Search results for: integral of time square error

Authors: Khelifi Mustapha, A. Moulay lakhdar, I. Elawady

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We will study the unequal error protection for VQ image. We have used the Reed Solomon (RS) Codes as Channel coding because they offer better performance in terms of channel error correction over a binary output channel. One such channel (binary input and output) should be considered if it is the case of the application layer, because it includes all the features of the layers located below and on the what it is usually not feasible to make changes.

Keywords: vector quantization, channel error correction, Reed-Solomon channel coding, application

Procedia PDF Downloads 332

Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, Rahmatullah Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

Procedia PDF Downloads 363

Authors: Salah Abdul Hameed Saleh, Ghada Hasan

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The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city- IRAQ. The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations. The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS). The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and root-mean-square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration with an acceptable level of accuracy.

Keywords: air pollution, PM10 concentration, Lansat8 OLI image, reflectance, multispectral algorithms, Kirkuk area

Procedia PDF Downloads 420

Authors: Chia-Yu Yao

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This paper presents experimental results on testing the symbol-error-rate (SER) performance of quadrature amplitude modulation (QAM) systems employing symmetric pulse-shaping square-root (SR) filters designed by minimizing the roughness function and by minimizing the peak-to-average power ratio (PAR). The device used in the experiments is the 'bladeRF' software-defined-radio platform. PAR is a well-known measurement, whereas the roughness function is a concept for measuring the jitter-induced interference. The experimental results show that the system employing minimum-roughness pulse-shaping SR filters outperforms the system employing minimum-PAR pulse-shaping SR filters in the sense of SER performance.

Keywords: pulse-shaping filters, FIR filters, jittering, QAM

Procedia PDF Downloads 318

Authors: Mohammad Sahadet Hossain, Shamsil Arifeen, Mehrab Hossian Likhon

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In this paper, we analyze a linear time invariant (LTI) descriptor system of large dimension. Since these systems are difficult to simulate, compute and store, we attempt to reduce this large system using Low Rank Cholesky Factorized Alternating Directions Implicit (LRCF-ADI) iteration followed by Square Root Balanced Truncation. LRCF-ADI solves the dual Lyapunov equations of the large system and gives low-rank Cholesky factors of the gramians as the solution. Using these cholesky factors, we compute the Hankel singular values via singular value decomposition. Later, implementing square root balanced truncation, the reduced system is obtained. The bode plots of original and lower order systems are used to show that the magnitude and phase responses are same for both the systems.

Keywords: low-rank cholesky factor alternating directions implicit iteration, LTI Descriptor system, Lyapunov equations, Square-root balanced truncation

Procedia PDF Downloads 385

Authors: Modupe O. Jimoh

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During manhole overflows, its contents pollute the immediate environment. Understanding the pollutant transfer characteristics between manhole’s incoming sewer and the overflow is therefore of great importance. A square manhole with sides 388 mm by 388 mm and height 700 mm with an overflow facility was used in the laboratory to carry out overflow concentration measurements. Two scenarios were investigated using three flow rates. The first scenario corresponded to when the exit of the pipe becomes blocked and the only exit for the flow is the manhole. The second scenario is when there is an overflow in combination with a pipe exit. The temporal concentration measurements showed that the peak concentration of pollutants in the flow was attenuated between the inlet and the overflow. A deconvolution software was used to predict the Residence time distribution (RTD) and consequently the Cumulative Residence time distribution (CRTD). The CRTDs suggest that complete mixing is occurring between the pipe inlet and the overflow, like what is obtained in a low surcharged manhole. The results also suggest that an instantaneous stirred tank reactor model can describe the mixing characteristics.

