Search results for: linear congruential algorithm

Authors: P. Subbuthai, S. Muruganand

Abstract:

Diabetic Retinopathy is one of the complicated diseases and it is caused by the changes in the blood vessels of the retina. Extraction of retina image through Fundus camera sometimes produced poor contrast and noises. Because of this noise, detection of blood vessels in the retina is very complicated. So preprocessing is needed, in this paper, a novel algorithm is implemented to remove the noisy pixel in the retina image. The proposed algorithm is Extended Median Filter and it is applied to the green channel of the retina because green channel vessels are brighter than the background. Proposed extended median filter is compared with the existing standard median filter by performance metrics such as PSNR, MSE and RMSE. Experimental results show that the proposed Extended Median Filter algorithm gives a better result than the existing standard median filter in terms of noise suppression and detail preservation.

Keywords: fundus retina image, diabetic retinopathy, median filter, microaneurysms, exudates

Procedia PDF Downloads 317

Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

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Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

Procedia PDF Downloads 504

Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

Procedia PDF Downloads 161

Authors: Essam Al Daoud

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This study solves a phylogeny problem by using modified wild dog pack optimization. The least squares error is considered as a cost function that needs to be minimized. Therefore, in each iteration, new distance matrices based on the constructed trees are calculated and used to select the alpha dog. To test the suggested algorithm, ten homologous genes are selected and collected from National Center for Biotechnology Information (NCBI) databanks (i.e., 16S, 18S, 28S, Cox 1, ITS1, ITS2, ETS, ATPB, Hsp90, and STN). The data are divided into three categories: 50 taxa, 100 taxa and 500 taxa. The empirical results show that the proposed algorithm is more reliable and accurate than other implemented methods.

Keywords: least square, neighbor joining, phylogenetic tree, wild dog pack

Procedia PDF Downloads 299

Authors: Abhijith Asok

Abstract:

This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

Procedia PDF Downloads 282

Authors: Soumaya Sallem, Marc Olivas

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This contribution presents a method for detecting, locating, and characterizing soft faults in a complex wired network. The proposed method is based on multi-carrier reflectometry MCTDR (Multi-Carrier Time Domain Reflectometry) combined with a multi-objective genetic algorithm. In order to ensure complete network coverage and eliminate diagnosis ambiguities, the MCTDR test signal is injected at several points on the network, and the data is merged between different reflectometers (sensors) distributed on the network. An adapted multi-objective genetic algorithm is used to merge data in order to obtain more accurate faults location and characterization. The proposed method performances are evaluated from numerical and experimental results.

Keywords: wired network, reflectometry, network distributed diagnosis, multi-objective genetic algorithm

Procedia PDF Downloads 172

Authors: M-M. Mohamed Ahmed, Nacer K. M’Sirdi, Aziz Naamane

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In this paper, a longitudinal and lateral control approach based on a nonlinear observer is proposed for a convoy of autonomous vehicles to follow a desired trajectory. To authors best knowledge, this topic has not yet been sufficiently addressed in the literature for the control of multi vehicles. The modeling of the convoy of the vehicles is revisited using a robotic method for simulation purposes and control design. With these models, a sliding mode observer is proposed to estimate the states of each vehicle in the convoy from the available sensors, then a sliding mode control based on this observer is used to control the longitudinal and lateral movement. The validation and performance evaluation are done using the well-known driving simulator Scanner-Studio. The results are presented for different maneuvers of 5 vehicles.

Keywords: autonomous vehicles, convoy, non-linear control, non-linear observer, sliding mode

Procedia PDF Downloads 119

Authors: ِAbdallah A. A. Shaheen

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Gaza Strip faces economic and political issues such as conflict, siege and urbanization; all these have led to an increase in the air pollution over Gaza Strip. In this study, Particulate matter 10 (PM10) concentration over Gaza Strip has been estimated by Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) data, based on a multispectral algorithm. Simultaneously, in-situ measurements for the corresponding particulate are acquired for selected time period. Landsat and ground data for eleven years are used to develop the algorithm while four years data (2002, 2006, 2010 and 2014) have been used to validate the results of algorithm. The developed algorithm gives highest regression, R coefficient value i.e. 0.86; RMSE value as 9.71 µg/m³; P values as 0. Average validation of algorithm show that calculated PM10 strongly correlates with measured PM10, indicating high efficiency of algorithm for the mapping of PM10 concentration during the years 2000 to 2014. Overall results show increase in minimum, maximum and average yearly PM10 concentrations, also presents similar trend over urban area. The rate of urbanization has been evaluated by supervised classification of the Landsat image. Urban sprawl from year 2000 to 2014 results in a high concentration of PM10 in the study area.

