Search results for: constant modulus algorithm

Authors: Maysam Saidi, Hassan Basirat Tabrizi, Reza Maddahian

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In this paper, numerical simulations are performed to investigate the effect of disturbance block on flow field of the classical square lid-driven cavity. Attentions are focused on vortex formation and studying the effect of block position on its structure. Corner vortices are different upon block position and new vortices are produced because of the block. Finite volume method is used to solve Navier-Stokes equations and PISO algorithm is employed for the linkage of velocity and pressure. Verification and grid independency of results are reported. Stream lines are sketched to visualize vortex structure in different block positions.

Keywords: Disturbance Block, Finite Volume Method, Lid-Driven Cavity

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Authors: Takayuki Shiina

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We consider power system expansion planning under uncertainty. In our approach, integer programming and stochastic programming provide a basic framework. We develop a multistage stochastic programming model in which some of the variables are restricted to integer values. By utilizing the special property of the problem, called block separable recourse, the problem is transformed into a two-stage stochastic program with recourse. The electric power capacity expansion problem is reformulated as the problem with first stage integer variables and continuous second stage variables. The L-shaped algorithm to solve the problem is proposed.

Keywords: electric power capacity expansion problem, integerprogramming, L-shaped method, stochastic programming

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Authors: A. Shahsavari, M. Nili-Ahmadabadi

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This paper presents an innovative one-dimensional optimization of a transonic compressor based on the radial equilibrium theory by means of increasing blade loading. Firstly, the rotor blade of the transonic compressor is redesigned based on the constant span-wise deHaller number and diffusion. The code is applied to extract compressor meridional plane and blade to blade geometry containing rotor and stator in order to design blade three-dimensional view. A structured grid is generated for the numerical domain of fluid. Finer grids are used for regions near walls to capture boundary layer effects and behavior. RANS equations are solved by finite volume method for rotating zones (rotor) and stationary zones (stator). The experimental data, available for the performance map of NASA Rotor67, is used to validate the results of simulations. Then, the capability of the design method is validated by CFD that is capable of predicting the performance map. The numerical results of new geometry show about 19% increase in pressure ratio and 11% improvement in overall efficiency of the transonic stage; however, the design point mass flow rate of the new compressor is 5.7% less than that of the original compressor.

Keywords: One dimensional design, deHaller number, radial equilibrium, transonic compressor.

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Authors: Yueming Wang, Zenghui Zhang, Rendong Ying, Peilin Liu

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Sparse representation has long been studied and several dictionary learning methods have been proposed. The dictionary learning methods are widely used because they are adaptive. In this paper, a new dictionary learning method for audio is proposed. Signals are at first decomposed into different degrees of Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD) technique. Then these IMFs form a learned dictionary. To reduce the size of the dictionary, the K-means method is applied to the dictionary to generate a K-EMD dictionary. Compared to K-SVD algorithm, the K-EMD dictionary decomposes audio signals into structured components, thus the sparsity of the representation is increased by 34.4% and the SNR of the recovered audio signals is increased by 20.9%.

Keywords: Dictionary Learning, EMD, K-means Method, Sparse Representation.

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Authors: Nikolai Krainiukov, Boris Melnikov

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We study the properties of maximal prefix codes. The codes have many applications in computer science, theory of formal languages, data processing and data classification. Our approaches to study use finite state automata (so-called flower automata) for the representation of prefix codes. An important task is the decomposition of prefix codes into prime prefix codes (factors). We discuss properties of such prefix code decompositions. A linear time algorithm is designed to find the prime decomposition. We used the GAP computer algebra system, which allows us to perform algebraic operations for free semigroups, monoids and automata.

Keywords: Maximal prefix code, regular languages, flower automata, prefix code decomposing.

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Authors: Tsuyoshi Okita

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For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.

Keywords: Formal language, statistical inference problem, reduction.

