Search results for: MaximumLikelihood Linear Regression (MLLR)
1588 Influence of Internal Heat Source on Thermal Instability in a Horizontal Porous Layer with Mass Flow and Inclined Temperature Gradient
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.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17101587 A New Quantile Based Fuzzy Time Series Forecasting Model
Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil
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Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19831586 Prediction of Road Accidents in Qatar by 2022
Authors: M. Abou-Amouna, A. Radwan, L. Al-kuwari, A. Hammuda, K. Al-Khalifa
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There is growing concern over increasing incidences of road accidents and consequent loss of human life in Qatar. In light to the future planned event in Qatar, World Cup 2022; Qatar should put into consideration the future deaths caused by road accidents, and past trends should be considered to give a reasonable picture of what may happen in the future. Qatar roads should be arranged and paved in a way that accommodate high capacity of the population in that time, since then there will be a huge number of visitors from the world. Qatar should also consider the risk issues of road accidents raised in that period, and plan to maintain high level to safety strategies. According to the increase in the number of road accidents in Qatar from 1995 until 2012, an analysis of elements affecting and causing road accidents will be effectively studied. This paper aims to identify and criticize the factors that have high effect on causing road accidents in the state of Qatar, and predict the total number of road accidents in Qatar 2022. Alternative methods are discussed and the most applicable ones according to the previous researches are selected for further studies. The methods that satisfy the existing case in Qatar were the multiple linear regression model (MLR) and artificial neutral network (ANN). Those methods are analyzed and their findings are compared. We conclude that by using MLR the number of accidents in 2022 will become 355,226 accidents, and by using ANN 216,264 accidents. We conclude that MLR gave better results than ANN because the artificial neutral network doesn’t fit data with large range varieties.
Keywords: Road Safety, Prediction, Accident, Model, Qatar.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26021585 Prevalence, Associated Factors, and Help-Seeking Behavior of Psychological Distress among International Students at the National University of Malaysia
Authors: Khadiga Kahwa, Aniza Ismail
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Depression, anxiety, and stress are associated with decreased role functioning, productivity, and quality of life. International students are more prone to psychological distress as they face many stressors while studying abroad. The objectives of the study were to determine the prevalence and associated factors of depression, anxiety, and stress among international students, their help-seeking behavior, and their awareness of the available on-campus mental support services. A cross-sectional study with a purposive sampling method was performed on 280 international students at Universiti Kebangsaan Malaysia (UKM) between the age of 18 and 35 years. The Depression Anxiety Stress Scale-21 (DASS-21) questionnaire was used anonymously to assess the mental health of students. Socio-demographic, help-seeking behavior, and awareness data were obtained. Independent sample t-test, one-way ANOVA test, and multiple linear regression were used to explore associated factors. The overall prevalence of depression, anxiety, and stress among international students were 58.9%, 71.8%, and 53.9%, respectively. Age was significantly associated with depression and anxiety. Ethnicity showed a significant association with depression and stress. No other factors were found to be significantly associated with psychological distress. Only 9.6% of the international students had sought help from on-campus mental support services. Students who were aware of the presence of such services were only 21.4% of the participants. In conclusion, this study addressed the gap in the literature on the mental health of international students and provided data that could be used in intervention programs to improve the mental health of the increasing number of international students in Malaysia.
