Search results for: Classification algorithms

Authors: Ewa. M. Sztendur, Neil T. Diamond

Abstract:

Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.

Keywords: Random Forests, Variable Importance, Fractional Factorial Designs, Student Attrition.

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Authors: Khattab Ibrahim Al Qaisi, Nicholas Bowring

Abstract:

This paper presents the simulation results of the effects of sampling frequency on the total harmonic distortion (THD) of three-phase inverters using the space vector pulse width modulation (SVPWM) and space vector control (SVC) algorithms. The relationship between the variables was studied using curve fitting techniques, and it has been shown that, for 50 Hz inverters, there is an exponential relation between the sampling frequency and THD up to around 8500 Hz, beyond which the performance of the model becomes irregular, and there is an negative exponential relation between the sampling frequency and the marginal improvement to the THD. It has also been found that the performance of SVPWM is better than that of SVC with the same sampling frequency in most frequency range, including the range where the performance of the former is irregular.

Keywords: SVPWM, THD, DC-AC Inverter, Sampling Frequency.

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Authors: Hayder Natiq Jasem, Zuriati Ahmad Zukarnain, Mohamed Othman, Shamala Subramaniam

Abstract:

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.

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Authors: Neda Shahidi, Hadi Esmaeilzadeh, Marziye Abdollahi, Caro Lucas

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In this paper, the problem of finding the optimal collision free path for a mobile robot, the path planning problem, is solved using an advanced evolutionary algorithm called memetic algorithm. What is new in this work is a novel representation of solutions for evolutionary algorithms that is efficient, simple and also compatible with memetic algorithm. The new representation makes it possible to solve the problem with a small population and in a few generations. It also makes the genetic operator simple and allows using an efficient local search operator within the evolutionary algorithm. The proposed algorithm is applied to two instances of path planning problem and the results are available.

Keywords: Path planning problem, Memetic Algorithm, Representation.

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Authors: Ali Zifan, Mohammad Hassan Moradi, Sohrab Saberi, Farzad Towhidkhah

Abstract:

Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG-s. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW). The results are tested on the QT database. We compared our results to Laguna-s two lead method. Our proposed approach has a comparable mean error, but yields a slightly higher standard deviation than Laguna-s method.

Keywords: Adaptive Piecewise Constant Approximation, Dynamic programming, ECG segmentation, Piecewise DerivativeDynamic Time Warping.

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Authors: Chahid K. Ghaddar

Abstract:

The spreadsheet engine is exploited via a non-conventional mechanism to enable novel worksheet solver functions for computational calculus. The solver functions bypass inherent restrictions on built-in math and user defined functions by taking variable formulas as a new type of argument while retaining purity and recursion properties. The enabling mechanism permits integration of numerical algorithms into worksheet functions for solving virtually any computational problem that can be modelled by formulas and variables. Several examples are presented for computing integrals, derivatives, and systems of deferential-algebraic equations. Incorporation of the worksheet solver functions with the ubiquitous spreadsheet extend the utility of the latter as a powerful tool for computational mathematics.

Keywords: Calculus functions, nonlinear systems, differential algebraic equations, solvers, spreadsheet.

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Authors: Parvinder S. Sandhu, Iqbaldeep Kaur, Amit Verma, Samriti Jindal, Shailendra Singh

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Biometric measures of one kind or another have been used to identify people since ancient times, with handwritten signatures, facial features, and fingerprints being the traditional methods. Of late, Systems have been built that automate the task of recognition, using these methods and newer ones, such as hand geometry, voiceprints and iris patterns. These systems have different strengths and weaknesses. This work is a two-section composition. In the starting section, we present an analytical and comparative study of common biometric techniques. The performance of each of them has been viewed and then tabularized as a result. The latter section involves the actual implementation of the techniques under consideration that has been done using a state of the art tool called, MATLAB. This tool aids to effectively portray the corresponding results and effects.

Keywords: Matlab, Recognition, Facial Vectors, Functions.

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Authors: Charifa Fergani, Adiba El Bouzekri El Idrissi, Suzanne Marcotte, Abdelowahed Hajjaji

Abstract:

Over the last years, a new paradigm known as Physical Internet has been developed, and studied in logistics management. The purpose of this global and open system is to deal with logistics grand challenge by setting up an efficient and sustainable Logistics Web. The purpose of this paper is to review scientific articles dedicated to Physical Internet topic, and to provide a clustering strategy enabling to classify the literature on the Physical Internet, to follow its evolution, as well as to criticize it. The classification is based on three factors: Logistics Web, organization, and resources. Several papers about Physical Internet have been classified and analyzed along the Logistics Web, resources and organization views at a strategic, tactical and operational level, respectively. A developed cluster analysis shows which topics of the Physical Internet that are the less covered actually. Future researches are outlined for these topics.