Keywords: CRTDs, instantaneous stirred tank reactor model, overflow, square manholes, surcharge, temporal concentration profiles

Procedia PDF Downloads 109

Authors: Sie Long Kek, Wah June Leong, Kok Lay Teo

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Linear quadratic Gaussian model is a standard mathematical model for the stochastic optimal control problem. The combination of the linear quadratic estimation and the linear quadratic regulator allows the state estimation and the optimal control policy to be designed separately. This is known as the separation principle. In this paper, an efficient computational method is proposed to solve the linear quadratic Gaussian problem. In our approach, the Hamiltonian function is defined, and the necessary conditions are derived. In addition to this, the output error is defined and the least-square optimization problem is introduced. By determining the first-order necessary condition, the gradient of the sum squares of output error is established. On this point of view, the stochastic approximation approach is employed such that the optimal control policy is updated. Within a given tolerance, the iteration procedure would be stopped and the optimal solution of the linear-quadratic Gaussian problem is obtained. For illustration, an example of the linear-quadratic Gaussian problem is studied. The result shows the efficiency of the approach proposed. In conclusion, the applicability of the approach proposed for solving the linear quadratic Gaussian problem is highly demonstrated.

Keywords: iteration procedure, least squares solution, linear quadratic Gaussian, output error, stochastic approximation

Procedia PDF Downloads 138

Authors: Anood Al-Shibli

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Designing a learner corpus is not an easy task to accomplish because dealing with learners’ language has many variables which might affect the results of any study based on learners’ language production (spoken and written). Also, it is very essential to systematically design a learner corpus especially when it is aimed to be a reference to language research. Therefore, designing the Omani Learner Corpus (OLEC) has undergone many explicit and systematic considerations. These criteria can be regarded as the foundation to design any learner corpus to be exploited effectively in language use and language learning studies. Added to that, OLEC is manually error-annotated corpus. Error-annotation in learner corpora is very essential; however, it is time-consuming and prone to errors. Consequently, a navigating tool is designed to help the annotators to insert errors’ codes in order to make the error-annotation process more efficient and consistent. To assure accuracy, error annotation procedure is followed to annotate OLEC and some preliminary findings are noted. One of the main results of this procedure is creating an error-annotation system based on the Omani learners of English language production. Because OLEC is still in the first stages, the primary findings are related to only one level of proficiency and one error type which is verb related errors. It is found that Omani learners in OLEC has the tendency to have more errors in forming the verb and followed by problems in agreement of verb. Comparing the results to other error-based studies indicate that the Omani learners tend to have basic verb errors which can found in lower-level of proficiency. To this end, it is essential to admit that examining learners’ errors can give insights to language acquisition and language learning and most errors do not happen randomly but they occur systematically among language learners.

Keywords: error-annotation system, error-annotation manual, learner corpora, verbs related errors

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Authors: Matteo Gentilucci, Marco Materazzi, Gilberto Pambianchi

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Climate change is an increasingly central issue in the world, because it affects many of human activities. In this context regional studies are of great importance because they sometimes differ from the general trend. This research focuses on a small area of central Italy which overlooks the Adriatic Sea, the province of Macerata. The aim is to analyze space-based climate changes, for precipitation and temperatures, in the last 3 climatological standard normals (1961-1990; 1971-2000; 1981-2010) through GIS software. The data collected from 30 weather stations for temperature and 61 rain gauges for precipitation were subject to quality controls: validation and homogenization. These data were fundamental for the spatialization of the variables (temperature and precipitation) through geostatistical techniques. To assess the best geostatistical technique for interpolation, the results of cross correlation were used. The co-kriging method with altitude as independent variable produced the best cross validation results for all time periods, among the methods analysed, with 'root mean square error standardized' close to 1, 'mean standardized error' close to 0, 'average standard error' and 'root mean square error' with similar values. The maps resulting from the analysis were compared by subtraction between rasters, producing 3 maps of annual variation and three other maps for each month of the year (1961/1990-1971/2000; 1971/2000-1981/2010; 1961/1990-1981/2010). The results show an increase in average annual temperature of about 0.1°C between 1961-1990 and 1971-2000 and 0.6 °C between 1961-1990 and 1981-2010. Instead annual precipitation shows an opposite trend, with an average difference from 1961-1990 to 1971-2000 of about 35 mm and from 1961-1990 to 1981-2010 of about 60 mm. Furthermore, the differences in the areas have been highlighted with area graphs and summarized in several tables as descriptive analysis. In fact for temperature between 1961-1990 and 1971-2000 the most areally represented frequency is 0.08°C (77.04 Km² on a total of about 2800 km²) with a kurtosis of 3.95 and a skewness of 2.19. Instead, the differences for temperatures from 1961-1990 to 1981-2010 show a most areally represented frequency of 0.83 °C, with -0.45 as kurtosis and 0.92 as skewness (36.9 km²). Therefore it can be said that distribution is more pointed for 1961/1990-1971/2000 and smoother but more intense in the growth for 1961/1990-1981/2010. In contrast, precipitation shows a very similar shape of distribution, although with different intensities, for both variations periods (first period 1961/1990-1971/2000 and second one 1961/1990-1981/2010) with similar values of kurtosis (1st = 1.93; 2nd = 1.34), skewness (1st = 1.81; 2nd = 1.62 for the second) and area of the most represented frequency (1st = 60.72 km²; 2nd = 52.80 km²). In conclusion, this methodology of analysis allows the assessment of small scale climate change for each month of the year and could be further investigated in relation to regional atmospheric dynamics.

