Paper Title, Authors, Abstract (Issue 1, Volume 3, 2009)

Pages

Numerical Simulation of Indonesian Tsunami 2004 at Penang Island in Peninsular Malaysia Using a Nested Grid Model
Md. Fazlul Karim, Ahmad Izani M Ismail, Mohammed Ashaque Meah

Abstract: Nested grid modelling techniques are increasingly being recognized as methodologies to aid in multiscale modelling of a variety of atmospheric and oceanic phenomena. A nested grid model with a fine resolution is used to simulate the Indonesian tsunami of 2004 along the coastal belt of Penang Island. The basic primitive model is depth averaged shallow water equations. A fine mesh numerical scheme for the Peninsular Malaysia covering the region between 5?10/ to 5?35/N and 100? to 100?30/E to record fine orographical detail of the region of Penang Island has been nested into a coarse mesh scheme covering the region approximately between 2° N to 14° N and 91° E to 100.5° E which includes the source region of the Indonesian tsunami of 2004. The nesting is accomplished using a scheme Arakawa C staggered grid arrangement. The solutions are obtained for two categories: (a) coarse mesh solution, and (b) nested solution. A nested model is employed in which a coarse grid model is used to supply the open boundary conditions for a finer grid. The major features of the event 2004 along Penang have been successfully simulated by the nested model.
 

FCM & FPCM Algorithm Based on Unsupervised Mahalanobis Distances with Better Initial Values and Separable Criterion
Jeng-Ming Yih, Yuan-Horng Lin, Hsiang-Chuan Liu

Abstract: The fuzzy partition clustering algorithms are most based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm, were developed to detect non-spherical structural clusters, but both of them based on semi-supervised Mahalanobis distance needed additional prior information. An improved Fuzzy CMean algorithm based on unsupervised Mahalanobis distance, FCM-M, was proposed by our previous work, but it didn’t consider the relationships between cluster centers in the objective function. In this paper, we proposed an improved Fuzzy C-Mean algorithm, FCM-MS, which is not only based on unsupervised Mahalanobis distance, but also considering the relationships between cluster centers, and the relationships between the center of all points and the cluster centers in the objective function, the singular and the initial values problems were also solved. Two real data sets was applied to prove that the performance of the FCMMS algorithm gave more accurate clustering results than the FCM and FCM-M methods, and the ratio method which is proposed by us is the better of the two methods for selecting the initial values.
 

The Transmission Model of P.falciparum and P.Vivax Malaria between Thai and Burmese
P. Pongsumpun, I. M. Tang

Abstract: The transmission of Plasmodium falciparum and Plasmodium vivax malaria of Thais and Burmese is studied through a mathematical model. The population is separated into two groups, Thai and Burmese. Each population is divided into susceptible and infectious subclasses. The loss of immunity by individuals in the infectious class causes them to move back into the susceptible class. Standard dynamical method is used to analyze the behavior of the model. Two stable equilibrium states, a disease free state and an epidemic state are found to be possible in each population. A disease free equilibrium state in the Thai population occurs when there are no infected Burmese entering into the community. When there are infected Burmese enters into the Thai community, the epidemic state can occur. It is found that the disease free state is stable when the threshold number R0 is less than one. The epidemic state is stable when (where these threshold numbers are for the individual populations) are greater than one. The numerical simulations of our model illustrate what the results would be for our theoretical model.
 

The Influence of Noise Kurtosis on the Dynamics of a Harmonic Oscillator with Fluctuating Frequency
Katrin Laas, Romi Mankin, Astrid Rekker

Abstract: The influence of noise kurtosis on underdamped motion of a harmonic oscillator with fluctuating frequency subjected to an external periodic force and an additive thermal noise is considered. The colored fluctuations of the oscillator frequency are modeled as a trichotomous noise. It is established that the spectral amplification and variance of the output signal exhibits a nonmonotonic dependence on the noise kurtosis, thus demonstrating the phenomenon of noise kurtosis controlled stochastic resonance. Some unexpected effects such as hypersensitive response of spectral amplification to small variations of noise amplitude, encountered in the case of a large kurtosis of colored noise are also discussed.
 

