Search results for: Transfer function.Mean of Squared Error

Authors: Hicham Gourgue, Ahmed Aharoune, Ahmed Ihlal

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We present in this work our model of road traffic emissions (line sources) and dispersion of these emissions, named DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission Model). In its emission part, this model was designed to keep the consistent bottom-up and top-down approaches. It also allows to generate emission inventories from reduced input parameters being adapted to existing conditions in Morocco and in the other developing countries. While several simplifications are made, all the performance of the model results are kept. A further important advantage of the model is that it allows the uncertainty calculation and emission rate uncertainty according to each of the input parameters. In the dispersion part of the model, an improved line source model has been developed, implemented and tested against a reference solution. It provides improvement in accuracy over previous formulas of line source Gaussian plume model, without being too demanding in terms of computational resources. In the case study presented here, the biggest errors were associated with the ends of line source sections; these errors will be canceled by adjacent sections of line sources during the simulation of a road network. In cases where the wind is parallel to the source line, the use of the combination discretized source and analytical line source formulas minimizes remarkably the error. Because this combination is applied only for a small number of wind directions, it should not excessively increase the calculation time.

Keywords: Air pollution, dispersion, emissions, line sources, road traffic, urban transport.

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Authors: Maninder Pal

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Medical digital images usually have low resolution because of nature of their acquisition. Therefore, this paper focuses on zooming these images to obtain better level of information, required for the purpose of medical diagnosis. For this purpose, a strategy for selecting pixels in zooming operation is proposed. It is based on the principle of analog clock and utilizes a combination of point and neighborhood image processing. In this approach, the hour hand of clock covers the portion of image to be processed. For alignment, the center of clock points at middle pixel of the selected portion of image. The minute hand is longer in length, and is used to gain information about pixels of the surrounding area. This area is called neighborhood pixels region. This information is used to zoom the selected portion of the image. The proposed algorithm is implemented and its performance is evaluated for many medical images obtained from various sources such as X-ray, Computerized Tomography (CT) scan and Magnetic Resonance Imaging (MRI). However, for illustration and simplicity, the results obtained from a CT scanned image of head is presented. The performance of algorithm is evaluated in comparison to various traditional algorithms in terms of Peak signal-to-noise ratio (PSNR), maximum error, SSIM index, mutual information and processing time. From the results, the proposed algorithm is found to give better performance than traditional algorithms.

Keywords: Zooming, interpolation, medical images, resolution.

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Authors: Kazuo Komatsu, Hitoshi Takata

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In this paper we consider a nonlinear control design for nonlinear systems by using two-stage formal linearization and twotype LQ controls. The ordinary LQ control is designed on almost linear region around the steady state point. On the other region, another control is derived as follows. This derivation is based on coordinate transformation twice with respect to linearization functions which are defined by polynomials. The linearized systems can be made up by using Taylor expansion considered up to the higher order. To the resulting formal linear system, the LQ control theory is applied to obtain another LQ control. Finally these two-type LQ controls are smoothly united to form a single nonlinear control. Numerical experiments indicate that this control show remarkable performances for a nonlinear system.

Keywords: Formal Linearization, LQ Control, Nonlinear Control, Taylor Expansion, Zero Function.

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Authors: M. Sedighizadeh, A. Rezazadeh

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In this paper a PID control strategy using neural network adaptive RASP1 wavelet for WECS-s control is proposed. It is based on single layer feedforward neural networks with hidden nodes of adaptive RASP1 wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. This particular neuro PID controller assumes a certain model structure to approximately identify the system dynamics of the unknown plant (WECS-s) and generate the control signal. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

Keywords: Adaptive PID Control, RASP1 Wavelets, WindEnergy Conversion Systems.