Keywords: climate change, GIS, interpolation, co-kriging

Procedia PDF Downloads 97

Authors: Fethi Soltani, Adel Almarashi, Idir Mechai

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Tikhonov regularization and reproducing kernels are the most popular approaches to solve ill-posed problems in computational mathematics and applications. And the Fourier multiplier operators are an essential tool to extend some known linear transforms in Euclidean Fourier analysis, as: Weierstrass transform, Poisson integral, Hilbert transform, Riesz transforms, Bochner-Riesz mean operators, partial Fourier integral, Riesz potential, Bessel potential, etc. Using the theory of reproducing kernels, we construct a simple and efficient representations for some class of Fourier multiplier operators Tm on the Paley-Wiener space Hh. In addition, we give an error estimate formula for the approximation and obtain some convergence results as the parameters and the independent variables approaches zero. Furthermore, using numerical quadrature integration rules to compute single and multiple integrals, we give numerical examples and we write explicitly the extremal function and the corresponding Fourier multiplier operators.

Keywords: fourier multiplier operators, Gauss-Kronrod method of integration, Paley-Wiener space, Tikhonov regularization

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Authors: Muhammad Ilyas, Awais Khan, Syed Ali Raza Shah

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DC motors are widely used in industries to provide mechanical power in speed and torque. The position and speed control of DC motors is getting the interest of the scientific community in robotics, especially in the robotic arm, a flexible joint manipulator. The current research work is based on position control of DC motors using experimental investigations in LabVIEW. The linear control strategy is applied to track the position and speed of the DC motor with comparative analysis in the LabVIEW platform and simulation analysis in MATLAB. The tracking error in hardware setup based on LabVIEW programming is slightly greater than simulation analysis in MATLAB due to the inertial load of the motor during steady-state conditions. The controller output shows the input voltage applied to the dc motor varies between 0-8V to ensure minimal steady error while tracking the position and speed of the DC motor.

Keywords: DC motor, labview, proportional integral derivative control, position tracking, speed tracking

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Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche

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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.

Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization

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Authors: Himayatullah Khan, Alena Fedorova

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The present paper analyses the income-consumption relationships in Pakistan using annual time series data from 1980-81 to 2010-1. The paper uses the Augmented Dickey-Fuller test to check the unit root and stationarity in these two time series. The paper finds that the two time series are nonstationary but stationary at their first difference levels. The Augmented Engle-Granger test and the Cointegrating Regression Durbin-Watson test imply that the two time series of consumption and income are cointegrated and that long-run marginal propensity to consume is 0.88 which is given by the estimated (static) equilibrium relation. The paper also used the error correction mechanism to find out to model dynamic relationship. The purpose of the ECM is to indicate the speed of adjustment from the short-run equilibrium to the long-run equilibrium state. The results show that MPC is equal to 0.93 and is highly significant. The coefficient of Engle-Granger residuals is negative but insignificant. Statistically, the equilibrium error term is zero, which suggests that consumption adjusts to changes in GDP in the same period. The short-run changes in GDP have a positive impact on short-run changes in consumption. The paper concludes that we may interpret 0.93 as the short-run MPC. The pair-wise Granger Causality test shows that both GDP and consumption Granger cause each other.

