Search results for: Search Algorithm
3040 Two Area Power Systems Economic Dispatch Problem Solving Considering Transmission Capacity Constraints
Authors: M. Zarei, A. Roozegar, R. Kazemzadeh, J.M. Kauffmann
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This paper describes an efficient and practical method for economic dispatch problem in one and two area electrical power systems with considering the constraint of the tie transmission line capacity constraint. Direct search method (DSM) is used with some equality and inequality constraints of the production units with any kind of fuel cost function. By this method, it is possible to use several inequality constraints without having difficulty for complex cost functions or in the case of unavailability of the cost function derivative. To minimize the number of total iterations in searching, process multi-level convergence is incorporated in the DSM. Enhanced direct search method (EDSM) for two area power system will be investigated. The initial calculation step size that causes less iterations and then less calculation time is presented. Effect of the transmission tie line capacity, between areas, on economic dispatch problem and on total generation cost will be studied; line compensation and active power with reactive power dispatch are proposed to overcome the high generation costs for this multi-area system.Keywords: Economic dispatch, Power System Operation, Direct Search Method, Transmission Capacity Constraint.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24833039 Active Vibration Control of Flexible Beam using Differential Evolution Optimisation
Authors: Mohd Sazli Saad, Hishamuddin Jamaluddin, Intan Zaurah Mat Darus
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This paper presents the development of an active vibration control using direct adaptive controller to suppress the vibration of a flexible beam system. The controller is realized based on linear parametric form. Differential evolution optimisation algorithm is used to optimize the controller using single objective function by minimizing the mean square error of the observed vibration signal. Furthermore, an alternative approach is developed to systematically search for the best controller model structure together with it parameter values. The performance of the control scheme is presented and analysed in both time and frequency domain. Simulation results demonstrate that the proposed scheme is able to suppress the unwanted vibration effectively.Keywords: flexible beam, finite difference method, active vibration control, differential evolution, direct adaptive controller
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25583038 Detection of Breast Cancer in the JPEG2000 Domain
Authors: Fayez M. Idris, Nehal I. AlZubaidi
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Breast cancer detection techniques have been reported to aid radiologists in analyzing mammograms. We note that most techniques are performed on uncompressed digital mammograms. Mammogram images are huge in size necessitating the use of compression to reduce storage/transmission requirements. In this paper, we present an algorithm for the detection of microcalcifications in the JPEG2000 domain. The algorithm is based on the statistical properties of the wavelet transform that the JPEG2000 coder employs. Simulation results were carried out at different compression ratios. The sensitivity of this algorithm ranges from 92% with a false positive rate of 4.7 down to 66% with a false positive rate of 2.1 using lossless compression and lossy compression at a compression ratio of 100:1, respectively.Keywords: Breast cancer, JPEG2000, mammography, microcalcifications.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15753037 Reentry Trajectory Optimization Based on Differential Evolution
Authors: Songtao Chang, Yongji Wang, Lei Liu, Dangjun Zhao
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Reentry trajectory optimization is a multi-constraints optimal control problem which is hard to solve. To tackle it, we proposed a new algorithm named CDEN(Constrained Differential Evolution Newton-Raphson Algorithm) based on Differential Evolution( DE) and Newton-Raphson.We transform the infinite dimensional optimal control problem to parameter optimization which is finite dimensional by discretize control parameter. In order to simplify the problem, we figure out the control parameter-s scope by process constraints. To handle constraints, we proposed a parameterless constraints handle process. Through comprehensive analyze the problem, we use a new algorithm integrated by DE and Newton-Raphson to solve it. It is validated by a reentry vehicle X-33, simulation results indicated that the algorithm is effective and robust.Keywords: reentry vehicle, trajectory optimization, constraint optimal, differential evolution.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17343036 Optimal Facility Layout Problem Solution Using Genetic Algorithm
Authors: Maricar G. Misola, Bryan B. Navarro
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Facility Layout Problem (FLP) is one of the essential problems of several types of manufacturing and service sector. It is an optimization problem on which the main objective is to obtain the efficient locations, arrangement and order of the facilities. In the literature, there are numerous facility layout problem research presented and have used meta-heuristic approaches to achieve optimal facility layout design. This paper presented genetic algorithm to solve facility layout problem; to minimize total cost function. The performance of the proposed approach was verified and compared using problems in the literature.