Keywords: Anxiety, depression, stress, help-seeking behavior, students.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7001584 A Diffusion Least-Mean Square Algorithm for Distributed Estimation over Sensor Networks
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.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18541583 Hybrid Method Using Wavelets and Predictive Method for Compression of Speech Signal
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.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13211582 Indexing and Searching of Image Data in Multimedia Databases Using Axial Projection
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.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13811581 A Non-Linear Eddy Viscosity Model for Turbulent Natural Convection in Geophysical Flows
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.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9381580 Global Chaos Synchronization of Identical and Nonidentical Chaotic Systems Using Only Two Nonlinear Controllers
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.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19471579 Hierarchically Modeling Cognition and Behavioral Problems of an Under-Represented Group
Authors: Zhidong Zhang, Zhi-Chao Zhang
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This study examined the mental health and behavioral problems in early adolescence with the instrument of Achenbach System of Empirically Based Assessment (ASEBA). The purpose of the study was stratified sampling method was used to collect data from 1975 participants. Multiple regression models and hierarchical regression models were applied to examine the relations between the background variables and internalizing problems, and the ones between students’ performance and internalizing problems. The results indicated that several background variables as predictors could significantly predict the anxious/depressed problem; reading and social study scores could significantly predict the anxious/depressed problem. However the class as a hierarchical macro factor did not indicate the significant effect. In brief, the majority of these models represented that the background variables, behaviors and academic performance were significantly related to the anxious/depressed problem.Keywords: Behavioral problems, anxious/depression problems, empirical-based assessment, hierarchical modeling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17491578 Hybrid Algorithm for Hammerstein System Identification Using Genetic Algorithm and Particle Swarm Optimization
Authors: Tomohiro Hachino, Kenji Shimoda, Hitoshi Takata
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This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm.Keywords: Hammerstein system, identification, automatic choosing function model, genetic algorithm, particle swarm optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14811577 Complex-Valued Neural Networks for Blind Equalization of Time-Varying Channels
Authors: Rajoo Pandey
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Most of the commonly used blind equalization algorithms are based on the minimization of a nonconvex and nonlinear cost function and a neural network gives smaller residual error as compared to a linear structure. The efficacy of complex valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present two neural network models for blind equalization of time-varying channels, for M-ary QAM and PSK signals. The complex valued activation functions, suitable for these signal constellations in time-varying environment, are introduced and the learning algorithms based on the CMA cost function are derived. The improved performance of the proposed models is confirmed through computer simulations.
Keywords: Blind Equalization, Neural Networks, Constant Modulus Algorithm, Time-varying channels.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18771576 Fuzzy Control of Macroeconomic Models
Authors: Andre A. Keller
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The optimal control is one of the possible controllers for a dynamic system, having a linear quadratic regulator and using the Pontryagin-s principle or the dynamic programming method . Stochastic disturbances may affect the coefficients (multiplicative disturbances) or the equations (additive disturbances), provided that the shocks are not too great . Nevertheless, this approach encounters difficulties when uncertainties are very important or when the probability calculus is of no help with very imprecise data. The fuzzy logic contributes to a pragmatic solution of such a problem since it operates on fuzzy numbers. A fuzzy controller acts as an artificial decision maker that operates in a closed-loop system in real time. This contribution seeks to explore the tracking problem and control of dynamic macroeconomic models using a fuzzy learning algorithm. A two inputs - single output (TISO) fuzzy model is applied to the linear fluctuation model of Phillips and to the nonlinear growth model of Goodwin.Keywords: fuzzy control, macroeconomic model, multiplier - accelerator, nonlinear accelerator, stabilization policy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19781575 Investigation of the Effect of Cavitator Angle and Dimensions for a Supercavitating Vehicle
Authors: Sri Raman A., A.K.Ghosh
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At very high speeds, bubbles form in the underwater vehicles because of sharp trailing edges or of places where the local pressure is lower than the vapor pressure. These bubbles are called cavities and the size of the cavities grows as the velocity increases. A properly designed cavitator can induce the formation of a single big cavity all over the vehicle. Such a vehicle travelling in the vaporous cavity is called a supercavitating vehicle and the present research work mainly focuses on the dynamic modeling of such vehicles. Cavitation of the fins is also accounted and the effect of the same on trajectory is well explained. The entire dynamics has been developed using the state space approach and emphasis is given on the effect of size and angle of attack of the cavitator. Control law has been established for the motion of the vehicle using Non-linear Dynamic Inverse (NDI) with cavitator as the control surface.