Keywords: cointegrating regression, Augmented Dickey Fuller test, Augmented Engle-Granger test, Granger causality, error correction mechanism

Procedia PDF Downloads 388

Authors: Chen-Yin Kuo, Yung-Hsin Lee

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Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

Procedia PDF Downloads 286

Authors: Hyeon-Kyo Lim, Tong-Il Jang, Yong-Hee Lee

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For an operator in a nuclear power plant, human error is one of the most dreaded factors that may result in unexpected accidents. The possibility of human errors may be low, but the risk of them would be unimaginably enormous. Thus, for accident prevention, it is quite indispensable to analyze the influence of any factors which may raise the possibility of human errors. During the past decades, not a few research results showed that performance of human operators may vary over time due to lots of factors. Among them, stress is known to be an indirect factor that may cause human errors and result in mental illness. Until now, not a few assessment tools have been developed to assess stress level of human workers. However, it still is questionable to utilize them for human performance anticipation which is related with human error possibility, because they were mainly developed from the viewpoint of mental health rather than industrial safety. Stress level of a person may go up or down with work time. In that sense, if they would be applicable in the safety aspect, they should be able to assess the variation resulted from work time at least. Therefore, this study aimed to compare their applicability for safety purpose. More than 10 kinds of work stress tools were analyzed with reference to assessment items, assessment and analysis methods, and follow-up measures which are known to close related factors with work stress. The results showed that most tools mainly focused their weights on some common organizational factors such as demands, supports, and relationships, in sequence. Their weights were broadly similar. However, they failed to recommend practical solutions. Instead, they merely advised to set up overall counterplans in PDCA cycle or risk management activities which would be far from practical human error prevention. Thus, it was concluded that application of stress assessment tools mainly developed for mental health seemed to be impractical for safety purpose with respect to human performance anticipation, and that development of a new assessment tools would be inevitable if anyone wants to assess stress level in the aspect of human performance variation and accident prevention. As a consequence, as practical counterplans, this study proposed a new scheme for assessment of work stress level of a human operator that may vary over work time which is closely related with the possibility of human errors.

Keywords: human error, human performance, work stress, assessment tool, time-variant, accident prevention

Procedia PDF Downloads 644

Authors: Sherif K. El Dyasti, Esam A. Hagras, Adel E. El-Hennawy

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This paper addresses the performance of antenna array beam-forming on Chip-Interleaved Code Division Multiple Access (CI_CDMA) system based on Minimum Mean Square Error (MMSE) detector in aeronautical mobile radio channel. Multipath fading, Doppler shifts caused by the speed of the aircraft, and Multiple Access Interference (MAI) are the most important reasons that affect and reduce the performance of aeronautical system. In this paper, we suggested the CI-CDMA with antenna array to combat this fading and improve the bit error rate (BER) performance. We further evaluate the performance of the proposed system in the four standard scenarios in aeronautical mobile radio channel.

