Search results for: Modeling of EHPS system

Authors: Hamidreza Bagheri, Alireza Shariati

Abstract:

There are many difficulties in the purification of raw components and products. However, researchers are seeking better ways for purification. One of the recent methods is extraction using supercritical fluids. In this study, the phase equilibria of benzoic acid -supercritical carbon dioxide system were investigated. Regarding the phase equilibria of this system, the modeling of solid-supercritical fluid behavior was performed using the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) and Peng-Robinson equations of state (PR EoS). For this purpose, five PC-SAFT EoS parameters for pure benzoic acid were obtained using its experimental vapor pressure. Benzoic acid has association sites and the behavior of the benzoic acid-supercritical fluid system was well predicted using both equations of state, while the binary interaction parameter values for PR EoS were negative. Genetic algorithm, which is one of the most accurate global optimization algorithms, was also used to optimize the pure benzoic acid parameters and the binary interaction parameters. The AAD% value for the PC-SAFT EoS, were 0.22 for the carbon dioxide-benzoic acid system.

Keywords: Supercritical fluids, Solubility, Solid, PC-SAFT EoS, Genetic algorithm.

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Authors: Reza Sedaghati, Mojtaba Hakimzadeh, Abdolmohamad Davodi, Navid Javidtash

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The advent of Flexible AC Transmission Systems (FACTS) is giving rise to a new family of power electronic equipment emerging for controlling and optimizing the performance of power system, e.g. STATCOM. Static synchronous Compensator (STATCOM) is a commonly used FACTS device and has been successfully applied in power systems. In this sense, superconducting magnetic energy storage (SMES) in integration with a static synchronous compensator (STATCOM) is capable of supplying power systems with both active and reactive powers simultaneously and very rapidly, and thus is able to enhance the security dramatically. In this paper the structure and characteristics of the STATCOM/SMES is proposed. In addition, using a proper control scheme, STATCOM/ SMES is tested on an IEEE 3-bus system and more effective performance of the presented STATCOM/SMES compensator is evaluated with alone STATCOM through the dynamic simulation by using PSCAD/EMTDC software.

Keywords: STATCOM/SMES, Oscillation Damping, Control, Power System.

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Authors: Rosmina Bustami, Charles Bong, Darrien Mah, Afnie Hamzah, Marina Patrick

Abstract:

The distressing flood scenarios that occur in recent years at the surrounding areas of Sarawak River have left damages of properties and indirectly caused disruptions of productive activities. This study is meant to reconstruct a 100-year flood event that took place in this river basin. Sarawak River Subbasin was chosen and modeled using the one-dimensional hydrodynamic modeling approach using InfoWorks River Simulation (RS), in combination with Geographical Information System (GIS). This produces the hydraulic response of the river and its floodplains in extreme flooding conditions. With different parameters introduced to the model, correlations of observed and simulated data are between 79% – 87%. Using the best calibrated model, flood mitigation structures are imposed along the sub-basin. Analysis is done based on the model simulation results. Result shows that the proposed retention ponds constructed along the sub-basin provide the most efficient reduction of flood by 34.18%.

Keywords: Flood, Flood mitigation structure, InfoWorks RS, Retention pond, Sarawak River sub-basin.

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Authors: M. H. Ayazi, M. Qamari, N.Hedayat, A. Rohani

Abstract:

Simulation of the flow and sedimentation process in the reservoir dams can be made by two methods of physical and mathematical modeling. The study area was within a region which ranged from the Jelogir hydrometric station to the Karkheh reservoir dam aimed at investigating the effects of stream tubes on the GSTARS-3 model behavior. The methodologies was to run the model based on 5 stream tubes in order to observe the influence of each scenario on longitudinal profiles, cross-section, flow velocity and bed load sediment size. Results further suggest that the use of two stream tubes or more which result in the semi-two-dimensional model will yield relatively closer results to the observational data than a singular stream tube modeling. Moreover, the results of modeling with three stream tubes shown to yield a relatively close results with the observational data. The overall conclusion of the paper is with applying various stream tubes; it would be possible to yield a significant influence on the modeling behavior Vis-a Vis the bed load sediment size.

