Search results for: linear combinatorial optimization

Authors: S. Esakkirajan, T. Veerakumar, V. Senthil Murugan, P. Navaneethan

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The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for signal processing. New class of wavelets called 'Multiwavelets' which posses more than one scaling function overcomes this problem. This paper presents a new image coding scheme based on non linear approximation of multiwavelet coefficients along with multistage vector quantization. The performance of the proposed scheme is compared with the results obtained from scalar wavelets.

Keywords: Image compression, Multiwavelets, Multi-stagevector quantization.

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Authors: AL.AL, M. B. I. Reaz, S. M. A. Motakabber, Mohd Alauddin Mohd Ali

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Nowadays it is a trend for electronic circuit designers to integrate all system components on a single-chip. This paper proposed the design of a single-chip proportional to absolute temperature (PTAT) sensor including a voltage reference circuit using CEDEC 0.18m CMOS Technology. It is a challenge to design asingle-chip wide range linear response temperature sensor for many applications. The channel widths between the compensation transistor and the reference transistor are critical to design the PTAT temperature sensor circuit. The designed temperature sensor shows excellent linearity between -100°C to 200° and the sensitivity is about 0.05mV/°C. The chip is designed to operate with a single voltage source of 1.6V.

Keywords: PTAT, single-chip circuit, linear temperature sensor, CMOS technology.

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Authors: Daniela I. Borissova, Ivan C. Mustakerov

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The paper describes an approach for defining of k-best night vision devices based on multi-criteria mixed-integer optimization modeling. The parameters of night vision devices are considered as criteria that have to be optimized. Using different user preferences for the relative importance between parameters different choice of k-best devices can be defined. An ideal device with all of its parameters at their optimum is used to determine how far the particular device from the ideal one is. A procedure for evaluation of deviation between ideal solution and k-best solutions is presented. The applicability of the proposed approach is numerically illustrated using real night vision devices data. The proposed approach contributes to quality of decisions about choice of night vision devices by making the decision making process more certain, rational and efficient. 

Keywords: K-best devices, mixed-integer model, multi-criteria problem, night vision devices.

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Authors: Pawel P. Czapski, Andrzej Sluzek

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This paper presents preliminary results regarding system-level power awareness for FPGA implementations in wireless sensor networks. Re-configurability of field programmable gate arrays (FPGA) allows for significant flexibility in its applications to embedded systems. However, high power consumption in FPGA becomes a significant factor in design considerations. We present several ideas and their experimental verifications on how to optimize power consumption at high level of designing process while maintaining the same energy per operation (low-level methods can be used additionally). This paper demonstrates that it is possible to estimate feasible power consumption savings even at the high level of designing process. It is envisaged that our results can be also applied to other embedded systems applications, not limited to FPGA-based.

Keywords: Power optimization, FPGA, system-level designing, wireless sensor networks.

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Authors: Ali Nouri Emamzadeh

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The experiment was performed to study the relationship between excreta viscosity and Nitrogen-corrected true metabolisable energy quantities of soybean meals using conventional addition method (CAM) in adult cockerels for 7 d: a 3-d preexperiment and a 4-d experiment period. Results indicated that differences between the excreta viscosity values were (P<0.01) significant for SBMs. The excreta viscosity values were less (P<0.01) for SBMs 6, 2, 8, 1 and 3 than other SBMs. The mean TMEn (kcal/kg) values were significant (P<0.01) between SBMs. The most TMEn values were (P<0.01) for SBMs 6, 2, 8 and 1, also the lowest TMEn values were (P<0.01) for SBMs 3, 7, 4, 9 and 5. There was a reverse linear relationship between the values of excreta viscosity and TMEn in SBMs. In conclusion, there was a reverse linear relationship between the values of excreta viscosity and TMEn in SBMs probably due to their various soluble NSPs.