Keywords: PM10, landsat, atmospheric reflectance, Gaza strip, urbanization

Procedia PDF Downloads 231

Authors: Sajana Suwal, Ganesh R. Nhemafuki

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Evaluation of ground response under earthquake shaking is crucial in geotechnical earthquake engineering. Damage due to seismic excitation is mainly correlated to local geological and geotechnical conditions. It is evident from the past earthquakes (e.g. 1906 San Francisco, USA, 1923 Kanto, Japan) that the local geology has strong influence on amplitude and duration of ground motions. Since then significant studies has been conducted on ground motion amplification revealing the importance of influence of local geology on ground. Observations from the damaging earthquakes (e.g. Nigata and San Francisco, 1964; Irpinia, 1980; Mexico, 1985; Kobe, 1995; L’Aquila, 2009) divulged that non-uniform damage pattern, particularly in soft fluvio-lacustrine deposit is due to the local amplification of seismic ground motion. Non-uniform damage patterns are also observed in Kathmandu Valley during 1934 Bihar Nepal earthquake and recent 2015 Gorkha earthquake seemingly due to the modification of earthquake ground motion parameters. In this study, site effects resulting from amplification of soft soil in Kathmandu are presented. A large amount of subsoil data was collected and used for defining the appropriate subsoil model for the Kathamandu valley. A comparative study of one-dimensional total-stress equivalent linear and non-linear site response is performed using four strong ground motions for six sites of Kathmandu valley. In general, one-dimensional (1D) site-response analysis involves the excitation of a soil profile using the horizontal component and calculating the response at individual soil layers. In the present study, both equivalent linear and non-linear site response analyses were conducted using the computer program DEEPSOIL. The results show that there is no significant deviation between equivalent linear and non-linear site response models until the maximum strain reaches to 0.06-0.1%. Overall, it is clearly observed from the results that non-linear site response model perform better as compared to equivalent linear model. However, the significant deviation between two models is resulted from other influencing factors such as assumptions made in 1D site response, lack of accurate values of shear wave velocity and nonlinear properties of the soil deposit. The results are also presented in terms of amplification factors which are predicted to be around four times more in case of non-linear analysis as compared to equivalent linear analysis. Hence, the nonlinear behavior of soil prevails the urgent need of study of dynamic characteristics of the soft soil deposit that can specifically represent the site-specific design spectra for the Kathmandu valley for building resilient structures from future damaging earthquakes.

Keywords: deep soil, equivalent linear analysis, non-linear analysis, site response

Procedia PDF Downloads 274

Authors: Thang V. Kieu, Long P. Nguyen

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Kernelized Correlation Filter (KCF) based trackers have gained a lot of attention recently because of their accuracy and fast calculation speed. However, this algorithm is not robust in cases where the object is lost by a sudden change of direction, being obscured or going out of view. In order to improve KCF performance in long-term tracking, this paper proposes an anomaly detection method for target loss warning by analyzing the response map of each frame, and a classification algorithm for reliable target re-locating mechanism by using Random fern. Being tested with Visual Tracker Benchmark and Visual Object Tracking datasets, the experimental results indicated that the precision and success rate of the proposed algorithm were 2.92 and 2.61 times higher than that of the original KCF algorithm, respectively. Moreover, the proposed tracker handles occlusion better than many state-of-the-art long-term tracking methods while running at 60 frames per second.

Keywords: correlation filter, long-term tracking, random fern, real-time tracking

Procedia PDF Downloads 117

Authors: J. G. Batista, T. S. Santiago, E. A. Ribeiro, G. A. P. Thé

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This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measure and/or expected values.

Keywords: modeling, AC servomotor, permanent magnet synchronous motor-PMSM, genetic algorithm, vector control, robotic manipulator, control

Procedia PDF Downloads 498

Authors: Shih-Pin Chen, Shih-Syuan You

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Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

Procedia PDF Downloads 414

Authors: Xu Runzhang, Yang Yanbing, Niu Yi, Zhang Mingyou, Liu Yu

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For a class of semilinear parabolic equations with linear dynamical boundary conditions in a bounded domain, we obtain both global solutions and finite time blow-up solutions when the initial data varies in the phase space H1(Ω). Our main tools are the comparison principle, the potential well method and the concavity method. In particular, we discuss the behavior of the solutions with the initial data at critical and high energy level.