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Authors: Negar Khezri, Golnaz Yaghoubnezhadzanganeh, Amirreza Attarzadeh

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Type 2 Diabetes (T2DM) and Alzheimer's disease (AD) are two main health problems influencing millions of people in the world. Neuron loss and synaptic impairment that interfere with cognition and memory cause for the behavioral indications of AD. While it is now accepted that insulin has central neuromodulatory purpose, it was contemplated for many years that brain is insusceptible to insulin, involving its function in memory and learning, which are impaired in AD. The common characteristics of both AD and T2D are impaired insulin signaling, oxidative stress, the excitation of inflammatory pathways and unqualified glucose metabolism. This review summarizes how the recognition of these mechanisms may lead to the development of alternative therapeutic approaches. Here we summarize how the recognition of these mechanisms may lead to the development of alternative therapeutic approaches.

Keywords: Alzheimer’s disease, diabetes, insulin resistance, neurodegenerative.

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: Segmentation, color-texture, neural networks, fractal, watershed.

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Authors: Mohammad Heidari

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Horizontal wells are proven to be better producers because they can be extended for a long distance in the pay zone. Engineers have the technical means to forecast the well productivity for a given horizontal length. However, experiences have shown that the actual production rate is often significantly less than that of forecasted. It is a difficult task, if not impossible to identify the real reason why a horizontal well is not producing what was forecasted. Often the source of problem lies in the drilling of horizontal section such as permeability reduction in the pay zone due to mud invasion or snaky well patterns created during drilling. Although drillers aim to drill a constant inclination hole in the pay zone, the more frequent outcome is a sinusoidal wellbore trajectory. The two factors, which play an important role in wellbore tortuosity, are the inclination and side force at bit. A constant inclination horizontal well can only be drilled if the bit face is maintained perpendicular to longitudinal axis of bottom hole assembly (BHA) while keeping the side force nil at the bit. This approach assumes that there exists no formation force at bit. Hence, an appropriate BHA can be designed if bit side force and bit tilt are determined accurately. The Artificial Neural Network (ANN) is superior to existing analytical techniques. In this study, the neural networks have been employed as a general approximation tool for estimation of the bit side forces. A number of samples are analyzed with ANN for parameters of bit side force and the results are compared with exact analysis. Back Propagation Neural network (BPN) is used to approximation of bit side forces. Resultant low relative error value of the test indicates the usability of the BPN in this area.

Keywords: Artificial Neural Network, BHA, Horizontal Well, Stabilizer.

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Authors: Teerapun Saeheaw, Nivit Charoenchai, Wichai Chattinnawat

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This paper proposes a meta-heuristic called Ant Colony Optimization to solve multi-objective production problems. The multi-objective function is to minimize lead time and work in process. The problem is related to the decision variables, i.e.; distance and process time. According to decision criteria, the mathematical model is formulated. In order to solve the model an ant colony optimization approach has been developed. The proposed algorithm is parameterized by the number of ant colonies and the number of pheromone trails. One example is given to illustrate the effectiveness of the proposed model. The proposed formulations; Max-Min Ant system are then used to solve the problem and the results evaluate the performance and efficiency of the proposed algorithm using simulation.

Keywords: Ant colony optimization, multi-objective problems.

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Authors: Sang-Rak Kim, Jea-Yong Park, Won-Goo Lee, Jin-Shik Yu, Seog-Young Han

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This paper presents a Reliability-Based Topology Optimization (RBTO) based on Evolutionary Structural Optimization (ESO). An actual design involves uncertain conditions such as material property, operational load and dimensional variation. Deterministic Topology Optimization (DTO) is obtained without considering of the uncertainties related to the uncertainty parameters. However, RBTO involves evaluation of probabilistic constraints, which can be done in two different ways, the reliability index approach (RIA) and the performance measure approach (PMA). Limit state function is approximated using Monte Carlo Simulation and Central Composite Design for reliability analysis. ESO, one of the topology optimization techniques, is adopted for topology optimization. Numerical examples are presented to compare the DTO with RBTO.

Keywords: Evolutionary Structural Optimization, PerformanceMeasure Approach, Reliability-Based Topology Optimization, Reliability Index Approach.