Keywords: High speed underwater vehicle, Non-Linear Dynamic Inverse (NDI), six-dof modeling, Supercavitation, Torpedo.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 715741574 Structural Optimization Method for 3D Reinforced Concrete Building Structure with Shear Wall
Authors: H. Nikzad, S. Yoshitomi
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In this paper, an optimization procedure is applied for 3D Reinforced concrete building structure with shear wall. In the optimization problem, cross sections of beams, columns and shear wall dimensions are considered as design variables and the optimal cross sections can be derived to minimize the total cost of the structure. As for final design application, the most suitable sections are selected to satisfy ACI 318-14 code provision based on static linear analysis. The validity of the method is examined through numerical example of 15 storied 3D RC building with shear wall. This optimization method is expected to assist in providing a useful reference in design early stage, and to be an effective and powerful tool for structural design of RC shear wall structures.
Keywords: Structural optimization, linear static analysis, ETABS, MATLAB, RC moment frame, RC shear wall structures.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13231573 Competitors’ Influence Analysis of a Retailer by Using Customer Value and Huff’s Gravity Model
Authors: Yepeng Cheng, Yasuhiko Morimoto
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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.Keywords: Customer value, Huff's Gravity Model, POS, retailer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5941572 An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions
Authors: R. Mallika, V. Saravanan
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This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers.Keywords: Support vector machines-one against all, cancerclassification, Linear Discriminant analysis, K nearest neighbour, microarray gene expression, gene pair ranking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25511571 Using Structural Equation Modeling in Causal Relationship Design for Balanced-Scorecards' Strategic Map
Authors: A. Saghaei, R. Ghasemi
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Through 1980s, management accounting researchers described the increasing irrelevance of traditional control and performance measurement systems. The Balanced Scorecard (BSC) is a critical business tool for a lot of organizations. It is a performance measurement system which translates mission and strategy into objectives. Strategy map approach is a development variant of BSC in which some necessary causal relations must be established. To recognize these relations, experts usually use experience. It is also possible to utilize regression for the same purpose. Structural Equation Modeling (SEM), which is one of the most powerful methods of multivariate data analysis, obtains more appropriate results than traditional methods such as regression. In the present paper, we propose SEM for the first time to identify the relations between objectives in the strategy map, and a test to measure the importance of relations. In SEM, factor analysis and test of hypotheses are done in the same analysis. SEM is known to be better than other techniques at supporting analysis and reporting. Our approach provides a framework which permits the experts to design the strategy map by applying a comprehensive and scientific method together with their experience. Therefore this scheme is a more reliable method in comparison with the previously established methods.Keywords: BSC, SEM, Strategy map.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26931570 Low-Level Modeling for Optimal Train Routing and Scheduling in Busy Railway Stations
Authors: Quoc Khanh Dang, Thomas Bourdeaud’huy, Khaled Mesghouni, Armand Toguy´eni
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This paper studies a train routing and scheduling problem for busy railway stations. Our objective is to allow trains to be routed in dense areas that are reaching saturation. Unlike traditional methods that allocate all resources to setup a route for a train and until the route is freed, our work focuses on the use of resources as trains progress through the railway node. This technique allows a larger number of trains to be routed simultaneously in a railway node and thus reduces their current saturation. To deal with this problem, this study proposes an abstract model and a mixed-integer linear programming formulation to solve it. The applicability of our method is illustrated on a didactic example.Keywords: Busy railway stations, mixed-integer linear programming, offline railway station management, train platforming, train routing, train scheduling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7181569 A New Weighted LDA Method in Comparison to Some Versions of LDA
Authors: Delaram Jarchi, Reza Boostani
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Linear Discrimination Analysis (LDA) is a linear solution for classification of two classes. In this paper, we propose a variant LDA method for multi-class problem which redefines the between class and within class scatter matrices by incorporating a weight function into each of them. The aim is to separate classes as much as possible in a situation that one class is well separated from other classes, incidentally, that class must have a little influence on classification. It has been suggested to alleviate influence of classes that are well separated by adding a weight into between class scatter matrix and within class scatter matrix. To obtain a simple and effective weight function, ordinary LDA between every two classes has been used in order to find Fisher discrimination value and passed it as an input into two weight functions and redefined between class and within class scatter matrices. Experimental results showed that our new LDA method improved classification rate, on glass, iris and wine datasets, in comparison to different versions of LDA.Keywords: Discriminant vectors, weighted LDA, uncorrelation, principle components, Fisher-face method, Bootstarp method.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15081568 Stability Analysis for a Multicriteria Problem with Linear Criteria and Parameterized Principle of Optimality “from Lexicographic to Slater“
Authors: Yury Nikulin
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A multicriteria linear programming problem with integer variables and parameterized optimality principle "from lexicographic to Slater" is considered. A situation in which initial coefficients of penalty cost functions are not fixed but may be potentially a subject to variations is studied. For any efficient solution, appropriate measures of the quality are introduced which incorporate information about variations of penalty cost function coefficients. These measures correspond to the so-called stability and accuracy functions defined earlier for efficient solutions of a generic multicriteria combinatorial optimization problem with Pareto and lexicographic optimality principles. Various properties of such functions are studied and maximum norms of perturbations for which an efficient solution preserves the property of being efficient are calculated.