Keywords: high energy level, critical energy level, linear dynamical boundary condition, semilinear parabolic equation

Procedia PDF Downloads 416

Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

Procedia PDF Downloads 443

Authors: Jamileh Darsareh, Mohammad Nikafrooz

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Some teachers and e-content producers, based on their experiences, try to produce educational videos using screen recording software. There are many challenges that they may encounter while producing screen-recorded videos. These are in the domains of technical and pedagogical challenges like designing the roadmap, preparing the screen, setting the recording software and recording the screen, editing, etc. This study is a descriptive study and tries to present some procedures for producing acceptable and well-made videos. These procedures are presented in the form of an algorithm for producing screen-recorded video. This algorithm presents the main producing phases, including design, pre-production, production, post-production, and distribution. These phases consist of some steps which are supported by several technical and pedagogical considerations. Following these phases and steps according to the suggested order helps the producers to produce their intended and desired video by saving time and also facing fewer technical problems. It is expected that by using this algorithm, e-content producers and teachers gain better performance in producing educational videos.

Keywords: e-content producing algorithm, screen-recorded videos, screen recording software, technical and pedagogical considerations

Procedia PDF Downloads 179

Authors: Rimmy Yadav, Avtar Singh

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—Ant Colony Optimization (ACO) is a promising modern approach to the unused combinatorial optimization. Here ACO is applied to finding the shortest during communication link failure. In this paper, the performances of the prim’s and ACO algorithm are made. By comparing the time complexity and program execution time as set of parameters, we demonstrate the pleasant performance of ACO in finding excellent solution to finding shortest path during communication link failure.

Keywords: ant colony optimization, link failure, prim’s algorithm, shortest path

Procedia PDF Downloads 373

Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

Abstract:

SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

Procedia PDF Downloads 46

Authors: Arslan Ellahi, Syed Amjad Hussain

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Software Testing is a vital process in software development life cycle. We can attain the quality of software after passing it through software testing phase. We have tried to find out automatic test data generation techniques that are a key research area of software testing to achieve test automation that can eventually decrease testing time. In this paper, we review some of the approaches presented in the literature which use evolutionary search based algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO), etc. to validate the test data generation process. We also look into the quality of test data generation which increases or decreases the efficiency of testing. We have proposed test data generation techniques for model-based testing. We have worked on tuning and fitness function of PSO algorithm.

Keywords: search based, evolutionary algorithm, particle swarm optimization, genetic algorithm, test data generation

Procedia PDF Downloads 162

Authors: P. K. Singhal, R. Naresh, V. Sharma

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This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: artificial bee colony algorithm, economic dispatch, unit commitment, wind power

Procedia PDF Downloads 355

Authors: Suman Dutta, Manish Agrawal, C. S. Jog

Abstract:

Accurate computation of hydrodynamic forces on floating structures and their deformation finds application in the ocean and naval engineering and wave energy harvesting. This manuscript presents a monolithic, finite element strategy for fluid-structure interaction involving hyper-elastic solids partly submerged in an incompressible fluid. A velocity-based Arbitrary Lagrangian-Eulerian (ALE) formulation has been used for the fluid and a displacement-based Lagrangian approach has been used for the solid. The flexibility of the ALE technique permits us to treat the free surface of the fluid as a Lagrangian entity. At the interface, the continuity of displacement, velocity and traction are enforced using the mortar method. In the mortar method, the constraints are enforced in a weak sense using the Lagrange multiplier method. In the literature, the mortar method has been shown to be robust in solving various contact mechanics problems. The time-stepping strategy used in this work reduces to the generalized trapezoidal rule in the Eulerian setting. In the Lagrangian limit, in the absence of external load, the algorithm conserves the linear and angular momentum and the total energy of the system. The use of monolithic coupling with an energy-conserving time-stepping strategy gives an unconditionally stable algorithm and allows the user to take large time steps. All the governing equations and boundary conditions have been mapped to the reference configuration. The use of the exact tangent stiffness matrix ensures that the algorithm converges quadratically within each time step. The robustness and good performance of the proposed method are demonstrated by solving benchmark problems from the literature.