Keywords: Logistics web, Physical Internet, PI characterization, taxonomy.

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Authors: Reza Nadimi, Fariborz Jolai

Abstract:

This article combines two techniques: data envelopment analysis (DEA) and Factor analysis (FA) to data reduction in decision making units (DMU). Data envelopment analysis (DEA), a popular linear programming technique is useful to rate comparatively operational efficiency of decision making units (DMU) based on their deterministic (not necessarily stochastic) input–output data and factor analysis techniques, have been proposed as data reduction and classification technique, which can be applied in data envelopment analysis (DEA) technique for reduction input – output data. Numerical results reveal that the new approach shows a good consistency in ranking with DEA.

Keywords: Effectiveness, Decision Making, Data EnvelopmentAnalysis, Factor Analysis

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Authors: Radhwan Yousif Sedik Al-Jawadi

Abstract:

Optimization is necessary for finding appropriate solutions to a range of real-life problems. In particular, genetic (or more generally, evolutionary) algorithms have proved very useful in solving many problems for which analytical solutions are not available. In this paper, we present an optimization algorithm called Dynamic Schema with Dissimilarity and Similarity of Chromosomes (DSDSC) which is a variant of the classical genetic algorithm. This approach constructs new chromosomes from a schema and pairs of existing ones by exploring their dissimilarities and similarities. To show the effectiveness of the algorithm, it is tested and compared with the classical GA, on 15 two-dimensional optimization problems taken from literature. We have found that, in most cases, our method is better than the classical genetic algorithm.

Keywords: Genetic algorithm, similarity and dissimilarity, chromosome injection, dynamic schema.

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Authors: Yerbol Sapargaliyev, Tatiana Kalganova

Abstract:

Evolvable Hardware (EHW) has been regarded as adaptive system acquired by wide application market. Consumer market of any good requires diversity to satisfy consumers- preferences. Adaptation of EHW is a key technology that could provide individual approach to every particular user. This situation raises a question: how to set target for evolutionary algorithm? The existing techniques do not allow consumer to influence evolutionary process. Only designer at the moment is capable to influence the evolution. The proposed consumer-triggered evolution overcomes this problem by introducing new features to EHW that help adaptive system to obtain targets during consumer stage. Classification of EHW is given according to responsiveness, imitation of human behavior and target circuit response. Home intelligent water heating system is considered as an example.

Keywords: Actuators, consumer-triggered evolution, evolvable hardware, sensors.

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Authors: A. Hossein Sahoolizadeh, B. Zargham Heidari, C. Hamid Dehghani

Abstract:

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Keywords: Face recognition Principal component analysis, Linear discriminant analysis, Neural networks.

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Authors: Ali Zifan, Sohrab Saberi, Mohammad Hassan Moradi, Farzad Towhidkhah

Abstract:

Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG's. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW). The results are tested on the QT database. We compared our results to Laguna's two lead method. Our proposed approach has a comparable mean error, but yields a slightly higher standard deviation than Laguna's method.

Keywords: Adaptive Piecewise Constant Approximation, Dynamic programming, ECG segmentation, Piecewise Derivative Dynamic Time Warping.

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Authors: Jin Sup Kim, Woo Young Jung, Minho Kwon

Abstract:

In general dynamic analyses, lower mode response is of interest, however the higher modes of spatially discretized equations generally do not represent the real behavior and not affects to global response much. Some implicit algorithms, therefore, are introduced to filter out the high-frequency modes using intended numerical error. The objective of this study is to introduce the P-method and PC α-method to compare that with dissipation method and Newmark method through the stability analysis and numerical example. PC α-method gives more accuracy than other methods because it based on the α-method inherits the superior properties of the implicit α-method. In finite element analysis, the PC α-method is more useful than other methods because it is the explicit scheme and it achieves the second order accuracy and numerical damping simultaneously.

Keywords: Dynamic, α-Method, P-Method, PC α-Method, Newmark method.