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Authors: Seema Sharma Kaushik, Dhananjoy Shaw

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Various length dimensions of the body have been a variable of interest in the research areas of kinanthropometry. However the inclusion of length measurements in various studies remains restricted to reflect characteristics of a particular game/sport at a particular time. Hence, the present investigation was conducted to study various length dimensions correlates of a longitudinal physical conditioning program on Indian male youth. The study was conducted on 90 Indian male youth. The sample was equally divided into three groups namely, progressive load training (PLT), constant load training (CLT) and no load training (NL). The variables included sitting height, leg length, arm length and foot length. The study was conducted by adopting the multi group repeated measure design. Three different groups were measured four times after completion of each of the three meso-cycles of six-weeks duration each. The measurements were taken using the standard landmarks and procedures. Mean, standard deviation and analysis of co-variance were computed to analyze the data statistically. The post-hoc analysis was conducted for the significant F-ratios at 0.05 level. The study concluded that the followed longitudinal physical conditioning program had significant effect on various length dimensions of Indian male youth.

Keywords: Indian male youth, longitudinal, length dimensions, physical conditioning.

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Authors: M. Vivodík, Ž. Balážová, Z. Gálová

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The aim of this work was to detect genetic variability among the set of 40 castor genotypes using 8 RAPD markers. Amplification of genomic DNA of 40 genotypes, using RAPD analysis, yielded in 66 fragments, with an average of 8.25 polymorphic fragments per primer. Number of amplified fragments ranged from 3 to 13, with the size of amplicons ranging from 100 to 1200 bp. Values of the polymorphic information content (PIC) value ranged from 0.556 to 0.895 with an average of 0.784 and diversity index (DI) value ranged from 0.621 to 0.896 with an average of 0.798. The dendrogram based on hierarchical cluster analysis using UPGMA algorithm was prepared and analyzed genotypes were grouped into two main clusters and only two genotypes could not be distinguished. Knowledge on the genetic diversity of castor can be used for future breeding programs for increased oil production for industrial uses.

Keywords: Dendrogram, polymorphism, RAPD technique, Ricinus communis L.

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Authors: Sheng-Tse Wu, Wu-Sung Yao

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In this paper, a drift assist control system is proposed for remote control (RC) cars to get the perfect drift angle. A steering servo control scheme is given powerfully to assist the drift driving. A gyroscope sensor is included to detect the machine's tail sliding and to achieve a better automatic counter-steering to prevent RC car from spinning. To analysis tire traction and vehicle dynamics is used to obtain the dynamic track of RC cars. It comes with a control gain to adjust counter-steering amount according to the sensor condition. An illustrated example of 1:10 RC drift car is given and the real-time control algorithm is realized by Arduino Uno.

Keywords: Drift assist control system, remote control cars, gyroscope, vehicle dynamics.

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Authors: Akram Khaleghei Ghosheh Balagh, Viliam Makis

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In this paper, we investigate the residual life prediction problem for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model with unknown parameters. The parameter estimation procedure based on an EM algorithm is developed and the formulas for the conditional reliability function and the mean residual life are derived, illustrated by a numerical example.

Keywords: Partially observable system, hidden Markov model, competing risks, residual life prediction.

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: Babak Tashakori Bafghi, Laleh Tashakori, Reza Allahyari Soeini, Mohammad Mokhtari

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Supplier selection is a multi criteria decision-making process that comprises tangible and intangible factors. The majority of previous supplier selection techniques do not consider strategic perspective. Besides, uncertainty is one of the most important obstacles in supplier selection. For the first, time in this paper, the idea of the algorithm " Knapsack " is used to select suppliers Moreover, an attempt has to be made to take the advantage of a simple numerical method for solving model .This is an innovation to resolve any ambiguity in choosing suppliers. This model has been tried in the suppliers selected in a competitive environment and according to all desired standards of quality and quantity to show the efficiency of the model, an industry sample has been uses.

Keywords: Knapsack, linear programming, supplier select, supply chain management.

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Authors: Udeme O. Ini, Edikan E. Akpanibah

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This paper studies the optimal investment strategies for a plan member (PM) in a defined contribution (DC) pension scheme with transaction cost, taxes on invested funds and couple risky assets (stocks) under the Ornstein-Uhlenbeck (O-U) process. The PM’s portfolio is assumed to consist of a risk-free asset and two risky assets where the two risky assets are driven by the O-U process. The Legendre transformation and dual theory is use to transform the resultant optimal control problem which is a nonlinear partial differential equation (PDE) into linear PDE and the resultant linear PDE is then solved for the explicit solutions of the optimal investment strategies for PM exhibiting constant absolute risk aversion (CARA) using change of variable technique. Furthermore, theoretical analysis is used to study the influences of some sensitive parameters on the optimal investment strategies with observations that the optimal investment strategies for the two risky assets increase with increase in the dividend and decreases with increase in tax on the invested funds, risk averse coefficient, initial fund size and the transaction cost.