Keywords: Karkheh, stream tubes, GSTARS-3 Model, Jelogir hydrometric station.

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Authors: Seyed Abolhasan Naeini, M. Mortezaee

Abstract:

Various factors such as the method of installation, the pile type, the pile material and the pile shape, can affect the final bearing capacity of a pile executed in the soil; among them, the method of installation is of special importance. The physical modeling is among the best options in the laboratory study of the piles behavior. Therefore, the current paper first presents and reviews the frustum confining vessel (FCV) as a suitable tool for physical modeling of deep foundations. Then, by describing the loading tests of two open-ended and closed-end steel piles, each of which has been performed in two methods, “with displacement" and "without displacement", the effect of end conditions and installation method on the final bearing capacity of the pile is investigated. The soil used in the current paper is silty sand of Firuzkuh, Iran. The results of the experiments show that in general the without displacement installation method has a larger bearing capacity in both piles, and in a specific method of installation the closed ended pile shows a slightly higher bearing capacity.

Keywords: physical modeling, frustum confining vessel, pile, bearing capacity, installation method

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Authors: Ε. Giovanis

Abstract:

In this paper we propose and examine an Adaptive Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition Autoregressive (STAR) modeling. Because STAR models follow fuzzy logic approach, in the non-linear part fuzzy rules can be incorporated or other training or computational methods can be applied as the error backpropagation algorithm instead to nonlinear squares. Furthermore, additional fuzzy membership functions can be examined, beside the logistic and exponential, like the triangle, Gaussian and Generalized Bell functions among others. We examine two macroeconomic variables of US economy, the inflation rate and the 6-monthly treasury bills interest rates.

Keywords: Forecasting, Neuro-Fuzzy, Smoothing transition, Time-series

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Authors: Y. N. Phang, E. F. Loh

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Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling overdispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling overdispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling overdispered medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling overdispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling overdispersed medical count data when ZIP and ZINB are inadequate.

Keywords: Zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit.

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Authors: S. Kulsri, M. Jaroensutasinee, K. Jaroensutasinee

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We developed a new method based on quasimolecular modeling to simulate the cavity flow in three cavity shapes: rectangular, half-circular and bucket beer in cgs units. Each quasi-molecule was a group of particles that interacted in a fashion entirely analogous to classical Newtonian molecular interactions. When a cavity flow was simulated, the instantaneous velocity vector fields were obtained by using an inverse distance weighted interpolation method. In all three cavity shapes, fluid motion was rotated counter-clockwise. The velocity vector fields of the three cavity shapes showed a primary vortex located near the upstream corners at time t ~ 0.500 s, t ~ 0.450 s and t ~ 0.350 s, respectively. The configurational kinetic energy of the cavities increased as time increased until the kinetic energy reached a maximum at time t ~ 0.02 s and, then, the kinetic energy decreased as time increased. The rectangular cavity system showed the lowest kinetic energy, while the half-circular cavity system showed the highest kinetic energy. The kinetic energy of rectangular, beer bucket and half-circular cavities fluctuated about stable average values 35.62 x 103, 38.04 x 103 and 40.80 x 103 ergs/particle, respectively. This indicated that the half-circular shapes were the most suitable shape for a shrimp pond because the water in shrimp pond flows best when we compared with rectangular and beer bucket shape.

Keywords: Quasi-molecular modelling, particle modelling, lid driven cavity flow.

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Authors: T.C. Manjunath, Student Member, B. Bandyopadhyay

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This paper features the mathematical modeling of a single input single output based Timoshenko smart beam. Further, this mathematical model is used to design a multirate output feedback based discrete sliding mode controller using Bartoszewicz law to suppress the flexural vibrations. The first 2 dominant vibratory modes is retained. Here, an application of the discrete sliding mode control in smart systems is presented. The algorithm uses a fast output sampling based sliding mode control strategy that would avoid the use of switching in the control input and hence avoids chattering. This method does not need the measurement of the system states for feedback as it makes use of only the output samples for designing the controller. Thus, this methodology is more practical and easy to implement.