Keywords: soybean meals (SBMs), Nitrogen-corrected true metabolisable energy (TMEn), viscosity

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Authors: See Zi Siang, Khairul Hazrin Hashim, Harold Thwaites, Lee Xia Sheng, Ooi Wooi Har

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The paper explores the development of an optimization of method and apparatus for retrieving extended high dynamic range from digital negative image. Architectural photo imaging can benefit from high dynamic range imaging (HDRI) technique for preserving and presenting sufficient luminance in the shadow and highlight clipping image areas. The HDRI technique that requires multiple exposure images as the source of HDRI rendering may not be effective in terms of time efficiency during the acquisition process and post-processing stage, considering it has numerous potential imaging variables and technical limitations during the multiple exposure process. This paper explores an experimental method and apparatus that aims to expand the dynamic range from digital negative image in HDRI environment. The method and apparatus explored is based on a single source of RAW image acquisition for the use of HDRI post-processing. It will cater the optimization in order to avoid and minimize the conventional HDRI photographic errors caused by different physical conditions during the photographing process and the misalignment of multiple exposed image sequences. The study observes the characteristics and capabilities of RAW image format as digital negative used for the retrieval of extended high dynamic range process in HDRI environment.

Keywords: High Dynamic Range Image, Photography Workflow Optimization, Digital Negative Image, Architectural Image

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Authors: Dana M. Ragab, Jasim A Ghaeb

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The Voltage Unbalance Factor (VUF) index is recommended to evaluate system performance under unbalanced operation. However, its calculation requires complex algebra which limits its use in the field. Furthermore, one system cycle is required at least to detect unbalance using the VUF. Ideally unbalance mitigation must be performed within 10 ms for 50 Hz systems. In this work, a linear relation for VUF evaluation in three-phase electrical power system using space vector (SV) is derived. It is proposed to determine the voltage unbalance quickly and accurately and to overcome the constraints associated with the traditional methods of VUF evaluation. Aqaba-Qatrana-South Amman (AQSA) power system is considered to study the system performance under unbalanced conditions. The results show that both the complexity of calculations and the time required to evaluate VUF are reduced significantly.

Keywords: Power quality, space vector, unbalance evaluation.

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: Crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest.

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Authors: Mohammad Najafi Nobar, Bahareh Pourmehr, Mehdi Hajimirarab

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One of the main processes of supply chain management is supplier selection process which its accurate implementation can dramatically increase company competitiveness. In presented article model developed based on the features of second tiers suppliers and four scenarios are predicted in order to help the decision maker (DM) in making up his/her mind. In addition two tiers of suppliers have been considered as a chain of suppliers. Then the proposed approach is solved by a method combined of concepts of fuzzy set theory (FST) and linear programming (LP) which has been nourished by real data extracted from an engineering design and supplying parts company. At the end results reveal the high importance of considering second tier suppliers features as criteria for selecting the best supplier.

Keywords: Supply Chain Management (SCM), SupplierSelection, Second Tier Supplier, Scenario Planning, Green Factor, Linear Programming, Fuzzy Set Theory

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Authors: Feng-Yi Huang, Yuan-Jye Tseng

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In the traditional concept of product life cycle management, the activities of design, manufacturing, and assembly are performed in a sequential way. The drawback is that the considerations in design may contradict the considerations in manufacturing and assembly. The different designs of components can lead to different assembly sequences. Therefore, in some cases, a good design may result in a high cost in the downstream assembly activities. In this research, an integrated design evaluation and assembly sequence planning model is presented. Given a product requirement, there may be several design alternative cases to design the components for the same product. If a different design case is selected, the assembly sequence for constructing the product can be different. In this paper, first, the designed components are represented by using graph based models. The graph based models are transformed to assembly precedence constraints and assembly costs. A particle swarm optimization (PSO) approach is presented by encoding a particle using a position matrix defined by the design cases and the assembly sequences. The PSO algorithm simultaneously performs design evaluation and assembly sequence planning with an objective of minimizing the total assembly costs. As a result, the design cases and the assembly sequences can both be optimized. The main contribution lies in the new concept of integrated design evaluation and assembly sequence planning model and the new PSO solution method. The test results show that the presented method is feasible and efficient for solving the integrated design evaluation and assembly planning problem. In this paper, an example product is tested and illustrated.