Keywords: Stability and accuracy, multicriteria optimization, lexicographic optimality.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10281567 Minimizing Makespan Subject to Budget Limitation in Parallel Flow Shop
Authors: Amin Sahraeian
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One of the criteria in production scheduling is Make Span, minimizing this criteria causes more efficiently use of the resources specially machinery and manpower. By assigning some budget to some of the operations the operation time of these activities reduces and affects the total completion time of all the operations (Make Span). In this paper this issue is practiced in parallel flow shops. At first we convert parallel flow shop to a network model and by using a linear programming approach it is identified in order to minimize make span (the completion time of the network) which activities (operations) are better to absorb the predetermined and limited budget. Minimizing the total completion time of all the activities in the network is equivalent to minimizing make span in production scheduling.Keywords: parallel flow shop, make span, linear programming, budget
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17681566 Economic Loss due to Ganoderma Disease in Oil Palm
Authors: K. Assis, K. P. Chong, A. S. Idris, C. M. Ho
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Oil palm or Elaeis guineensis is considered as the golden crop in Malaysia. But oil palm industry in this country is now facing with the most devastating disease called as Ganoderma Basal Stem Rot disease. The objective of this paper is to analyze the economic loss due to this disease. There were three commercial oil palm sites selected for collecting the required data for economic analysis. Yield parameter used to measure the loss was the total weight of fresh fruit bunch in six months. The predictors include disease severity, change in disease severity, number of infected neighbor palms, age of palm, planting generation, topography, and first order interaction variables. The estimation model of yield loss was identified by using backward elimination based regression method. Diagnostic checking was conducted on the residual of the best yield loss model. The value of mean absolute percentage error (MAPE) was used to measure the forecast performance of the model. The best yield loss model was then used to estimate the economic loss by using the current monthly price of fresh fruit bunch at mill gate.
Keywords: Ganoderma, oil palm, regression model, yield loss, economic loss.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32071565 A Deterministic Dynamic Programming Approach for Optimization Problem with Quadratic Objective Function and Linear Constraints
Authors: S. Kavitha, Nirmala P. Ratchagar
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This paper presents the novel deterministic dynamic programming approach for solving optimization problem with quadratic objective function with linear equality and inequality constraints. The proposed method employs backward recursion in which computations proceeds from last stage to first stage in a multi-stage decision problem. A generalized recursive equation which gives the exact solution of an optimization problem is derived in this paper. The method is purely analytical and avoids the usage of initial solution. The feasibility of the proposed method is demonstrated with a practical example. The numerical results show that the proposed method provides global optimum solution with negligible computation time.
Keywords: Backward recursion, Dynamic programming, Multi-stage decision problem, Quadratic objective function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35681564 Texture Characterization Based on a Chandrasekhar Fast Adaptive Filter
Authors: Mounir Sayadi, Farhat Fnaiech
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In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.