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Authors: Clio G. Vossou, Ioannis N. Koukoulis, Christopher G. Provatidis

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The present work encounters the solution of the defect identification problem with the use of an evolutionary algorithm combined with a simplex method. In more details, a Matlab implementation of Genetic Algorithms is combined with a Simplex method in order to lead to the successful identification of the defect. The influence of the location and the orientation of the depressed ellipsoidal flaw was investigated as well as the use of different amount of static data in the cost function. The results were evaluated according to the ability of the simplex method to locate the global optimum in each test case. In this way, a clear impression regarding the performance of the novel combination of the optimization algorithms, and the influence of the geometrical parameters of the flaw in defect identification problems was obtained.

Keywords: Defect identification, genetic algorithms, optimization.

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Authors: K. Cormican, G. Coppola, S. Farina

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Knowledge management is a critical component of competitive success in service organizations. Knowledge management centers on creating new knowledge and utilizing existing knowledge. While utilizing existing knowledge relates to input and control and can lead to a reduction in costs; creating new knowledge relates to output and growth and can lead to an increase in revenue. Therefore managers must ensure that they can successfully optimize the knowledge and talent in their organizations. To do this they and must try to develop an environment that promotes the generation, acquisition, transfer and use of valuable knowledge in creative ways. However knowledge management is complex and diverse. Research suggests that organizations in general and SMEs in particular are finding it difficult to implement successful knowledge management initiatives. Our research attempts to understand whether organizations are adopting best practice initiatives in their organizations. This paper presents findings from an exploratory study of 139 SMEs operating in the tourism sector across Europe. The goals of the survey is to assess the level of awareness of knowledge and talent management strategies and methodologies and to determine whether the responding companies implement best practice knowledge management initiatives in their organizations Analysis of the findings from the study are presented and discussed.

Keywords: service sector, small enterprise, success factors, survey

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Authors: Juan H. Sosa-Arnao, Daniel J. O. Ferreira, Caice G. Santos, Justo E. Alvarez, Leonardo P. Rangel, Song W. Park

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A comprehensive CFD model is developed to represent heterogeneous combustion and two burner designs of supply sugar-cane bagasse into a furnace. The objective of this work is to compare the insertion and burning of a Brazilian south-eastern sugar-cane bagasse using a new swirl burner design against an actual geometry under operation. The new design allows control the particles penetration and scattering inside furnace by adjustment of axial/tangential contributions of air feed without change their mass flow. The model considers turbulence using RNG k-, combustion using EDM, radiation heat transfer using DTM with 16 ray directions and bagasse particle tracking represented by Schiller-Naumann model. The obtained results are favorable to use of new design swirl burner because its axial/tangential control promotes more penetration or more scattering than actual design and allows reproduce the actual design operation without change the overall mass flow supply.

Keywords: Comprehensive CFD model, sugar-cane bagasse combustion, swirl burner.

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Authors: Toshihiko Matsuka, James E. Corter

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Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.

Keywords: category learning, cognitive modeling, radial basis function, stochastic optimization.

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Authors: M. Hanif, M. Salik

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In the present research work we present the optical emission studies of the Indium (In) – Tin (Sn) plasma produced by the first (1064 nm) harmonic of an Nd: YAG nanosecond pulsed laser. The experimentally observed line profiles of neutral Indium (In I) and Tin (SnI) are used to extract the electron temperature (Te) using the Boltzmann plot method. Whereas, the electron number density (Ne) has been determined from the Stark broadening line profile method. The Te is calculated by varying the distance from the target surface along the line of propagation of plasma plume and also by varying the laser irradiance. Beside we have studied the variation of Ne as a function of laser irradiance as well as its variation with distance from the target surface.

Keywords: Indium – Tin plasma, laser ablation, optical emission spectroscopy, electron temperature, and electron number density.