Keywords: Ornstein-Uhlenbeck process, portfolio management, Legendre transforms, CARA utility.

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Authors: Wei-hua Ai, Wei Yan, Xiang Li

Abstract:

Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.

Keywords: Lossless compression, weather radar data, optical linear prediction, PPI image

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Authors: S. Balaji

Abstract:

An edge based local search algorithm, called ELS, is proposed for the maximum clique problem (MCP), a well-known combinatorial optimization problem. ELS is a two phased local search method effectively £nds the near optimal solutions for the MCP. A parameter ’support’ of vertices de£ned in the ELS greatly reduces the more number of random selections among vertices and also the number of iterations and running times. Computational results on BHOSLIB and DIMACS benchmark graphs indicate that ELS is capable of achieving state-of-the-art-performance for the maximum clique with reasonable average running times.

Keywords: Maximum clique, local search, heuristic, NP-complete.

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Authors: H. C. Chinwenyi, H. D. Ibrahim, F. A. Ahmed

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In modern financial mathematics, valuing derivatives such as options is often a tedious task. This is simply because their fair and correct prices in the future are often probabilistic. This paper examines three different Stochastic Differential Equation (SDE) models in finance; the Constant Elasticity of Variance (CEV) model, the Balck-Karasinski model, and the Heston model. The various Martingales option price valuation formulas for these three models were obtained using the replicating portfolio method. Also, the numerical solution of the derived Martingales options price valuation equations for the SDEs models was carried out using the Monte Carlo method which was implemented using MATLAB. Furthermore, results from the numerical examples using published data from the Nigeria Stock Exchange (NSE), all share index data show the effect of increase in the underlying asset value (stock price) on the value of the European Put Option for these models. From the results obtained, we see that an increase in the stock price yields a decrease in the value of the European put option price. Hence, this guides the option holder in making a quality decision by not exercising his right on the option.

Keywords: Equivalent Martingale Measure, European Put Option, Girsanov Theorem, Martingales, Monte Carlo method, option price valuation, option price valuation formula.

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

Abstract:

We developed a method based on quasi-molecular modelling to simulate the fall of water drops on horizontally smooth and rough surfaces. Each quasi-molecule was a group of particles that interacted in a fashion entirely analogous to classical Newtonian molecular interactions. When a falling water droplet was simulated at low impact velocity on both smooth and rough surfaces, the droplets moved periodically (i.e. the droplets moved up and down for a certain period, finally they stopped moving and reached a steady state), spreading and recoiling without splash or break-up. Spreading rates of falling water droplets increased rapidly as time increased until the spreading rate reached its steady state at time t ~ 0.25 s for rough surface and t ~ 0.40 s for smooth surface. The droplet height above both surfaces decreased as time increased, remained constant after the droplet diameter attained a maximum value and reached its steady state at time t ~ 0.4 s. However, rough surface had higher spreading rates of falling water droplets and lower height on the surface than smooth one.

Keywords: Quasi-molecular modelling, particle modelling, molecular aggregate approach.

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Authors: S. Pathomsiri, P. Sukaboon

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The National Blood Center, Thai Red Cross Society is responsible for providing blood to hospitals all over the country. When any hospital needs blood, it will have to send the vehicle to pick up at the NBC. There are a lot of vehicles to pick up blood at the NBC every day. Each vehicle is usually empty for inbound trip and a little loaded for outbound. The NBC realized such waste or loss and there have been the third party offered to distribute blood and charge for fee. This paper proposes to apply the vehicle routing problem (VRP) for estimating the fair price. The idea is tested with the real data during seven-day period of 6 – 12 July 2010 to estimate the fair price for transporting blood in Bangkok Metropolitan Region.

Keywords: Blood Supply Chain, Vehicle Routing Problem, Heuristic, Saving Algorithm, Fair Price.