Keywords: assembly sequence planning, design evaluation, design for assembly, particle swarm optimization

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Authors: Anjanna Matta, P. A. L. Narayana

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An investigation has been presented to analyze the effect of internal heat source on the onset of Hadley-Prats flow in a horizontal fluid saturated porous medium. We examine a better understanding of the combined influence of the heat source and mass flow effect by using linear stability analysis. The resultant eigenvalue problem is solved by using shooting and Runga-Kutta methods for evaluate critical thermal Rayleigh number with respect to various flow governing parameters. It is identified that the flow is switch from stabilizing to destabilizing as the horizontal thermal Rayleigh number is enhanced. The heat source and mass flow increases resulting a stronger destabilizing effect.

Keywords: Linear stability analysis, heat source, porous medium, mass flow.

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Authors: G. Venkata Hari Prasad, P. Rajesh Kumar

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Phonocardiography is important in appraisal of congenital heart disease and pulmonary hypertension as it reflects the duration of right ventricular systoles. The systolic murmur in patients with intra-cardiac shunt decreases as pulmonary hypertension develops and may eventually disappear completely as the pulmonary pressure reaches systemic level. Phonocardiography and auscultation are non-invasive, low-cost, and accurate methods to assess heart disease. In this work an objective signal processing tool to extract information from phonocardiography signal using Wavelet is proposed to classify the murmur as normal or abnormal. Since the feature vector is large, a Binary Particle Swarm Optimization (PSO) with mutation for feature selection is proposed. The extracted features improve the classification accuracy and were tested across various classifiers including Naïve Bayes, kNN, C4.5, and SVM.

Keywords: Phonocardiography, Coiflet, Feature selection, Particle Swarm Optimization.

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Authors: Bahare Fatemi, Seyed Mehran Kazemi, Nazanin Mehrasa

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Sudoku is a logic-based combinatorial puzzle game which people in different ages enjoy playing it. The challenging and addictive nature of this game has made it a ubiquitous game. Most magazines, newspapers, puzzle books, etc. publish lots of Sudoku puzzles every day. These puzzles often come in different levels of difficulty so that all people, from beginner to expert, can play the game and enjoy it. Generating puzzles with different levels of difficulty is a major concern of Sudoku designers. There are several works in the literature which propose ways of generating puzzles having a desirable level of difficulty. In this paper, we propose a method based on constraint satisfaction problems to evaluate the difficulty of the Sudoku puzzles. Then we propose a hill climbing method to generate puzzles with different levels of difficulty. Whereas other methods are usually capable of generating puzzles with only few number of difficulty levels, our method can be used to generate puzzles with arbitrary number of different difficulty levels. We test our method by generating puzzles with different levels of difficulty and having a group of 15 people solve all the puzzles and recording the time they spend for each puzzle.

Keywords: Constraint satisfaction problem, generating Sudoku puzzles, hill climbing.

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Authors: A. Shyamala Devi, K. Chitra Devi, R. Rajendiran

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A total of 6 isolates of Bacillus subtilis were isolated from oil mill waste collected in Namakkal district, Tamilnadu, India. The isolated bacteria were screened using lipase screening medium containing Tween 80. BS-3 isolate exhibited a greater clear zone than the others, indicating higher lipase activity. Therefore, this isolate was selected for media optimization studies. Ten process variables were screened using Plackett–Burman design and were further optimized by central composite design of response surface methodology for lipase production in submerged fermentation. Maximum lipase production of 16.627 U/min/ml were predicted in medium containing yeast extract (9.3636g), CaCl2 (0.8986g) and incubation periods (1.813 days). A mean value of 16.98 ± 0.2286 U/min/ml of lipase was acquired from real experiments.

Keywords: Bacillus subtilis, extracellular lipase, Plackett–Burman design, response surface methodology.

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Authors: Farhad Kolahan, A. Hamid Khajavi

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In this paper, a set of experimental data has been used to assess the influence of abrasive water jet (AWJ) process parameters in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. The effects of these input parameters are studied on depth of cut (h); one of most important characteristics of AWJ. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the AWJ process parameters. The objective is to determine a suitable set of process parameters that can produce a desired depth of cut, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.