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Authors: J. P. Dubois, Omar M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool developed to a more complex concept of structural risk minimization (SRM). In this paper, SVM is applied to signal detection in communication systems in the presence of channel noise in various environments in the form of Rayleigh fading, additive white Gaussian background noise (AWGN), and interference noise generalized as additive color Gaussian noise (ACGN). The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these advanced stochastic noise models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to conventional binary signaling optimal model-based detector driven by binary phase shift keying (BPSK) modulation. We show that the SVM performance is superior to that of conventional matched filter-, innovation filter-, and Wiener filter-driven detectors, even in the presence of random Doppler carrier deviation, especially for low SNR (signal-to-noise ratio) ranges. For large SNR, the performance of the SVM was similar to that of the classical detectors. However, the convergence between SVM and maximum likelihood detection occurred at a higher SNR as the noise environment became more hostile.

Keywords: Colour noise, Doppler shift, innovation filter, least square-support vector machine, matched filter, Rayleigh fading, Wiener filter.

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Authors: Marina Velikova, Hennie Daniels, Ad Feelders

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In many applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. Here we consider partially monotone problems, where the target variable depends monotonically on some of the predictor variables but not all. We propose an approach to build partially monotone models based on the convolution of monotone neural networks and kernel functions. The results from simulations and a real case study on house pricing show that our approach has significantly better performance than partially monotone linear models. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: Mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: Homa Torabizadeh

Abstract:

This study proposes a basic molecular formula for all proteins. A total of 10,739 proteins belonging to 9 different protein groups classified on the basis of their functions were selected randomly. They included enzymes, storage proteins, hormones, signalling proteins, structural proteins, transport proteins, immunoglobulins or antibodies, motor proteins and receptor proteins. After obtaining the protein molecular formula using the ProtParam tool, the H/C, N/C, O/C, and S/C ratios were determined for each randomly selected sample. In this case, H, N, O, and S coefficients were specified per carbon atom. Surprisingly, the results demonstrated that H, N, O, and S coefficients for all 10,739 proteins are similar and highly correlated. This study demonstrates that despite differences in the structure and function, all known proteins have a similar basic molecular formula CnH1.58 ± 0.015nN0.28 ± 0.005nO0.30 ± 0.007nS0.01 ± 0.002n. The total correlation between all coefficients was found to be 0.9999.

Keywords: Protein molecular formula, Basic unit formula, Protparam tool.

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Authors: Samir Abdelhamid, Noureddine Bouguechal

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We report in this paper the procedure of a system of automatic speech recognition based on techniques of the dynamic programming. The technique of temporal retiming is a technique used to synchronize between two forms to compare. We will see how this technique is adapted to the field of the automatic speech recognition. We will expose, in a first place, the theory of the function of retiming which is used to compare and to adjust an unknown form with a whole of forms of reference constituting the vocabulary of the application. Then we will give, in the second place, the various algorithms necessary to their implementation on machine. The algorithms which we will present were tested on part of the corpus of words in Arab language Arabdic-10 [4] and gave whole satisfaction. These algorithms are effective insofar as we apply them to the small ones or average vocabularies.

Keywords: Continuous speech recognition, temporal retiming, phonetic decoding, algorithms, vocal signal, dynamic programming.

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Authors: So-Jeong Youn, Kyung-Whan Oh

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The human friendly interaction is the key function of a human-centered system. Over the years, it has received much attention to develop the convenient interaction through intention recognition. Intention recognition processes multimodal inputs including speech, face images, and body gestures. In this paper, we suggest a novel approach of intention recognition using a graph representation called Intention Graph. A concept of valid intention is proposed, as a target of intention recognition. Our approach has two phases: goal recognition phase and intention recognition phase. In the goal recognition phase, we generate an action graph based on the observed actions, and then the candidate goals and their plans are recognized. In the intention recognition phase, the intention is recognized with relevant goals and user profile. We show that the algorithm has polynomial time complexity. The intention graph is applied to a simple briefcase domain to test our model.

Keywords: Intention recognition, intention, graph, HCI.