Keywords: Facility Layout Problem, Genetic Algorithm, Material Handling Cost, Meta-heuristic Approach.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 47383035 Recognition Machine (RM) for On-line and Isolated Flight Deck Officer (FDO) Gestures
Authors: Deniz T. Sodiri, Venkat V S S Sastry
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The paper presents an on-line recognition machine (RM) for continuous/isolated, dynamic and static gestures that arise in Flight Deck Officer (FDO) training. RM is based on generic pattern recognition framework. Gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of gestures via dynamic programming and Markovian process. The algorithm predicts corresponding index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. The algorithm provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. In the present paper, we consider isolated gestures. The performance of RM is evaluated using four datasets - artificial (W TTest), hand motion (Yang) and FDO (tracker, vision-based ). RM achieves comparable results which are in agreement with other on-line and off-line algorithms such as hidden Markov model (HMM) and dynamic time warping (DTW). The proposed algorithm has the additional advantage of providing timely feedback for training purposes.Keywords: On-line Recognition Algorithm, IsolatedDynamic/Static Gesture Recognition, On-line Markovian/DynamicProgramming, Training in Virtual Environments.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14613034 A Phenomic Algorithm for Reconstruction of Gene Networks
Authors: Rio G. L. D'Souza, K. Chandra Sekaran, A. Kandasamy
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The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Keywords: Evolutionary computing, gene expression analysis, gene networks, microarray data analysis, phenomic algorithms.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19243033 A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata
Authors: Pavan K. Rallabandi, Kailash C. Patidar
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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence/pattern recognition/classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.Keywords: Hybrid systems, Hidden Markov Models, Recurrent neural networks, Deterministic finite state automata.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28833032 A New Implementation of Miura-Arita Algorithm for Miura Curves
Authors: A. Basiri, S. Rahmany, D. Khatibi
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The aim of this paper is to review some of standard fact on Miura curves. We give some easy theorem in number theory to define Miura curves, then we present a new implementation of Arita algorithm for Miura curves.
Keywords: Miura curve, discrete logarithm problem, algebraic curve cryptography, Jacobian group.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14623031 A Comparison of Real Valued Transforms for Image Compression
Authors: Shivali D. Kulkarni, Ameya K. Naik, Nitin S. Nagori
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In this paper we present simulation results for the application of a bandwidth efficient algorithm (mapping algorithm) to an image transmission system. This system considers three different real valued transforms to generate energy compact coefficients. First results are presented for gray scale and color image transmission in the absence of noise. It is seen that the system performs its best when discrete cosine transform is used. Also the performance of the system is dominated more by the size of the transform block rather than the number of coefficients transmitted or the number of bits used to represent each coefficient. Similar results are obtained in the presence of additive white Gaussian noise. The varying values of the bit error rate have very little or no impact on the performance of the algorithm. Optimum results are obtained for the system considering 8x8 transform block and by transmitting 15 coefficients from each block using 8 bits.Keywords: Additive white Gaussian noise channel, mapping algorithm, peak signal to noise ratio, transform encoding.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14983030 Comparative Study of IC and Perturb and Observe Method of MPPT Algorithm for Grid Connected PV Module
Authors: Arvind Kumar, Manoj Kumar, Dattatraya H. Nagaraj, Amanpreet Singh, Jayanthi Prattapati