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Authors: Abdussamad Ismail, Dong-Sheng Jeng

Abstract:

A self-evolution algorithm for optimizing neural networks using a combination of PSO and JPSO is proposed. The algorithm optimizes both the network topology and parameters simultaneously with the aim of achieving desired accuracy with less complicated networks. The performance of the proposed approach is compared with conventional back-propagation networks using several synthetic functions, with better results in the case of the former. The proposed algorithm is also implemented on slope stability problem to estimate the critical factor of safety. Based on the results obtained, the proposed self evolving network produced a better estimate of critical safety factor in comparison to conventional BPN network.

Keywords: Neural networks, Topology evolution, Particle swarm optimization.

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Authors: R. Xu, X. Zhao, X. Li, C. Kwan, C.-I Chang

Abstract:

An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.

Keywords: Image texture analysis, feature extraction, target detection, pattern classification.

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Authors: Leo Galambos, Jan Lansky, Katsiaryna Chernik

Abstract:

EGOTHOR is a search engine that indexes the Web and allows us to search the Web documents. Its hit list contains URL and title of the hits, and also some snippet which tries to shortly show a match. The snippet can be almost always assembled by an algorithm that has a full knowledge of the original document (mostly HTML page). It implies that the search engine is required to store the full text of the documents as a part of the index. Such a requirement leads us to pick up an appropriate compression algorithm which would reduce the space demand. One of the solutions could be to use common compression methods, for instance gzip or bzip2, but it might be preferable if we develop a new method which would take advantage of the document structure, or rather, the textual character of the documents. There already exist a special compression text algorithms and methods for a compression of XML documents. The aim of this paper is an integration of the two approaches to achieve an optimal level of the compression ratio

Keywords: Compression, search engine, HTML, XML.

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Authors: Antonis Sideris, Elias Chlis Kalogeropoulos, Konstantia Moirogiorgou

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The present study proposes a methodology for the efficient daily management of fleet vehicles and construction machinery. The application covers the area of remote monitoring of heavy-duty vehicles operation parameters, where specific sensor data are stored and examined in order to provide information about the vehicle’s health. The vehicle diagnostics allow the user to inspect whether maintenance tasks need to be performed before a fault occurs. A properly designed machine learning model is proposed for the detection of two different types of faults through classification. Cross validation is used and the accuracy of the trained model is checked with the confusion matrix.

Keywords: Fault detection, feature selection, machine learning, predictive maintenance.

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Authors: Mohammed Sabri Ali Akresh

Abstract:

The modern technologies and developments in computer and Global Positioning System (GPS) as well as Geographic Information System (GIS) and total station TS. This paper presents a new proposal for coordinates system by a harmonic equations “United projections”, which have five projections (Mercator, Lambert, Russell, Lagrange, and compound of projection) in one zone coordinate system width 14 degrees, also it has one degree for overlap between zones, as well as two standards parallels for zone from 10 S to 45 S. Also this paper presents two cases; first case is to compare distances between a new coordinate system and UTM, second case creating local coordinate system for the city of Sydney to measure the distances directly from rectangular coordinates using projection of Mercator, Lambert and UTM.

Keywords: Harmonic equations, coordinate system, projections, algorithms and parallels.

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Authors: Marco Antonio Cruz-Chávez, Juan Frausto-Solís, Fernando Ramos-Quintana

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A procedure commonly used in Job Shop Scheduling Problem (JSSP) to evaluate the neighborhoods functions that use the non-deterministic algorithms is the calculation of the critical path in a digraph. This paper presents an experimental study of the cost of computation that exists when the calculation of the critical path in the solution for instances in which a JSSP of large size is involved. The results indicate that if the critical path is use in order to generate neighborhoods in the meta-heuristics that are used in JSSP, an elevated cost of computation exists in spite of the fact that the calculation of the critical path in any digraph is of polynomial complexity.

Keywords: Job Shop, CPM, critical path, neighborhood, meta-heuristic.

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Authors: Nermin Ozgulbas, Ali Serhan Koyuncugil

Abstract:

One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

Keywords: Early Warning Systems, Data Mining, Financial Risk, SMEs.

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Authors: Farzaneh Hadafi, Alireza Fallahi

Abstract:

Natural Disasters have always occurred through earth life. As human life developed on earth, he faced with different disasters. Since disasters would destroy his living areas and ruin his life, he learned how to respond and overcome to these matters. Nowadays, in the era of industrialized world and informatics, the man kind seeks for stages and classification of pre and post disaster process in order to identify a framework in these circumstances. Because too many parameters complicate these frameworks and proceedings, it seems that this goal has not been properly established yet and the only resource is guidelines of UNDRO (1982) [1]. This paper will discuss about temporary housing as one of an approved stage in disaster management field and investigate the affects of disapproval or dismissal of this at two earthquakes which took place in Iran.