Keywords: AWJ machining, Mathematical modeling, Simulated Annealing, Optimization

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Authors: R. Abdulrahman, I. Sebastine

Abstract:

Natural gas is the most popular fossil fuel in the current era and future as well. Natural gas is existed in underground reservoirs so it may contain many of non-hydrocarbon components for instance, hydrogen sulfide, nitrogen and water vapor. These impurities are undesirable compounds and cause several technical problems for example, corrosion and environment pollution. Therefore, these impurities should be reduce or removed from natural gas stream. Khurmala dome is located in southwest Erbil-Kurdistan region. The Kurdistan region government has paid great attention for this dome to provide the fuel for Kurdistan region. However, the Khurmala associated natural gas is currently flaring at the field. Moreover, nowadays there is a plan to recover and trade this gas and to use it either as feedstock to power station or to sell it in global market. However, the laboratory analysis has showed that the Khurmala sour gas has huge quantities of H2S about (5.3%) and CO2 about (4.4%). Indeed, Khurmala gas sweetening process has been removed in previous study by using Aspen HYSYS. However, Khurmala sweet gas still contents some quintets of water about 23 ppm in sweet gas stream. This amount of water should be removed or reduced. Indeed, water content in natural gas cause several technical problems such as hydrates and corrosion. Therefore, this study aims to simulate the prospective Khurmala gas dehydration process by using Aspen HYSYS V. 7.3 program. Moreover, the simulation process succeeded in reducing the water content to less than 0.1ppm. In addition, the simulation work is also achieved process optimization by using several desiccant types for example, TEG and DEG and it also study the relationship between absorbents type and its circulation rate with HCs losses from glycol regenerator tower.

Keywords: Aspen Hysys, Process simulation, gas dehydration, process optimization.

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Authors: Kang Zijian, Zhang Tingyu, Burra Venkata Durga Kumar

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This article investigates the challenges in memory migration during the live migration of virtual machines. We found three challenges probably existing in pre-copy technology. One of the main challenges is the challenge of downtime migration. Decreasing the downtime could promise the normal work for a virtual machine. Although pre-copy technology is greatly decreasing the downtime, we still need to shut down the machine in order to finish the last round of data transfer. This paper provides an optimization scheme for the problems existing in pro-copy technology, mainly the optimization of the dirty page migration mechanism. The typical pre-copy technology copies n-1th’s dirty pages in nth turn. However, our idea is to create a double iteration method to solve this problem.

Keywords: Virtual machine, pre-copy technology, memory migration process, downtime, dirty pages migration method.

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Authors: A. M. Pashayev, C. Ardil, D. D. Askerov, R. A. Sadiqov, P. S. Abdullayev

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In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.

Keywords: Gas turbine engines, neural networks, fuzzy logic, fuzzy statistics.

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Authors: Amir Rastegarnia, Mohammad Ali Tinati, Azam Khalili

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In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor-s observation noise variance and II) using the estimated variances to obtain the desired parameter. Our proposed algorithm is based on a diffusion least mean square (LMS) implementation with linear combiner model. In the proposed algorithm, the step-size parameter the coefficients of linear combiner are adjusted according to estimated observation noise variances. As the simulation results show, the proposed algorithm considerably improves the diffusion LMS algorithm given in literature.

Keywords: Adaptive filter, distributed estimation, sensor network, diffusion.

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Authors: Karima Siham Aoubid, Mohamed Boulemden

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The development of the signal compression algorithms is having compressive progress. These algorithms are continuously improved by new tools and aim to reduce, an average, the number of bits necessary to the signal representation by means of minimizing the reconstruction error. The following article proposes the compression of Arabic speech signal by a hybrid method combining the wavelet transform and the linear prediction. The adopted approach rests, on one hand, on the original signal decomposition by ways of analysis filters, which is followed by the compression stage, and on the other hand, on the application of the order 5, as well as, the compression signal coefficients. The aim of this approach is the estimation of the predicted error, which will be coded and transmitted. The decoding operation is then used to reconstitute the original signal. Thus, the adequate choice of the bench of filters is useful to the transform in necessary to increase the compression rate and induce an impercevable distortion from an auditive point of view.

Keywords: Compression, linear prediction analysis, multiresolution analysis, speech signal.