Keywords: Smart structure, Timoshenko beam theory, Discretesliding mode control, Bartoszewicz law, Finite Element Method, State space model, Vibration control, Mathematical model, SISO.

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Authors: Parvinder S. Sandhu, Hardeep Singh

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Automatic reusability appraisal could be helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this paper, we have mentioned two-tier approach by studying the structural attributes as well as usability or relevancy of the component to a particular domain. Latent semantic analysis is used for the feature vector representation of various software domains. It exploits the fact that FeatureVector codes can be seen as documents containing terms -the idenifiers present in the components- and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. Further, we devised Neuro- Fuzzy hybrid Inference System, which takes structural metric values as input and calculates the reusability of the software component. Decision tree algorithm is used to decide initial set of fuzzy rules for the Neuro-fuzzy system. The results obtained are convincing enough to propose the system for economical identification and retrieval of reusable software components.

Keywords: Clustering, ID3, LSA, Neuro-fuzzy System, SVD

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Authors: Aini Hussain, M A Hannan, Azah Mohamed, Hilmi Sanusi, Burhanuddin Yeop Majlis

Abstract:

Airbag deployment has been known to be responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decisions. Therefore, the authorities and industries have been looking for more innovative and intelligent products to be realized for future enhancements in the vehicle safety systems (VSSs). Although the VSSs technologies have advanced considerably, they still face challenges such as how to avoid unnecessary and untimely airbag deployments that can be hazardous and fatal. Currently, most of the existing airbag systems deploy without regard to occupant size and position. As such, this paper will focus on the occupant and crash sensing performances due to frontal collisions for the new breed of so called smart airbag systems. It intends to provide a thorough discussion relating to the occupancy detection, occupant size classification, occupant off-position detection to determine safe distance zone for airbag deployment, crash-severity analysis and airbag decision algorithms via a computer modeling. The proposed system model consists of three main modules namely, occupant sensing, crash severity analysis and decision fusion. The occupant sensing system module utilizes the weight sensor to determine occupancy, classify the occupant size, and determine occupant off-position condition to compute safe distance for airbag deployment. The crash severity analysis module is used to generate relevant information pertinent to airbag deployment decision. Outputs from these two modules are fused to the decision module for correct and efficient airbag deployment action. Computer modeling work is carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes.

Keywords: Crash severity analysis, occupant size classification, smart airbag, vehicle safety system.

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Authors: Farhan Beg

Abstract:

The Indian subcontinent is facing a massive challenge with regards to the energy security in member countries, i.e. providing a reliable source of electricity to facilitate development across various sectors of the economy and thereby achieve the developmental targets it has set for itself. A highly precarious situation exists in the subcontinent which is observed in the series of system failures which most of the times leads to system collapses-blackouts. To mitigate the issues related with energy security as well as keep in check the increasing supply demand gap, a possible solution that stands in front of the subcontinent is the deployment of an interconnected electricity ‘Supergrid’ designed to carry huge quanta of power across the sub continent as well as provide the infra structure for RES integration. This paper assesses the need and conditions for a Supergrid deployment and consequently proposes a meshed topology based on VSC HVDC converters for the Supergrid modeling.

Keywords: Super grid, Wind and Solar Energy, HVDC, Electricity management, Load Flow Analysis.

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Authors: Zhidong Zhang, Zhi-Chao Zhang

Abstract:

This study examined the mental health and behavioral problems in early adolescence with the instrument of Achenbach System of Empirically Based Assessment (ASEBA). The purpose of the study was stratified sampling method was used to collect data from 1975 participants. Multiple regression models and hierarchical regression models were applied to examine the relations between the background variables and internalizing problems, and the ones between students’ performance and internalizing problems. The results indicated that several background variables as predictors could significantly predict the anxious/depressed problem; reading and social study scores could significantly predict the anxious/depressed problem. However the class as a hierarchical macro factor did not indicate the significant effect. In brief, the majority of these models represented that the background variables, behaviors and academic performance were significantly related to the anxious/depressed problem.