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Authors: Walid Aydi, Nouri Masmoudi, Lotfi Kamoun

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Images of human iris contain specular highlights due to the reflective properties of the cornea. This corneal reflection causes many errors not only in iris and pupil center estimation but also to locate iris and pupil boundaries especially for methods that use active contour. Each iris recognition system has four steps: Segmentation, Normalization, Encoding and Matching. In order to address the corneal reflection, a novel reflection removal method is proposed in this paper. Comparative experiments of two existing methods for reflection removal method are evaluated on CASIA iris image databases V3. The experimental results reveal that the proposed algorithm provides higher performance in reflection removal.

Keywords: iris, pupil, specular highlights, reflection removal

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Authors: M. Lakshmi, J. Senthil Kumar

Abstract:

Digital cameras to reduce cost, use an image sensor to capture color images. Color Filter Array (CFA) in digital cameras permits only one of the three primary (red-green-blue) colors to be sensed in a pixel and interpolates the two missing components through a method named demosaicking. Captured data is interpolated into a full color image and compressed in applications. Color interpolation before compression leads to data redundancy. This paper proposes a new Vector Quantization (VQ) technique to construct a VQ codebook with Differential Evolution (DE) Algorithm. The new technique is compared to conventional Linde- Buzo-Gray (LBG) method.

Keywords: Color Filter Array (CFA), Biorthogonal Wavelet, Vector Quantization (VQ), Differential Evolution (DE).

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Authors: Aly Farag, Ayman El-Baz, Refaat Mohamed

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In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.

Keywords: Logistic regression model, Expectationmaximization, Segmentation.

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Authors: Abduladheem A. Ali, Easa A. Abd

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The robot is a repeated task plant. The control of such a plant under parameter variations and load disturbances is one of the important problems. The aim of this work is to design Geno-Fuzzy controller suitable for online applications to control single link rigid robot arm plant. The genetic-fuzzy online controller (indirect controller) has two genetic-fuzzy blocks, the first as controller, the second as identifier. The identification method is based on inverse identification technique. The proposed controller it tested in normal and load disturbance conditions.

Keywords: Fuzzy network, genetic algorithm, robot control, online genetic control, parameter identification.

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Authors: Mazhar B. Tayel, Amr H. Yassin

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A proposed small-signal model parameters for a pseudomorphic high electron mobility transistor (PHEMT) is presented. Both extrinsic and intrinsic circuit elements of a smallsignal model are determined using genetic algorithm (GA) as a stochastic global search and optimization tool. The parameters extraction of the small-signal model is performed on 200-μm gate width AlGaAs/InGaAs PHEMT. The equivalent circuit elements for a proposed 18 elements model are determined directly from the measured S- parameters. The GA is used to extract the parameters of the proposed small-signal model from 0.5 up to 18 GHz.

Keywords: PHEMT, Genetic Algorithms, small signal modeling, optimization.

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Authors: Soumya Banerjee, Indrajit Mukherjee, P.K. Mahanti

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Scheduling of diversified service requests in distributed computing is a critical design issue. Cloud is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. It is not only the clusters and grid but also it comprises of next generation data centers. The paper proposes an initial heuristic algorithm to apply modified ant colony optimization approach for the diversified service allocation and scheduling mechanism in cloud paradigm. The proposed optimization method is aimed to minimize the scheduling throughput to service all the diversified requests according to the different resource allocator available under cloud computing environment.

Keywords: Ant Colony, Cloud Computing, Grid, Resource allocator, Service Request.

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Authors: Siong W. Lee, Kang H. Tan, En H. Yang

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This paper presents the results of an experimental study undertaken to evaluate the local bond stress-slip response of short embedment of reinforcing bars in normal concrete (NC) and high performance fiber reinforced cement composites (HPFRCC) blocks. Long embedment was investigated as well to gain insights on the distribution of strain, slip, bar stress and bond stress along the bar especially in post-yield range. A total of 12 specimens were tested, by means of pull-out of the reinforcing bars from concrete blocks. It was found that the enhancement of local bond strength can be reached up to 50% and ductility of the bond behavior was improved significantly if HPFRCC is used. Also, under a constant strain at loaded end, HPFRCC has delayed yielding of bars at other location from the loaded end. Hence, the reduction of bond stress was slower for HPFRCC in comparison with NC. Due to the same reason, the total slips at loaded end for HPFRCC was smaller than NC as expected. Test results indicated that HPFRCC has better bond slip behavior which makes it a suitable material to be employed in anchorage zone such as beam-column joints.