Computer-Aided Simulation on the Reversing Operation of the Two-Phase Induction Machine
Alecsandru Simion, Leonard Livadaru, Dorin Lucache

Abstract: The paper presents a new mathematical model of the two-phase induction machine, called "in total fluxes", which is very appropriate for the study of the reversing regime. The equations of the model use as main quantities the rotation angle and the total fluxes of the windings and exclude the rotation speed, which now become a secondary quantity that can be calculated from rotation angle expression. On the basis of this model, the computer simulation looks into the behavior of a two-phase induction servomotor with low inertia when the supply voltages of the two separate windings have the same magnitude but a different frequency, under load and no load operation. The reversing regime is also simulated under unbalanced supply conditions. The results offer the perspective to design electromechanical systems with speed or rotation angle expressed as harmonic, quasi-harmonic or even random variation laws.
 

The Methods of Multi Attribute Analysis in Application to Assess Optimal Factor Combination in One Experiment
D. Randjelovic, C. Dolicanin

Abstract: Experiments are used by scientists to affirm their hypothesis, these experiments are called tests in research, or to choose the best from available possibilities, these experiments are called valuations in research in which group belongs also optimal factor combination choice in one multifactor and often multivariate experiments. For decreasing influence ever present uncontrolled factors i.e. experimental error researchers make different plans. Mathematical instruments of most effective plans for experiment organization are possible to search on the basis of total random distribution, random block distribution and some special organized block distribution while they can most effectively represent complex multifactor and multivariate experiments. Statistic analysis for any experiment plan is very complex in the standard way with analysis of variance and multiple linear regression and especially in the case of the optimal factor combination choice. From other side multiple criteria analysis like modern science discipline enables an easier way to make analysis of results of one experiment just in the case of optimal factor combination choice of one multifactor and multivariate experiment. Therefore authors propose multiple criteria analysis application in analysis of experiment results and in this paper authors consider application of one subgroup of these methods, so called multi attribute decision methods, to which belong and ELECTRA method. One example of multiple attribute analysis application in analysis of results of one experiment is given in the end of this paper.
 

Efficient Mixing in Microchannel by using Magnetic Nanoparticles
T. N. Le, Y. K. Suh, S. Kang

Abstract: Rapid and efficient mixing in microchannel using magnetic nanoparticles has been numerically investigated. The magnetic nanoparticles are introduced into the microchannel and are exerted by the external magnetic force to cause the vortex motion of the fluid for mixing. The velocity field of the flow and trajectories of the particles are solved implicitly by using the Finite Volume Method (FVM). The obtained results illustrate the significant effects of the magnetic actuation force, the switching frequency, number of magnetic nanoparticles on the mixing efficiency. The mixing properties of the flow predicted by numerical simulation are studied under the concentration field, mixing index and Poincare section.
 

Excitation Control of Self Excited Induction Generator using Genetic Algorithm and Artificial Neural Network
Dheeraj Joshi, K. S. Sandhu

Abstract: Induction generators which may be operated in grid or self-excited mode, are found to be successful machines for wind energy conversion. Out of these two self-excited mode is gaining importance due to its ability to convert the wind energy into electrical energy for large variations in operating speed. However it has been found that these machine exhibits a poor voltage regulation. Steady-state analysis of self excited induction generator reveals that such generators are not capable to maintain the terminal voltage and frequency in the absence of expensive controllers. In turn addition of such controllers may result into a fall in popularity of this machine due to its simplicity. Another simple way to control the terminal voltage is through excitation control using series compensation. In this paper artificial intelligent techniques are used to model the control strategy for proper reactive compensation under different operating conditions. Genetic algorithm along with artificial neural network has been proposed to estimate the values of shunt and series excitation capacitance to maintain the terminal and load voltage. Simulated results as found using proposed control technique are verified using experimental results on a test machine. Simulated results are found to be in close agreement with experimental results.
 