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The purpose of this paper is to study and compare two maximum power point tracking (MPPT) algorithms in a photovoltaic simulation system and also show a simulation study of maximum power point tracking (MPPT) for photovoltaic systems using perturb and observe algorithm and Incremental conductance algorithm. Maximum power point tracking (MPPT) plays an important role in photovoltaic systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency and minimize the overall system cost. Since the maximum power point (MPP) varies, based on the irradiation and cell temperature, appropriate algorithms must be utilized to track the (MPP) and maintain the operation of the system in it. MATLAB/Simulink is used to establish a model of photovoltaic system with (MPPT) function. This system is developed by combining the models established of solar PV module and DC-DC Boost converter. The system is simulated under different climate conditions. Simulation results show that the photovoltaic simulation system can track the maximum power point accurately.Keywords: Incremental conductance Algorithm, Perturb and Observe Algorithm, Photovoltaic System and Simulation Results.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12573029 Deep Web Content Mining
Authors: Shohreh Ajoudanian, Mohammad Davarpanah Jazi
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The rapid expansion of the web is causing the constant growth of information, leading to several problems such as increased difficulty of extracting potentially useful knowledge. Web content mining confronts this problem gathering explicit information from different web sites for its access and knowledge discovery. Query interfaces of web databases share common building blocks. After extracting information with parsing approach, we use a new data mining algorithm to match a large number of schemas in databases at a time. Using this algorithm increases the speed of information matching. In addition, instead of simple 1:1 matching, they do complex (m:n) matching between query interfaces. In this paper we present a novel correlation mining algorithm that matches correlated attributes with smaller cost. This algorithm uses Jaccard measure to distinguish positive and negative correlated attributes. After that, system matches the user query with different query interfaces in special domain and finally chooses the nearest query interface with user query to answer to it.Keywords: Content mining, complex matching, correlation mining, information extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22773028 Optimum Design of an Absorption Heat Pump Integrated with a Kraft Industry using Genetic Algorithm
Authors: B. Jabbari, N. Tahouni, M. H. Panjeshahi
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In this study the integration of an absorption heat pump (AHP) with the concentration section of an industrial pulp and paper process is investigated using pinch technology. The optimum design of the proposed water-lithium bromide AHP is then achieved by minimizing the total annual cost. A comprehensive optimization is carried out by relaxation of all stream pressure drops as well as heat exchanger areas involving in AHP structure. It is shown that by applying genetic algorithm optimizer, the total annual cost of the proposed AHP is decreased by 18% compared to one resulted from simulation.Keywords: Absorption Heat Pump, Genetic Algorithm, Kraft Industry, Pinch Technology
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19413027 Edge Detection Algorithm Based on Wavelet De-nosing Applied tothe X-ray Image Enhancement of the Electric Equipment
Authors: Fei Xue, Hong Yu, Da-da Wang, Wei Zhang, Rong-min Zou, Xiao-lanCai
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The X-ray technology has been used in non-destructive evaluation in the Power System, in which a visual non-destructive inspection method for the electrical equipment is provided. However, lots of noise is existed in the images that are got from the X-ray digital images equipment. Therefore, the auto defect detection which based on these images will be very difficult to proceed. A theory on X-ray image de-noising algorithm based on wavelet transform is proposed in this paper. Then the edge detection algorithm is used so that the defect can be pushed out. The result of experiment shows that the method which utilized by this paper is very useful for de-noising on the X-ray images.