Keywords: aeronautical channel, CI-CDMA, beamforming, communication, information

Procedia PDF Downloads 375

Authors: Heeba A. Gurku

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Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

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Authors: Jorge Gonzalez-Camus

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In this work is proved the existence of at least one minimal and maximal mild solutions to the Cauchy problem, for fractional evolution equation of neutral type, involving a general kernel. An operator A generating a resolvent family and integral resolvent family on a Banach space X and a kernel belonging to a large class appears in the equation, which covers many relevant cases from physics applications, in particular, the important case of time - fractional evolution equations of neutral type. The main tool used in this work was the Kuratowski measure of noncompactness and fixed point theorems, specifically Darbo-type, and an iterative method of lower and upper solutions, based in an order in X induced by a normal cone P. Initially, the equation is a Cauchy problem, involving a fractional derivate in Caputo sense. Then, is formulated the equivalent integral version, and defining a convenient functional, using the theory of resolvent families, and verifying the hypothesis of the fixed point theorem of Darbo type, give us the existence of mild solution for the initial problem. Furthermore, the existence of minimal and maximal mild solutions was proved through in an iterative method of lower and upper solutions, using the Azcoli-Arzela Theorem, and the Gronwall’s inequality. Finally, we recovered the case derivate in Caputo sense.

Keywords: fractional evolution equations, Volterra integral equations, minimal and maximal mild solutions, neutral type equations, non-local in time equations

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Authors: Nor Zila Abd Hamid, Mohd Salmi 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: Bina Kumari, Subir K. Sarkar, Pradipta Bandyopadhyay

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The autocorrelation function of the density fluctuation is studied in each of the two phases in a Gibbs Ensemble Monte Carlo (GEMC) simulation of the problem of phase separation for a square well potential with various values of its range. We find that the normalized autocorrelation function is described very well as a linear combination of an exponential function with a time scale τ₂ and a stretched exponential function with a time scale τ₁ and an exponent α. Dependence of (α, τ₁, τ₂) on the parameters of the GEMC algorithm and the range of the square well potential is investigated and interpreted. We also analyse the issue of how to choose the parameters of the GEMC simulation optimally.

Keywords: autocorrelation function, density fluctuation, GEMC, simulation

Procedia PDF Downloads 156

Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Mahmood Naghizadeh, Maryam Memarian, Hourshad Irvash

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Following the rise in the world population and the upward growth of urbanization, the design, planning, and management of the site scenery for the purpose of presentation and expansion of sustainable site scenery has turned to be the greatest concern to experts. Since the fundamental principles of the site scenery change more and less haphazardly over time, sustainable site scenery can be viewed as an ideal goal because both sustainability and dynamism come into view in urban site scenery and it wouldn’t be designed according to a set of pre-determined principles. Sustainable site scenery, as the ongoing interaction between idealism and pragmatism with sustainability factors, is a dynamic phenomenon created by bringing cultural, historical, social and natural scenery together. Such an interaction is not to subdue other factors but to reinforce the aforementioned factors. The sustainable site scenery is a persistently occurring event not only has attenuated over time but has gained strength. The sustainability of a site scenery or an event over time depends on its site identity which grows out of its continuous association with the past. The sustainability of a site scene or an event in a time frame intertwined with the identity of the place from past to present. This past history supports the present and future of the scene. The result of such a supportive role is the sustainability of site scenery. Isfahan Naqsh-e-Jahan Square is one of the most outstanding squares in the world and the best embodiment of Iranian site scenery architecture. This square is an arena that brings people together and a dynamic city center comprising various urban and religious complexes, spaces and facilities and is considered as one of the most favorable traditional urban space of Iran. Such a place can illustrate many factors related to sustainable site scenery. One the other hand, there are still no specific principles concerning sustainability in the architecture of site scenery. Meanwhile, sustainability is recognized as a rather modern view in architecture. The purpose of this research is to identify factors involved in sustainability in general and to examine their effects on site scenery architecture in particular. Finally, these factors will be studied with taking Naqsh-e-Jahan Square into account. This research adopts an analytic-descriptive approach that has benefited from the review of literature available in library studies and the documents related to sustainability and site scenery architecture. The statistical population used for the purpose of this research includes square constructed during the Safavid dynasty and Naqsh-e-Jahan Square was picked out as the case study. The purpose of this paper is to come up with a rough definition of sustainable site scenery and demonstrate this concept by analyzing it and recognizing the social, economic and ecological aspects of this project.

