well-known regression modeling methods, using publicly available data sets.

Keywords: Basis function construction, heuristic search, modelensembles, polynomial regression.

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Authors: P. Tueku, P. Supnithi, R. Wongsathan

Abstract:

Heat-Assisted Magnetic Recording (HAMR) is one of the leading technologies identified to enable areal density beyond 1 Tb/in² of magnetic recording systems. A key challenge to HAMR designing is accuracy of positioning, timing of the firing laser, power of the laser, thermo-magnetic head, head-disk interface and cooling system. We study the effect of HAMR parameters on transition center and transition width. The HAMR is model using Thermal Williams-Comstock (TWC) and microtrack model. The target and equalizer are designed by the minimum mean square error (MMSE). The result shows that the unit energy constraint outperforms other constraints.

Keywords: Heat-Assisted Magnetic Recording, Thermal Williams-Comstock equation, Microtrack model, Equalizer.

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Authors: Revanth Sai Kosaraju, Michael Ramscar, Melody Dye

Abstract:

Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.

Keywords: Abstractness, child psychology, language acquisition, prediction and error.

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Authors: Jiangyong Liu, Xiangxiang Xu, Bote Luo, Xiaoxue Luo, Jiang Zhu, Lingzhi Yi

Abstract:

To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the least square support vector machine (LSSVM) optimized by an improved sparrow search algorithm combined with the variational mode decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of intrinsic mode functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the LSSVM. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy.

Keywords: Load forecasting, variational mode decomposition, improved sparrow search algorithm, least square support vector machine.

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Authors: K.Ramesh, A.Nirmalkumar, G.Gurusamy

Abstract:

In this paper, a new model order reduction phenomenon is introduced at the design stage of linear phase digital IIR filter. The complexity of a system can be reduced by adopting the model order reduction method in their design. In this paper a mixed method of model order reduction is proposed for linear IIR filter. The proposed method employs the advantages of factor division technique to derive the reduced order denominator polynomial and the reduced order numerator is obtained based on the resultant denominator polynomial. The order reduction technique is used to reduce the delay units at the design stage of IIR filter. The validity of the proposed method is illustrated with design example in frequency domain and stability is also examined with help of nyquist plot.

Keywords: Error index (J), Factor division method, IIR filter, Nyquist plot, Order reduction.

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Authors: O. Hassanein, Sreenatha G. Anavatti, Tapabrata Ray

Abstract:

The dynamics of the Autonomous Underwater Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate accurately because of the variations of these coefficients with different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line adaptive fuzzy model and adaptive neural fuzzy network (ANFN) model techniques to overcome the uncertain external disturbance and the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according to the back propagation algorithm based upon the error between the identified model and the actual output of the plant. The proposed ANFN model adopts a functional link neural network (FLNN) as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN model is a nonlinear combination of input variables. Fuzzy control system is applied to guide and control the AUV using both adaptive models and mathematical model. Simulation results show the superiority of the proposed adaptive neural fuzzy network (ANFN) model in tracking of the behavior of the AUV accurately even in the presence of noise and disturbance.

Keywords: AUV, AUV dynamic model, fuzzy control, fuzzy modelling, adaptive fuzzy control, back propagation, system identification, neural fuzzy model, FLNN.

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Authors: Young-San Shin, Seongsoo Lee

Abstract:

Hall sensor is widely used to measure rotation angle. When the Hall voltage is measured for linear displacement, it is converted to angular displacement using arctangent function, which requires a large lookup table. In this paper, a lookup table reduction technique is presented for angle measurement. When the input of the lookup table is small within a certain threshold, the change of the outputs with respect to the change of the inputs is relatively small. Thus, several inputs can share same output, which significantly reduce the lookup table size. Its error analysis was also performed, and the threshold was determined so as to maintain the error less than 1°. When the Hall voltage has 11-bit resolution, the lookup table size is reduced from 1,024 samples to 279 samples.

Keywords: Hall sensor, angle measurement, lookup table, arctangent.

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Authors: P. Bárta

Abstract:

Increasing demand on the performance of Subsea Production Systems (SPS) suggests a need for more detailed investigation of fluid behavior taking place in subsea equipment. Complete CFD cool down analyses of subsea equipment are very time demanding. The objective of this paper is to investigate a Locked CFD approach, which enables significant reduction of the computational time and at the same time maintains sufficient accuracy during thermal cool down simulations. The result comparison of a dead leg simulation using the Full CFD and the three LCFD-methods confirms the validity of the locked flow field assumption for the selected case. For the tested case the LCFD simulation speed up by factor of 200 results in the absolute thermal error of 0.5 °C (3% relative error), speed up by factor of 10 keeps the LCFD results within 0.1 °C (0.5 % relative error) comparing to the Full CFD.

Keywords: CFD, Locked Flow Field, Speed up of CFD simulation time, Subsea

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Authors: B. Dora Arul Selvi, .N. Kamaraj

Abstract:

Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Machines (FSVM) to investigate the stability status of power systems and a modified generation rescheduling scheme to bring back the identified unstable cases to a more economical and stable operating point. FSVM improves the traditional SVM (Support Vector Machines) by adding fuzzy membership to each training sample to indicate the degree of membership of this sample to different classes. The preventive control based on economic generator rescheduling avoids the instability of the power systems with minimum change in operating cost under disturbed conditions. Numerical results on the New England 39 bus test system show the effectiveness of the proposed method.

Keywords: Fuzzy Support Vector Machine (FSVM), Incremental Cost, Preventive Control, Transient stability

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Authors: H. Barati, M. Wu, A. Kharicha, A. Ludwig

Abstract:

A transient model for nozzle clogging has been developed and successfully validated against a laboratory experiment. Key steps of clogging are considered: transport of particles by turbulent flow towards the nozzle wall; interactions between fluid flow and nozzle wall, and the adhesion of the particle on the wall; the growth of the clog layer and its interaction with the flow. The current paper is to investigate the mesh (size and type) sensitivity of the model in both two and three dimensions. It is found that the algorithm for clog growth alone excluding the flow effect is insensitive to the mesh type and size, but the calculation including flow becomes sensitive to the mesh quality. The use of 2D meshes leads to overestimation of the clog growth because the 3D nature of flow in the boundary layer cannot be properly solved by 2D calculation. 3D simulation with tetrahedron mesh can also lead to an error estimation of the clog growth. A mesh-independent result can be achieved with hexahedral mesh, or at least with triangular prism (inflation layer) for near-wall regions.

Keywords: Clogging, nozzle, numerical model, simulation.

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Authors: Melih Turgut, Süha Yılmaz

Abstract:

In this paper, first, a characterization of spherical Pseudo null curves in Semi-Euclidean space is given. Then, to investigate position vector of a pseudo null curve, a system of differential equation whose solution gives the components of the position vector of a pseudo null curve on the Frenet axis is established by means of Frenet equations. Additionally, in view of some special solutions of mentioned system, characterizations of some special pseudo null curves are presented.

Keywords: Semi-Euclidean Space, Pseudo Null Curves, Position Vectors.

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