Keywords: de-noising, edge detection, wavelet transform, X-ray
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15483026 Identification of Optimum Parameters of Deep Drawing of a Cylindrical Workpiece using Neural Network and Genetic Algorithm
Authors: D. Singh, R. Yousefi, M. Boroushaki
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Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Keywords: Deep-drawing, Neural network, Genetic algorithm, Sheet metal forming.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22013025 Operating Conditions Optimization of Steam Injection in Enhanced Oil Recovery Using Duelist Algorithm
Authors: Totok R. Biyanto, Sonny Irawan, Hiskia J. Ginting, Matradji, Ya’umar, A. I. Fitri
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Steam injection is the most suitable of Enhanced Oil Recovery (EOR) methods to recover high viscosity oil. This is due to the capabilities of steam to reduce oil viscosity and increase the sweep capability of oil from the injection well toward the production well. Oil operating conditions in production should be match well with the operating condition target at the bottom of the production well. It is influenced by oil properties and reservoir rock properties. Hence, the operating condition should be optimized. Optimization requires three components i.e., objective function, model, and optimization technique. In this paper, the objective function is to obtain the optimum operating condition at the production well. The model was built using Darcy equation and mass-energy balance. The optimization technique utilizes Duelist Algorithm due to the effectiveness of its algorithm to obtain the desirable optimization results at the optimum operating condition.Keywords: Enhanced oil recovery, steam injection, operating conditions, modeling, optimization, Duelist algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15753024 System Reduction by Eigen Permutation Algorithm and Improved Pade Approximations
Authors: Jay Singh, Kalyan Chatterjee, C. B. Vishwakarma
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A mixed method by combining a Eigen algorithm and improved pade approximations is proposed for reducing the order of the large-scale dynamic systems. The most dominant Eigen value of both original and reduced order systems remain same in this method. The proposed method guarantees stability of the reduced model if the original high-order system is stable and is comparable in quality with the other well known existing order reduction methods. The superiority of the proposed method is shown through examples taken from the literature.
Keywords: Eigen algorithm, Order reduction, improved pade approximations, Stability, Transfer function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20933023 Incremental Algorithm to Cluster the Categorical Data with Frequency Based Similarity Measure
Authors: S.Aranganayagi, K.Thangavel
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Clustering categorical data is more complicated than the numerical clustering because of its special properties. Scalability and memory constraint is the challenging problem in clustering large data set. This paper presents an incremental algorithm to cluster the categorical data. Frequencies of attribute values contribute much in clustering similar categorical objects. In this paper we propose new similarity measures based on the frequencies of attribute values and its cardinalities. The proposed measures and the algorithm are experimented with the data sets from UCI data repository. Results prove that the proposed method generates better clusters than the existing one.Keywords: Clustering, Categorical, Incremental, Frequency, Domain
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18203022 Association Rules Mining and NOSQL Oriented Document in Big Data
Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub
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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.
Keywords: Apriori, Association rules mining, Big Data, data mining, Hadoop, Map Reduce, MongoDB, NoSQL.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6933021 Automated Inspection Algorithm for Thick Plate Using Dual Light Switching Lighting Method
Authors: Yong-JuJeon, Doo-chul Choi, Jong Pil Yun, Changhyun Park, Homoon Bae, Sang Woo Kim
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This paper presents an automated inspection algorithm for a thick plate. Thick plates typically have various types of surface defects, such as scabs, scratches, and roller marks. These defects have individual characteristics including brightness and shape. Therefore, it is not simple to detect all the defects. In order to solve these problems and to detect defects more effectively, we propose a dual light switching lighting method and a defect detection algorithm based on Gabor filters.Keywords: Thick plate, Defect, Inspection, Gabor filter, Dual Light Switching.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18743020 Feasibility of the Evolutionary Algorithm using Different Behaviours of the Mutation Rate to Design Simple Digital Logic Circuits
Authors: Konstantin Movsovic, Emanuele Stomeo, Tatiana Kalganova
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The evolutionary design of electronic circuits, or evolvable hardware, is a discipline that allows the user to automatically obtain the desired circuit design. The circuit configuration is under the control of evolutionary algorithms. Several researchers have used evolvable hardware to design electrical circuits. Every time that one particular algorithm is selected to carry out the evolution, it is necessary that all its parameters, such as mutation rate, population size, selection mechanisms etc. are tuned in order to achieve the best results during the evolution process. This paper investigates the abilities of evolution strategy to evolve digital logic circuits based on programmable logic array structures when different mutation rates are used. Several mutation rates (fixed and variable) are analyzed and compared with each other to outline the most appropriate choice to be used during the evolution of combinational logic circuits. The experimental results outlined in this paper are important as they could be used by every researcher who might need to use the evolutionary algorithm to design digital logic circuits.Keywords: Evolvable hardware, evolutionary algorithm, digitallogic circuit, mutation rate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15003019 A Cognitive Robot Collaborative Reinforcement Learning Algorithm