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Authors: Khalid A. Kaabneh

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This paper introduces and studies new indexing techniques for content-based queries in images databases. Indexing is the key to providing sophisticated, accurate and fast searches for queries in image data. This research describes a new indexing approach, which depends on linear modeling of signals, using bases for modeling. A basis is a set of chosen images, and modeling an image is a least-squares approximation of the image as a linear combination of the basis images. The coefficients of the basis images are taken together to serve as index for that image. The paper describes the implementation of the indexing scheme, and presents the findings of our extensive evaluation that was conducted to optimize (1) the choice of the basis matrix (B), and (2) the size of the index A (N). Furthermore, we compare the performance of our indexing scheme with other schemes. Our results show that our scheme has significantly higher performance.

Keywords: Axial Projection, images, indexing, multimedia database, searching.

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Authors: Tim Giesen, Christian Fischmann, Fabian Böttinger, Alexander Ehm, Alexander Verl

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In a state-of-the-art industrial production line of photovoltaic products the handling and automation processes are of particular importance and implication. While processing a fully functional crystalline solar cell an as-cut photovoltaic wafer is subject to numerous repeated handling steps. With respect to stronger requirements in productivity and decreasing rejections due to defects the mechanical stress on the thin wafers has to be reduced to a minimum as the fragility increases by decreasing wafer thicknesses. In relation to the increasing wafer fragility, researches at the Fraunhofer Institutes IPA and CSP showed a negative correlation between multiple handling processes and the wafer integrity. Recent work therefore focused on the analysis and optimization of the dry wafer stack separation process with compressed air. The achievement of a wafer sensitive process capability and a high production throughput rate is the basic motivation in this research.

Keywords: Automation, Photovoltaic Manufacturing, Thin Wafer, Material Handling

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Authors: J. P. Panda, K. Sasmal, H. V. Warrior

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Eddy viscosity models in turbulence modeling can be mainly classified as linear and nonlinear models. Linear formulations are simple and require less computational resources but have the disadvantage that they cannot predict actual flow pattern in complex geophysical flows where streamline curvature and swirling motion are predominant. A constitutive equation of Reynolds stress anisotropy is adopted for the formulation of eddy viscosity including all the possible higher order terms quadratic in the mean velocity gradients, and a simplified model is developed for actual oceanic flows where only the vertical velocity gradients are important. The new model is incorporated into the one dimensional General Ocean Turbulence Model (GOTM). Two realistic oceanic test cases (OWS Papa and FLEX' 76) have been investigated. The new model predictions match well with the observational data and are better in comparison to the predictions of the two equation k-epsilon model. The proposed model can be easily incorporated in the three dimensional Princeton Ocean Model (POM) to simulate a wide range of oceanic processes. Practically, this model can be implemented in the coastal regions where trasverse shear induces higher vorticity, and for prediction of flow in estuaries and lakes, where depth is comparatively less. The model predictions of marine turbulence and other related data (e.g. Sea surface temperature, Surface heat flux and vertical temperature profile) can be utilized in short term ocean and climate forecasting and warning systems.

Keywords: Eddy viscosity, turbulence modeling, GOTM, CFD.

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Authors: Dana Kristalova, Jan Mazal

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This article deals with geographical conditions in terrain and their effect on the movement of vehicles, their effect on speed and safety of movement of people and vehicles. Finding of the optimal routes outside the communication is studied in the Army environment, but it occur in civilian as well, primarily in crisis situation, or by the provision of assistance when natural disasters such as floods, fires, storms etc., have happened. These movements require the optimization of routes when effects of geographical factors should be included. The most important factor is the surface of a terrain. It is based on several geographical factors as are slopes, soil conditions, micro-relief, a type of surface and meteorological conditions. Their mutual impact has been given by coefficient of deceleration. This coefficient can be used for the commander`s decision. New approaches and methods of terrain testing, mathematical computing, mathematical statistics or cartometric investigation are necessary parts of this evaluation.

Keywords: Movement in a terrain, geographical factors, surface of a field, mathematical evaluation, optimization and searching paths.