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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: Yew Mun Hung, Ching Sze Lim, Tiew Wei Ting, Ningqun Guo

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The effect of streamwise conduction on the thermal characteristics of forced convection for nanofluidic flow in rectangular microchannel heat sinks under isothermal wall has been investigated. By applying the fin approach, models with and without streamwise conduction term in the energy equation were developed for hydrodynamically and thermally fully-developed flow. These two models were solved to obtain closed form analytical solutions for the nanofluid and solid wall temperature distributions and the analysis emphasized details of the variations induced by the streamwise conduction on the nanofluid heat transport characteristics. The effects of the Peclet number, nanoparticle volume fraction, thermal conductivity ratio on the thermal characteristics of forced convection in microchannel heat sinks are analyzed. Due to the anomalous increase in the effective thermal conductivity of nanofluid compared to its base fluid, the effect of streamwise conduction is expected to be more significant. This study reveals the significance of the effect of streamwise conduction under certain conditions of which the streamwise conduction should not be neglected in the forced convective heat transfer analysis of microchannel heat sinks.

Keywords: fin approach, microchannel heat sink, nanofluid, streamwise conduction

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Authors: Basheer M. Al-Maqaleh, Kamal K. Bharadwaj

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Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Genetic Programming, Hierarchy, Knowledge Discovery in Database, Subsumption Matrix.

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Authors: Keunhong Chae, Youngpo Lee, Seokho Yoon

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In cognitive radio (CR) systems, the primary user (PU) signal would randomly depart or arrive during the sensing period of a CR user, which is referred to as the high traffic environment. In this paper, we propose a novel spectrum sensing scheme based on the cyclostationarity of PU signals in high traffic environments. Specifically, we obtain a test statistic by applying an estimate of spectral autocoherence function of the PU signal to the generalized- likelihood ratio. From numerical results, it is confirmed that the proposed scheme provides a better spectrum sensing performance compared with the conventional spectrum sensing scheme based on the energy of the PU signals in high traffic environments.

Keywords: Spectrum sensing, cyclostationarity, high traffic environments.

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Authors: Praveen Kumar S P, Bong-Su Sin, Kwon-Hee Lee

Abstract:

Finned-tube heat exchangers are predominantly used in space conditioning systems, as well as other applications requiring heat exchange between two fluids. The design of finned-tube heat exchangers requires the selection of over a dozen design parameters by the designer such as tube pitch, tube diameter, tube thickness, etc… Finned-tube heat exchangers are common devices; however, their performance characteristics are complicated. In this paper numerical studies have been carried out to analyze the performances of finned tube heat exchanger (without fins considered for experimental purpose) by predicting the characteristics of temperature difference and pressure drop. In this study, a design considering 5 design variables and also maximizing the temperature difference and pressure drop was suggested by applying DOE. During this process, L₁₈ orthogonal array was adopted. Parametric analytical studies have been carried out using ANOVA to determine the relative importance of each variable with respect to the temperature difference and the pressure drop. Following the results, the final design was suggested by predicting the optimum design therefore confirming the optimized condition.

Keywords: Heat Exchanger, Fluid Analysis, Heat Transfer, Design of Experiment (DOE), Analysis of Variance (ANOVA).

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Authors: Morteza Pourazar, Fatemeh Mirakhori, Fazlolah Bagherzadeh, Rasool Hemayattalab

Abstract:

The purpose of study was to examine the effects of external and internal focus of attention in the motor learning of children with cerebral palsy. The study involved 30 boys (7 to 12 years old) with CP type 1 who practiced throwing beanbags. The participants were randomly assigned to the internal focus, external focus, and control groups, and performed six blocks of 10-trial with attentional focus reminders during a practice phase and no reminders during retention and transfer tests. Analysis of variance (ANOVA) with repeated measures on the last factor was used. The results show that significant main effects were found for time and group. However, the interaction of time and group was not significant. Retention scores were significantly higher for the external focus group. The external focus group performed better than other groups; however, the internal focus and control groups’ performance did not differ. The study concluded that motor skills in Spastic Hemiparetic Cerebral Palsy (SHCP) children could be enhanced by external attention.

Keywords: Cerebral Palsy, external attention, internal attention, throwing task.