Authors: Amit Gil, Helman Stern, Yael Edan
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A cognitive collaborative reinforcement learning algorithm (CCRL) that incorporates an advisor into the learning process is developed to improve supervised learning. An autonomous learner is enabled with a self awareness cognitive skill to decide when to solicit instructions from the advisor. The learner can also assess the value of advice, and accept or reject it. The method is evaluated for robotic motion planning using simulation. Tests are conducted for advisors with skill levels from expert to novice. The CCRL algorithm and a combined method integrating its logic with Clouse-s Introspection Approach, outperformed a base-line fully autonomous learner, and demonstrated robust performance when dealing with various advisor skill levels, learning to accept advice received from an expert, while rejecting that of less skilled collaborators. Although the CCRL algorithm is based on RL, it fits other machine learning methods, since advisor-s actions are only added to the outer layer.Keywords: Robot learning, human-robot collaboration, motion planning, reinforcement learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17233018 Computational Method for Annotation of Protein Sequence According to Gene Ontology Terms
Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias
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Annotation of a protein sequence is pivotal for the understanding of its function. Accuracy of manual annotation provided by curators is still questionable by having lesser evidence strength and yet a hard task and time consuming. A number of computational methods including tools have been developed to tackle this challenging task. However, they require high-cost hardware, are difficult to be setup by the bioscientists, or depend on time intensive and blind sequence similarity search like Basic Local Alignment Search Tool. This paper introduces a new method of assigning highly correlated Gene Ontology terms of annotated protein sequences to partially annotated or newly discovered protein sequences. This method is fully based on Gene Ontology data and annotations. Two problems had been identified to achieve this method. The first problem relates to splitting the single monolithic Gene Ontology RDF/XML file into a set of smaller files that can be easy to assess and process. Thus, these files can be enriched with protein sequences and Inferred from Electronic Annotation evidence associations. The second problem involves searching for a set of semantically similar Gene Ontology terms to a given query. The details of macro and micro problems involved and their solutions including objective of this study are described. This paper also describes the protein sequence annotation and the Gene Ontology. The methodology of this study and Gene Ontology based protein sequence annotation tool namely extended UTMGO is presented. Furthermore, its basic version which is a Gene Ontology browser that is based on semantic similarity search is also introduced.
Keywords: automatic clustering, bioinformatics tool, gene ontology, protein sequence annotation, semantic similarity search
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31273017 BIDENS: Iterative Density Based Biclustering Algorithm With Application to Gene Expression Analysis
Authors: Mohamed A. Mahfouz, M. A. Ismail
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Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19623016 Progressive AAM Based Robust Face Alignment
Authors: Daehwan Kim, Jaemin Kim, Seongwon Cho, Yongsuk Jang, Sun-Tae Chung, Boo-Gyoun Kim
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AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feature parameter vector fitting the inner facial feature points of the face and later localize the feature points of the whole face using the first information. The proposed progressive AAM-based face alignment algorithm utilizes the fact that the feature points of the inner part of the face are less variant and less affected by the background surrounding the face than those of the outer part (like the chin contour). The proposed algorithm consists of two stages: modeling and relation derivation stage and fitting stage. Modeling and relation derivation stage first needs to construct two AAM models: the inner face AAM model and the whole face AAM model and then derive relation matrix between the inner face AAM parameter vector and the whole face AAM model parameter vector. In the fitting stage, the proposed algorithm aligns face progressively through two phases. In the first phase, the proposed algorithm will find the feature parameter vector fitting the inner facial AAM model into a new input face image, and then in the second phase it localizes the whole facial feature points of the new input face image based on the whole face AAM model using the initial parameter vector estimated from using the inner feature parameter vector obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment algorithm is more robust with respect to pose, illumination, and face background than the conventional basic AAM-based face alignment algorithm.Keywords: Face Alignment, AAM, facial feature detection, model matching.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16383015 GMDH Modeling Based on Polynomial Spline Estimation and Its Applications
Authors: LI qiu-min, TIAN yi-xiang, ZHANG gao-xun
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GMDH algorithm can well describe the internal structure of objects. In the process of modeling, automatic screening of model structure and variables ensure the convergence rate.This paper studied a new GMDH model based on polynomial spline stimation. The polynomial spline function was used to instead of the transfer function of GMDH to characterize the relationship between the input variables and output variables. It has proved that the algorithm has the optimal convergence rate under some conditions. The empirical results show that the algorithm can well forecast Consumer Price Index (CPI).