Keywords: ALE, floating body, fluid-structure interaction, monolithic, mortar method

Procedia PDF Downloads 257

Authors: Deepa Das, Susmita Das

Abstract:

Cognitive Radio (CR) is an emerging technology to combat the spectrum scarcity issues. This is achieved by consistently sensing the spectrum, and detecting the under-utilized frequency bands without causing undue interference to the primary user (PU). In soft decision fusion (SDF) based cooperative spectrum sensing, various evolutionary algorithms have been discussed, which optimize the weight coefficient vector for maximizing the detection performance. In this paper, we propose the hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm as a fast and global optimization method, which improves the detection probability with a lesser sensing time. Then, the efficiency of this algorithm is compared with the standard invasive weed optimization (IWO), particle swarm optimization (PSO), genetic algorithm (GA) and other conventional SDF based methods on the basis of convergence and detection probability.

Keywords: cognitive radio, spectrum sensing, soft decision fusion, GA, PSO, IWO, hybrid IWO/PSO

Procedia PDF Downloads 437

Authors: Robert van Ling, Alexander Schwahn, Shanhua Lin, Ken Cook, Frank Steiner, Rowan Moore, Mauro de Pra

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Purpose: Demonstration of fast high resolution charge variant analysis for monoclonal antibody (mAb) therapeutics within 5 minutes. Methods: Three commercially available mAbs were used for all experiments. The charge variants of therapeutic mAbs (Bevacizumab, Cetuximab, Infliximab, and Trastuzumab) are analyzed on a strong cation exchange column with a linear pH gradient separation method. The linear gradient from pH 5.6 to pH 10.2 is generated over time by running a linear pump gradient from 100% Thermo Scientific™ CX-1 pH Gradient Buffer A (pH 5.6) to 100% CX-1 pH Gradient Buffer B (pH 10.2), using the Thermo Scientific™ Vanquish™ UHPLC system. Results: The pH gradient method is generally applicable to monoclonal antibody charge variant analysis. In conjunction with state-of-the-art column and UHPLC technology, ultra fast high-resolution separations are consistently achieved in under 5 minutes for all mAbs analyzed. Conclusion: The linear pH gradient method is a platform method for mAb charge variant analysis. The linear pH gradient method can be easily optimized to improve separations and shorten cycle times. Ultra-fast charge variant separation is facilitated with UHPLC that complements, and in some instances outperforms CE approaches in terms of both resolution and throughput.

Keywords: charge variants, ion exchange chromatography, monoclonal antibody, UHPLC

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

Abstract:

The aim of this study was to improve the effects of human body 3D reconstruction. The MvP algorithm was adopted to obtain key point information from multiple perspectives. This algorithm allowed the capture of human posture and joint positions from multiple angles, providing more comprehensive and accurate data. The study also incorporated the SMPL-X model, which has been widely used for human body modeling, to achieve more accurate 3D reconstruction results. The use of the MvP algorithm made it possible to observe the reconstructed object from multiple angles, thus reducing the problems of blind spots and missing information. This algorithm was able to effectively capture key point information, including the position and rotation angle of limbs, providing key data for subsequent 3D reconstruction. Compared with traditional single-view methods, the method of multi-view fusion significantly improved the accuracy and stability of reconstruction. By combining the MvP algorithm with the SMPL-X model, we successfully achieved better human body 3D reconstruction effects. The SMPL-X model is highly scalable and can generate highly realistic 3D human body models, thus providing more detail and shape information.

Keywords: 3D human reconstruction, multi-view, joint point, SMPL-X

Procedia PDF Downloads 41

Authors: J. Ritonja, R. Brezovnik, M. Petrun, B. Polajžer

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Many modern synchronous generators in power systems are extremely weakly damped. The reasons are cost optimization of the machine building and introduction of the additional control equipment into power systems. Oscillations of the synchronous generators and related stability problems of the power systems are harmful and can lead to failures in operation and to damages. The only useful solution to increase damping of the unwanted oscillations represents the implementation of the power system stabilizers. Power system stabilizers generate the additional control signal which changes synchronous generator field excitation voltage. Modern power system stabilizers are integrated into static excitation systems of the synchronous generators. Available commercial power system stabilizers are based on linear control theory. Due to the nonlinear dynamics of the synchronous generator, current stabilizers do not assure optimal damping of the synchronous generator’s oscillations in the entire operating range. For that reason the use of the robust power system stabilizers which are convenient for the entire operating range is reasonable. There are numerous robust techniques applicable for the power system stabilizers. In this paper the use of sliding mode control for synchronous generator stability improvement is studied. On the basis of the sliding mode theory, the robust power system stabilizer was developed. The main advantages of the sliding mode controller are simple realization of the control algorithm, robustness to parameter variations and elimination of disturbances. The advantage of the proposed sliding mode controller against conventional linear controller was tested for damping of the synchronous generator oscillations in the entire operating range. Obtained results show the improved damping in the entire operating range of the synchronous generator and the increase of the power system stability. The proposed study contributes to the progress in the development of the advanced stabilizer, which will replace conventional linear stabilizers and improve damping of the synchronous generators.