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Authors: M.E. Masoumi, S. Kadkhodaie

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Distillation column is one of the most common operations in process industries and is while the most expensive unit of the amount of energy consumption. Many ideas have been presented in the related literature for optimizing energy consumption in distillation columns. This paper studies the different heat integration methods in a distillation column which separate Benzene, Toluene, Xylene, and C9+. Three schemes of heat integration including, indirect sequence (IQ), indirect sequence with forward energy integration (IQF), and indirect sequence with backward energy integration (IQB) has been studied in this paper. Using shortcut method these heat integration schemes were simulated with Aspen HYSYS software and compared with each other with regarding economic considerations. The result shows that the energy consumption has been reduced 33% in IQF and 28% in IQB in comparison with IQ scheme. Also the economic result shows that the total annual cost has been reduced 12% in IQF and 8% in IQB regarding with IQ scheme. Therefore, the IQF scheme is most economic than IQB and IQ scheme.

Keywords: Optimization, Distillation Column Sequence, Energy Savings

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Authors: M. Moeinaddini, Z. Asadi-Shekari, M. Zaly Shah, A. Hamzah

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Currently, planners try to have more green travel options to decrease economic, social and environmental problems. Therefore, this study tries to find significant urban travel factors to be used to increase the usage of alternative urban travel modes. This paper attempts to identify the relationship between prominent urban mobility indicators and daily trips by public transport in 30 cities from various parts of the world. Different travel modes, infrastructures and cost indicators were evaluated in this research as mobility indicators. The results of multi-linear regression analysis indicate that there is a significant relationship between mobility indicators and the daily usage of public transport.

Keywords: Green travel modes, urban travel indicators, daily trips by public transport, multi-linear regression analysis.

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Authors: Azizan Bin Saaban, Adyda Binti Ibrahim, Mohammad Shehzad, Israr Ahmad

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In chaos synchronization, the main goal is to design such controller(s) that synchronizes the states of master and slave system asymptotically globally. This paper studied and investigated the synchronization problem of two identical Chen, and identical Tigan chaotic systems and two non-identical Chen and Tigan chaotic systems using Non-linear active control algorithm. In this study, based on Lyapunov stability theory and using non-linear active control algorithm, it has been shown that the proposed schemes have excellent transient performance using only two nonlinear controllers and have shown analytically as well as graphically that synchronization is asymptotically globally stable.

Keywords: Nonlinear Active Control, Chen and Tigan Chaotic systems, Lyapunov Stability theory, Synchronization.

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Authors: Leila Torkaman, Nasser Ghassembaglou

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Significant quota of Municipal Electrical Energy consumption is related to Decentralized Air Conditioning which is mostly provided by evaporative coolers. So the aim is to optimize design of air conditioners to increase their efficiencies. To achieve this goal, results of practical standardized tests for 40 evaporative coolers in different types collected and simultaneously results for same coolers based on one of EER (Energy Efficiency Ratio) modeling styles are figured out. By comparing experimental results of different coolers standardized tests with modeling results, preciseness of used model is assessed and after comparing gained preciseness with international standards based on EER for cooling capacity, aeration, and also electrical energy consumption, energy label from A (most effective) to G (less effective) is classified; finally needed methods to optimize energy consumption and coolers’ classification are provided.

Keywords: Cooler, EER, Energy Label, Optimization.

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Authors: H. Shayeghi, H. A. Shayanfar, G. Azimi

Abstract:

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Keywords: Large Neural Network, Short-Term Load Forecasting, Particle Swarm Optimization.

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Authors: M. F. Omar, R. A. Salam, R. Abdullah, N. A. Rashid

Abstract:

Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-sequence alignment. In multiple sequence alignment, the emphasis is to find optimal alignment for a group of sequences. Several applicable techniques were observed in this research, from traditional method such as dynamic programming to the extend of widely used stochastic optimization method such as Genetic Algorithms (GAs) and Simulated Annealing. A framework with combination of Genetic Algorithm and Simulated Annealing is presented to solve Multiple Sequence Alignment problem. The Genetic Algorithm phase will try to find new region of solution while Simulated Annealing can be considered as an alignment improver for any near optimal solution produced by GAs.

Keywords: Simulated annealing, genetic algorithm, sequence alignment, multiple sequence alignment.

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