Keywords: Behavioral problems, anxious/depression problems, empirical-based assessment, hierarchical modeling.

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Authors: J. Fernandez de Canete, S. Fernandez-Calvo, I. García-Moral

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This paper presents and discusses the application of the object-oriented modelling software SIMSCAPE to hydraulic systems, with particular reference to multivariable proportional-integral-derivative (PID) control. As a result, a particular modelling approach of a double cylinder-piston coupled system is proposed and motivated, and the SIMULINK based PID tuning tool has also been used to select the proper controller parameters. The paper demonstrates the usefulness of the object-oriented approach when both physical modelling and control are tackled.

Keywords: Object-oriented modeling, multivariable hydraulic system, multivariable PID control, computer simulation.

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Authors: M. Behbahani-Nejad, Y. Shekari

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A reduced order modeling approach for natural gas transient flow in pipelines is presented. The Euler equations are considered as the governing equations and solved numerically using the implicit Steger-Warming flux vector splitting method. Next, the linearized form of the equations is derived and the corresponding eigensystem is obtained. Then, a few dominant flow eigenmodes are used to construct an efficient reduced-order model. A well-known test case is presented to demonstrate the accuracy and the computational efficiency of the proposed method. The results obtained are in good agreement with those of the direct numerical method and field data. Moreover, it is shown that the present reduced-order model is more efficient than the conventional numerical techniques for transient flow analysis of natural gas in pipelines.

Keywords: Eigenmode, Natural Gas, Reduced Order Modeling, Transient Flow.

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Authors: Yuan L. Lai, Yong R. Chen, Jui P. Hung, Tzuo L. Luo, Hsi H. Hsiao

Abstract:

Chatter vibration has been a troublesome problem for a machine tool toward the high precision and high speed machining. Essentially, the machining performance is determined by the dynamic characteristics of the machine tool structure and dynamics of cutting process. Therefore the dynamic vibration behavior of spindle tool system greatly determines the performance of machine tool. The purpose of this study is to investigate the influences of the machine frame structure on the dynamic frequency of spindle tool unit through finite element modeling approach. To this end, a realistic finite element model of the vertical milling system was created by incorporated the spindle-bearing model into the spindle head stock of the machine frame. Using this model, the dynamic characteristics of the milling machines with different structural designs of spindle head stock and identical spindle tool unit were demonstrated. The results of the finite element modeling reveal that the spindle tool unit behaves more compliant when the excited frequency approaches the natural mode of the spindle tool; while the spindle tool show a higher dynamic stiffness at lower frequency that may be initiated by the structural mode of milling head. Under this condition, it is concluded that the structural configuration of spindle head stock associated with the vertical column of milling machine plays an important role in determining the machining dynamics of the spindle unit.

Keywords: Machine tools, Compliance, Frequency response function, Machine frame structure, Spindle unit

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Authors: Hanaa T. El-Madany, Faten H. Fahmy, Ninet M. A. El-Rahman, Hassen T. Dorrah

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Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.

Keywords: Satellite, neural network, MATLAB, power system.

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Authors: Yuanyuan Chai, Limin Jia, Zundong Zhang

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Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.

Keywords: Fuzzy neural networks, Mamdani fuzzy inference, M-ANFIS

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Authors: Jouhra Dad, Mohammed Ouali, Yahia Lebbah

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Urban road network traffic has become one of the most studied research topics in the last decades. This is mainly due to the enlargement of the cities and the growing number of motor vehicles traveling in this road network. One of the most sensitive problems is to verify if the network is congestion-free. Another related problem is the automatic reconfiguration of the network without building new roads to alleviate congestions. These problems require an accurate model of the traffic to determine the steady state of the system. An alternative is to simulate the traffic to see if there are congestions and when and where they occur. One key issue is to find an adequate model for road intersections. Once the model established, either a large scale model is built or the intersection is represented by its performance measures and simulation for analysis. In both cases, it is important to seek the queueing model to represent the road intersection. In this paper, we propose to model the road intersection as a BCMP queueing network and we compare this analytical model against a simulation model for validation.