Keywords: Temporary Housing, Temporary Sheltering, DisasterManagement, Iran

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Authors: Giovanni Incerti

Abstract:

In this paper the vibration of a synchronous belt drive during start-up is analyzed and discussed. Besides considering the belt elasticity, the model here proposed also takes into consideration the electromagnetic response of the DC motor. The solution of the motion equations is obtained by means of the modal analysis in state space, which allows to obtain the decoupling of all equations, without introducing the hypothesis of proportional damping. The mathematical model of the transmission and the solution algorithms have been implemented within a computing software that allows the user to simulate the dynamics of the system and to evaluate the effects due to the elasticity of the belt branches and to the electromagnetic behavior of the DC motor. In order to show the details of the calculation procedure, the paper presents a case study developed with the aid of the above-mentioned software.

Keywords: Belt drive, Vibrations, Startup, DC motor.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

Abstract:

Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

Abstract:

Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, three feature selection methods are evaluated: Random Selection, Information Gain (IG) and Support Vector Machine feature selection (called SVM_FS). We show that the best results were obtained with SVM_FS method for a relatively small dimension of the feature vector. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Feature Selection, Learning with Kernels, SupportVector Machine, and Classification.

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Authors: In Lee, Kyung-Kyu Kang, Jaewoon Lee, Dongho Kim

Abstract:

Previous the 3D model texture generation from multi-view images and mapping algorithms has issues in the texture chart generation which are the self-intersection and the concentration of the texture in texture space. Also we may suffer from some problems due to the occluded areas, such as inside parts of thighs. In this paper we propose a texture mapping technique for 3D models using multi-view images on the GPU. We do texture mapping directly on the GPU fragment shader per pixel without generation of the texture map. And we solve for the occluded area using the 3D model depth information. Our method needs more calculation on the GPU than previous works, but it has shown real-time performance and previously mentioned problems do not occur.

Keywords: Texture Mapping, Multi-view Images, Camera Calibration, GPU Shader.

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Authors: Chun-Yao Lee, Yi-Xing Shen

Abstract:

This paper presents an application of particle swarm optimization (PSO) to the grounding grid planning which compares to the application of genetic algorithm (GA). Firstly, based on IEEE Std.80, the cost function of the grounding grid and the constraints of ground potential rise, step voltage and touch voltage are constructed for formulating the optimization problem of grounding grid planning. Secondly, GA and PSO algorithms for obtaining optimal solution of grounding grid are developed. Finally, a case of grounding grid planning is shown the superiority and availability of the PSO algorithm and proposal planning results of grounding grid in cost and computational time.

Keywords: Genetic algorithm, particle swarm optimization, grounding grid.

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Authors: Rachid Ahdid, Khaddouj Taifi, Said Safi, Bouzid Manaut

Abstract:

Automatic detection of facial feature points plays an important role in applications such as facial feature tracking, human-machine interaction and face recognition. The majority of facial feature points detection methods using two-dimensional or three-dimensional data are covered in existing survey papers. In this article chosen approaches to the facial features detection have been gathered and described. This overview focuses on the class of researches exploiting facial feature points detection to represent facial surface for two-dimensional or three-dimensional face. In the conclusion, we discusses advantages and disadvantages of the presented algorithms.

Keywords: Facial feature points, face recognition, facial feature tracking, two-dimensional data, three-dimensional data.

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Authors: Shehzad Ashraf Ch., Aftab Ahmed Khan, Zahid Mehmood, Muhammad Ahsan Habib, Qasim Mehmood

Abstract:

Energy efficiency is the key requirement in wireless sensor network as sensors are small, cheap and are deployed in very large number in a large geographical area, so there is no question of replacing the batteries of the sensors once deployed. Different ways can be used for efficient energy transmission including Multi-Hop algorithms, collaborative communication, cooperativecommunication, Beam- forming, routing algorithm, phase, frequency and time synchronization. The paper reviews the need for time synchronization and proposed a BFS based synchronization algorithm to achieve energy efficiency. The efficiency of our protocol has been tested and verified by simulation

Keywords: time synchronization, sensor networks, energy efficiency, breadth first search

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