    Paper Title, Authors, Abstract (Issue 2, Volume 3, 2009)

Pages

Estimate to the Trajectory of Maneuvering Targets by Combining Sensor Scheduling with Energy Efficient in WSNs
Joy Iong-Zong Chen, Chih-Chung Yu

Abstract: An algorithm by combining sensor scheduling with energy efficient for tracking the maneuvering targets with mobile sensor deployed in WSNs (wireless sensor networks) is proposed to investigate the tracking performance in the article. In order to minimize the estimated error, the sensor sequence and the optimal sensor movement are scheduled previously and determined first. Thus, the sensor scheduling is depending on the results from the evaluation of energy efficient of a sensor node. Moreover, due to the targets is varying with time in the estimation process the EKF (extended Kalman filtering) technique is applied to predict MSE (mean square error) of a predicted target. Finally, simulations by using of the scenario with two and four maneuvering targets tracking are held to validate the accuracy of the proposed algorithm, and the results definitely show the fact that the MSE will decrease when the right way of the sensor scheduling is arranged previously.
 

Time Series Modeling using an Adaptive Gene Expression Programming Algorithm
Alina Barbulescu, Elena Bautu

Abstract: Meteorological time series are characterized by important spatial and temporal variation. Model determination and the prediction of evolution of such series is of high importance for different practical purposes, even if discovering evolution patterns in such series is a very difficult problem. In this article we describe an adaptive evolutionary technique and we apply it for modeling the precipitation and temperatures collected in a region of Romania. The results are promising for the analysis of such time series.
 

A Multi-Item Production Lot Size Inventory Model with Cycle Dependent Parameters
Zaid T. Balkhi, Abdelaziz Foul

Abstract: In this paper, a multi-item production inventory model is considered within a given time horizon that consists of different time periods. For each product, production, demand, and deterioration rates in each period are known. Shortage for each product is allowed but it is completely backlogged . The objective is to find the optimal production and restarting times for each product in each period so that the overall total inventory cost for all products is minimized. In this paper, a formulation of the problem is developed and optimization techniques are performed to show uniqueness and global optimality of the solution.
 

Stochastic Geolithological Reconstruction coupled with Artificial Neural Networks Approach for Hydrogeological Modeling
Claudia Cherubini, Fausta Musci, Nicola Pastore

Abstract: When simulating fluid flow and solute transport a more accurate modeling of the lithologic, geological and structural characters of an aquifer is of extreme importance in order to improve the reliability of the numerical simulations. On the other hand the information available for the setting up of a hydrogeological model is subjected to ambiguities due to not univocal interpretations or to uncertainties linked to the methodologies of measurement of the variables of interest. Therefore, hydrogeological characterization of heterogeneous aquifers, if carried out up to a high degree of detail, should not identify a univocal model but a set of “equifinal” solutions. In the present paper the application of Artificial Neural Network approach coupled with a Nested Sequential Indicator simulation has allowed to obtain the distribution of hydrogeologic parameters that are not only conditioned by the in situ measured values but also by the soft information coming from geolithology. The results show a fairly good relationship between parameters such as Transmissivity and Storage coefficient and the geolithologic architecture of the examined aquifer.
 

Formal Transformation from NFA to Z Notation by Constructing Union of Regular Languages
Nazir Ahmad Zafar, Nabeel Sabir, Amir Ali

Abstract: Capturing functionalities and modeling control behavior are primary requirements in design and development of a complex system. Automata theory plays a vital role in modeling behavior while Z notation is an ideal specification language for describing state space of a system. Consequently, integration of automata and Z notation will be a useful tool facilitating and increasing modeling power for complex systems. Further, nondeterministic finite automata (NFA) may have different implementations and therefore it is needed to verify the transformation from diagrams to code. If we describe formal specification of a given nondeterministic finite automata before implementing then confidence over transformation can be increased. In this paper, we have combined NFA and Z and a linkage is established between these approaches. At this level of integration, we have given a formal procedure to transform NFA to Z. A string accepter is designed and then extended to the language accepter. Finally, NFA accepting union of two regular languages is constructed by describing formal specification of their relationships. The specification is analyzed and validated using Z/EVES tool.
 