Keywords: Queueing theory, transportation systems, BCMPqueueing network, performance measures, modeling, simulation

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Authors: R. Walha, F. Drira, H. El-Abed, A. M. Alimi

Abstract:

The Automatic Speech Recognition (ASR) applied to Arabic language is a challenging task. This is mainly related to the language specificities which make the researchers facing multiple difficulties such as the insufficient linguistic resources and the very limited number of available transcribed Arabic speech corpora. In this paper, we are interested in the development of a HMM-based ASR system for Standard Arabic (SA) language. Our fundamental research goal is to select the most appropriate acoustic parameters describing each audio frame, acoustic models and speech recognition unit. To achieve this purpose, we analyze the effect of varying frame windowing (size and period), acoustic parameter number resulting from features extraction methods traditionally used in ASR, speech recognition unit, Gaussian number per HMM state and number of embedded re-estimations of the Baum-Welch Algorithm. To evaluate the proposed ASR system, a multi-speaker SA connected-digits corpus is collected, transcribed and used throughout all experiments. A further evaluation is conducted on a speaker-independent continue SA speech corpus. The phonemes recognition rate is 94.02% which is relatively high when comparing it with another ASR system evaluated on the same corpus.

Keywords: ASR, HMM, acoustical analysis, acoustic modeling, Standard Arabic language

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Authors: Jonah Gamba

Abstract:

In this paper the problem of estimating the time delay between two spatially separated noisy sinusoidal signals by system identification modeling is addressed. The system is assumed to be perturbed by both input and output additive white Gaussian noise. The presence of input noise introduces bias in the time delay estimates. Normally the solution requires a priori knowledge of the input-output noise variance ratio. We utilize the cascade of a self-tuned filter with the time delay estimator, thus making the delay estimates robust to input noise. Simulation results are presented to confirm the superiority of the proposed approach at low input signal-to-noise ratios.

Keywords: LMS algorithm, Self-tuned filter, Systemidentification, Time delay estimation, .

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Authors: A. Saghaei, R. Ghasemi

Abstract:

Through 1980s, management accounting researchers described the increasing irrelevance of traditional control and performance measurement systems. The Balanced Scorecard (BSC) is a critical business tool for a lot of organizations. It is a performance measurement system which translates mission and strategy into objectives. Strategy map approach is a development variant of BSC in which some necessary causal relations must be established. To recognize these relations, experts usually use experience. It is also possible to utilize regression for the same purpose. Structural Equation Modeling (SEM), which is one of the most powerful methods of multivariate data analysis, obtains more appropriate results than traditional methods such as regression. In the present paper, we propose SEM for the first time to identify the relations between objectives in the strategy map, and a test to measure the importance of relations. In SEM, factor analysis and test of hypotheses are done in the same analysis. SEM is known to be better than other techniques at supporting analysis and reporting. Our approach provides a framework which permits the experts to design the strategy map by applying a comprehensive and scientific method together with their experience. Therefore this scheme is a more reliable method in comparison with the previously established methods.

Keywords: BSC, SEM, Strategy map.

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Authors: Erisasadat Sahebzamani, Núria Forcada, Francisco Lendinez

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Global climate change has become increasingly problematic over the past few decades. The construction industry has contributed to greenhouse gas emissions in recent decades. Considering these issues and the high demand for materials in the construction industry, Circular Economy (CE) is considered necessary to keep materials in the loop and extend their useful lives. By providing tangible benefits, Construction 4.0 facilitates the adoption of CE by reducing waste, updating standard work, sharing knowledge, and increasing transparency and stability. This study aims to present a framework for integrating CE and digital tools like Building Information Modeling (BIM) and Virtual Reality (VR) to examine the impact on the construction industry based on stakeholders' perspectives.

Keywords: Circular Economy, Building Information Modeling, Virtual Reality, stakeholder engagement.