Keywords: spline, GMDH, nonparametric, bias, forecast.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21333014 On Face Recognition using Gabor Filters
Authors: Al-Amin Bhuiyan, Chang Hong Liu
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Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.Keywords: Fuzzily skewed filter, Gabor filter, rms contrast, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30993013 Reducing Power in Error Correcting Code using Genetic Algorithm
Authors: Heesung Lee, Joonkyung Sung, Euntai Kim
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This paper proposes a method which reduces power consumption in single-error correcting, double error-detecting checker circuits that perform memory error correction code. Power is minimized with little or no impact on area and delay, using the degrees of freedom in selecting the parity check matrix of the error correcting codes. The genetic algorithm is employed to solve the non linear power optimization problem. The method is applied to two commonly used SEC-DED codes: standard Hamming and odd column weight Hsiao codes. Experiments were performed to show the performance of the proposed method.Keywords: Error correcting codes, genetic algorithm, non-linearpower optimization, Hamming code, Hsiao code.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21843012 The New AIMD Congestion Control Algorithm
Authors: Hayder Natiq Jasem, Zuriati Ahmad Zukarnain, Mohamed Othman, Shamala Subramaniam
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Congestion control is one of the fundamental issues in computer networks. Without proper congestion control mechanisms there is the possibility of inefficient utilization of resources, ultimately leading to network collapse. Hence congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. AIMD (Additive Increase Multiplicative Decrease) is the best algorithm among the set of liner algorithms because it reflects good efficiency as well as good fairness. Our control model is based on the assumption of the original AIMD algorithm; we show that both efficiency and fairness of AIMD can be improved. We call our approach is New AIMD. We present experimental results with TCP that match the expectation of our theoretical analysis.
Keywords: Congestion control, Efficiency, Fairness, TCP, AIMD.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24193011 Energy Efficient Clustering Algorithm with Global and Local Re-clustering for Wireless Sensor Networks
Authors: Ashanie Guanathillake, Kithsiri Samarasinghe
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Wireless Sensor Networks consist of inexpensive, low power sensor nodes deployed to monitor the environment and collect data. Gathering information in an energy efficient manner is a critical aspect to prolong the network lifetime. Clustering algorithms have an advantage of enhancing the network lifetime. Current clustering algorithms usually focus on global re-clustering and local re-clustering separately. This paper, proposed a combination of those two reclustering methods to reduce the energy consumption of the network. Furthermore, the proposed algorithm can apply to homogeneous as well as heterogeneous wireless sensor networks. In addition, the cluster head rotation happens, only when its energy drops below a dynamic threshold value computed by the algorithm. The simulation result shows that the proposed algorithm prolong the network lifetime compared to existing algorithms.
Keywords: Energy efficient, Global re-clustering, Local re-clustering, Wireless sensor networks.
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