Keywords: control theory, power system stabilizer, robust control, sliding mode control, stability, synchronous generator

Procedia PDF Downloads 203

Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm

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Authors: Jia Li, Huacong Li, Xiaobao Han

Abstract:

Due to the more variable geometry parameters of VCE (variable cycle aircraft engine), presents an adaptive controller method based on the full-state linear model of VCE and has simulated to solve the multivariate controller design problem of the whole flight envelops. First, analyzes the static and dynamic performances of bypass ratio and other state parameters caused by variable geometric components, and develops nonlinear component model of VCE. Then based on the component model, through small deviation linearization of main fuel (Wf), the area of tail nozzle throat (A8) and the angle of rear bypass ejector (A163), setting up multiple linear model which variable geometric parameters can be inputs. Second, designs the adaptive controllers for VCE linear models of different nominal points. Among them, considering of modeling uncertainties and external disturbances, derives the adaptive law by lyapunov function. The simulation results showed that, the adaptive controller method based on full-state linear model used the angle of rear bypass ejector as input and effectively solved the multivariate control problems of VCE. The performance of all nominal points could track the desired closed-loop reference instructions. The adjust time was less than 1.2s, and the system overshoot was less than 1%, at the same time, the errors of steady states were less than 0.5% and the dynamic tracking errors were less than 1%. In addition, the designed controller could effectively suppress interference and reached the desired commands with different external random noise signals.

Keywords: variable cycle engine (VCE), full-state linear model, adaptive control, by-pass ratio

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Authors: Qazi Salman Khalid, Muhammad Khalid, Shahid Maqsood

Abstract:

This paper presents a solution for optimizing the cycle time in an assembly line with human-robot collaboration and diverse operators. A genetic algorithm with tailored parameters is used to address the assembly line balancing problem in the automobile sector. A mathematical model is developed, depicting the problem. Currently, the firm runs on the largest candidate rule; however, it causes a lag in orders, which ultimately gets penalized. The results of the study show that the proposed GA is effective in providing efficient solutions and that the cycle time has significantly impacted productivity.

Keywords: line balancing, cycle time, genetic algorithm, productivity

Procedia PDF Downloads 111

Authors: Takashi Kaburagi, Takamasa Komura, Yosuke Kurihara

Abstract:

Absolute pitch is the ability to identify a musical note without a reference tone. Training for absolute pitch often occurs in preschool education. It is necessary to clarify how well the trainee can make use of synesthesia in order to evaluate the effect of the training. To the best of our knowledge, there are no existing methods for objectively confirming whether the subject is using synesthesia. Therefore, in this study, we present a method to distinguish the use of color-auditory synesthesia from the separate use of color and audition during absolute pitch training. This method measures blood volume in the prefrontal cortex using functional Near-infrared spectroscopy (fNIRS) and assumes that the cognitive step has two parts, a non-linear step and a linear step. For the linear step, we assume a second order ordinary differential equation. For the non-linear part, it is extremely difficult, if not impossible, to create an inverse filter of such a complex system as the brain. Therefore, we apply a method based on a self-organizing map (SOM) and are guided by the available data. The presented method was tested using 15 subjects, and the estimation accuracy is reported.

Keywords: absolute pitch, functional near-infrared spectroscopy, prefrontal cortex, synesthesia

Procedia PDF Downloads 244

Authors: Yi-Fei Yang, Nai-Bao He, Shao-Bang Xing

Abstract:

This study investigates the permanent magnet synchronous linear motor (PMSLM) chaotic motion under the specific physical parameters, the stability and the security of motor-driven system will be unavoidably influenced. Therefore, it is really necessary to investigate the methods of controlling or suppressing chaos in PMSLM. Firstly, we derive a chaotic model of PMSLM in the closed-loop system. Secondly, in order to realize the local asymptotic stabilization of the mechanical subsystem and the global stabilization of the motor-driven system including electrical subsystem, we propose an improved uniting control lyapunov functions by introducing backstepping approach. Finally, an illustrated example is also given to show the electiveness of the obtained results.

Keywords: linear motor, lyapunov functions, chao control, hybrid controller

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Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

Abstract:

The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

Procedia PDF Downloads 381