Identification of the De-synchronization, Synchronization and Forced Oscillation Phenomenon of a Nonlinear System
Marius-Constantin O.S. Popescu, Onisifor V. Olaru, Valentina E. Balas

Abstract: The phenomena of de-synchronization, synchronization, and forced oscillation has been investigation using describing function theory for a two input and two output nonlinear system containing saturation-type nonlinearities and subjected to high-frequency deterministic signal for the purpose of limit cycle quenching. The analytical results have been compared with the results of digital simulation Matlab-Simulink for a typical example varying the nonlinear element.
 

Equilibrium Dynamic Systems Intelligence
Marius-Constantin O.S. Popescu, Onisifor V. Olaru, Nikos E. Mastorakis

Abstract: Most work in Artificial Intelligence reviews the balance of classic game theory to predict agent behavior in different positions. In this paper we introduce steady competitive analysis. This approach bridges the gap between the standards of desired paths of artificial intelligence, where a strategy must be selected in order to ensure an end result and a balanced analysis. We show that a strategy without risk level is able to guarantee the value obtained in the Nash equilibrium, by more scientific methods of classical computers. Then we will discuss the concept of competitive strategy and illustrate how it is used in a decentralized load balanced position, typical for network problems. In particular, we will show that when there are many agents, it is possible to guarantee an expected final result, which is a 8/9 factor of the final result obtained in the Nash equilibrium. Finally, we will discuss about extending the above concept in Bayesian game and illustrate its use in a basic structure of an auction.
 

Suggestions of Nanotechnology Park and Observations on Industrial Challenges
Ahmet Karakas

Abstract: This study aims to clarify the establishment of the Nanotechnology Park in South Wales .The feasibility is observed through a survey, and reliability of the survey participants is justified with question structures. As the idea is a unique concept, the survey outcome is analyzed together with recent research and it is aimed to fill the gap in the field. Due to the nature of nanotechnology organizations, the challenges of the industry as well as the researchers are observed. Financial and organizational difficulties of the start-up companies are observed, including the constraints of the industry and research institutions. The outline and proposed issues to be considered are addressed for a nanotechnology park. Multi-disciplined field structure is observed and criticized with the current applications. Further research recommendations are pointed out through finalizing this study.
 

Checking Simulations of a Geolithological Model Obtained by Means of Nested Truncated Bigaussian Method
Claudia Cherubini, Fausta Musci, Nicola Pastore

Abstract: Characterizing the spatial distribution of major lithotypes and their relationships is a key aspect in the process of hydrogeological modeling of aquifers in that assignment of lithotypes-specific hydraulic and hydrochemical properties requires the knowledge of the layout of the lithotypes themselves. Truncated bigaussian simulation is a procedure derived from the truncated Gaussian model, used to simulate random sets, and, in particular, variable geological characteristics, expressed as categorical variables. Anyway, in cases of many lithotypes having not homogeneous spatial behaviors, this methodology might not explain at best the relations existing among the lithotypes themselves; a more general method is therefore required to represent this variability. In this paper, that concerns a site whose geologic asset has already been reconstructed, in order to better characterize the aquifer geolithological architecture, nested simulation for a macro-unit of the previously realized geolithologic model has been carried out, together with a check phase of the results obtained by the mentioned simulation. The proposed methodology can represent a useful instrument for the modeling of complex geological layouts other than in the detailed characterizations of hydrogeological studies, for a better interpretation of the complex phenomena that take place in groundwater circulation and contaminant propagation.
 

A Simulation Study of Additive Outlier in ARMA (1, 1) Model
Azami Zaharim, Rafizah Rajali, Raden Mohamad Atok, Ibrahim Mohamed, Khamisah Jafar

Abstract: Abnormal observation due to an isolated incident such as a recording error is known as additive outlier and it is often found in time series. Since extreme value of additive outliers may contribute to the inaccuracy of model specification, proper detection procedure is significant to avoid such error. Equations that explain the nature of an additive outlier and the test statistics pertaining to it are discussed in this article. This is followed by two separate simulation studies that are conducted to investigate the sampling behavior and detection performance of the test statistics in ARMA (1, 1) models. Results for the first simulation study show that the test statistics is an increasing function of sample size. Whilst in the other simulation study we see that the performance of the test statistics improves as large magnitudes of outlier effect are used.