Keywords: Naqsh-e-Jahan Square, site scenery architecture, sustainability, sustainable site scenery

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Authors: Shai Sarussi

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Let S be an integral domain which is not a field, let F be its field of fractions, and let A be an F-algebra. An S-subalgebra R of A is called S-nice if R ∩ F = S and F R = A. A topological space whose set of open sets is closed under arbitrary intersections is called an Alexandroff space. Inspired by the well-known Zariski-Riemann space and the Zariski topology on the set of prime ideals of a commutative ring, we define a topology on the set of all S-nice subalgebras of A. Consequently, we get an interplay between Algebra and topology, that gives us a better understanding of the S-nice subalgebras of A. It is shown that every irreducible subset of S-nice subalgebras of A has a supremum; and a characterization of the irreducible components is given, in terms of maximal S-nice subalgebras of A.

Keywords: Alexandroff topology, integral domains, Zariski-Riemann space, S-nice subalgebras

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Authors: Bao Li, Aike Qiao, Gaoyang Li, Youjun Liu

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Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics.

Keywords: blood circulatory system, individual physiological indicators, lumped parameter model, optimization algorithm

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Authors: Zainab Almukhtar, Adel Merabet

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In this paper, a method for maximum power point tracking of a photovoltaic energy conversion system is presented. This method is based on using the difference between the power from the solar panel and an estimated power value to control the DC-DC converter of the photovoltaic system. The difference is continuously compared with a preset error permitted value. If the power difference is more than the error, the estimated power is multiplied by a factor and the operation is repeated until the difference is less or equal to the threshold error. The difference in power will be used to trigger a DC-DC boost converter in order to raise the voltage to where the maximum power point is achieved. The proposed method was experimentally verified through a PV energy conversion system driven by the OPAL-RT real time controller. The method was tested on varying radiation conditions and load requirements, and the Photovoltaic Panel was operated at its maximum power in different conditions of irradiation.

Keywords: control system, error, solar panel, MPPT tracking

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Authors: M. Qamarul Islam, Ergun Dogan, Mehmet Yazici

Abstract:

There is a growing body of evidence that non-normal data is more prevalent in nature than the normal one. Examples can be quoted from, but not restricted to, the areas of Economics, Finance and Actuarial Science. The non-normality considered here is expressed in terms of fat-tailedness and asymmetry of the relevant distribution. In this study a skew t distribution that can be used to model a data that exhibit inherent non-normal behavior is considered. This distribution has tails fatter than a normal distribution and it also exhibits skewness. Although maximum likelihood estimates can be obtained by solving iteratively the likelihood equations that are non-linear in form, this can be problematic in terms of convergence and in many other respects as well. Therefore, it is preferred to use the method of modified maximum likelihood in which the likelihood estimates are derived by expressing the intractable non-linear likelihood equations in terms of standardized ordered variates and replacing the intractable terms by their linear approximations obtained from the first two terms of a Taylor series expansion about the quantiles of the distribution. These estimates, called modified maximum likelihood estimates, are obtained in closed form. Hence, they are easy to compute and to manipulate analytically. In fact the modified maximum likelihood estimates are equivalent to maximum likelihood estimates, asymptotically. Even in small samples the modified maximum likelihood estimates are found to be approximately the same as maximum likelihood estimates that are obtained iteratively. It is shown in this study that the modified maximum likelihood estimates are not only unbiased but substantially more efficient than the commonly used moment estimates or the least square estimates that are known to be biased and inefficient in such cases. Furthermore, in conventional regression analysis, it is assumed that the error terms are distributed normally and, hence, the well-known least square method is considered to be a suitable and preferred method for making the relevant statistical inferences. However, a number of empirical researches have shown that non-normal errors are more prevalent. Even transforming and/or filtering techniques may not produce normally distributed residuals. Here, a study is done for multiple linear regression models with random error having non-normal pattern. Through an extensive simulation it is shown that the modified maximum likelihood estimates of regression parameters are plausibly robust to the distributional assumptions and to various data anomalies as compared to the widely used least square estimates. Relevant tests of hypothesis are developed and are explored for desirable properties in terms of their size and power. The tests based upon modified maximum likelihood estimates are found to be substantially more powerful than the tests based upon least square estimates. Several examples are provided from the areas of Economics and Finance where such distributions are interpretable in terms of efficient market hypothesis with respect to asset pricing, portfolio selection, risk measurement and capital allocation, etc.