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Authors: M. Chomczyńska

Abstract:

The studies concerned an effect of six variants of ion exchange substrate (nutrient carriers with a different potential impact on pH of soil solution) on vegetation of orchard grass during two different periods (42 and 84 days). In the pot experiment plants were grown on sand (model of degraded soil) and six mixtures of sand and 2% (v/v) additions of particular variants of ion exchange substrate (with pH ranged from 5.5 to 8.0). The study results showed that the addition of the substrate at pH=6.5 caused the highest increase in plant yield after shorter vegetation period whereas the addition of the substrate at pH=5.5 increased dry stem and root biomass of orchard grass after longer vegetation period. Thus, the ion exchange substrate at pH=6.5 can be recommended for restoration of exhausted soils when shorter vegetation period is planned; the ion exchange substrate at pH=5.5 can be used for the same purpose when longer periods of vegetative growth are considered.

Keywords: ion exchanger, ion exchange substrate, soilrestoration

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Authors: Fereydoon Sarmadian, Ali Keshavarzi

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Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.

Keywords: Artificial neural network, Field capacity, Permanentwilting point, Pedotransfer functions.

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Authors: M.Sujaritha, S. Annadurai

Abstract:

An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.

Keywords: Adaptive; Spatial, Mixture model, Segmentation, Color.

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Authors: K. Pana-Suppamassadu, P. Jeimrittiwong, P. Narataruksa, S. Tungkamani

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The aim of this work is to investigate on the internalflow patterns in a plate heat exchanger channel, which affect the rate of sedimentation fouling on the heat transfer surface of the plate heat exchanger. The research methodologies were the computer simulation using Computational Fluid Dynamics (CFD) and the experimental works. COMSOL MULTIPHYSICS™ Version 3.3 was used to simulate the velocity flow fields to verify the low and high flow regions. The results from the CFD technique were then compared with the images obtained from the experiments in which the fouling test rig was set up with a singlechannel plate heat exchanger to monitor the fouling of calcium carbonate. Two parameters were varied i.e., the crossing angle of the two plate: 55/55, 10/10, and 55/10 degree, and the fluid flow rate at the inlet: 0.0566, 0.1132 and 0.1698 m/s. The type of plate “GX-12" (the surface area 0.12 m2, the depth 2.9 mm, the width of fluid flow 215 mm and the thickness of stainless plate of 0.5 mm) was used in this study. The results indicated that the velocity distribution for the case of 55/55 degree seems to be very well organized when compared with the others. Also, an increase in the inlet velocity resulted in the reduction of fouling rate on the surface of plate heat exchangers.

Keywords: Computational fluid dynamics, crossing angles, finite element method, plate heat exchanger.

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Authors: Md Rashadul Islam, Umme Amina Mannan, Rafiqul A. Tarefder

Abstract:

Stiffness of Hot Mix Asphalt (HMA) in flexible pavement is largely dependent of temperature, mode of testing and age of pavement. Accurate measurement of HMA stiffness is thus quite challenging. This study determines HMA stiffness based on Finite Element Model (FEM) and validates the results using field data. As a first step, stiffnesses of different layers of a pavement section on Interstate 40 (I-40) in New Mexico were determined by Falling Weight Deflectometer (FWD) test. Pavement temperature was not measured at that time due to lack of temperature probe. Secondly, a FE model is developed in ABAQUS. Stiffness of the base, subbase and subgrade were taken from the FWD test output obtained from the first step. As HMA stiffness largely varies with temperature it was assigned trial and error approach. Thirdly, horizontal strain and vertical stress at the bottom of the HMA and temperature at different depths of the pavement were measured with installed sensors on the whole day on December 25th, 2012. Fourthly, outputs of FEM were correlated with measured stress-strain responses. After a number of trials a relationship was developed between the trial stiffness of HMA and measured mid-depth HMA temperature. At last, the obtained relationship between stiffness and temperature is verified by further FWD test when pavement temperature was recorded. A promising agreement between them is observed. Therefore, conclusion can be drawn that linear elastic FEM can accurately predict the stiffness and the structural response of flexible pavement.

Keywords: Asphalt pavement, falling weight deflectometer test, field instrumentation, finite element model, horizontal strain, temperature probes.

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