Keywords: Bond stress, high performance fiber reinforced cement composites, slip, strain.

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Authors: Kirill Trapezon, Alexandr Trapezon

Abstract:

A problem is formulated for the natural oscillations of a circular plate of linearly variable thickness on the basis of the symmetry method. The equations of natural frequencies and forms for a plate are obtained, providing that it is rigidly fixed along the inner contour. The first three eigenfrequencies are calculated, and the eigenmodes of the oscillations of the acoustic element are constructed. An algorithm for applying the symmetry method and the factorization method for solving problems in the theory of oscillations for plates of variable thickness is shown. The effectiveness of the approach is demonstrated on the basis of comparison of known results and those obtained in the article. It is shown that the results are more accurate and reliable.

Keywords: Vibrations, plate, thickness, symmetry, factorization, approximation.

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Authors: S. Jalilzadeh, A. Kimiyaghalam, A. Ashouri

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This paper presents a mathematical model and a methodology to analyze the losses in transmission expansion planning (TEP) under uncertainty in demand. The methodology is based on discrete particle swarm optimization (DPSO). DPSO is a useful and powerful stochastic evolutionary algorithm to solve the large-scale, discrete and nonlinear optimization problems like TEP. The effectiveness of the proposed idea is tested on an actual transmission network of the Azerbaijan regional electric company, Iran. The simulation results show that considering the losses even for transmission expansion planning of a network with low load growth is caused that operational costs decreases considerably and the network satisfies the requirement of delivering electric power more reliable to load centers.

Keywords: DPSO, TEP, Uncertainty

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Authors: Reinhold Decker, Christian Holsing, Sascha Lerke

Abstract:

This paper presents a new growing neural network for cluster analysis and market segmentation, which optimizes the size and structure of clusters by iteratively checking them for multivariate normality. We combine the recently published SGNN approach [8] with the basic principle underlying the Gaussian-means algorithm [13] and the Mardia test for multivariate normality [18, 19]. The new approach distinguishes from existing ones by its holistic design and its great autonomy regarding the clustering process as a whole. Its performance is demonstrated by means of synthetic 2D data and by real lifestyle survey data usable for market segmentation.

Keywords: Artificial neural network, clustering, multivariatenormality, market segmentation, self-organization

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Authors: A. Jamekhorshid, G. Karimi, I. Noshadi, A. Jahangiri

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Non-uniform current distribution in polymer electrolyte membrane fuel cells results in local over-heating, accelerated ageing, and lower power output than expected. This issue is very critical when fuel cell experiences water flooding. In this work, the performance of a PEM fuel cell is investigated under cathode flooding conditions. Two-dimensional partially flooded GDL models based on the conservation laws and electrochemical relations are proposed to study local current density distributions along flow fields over a wide range of cell operating conditions. The model results show a direct association between cathode inlet humidity increases and that of average current density but the system becomes more sensitive to flooding. The anode inlet relative humidity shows a similar effect. Operating the cell at higher temperatures would lead to higher average current densities and the chance of system being flooded is reduced. In addition, higher cathode stoichiometries prevent system flooding but the average current density remains almost constant. The higher anode stoichiometry leads to higher average current density and higher sensitivity to cathode flooding.

Keywords: Current distribution, Flooding, Hydrogen energysystem, PEM fuel cell.

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Authors: Sanmohan Krüger, Volker Krüger

Abstract:

Imitation learning is considered to be an effective way of teaching humanoid robots and action recognition is the key step to imitation learning. In this paper an online algorithm to recognize parametric actions with object context is presented. Objects are key instruments in understanding an action when there is uncertainty. Ambiguities arising in similar actions can be resolved with objectn context. We classify actions according to the changes they make to the object space. Actions that produce the same state change in the object movement space are classified to belong to the same class. This allow us to define several classes of actions where members of each class are connected with a semantic interpretation.

Keywords: Parametric actions, Action primitives, Hand gesture recognition, Imitation learning

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