Keywords: least square estimates, linear regression, maximum likelihood estimates, modified maximum likelihood method, non-normality, robustness

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Authors: Ting-Ting Wen, Jung-Ruey Tsai

Abstract:

This paper reports a method for the replication of micro-scale structures. By using electromagnetic force-assisted imprinting system with magnetic soft stamp written square-microlens cavity, a photopolymer square-microlens structures can be rapidly fabricated. Under the proper processing conditions, the polymeric square-microlens structures with feature size of width 100.3um and height 15.2um across a large area can be successfully fabricated. Scanning electron microscopy (SEM) and surface profiler observations confirm that the micro-scale polymer structures are produced without defects or distortion and with good pattern fidelity over a 60x60mm2 area. This technique shows great potential for the efficient replication of the micro-scale structure array at room temperature and with high productivity and low cost.

Keywords: square-microlens structures, electromagnetic force-assisted imprinting, magnetic soft stamp

Procedia PDF Downloads 298

Authors: Illyas Md Isa, Mohamad Idris Saidin, Mustaffa Ahmad, Norhayati Hashim

Abstract:

A highly selective and sensitive carbon paste electrode modified with multiwall carbon nanotubes and 2,6–diacetylpyridine-di-(1R)–(-)–fenchone diazine tetracarbonylmolybdenum(0) complex was used for determination of trace amounts of Cu(II) using square wave anodic stripping voltammetry (SWASV). The influences of experimental variables on the proposed electrode such as pH, supporting electrolyte, preconcentration potential and time, and square wave parameters were investigated. Under optimal conditions, the proposed electrode showed a linear relationship with concentration in the range of 1.0 × 10–10 to 1.0 × 10– 6 M Cu(II) with a limit of detection 8.0 × 10–11 M. The relative standard deviation (n = 5) for a solution containing 1.0 × 10– 6 M of Cu(II) was 0.036. The presence of various cations (in 10 and 100-folds concentration) did not interfere. Electrochemical impedance spectroscopy (EIS) showed that the charge transfer at the electrode-solution interface was favourable. The proposed electrode was applied for the determination of Cu(II) in several water samples. Results agreed very well with those obtained by inductively coupled plasma-optical emission spectrometry. The modified electrode was then proposed as an alternative for determination of Cu(II).

Keywords: chemically modified electrode, Cu(II), square wave anodic stripping voltammetry, tetracarbonylmolybdenum(0)

Procedia PDF Downloads 247

Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

Abstract:

In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

Procedia PDF Downloads 271

Authors: E. Ghayoula, M. Haj Taieb, A. Bouallegue, J. Y. Chouinard, R. Ghayoula

Abstract:

This paper evaluates the bit error rate (BER) performance of MIMO systems for wireless communication. MIMO uses multiple transmitting antennas, multiple receiving antennas and the space-time block codes to provide diversity. MIMO transmits signal encoded by space-time block (STBC) encoder through different transmitting antennas. These signals arrive at the receiver at slightly different times. Spatially separated multiple receiving antennas are employed to provide diversity reception to combat the effect of fading in the channel. This paper presents a detailed study of diversity coding for MIMO systems. STBC techniques are implemented and simulation results in terms of the BER performance with varying number of MIMO transmitting and receiving antennas are presented. Our results show how increasing the number of both transmit and receive antenna improves system performance and reduces the bit error rate.

Keywords: MIMO systems, diversity, BER, MRRC, SIMO, MISO, STBC, alamouti, SNR

Procedia PDF Downloads 467