Keywords: Texture analysis, statistical features, adaptive filters, Chandrasekhar algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16061563 Feature Vector Fusion for Image Based Human Age Estimation
Authors: D. Karthikeyan, G. Balakrishnan
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Human faces, as important visual signals, express a significant amount of nonverbal info for usage in human-to-human communication. Age, specifically, is more significant among these properties. Human age estimation using facial image analysis as an automated method which has numerous potential real‐world applications. In this paper, an automated age estimation framework is presented. Support Vector Regression (SVR) strategy is utilized to investigate age prediction. This paper depicts a feature extraction taking into account Gray Level Co-occurrence Matrix (GLCM), which can be utilized for robust face recognition framework. It applies GLCM operation to remove the face's features images and Active Appearance Models (AAMs) to assess the human age based on image. A fused feature technique and SVR with GA optimization are proposed to lessen the error in age estimation.
Keywords: Support vector regression, feature extraction, gray level co-occurrence matrix, active appearance models.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13001562 Weight Functions for Signal Reconstruction Based On Level Crossings
Authors: Nagesha, G. Hemantha Kumar
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Although the level crossing concept has been the subject of intensive investigation over the last few years, certain problems of great interest remain unsolved. One of these concern is distribution of threshold levels. This paper presents a new threshold level allocation schemes for level crossing based on nonuniform sampling. Intuitively, it is more reasonable if the information rich regions of the signal are sampled finer and those with sparse information are sampled coarser. To achieve this objective, we propose non-linear quantization functions which dynamically assign the number of quantization levels depending on the importance of the given amplitude range. Two new approaches to determine the importance of the given amplitude segment are presented. The proposed methods are based on exponential and logarithmic functions. Various aspects of proposed techniques are discussed and experimentally validated. Its efficacy is investigated by comparison with uniform sampling.
Keywords: speech signals, sampling, signal reconstruction, asynchronousdelta modulation, non-linear quantization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16391561 Prediction of Compressive Strength Using Artificial Neural Network
Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal
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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.
Keywords: Rebound, ultra-sonic pulse, penetration, ANN, NDT, regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 43611560 Development of a Comprehensive Electricity Generation Simulation Model Using a Mixed Integer Programming Approach
Authors: Erik Delarue, David Bekaert, Ronnie Belmans, William D'haeseleer
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This paper presents the development of an electricity simulation model taking into account electrical network constraints, applied on the Belgian power system. The base of the model is optimizing an extensive Unit Commitment (UC) problem through the use of Mixed Integer Linear Programming (MILP). Electrical constraints are incorporated through the implementation of a DC load flow. The model encloses the Belgian power system in a 220 – 380 kV high voltage network (i.e., 93 power plants and 106 nodes). The model features the use of pumping storage facilities as well as the inclusion of spinning reserves in a single optimization process. Solution times of the model stay below reasonable values.
Keywords: Electricity generation modeling, Unit Commitment(UC), Mixed Integer Linear Programming (MILP), DC load flow.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20271559 Development of Orbital TIG Welding Robot System for the Pipe
Authors: Dongho Kim, Sung Choi, Kyowoong Pee, Youngsik Cho, Seungwoo Jeong, Soo-Ho Kim
Abstract:
This study is about the orbital TIG welding robot system which travels on the guide rail installed on the pipe, and welds and tracks the pipe seam using the LVS (Laser Vision Sensor) joint profile data. The orbital welding robot system consists of the robot, welder, controller, and LVS. Moreover we can define the relationship between welding travel speed and wire feed speed, and we can make the linear equation using the maximum and minimum amount of weld metal. Using the linear equation we can determine the welding travel speed and the wire feed speed accurately corresponding to the area of weld captured by LVS. We applied this orbital TIG welding robot system to the stainless steel or duplex pipe on DSME (Daewoo Shipbuilding and Marine Engineering Co. Ltd.,) shipyard and the result of radiographic test is almost perfect. (Defect rate: 0.033%).
Keywords: Adaptive welding, automatic welding, Pipe welding, Orbital welding, Laser vision sensor, LVS, welding D/B.
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