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Authors: Abeer Samy, Oliver C. Saavedra Valeriano, Abdelazim Negm

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The Blue Nile Basin is the most important tributary of the Nile River. Egypt and Sudan are almost dependent on water originated from the Blue Nile. This multi-dependency creates conflicts among the three countries Egypt, Sudan, and Ethiopia making the management of these conflicts as an international issue. Good assessment of the water resources of the Blue Nile is an important to help in managing such conflicts. Hydrological models are good tool for such assessment. This paper presents a critical review of the nature and variability of the climate and hydrology of the Blue Nile Basin as a first step of using hydrological modeling to assess the water resources of the Blue Nile. Many several attempts are done to develop basin-scale hydrological modeling on the Blue Nile. Lumped and semi distributed models used averages of meteorological inputs and watershed characteristics in hydrological simulation, to analyze runoff for flood control and water resource management. Distributed models include the temporal and spatial variability of catchment conditions and meteorological inputs to allow better representation of the hydrological process. The main challenge of all used models was to assess the water resources of the basin is the shortage of the data needed for models calibration and validation. It is recommended to use distributed model for their higher accuracy to cope with the great variability and complexity of the Blue Nile basin and to collect sufficient data to have more sophisticated and accurate hydrological modeling.

Keywords: Blue Nile Basin, Climate Change, Hydrological Modeling, Watershed.

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Authors: Thiang, Handry Khoswanto, Rendy Pangaldus

Abstract:

Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.

Keywords: Artificial neural network, back propagation, inverse kinematics, manipulator, robot.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

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In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is observed that the proposed algorithm improves the recognition accuracy and F-measure up to 3% as compared to that of the existing algorithms for the speech signal from various speakers. In addition, it reduces the Word Error Rate, Error Rate and Error up to 4% as compared to that of the existing algorithms. In all aspects, the proposed hybrid modeling for Tamil speech recognition provides the significant improvements for speechto- text conversion in various applications.

Keywords: Speech Segmentation, Feature Extraction, Clustering, HMM, EM-GMM, CSR.

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Authors: Enrico Rukzio, George N. Prezerakos, Giovanni Cortese, Eleftherios Koutsoloukas, Sofia Kapellaki

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The paper focuses on the area of context modeling with respect to the specification of context-aware systems supporting ubiquitous applications. The proposed approach, followed within the SIMPLICITY IST project, uses a high-level system ontology to derive context models for system components which consequently are mapped to the system's physical entities. For the definition of user and device-related context models in particular, the paper suggests a standard-based process consisting of an analysis phase using the Common Information Model (CIM) methodology followed by an implementation phase that defines 3GPP based components. The benefits of this approach are further depicted by preliminary examples of XML grammars defining profiles and components, component instances, coupled with descriptions of respective ubiquitous applications.

Keywords: 3GPP, context, context-awareness, context model, information model, user model, XML

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Authors: Akbar Zamaniyan, Alireza Behroozsarand, Hadi Ebrahimi

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Partial oxidation (POX) of light hydrocarbons (e.g. methane) is occurred in the first part of the autothermal reformer (ATR). The results of the detailed modeling of the reformer based on the thermodynamic model of the POX and 1D heterogeneous catalytic model for the fixed bed section are considered here. According to the results, the overall performance of the ATR can be improved by changing the important feed parameters.

Keywords: Autothermal Reformer, Partial Oxidation, Mathematical Modeling, Process Simulation, Syngas.

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Authors: M. Ghareh, L. Sepahi

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Temperature rise in a transformer depends on variety of parameters such as ambient temperature, output current and type of the core. Considering these parameters, temperature rise estimation is still complicated procedure. In this paper, we present a new model based on simple electrical equivalent circuit. This method avoids the complication associated to accurate estimation and is in very good agreement with practice.

Keywords: Thermal modeling, temperature rise, equivalent thermal circuit.

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

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This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory. The proposed approach is based on modeling the potential function associated with each clique shape of the GMRF model as a Gaussian-shaped kernel. In turn, the energy function of the GMRF will be in the form of a weighted sum of Gaussian kernels. This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function. The approach has been tested on synthetic texture images and is shown to provide satisfactory results in retrieving the synthesizing parameters.

Keywords: Image Modeling, MRF, Parameters Estimation, SVM Learning.

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