Search results for: Physical machines

Authors: Refaat M Mohamed, Ayman El-Baz, Aly A. Farag

Abstract:

This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory. The proposed approach is based on modeling the potential function associated with each clique shape of the GMRF model as a Gaussian-shaped kernel. In turn, the energy function of the GMRF will be in the form of a weighted sum of Gaussian kernels. This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function. The approach has been tested on synthetic texture images and is shown to provide satisfactory results in retrieving the synthesizing parameters.

Keywords: Image Modeling, MRF, Parameters Estimation, SVM Learning.

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Authors: N. Yogal, C. Lehrmann

Abstract:

The use of permanent magnets (PM) is increasing in permanent magnet synchronous machines (PMSM) to fulfill the requirements of high efficiency machines in modern industry. PMSM are widely used in industrial applications, wind power plants and the automotive industry. Since PMSM are used in different environmental conditions, the long-term effect of NdFeB-based magnets at high temperatures and their corrosion behavior have to be studied due to the irreversible loss of magnetic properties. In this paper, the effect of magnetic properties due to corrosion and increasing temperature in a climatic chamber has been presented. The magnetic moment and magnetic field of the magnets were studied experimentally.

Keywords: Permanent magnets (PM), NdFeB, corrosion behavior, temperature effect, permanent magnet synchronous machine (PMSM).

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments.

Keywords: Ensembles, WEKA, Neural networks [NN], SupportVector Machines [SVM], Random Forests [RF].

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Authors: A. Abbou, H. Mahmoudi, M. Akherraz

Abstract:

In contrast to conventional generators, self-excited induction generators are found to be most suitable machines for wind energy conversion in remote and windy areas due to many advantages over grid connected machines. This papers presents a Self-Excited Induction Generator (SEIG) driven by wind turbine and supplying an induction motor which is coupled to a centrifugal pump. A method to describe the steady state performance based on nodal analysis is presented. Therefore the advanced knowledge of the minimum excitation capacitor value is required. The effects of variation of excitation capacitance on system and rotor speed under different loading conditions have been analyzed and considered to optimize induction motor pump performances.

Keywords: SEIG, Induction Motor, Centrifugal Pump, capacitance requirements, wind rotor speed.

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Authors: Essam Al Daoud

Abstract:

Intrusion detection is a mechanism used to protect a system and analyse and predict the behaviours of system users. An ideal intrusion detection system is hard to achieve due to nonlinearity, and irrelevant or redundant features. This study introduces a new anomaly-based intrusion detection model. The suggested model is based on particle swarm optimisation and nonlinear, multi-class and multi-kernel support vector machines. Particle swarm optimisation is used for feature selection by applying a new formula to update the position and the velocity of a particle; the support vector machine is used as a classifier. The proposed model is tested and compared with the other methods using the KDD CUP 1999 dataset. The results indicate that this new method achieves better accuracy rates than previous methods.

Keywords: Feature selection, Intrusion detection, Support vector machine, Particle swarm.

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Authors: Wernhuar Tarng, Yuan-Yuan Chen, Chien-Lung Li, Kun-Rong Hsie, Mingteh Chen

Abstract:

An emotional speech recognition system for the applications on smart phones was proposed in this study to combine with 3G mobile communications and social networks to provide users and their groups with more interaction and care. This study developed a mechanism using the support vector machines (SVM) to recognize the emotions of speech such as happiness, anger, sadness and normal. The mechanism uses a hierarchical classifier to adjust the weights of acoustic features and divides various parameters into the categories of energy and frequency for training. In this study, 28 commonly used acoustic features including pitch and volume were proposed for training. In addition, a time-frequency parameter obtained by continuous wavelet transforms was also used to identify the accent and intonation in a sentence during the recognition process. The Berlin Database of Emotional Speech was used by dividing the speech into male and female data sets for training. According to the experimental results, the accuracies of male and female test sets were increased by 4.6% and 5.2% respectively after using the time-frequency parameter for classifying happy and angry emotions. For the classification of all emotions, the average accuracy, including male and female data, was 63.5% for the test set and 90.9% for the whole data set.

Keywords: Smart phones, emotional speech recognition, socialnetworks, support vector machines, time-frequency parameter, Mel-scale frequency cepstral coefficients (MFCC).

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Authors: L. Rodriguez-Benitez, J. Moreno-Garcia, J.J. Castro-Schez, C. Solana, L. Jimenez

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In this paper the use of sequential machines for recognizing actions taken by the objects detected by a general tracking algorithm is proposed. The system may deal with the uncertainty inherent in medium-level vision data. For this purpose, fuzzification of input data is performed. Besides, this transformation allows to manage data independently of the tracking application selected and enables adding characteristics of the analyzed scenario. The representation of actions by means of an automaton and the generation of the input symbols for finite automaton depending on the object and action compared are described. The output of the comparison process between an object and an action is a numerical value that represents the membership of the object to the action. This value is computed depending on how similar the object and the action are. The work concludes with the application of the proposed technique to identify the behavior of vehicles in road traffic scenes.

Keywords: Approximate reasoning, finite state machines, video analysis.

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Authors: Vladimir Rastunkov, Jae-Eun Park, Abhijit Mitra, Brian Quanz, Steve Wood, Christopher Codella, Heather Higgins, Joseph Broz

Abstract:

Quantum Support Vector Machines (QSVM) have become an important tool in research and applications of quantum kernel methods. In this work we propose a boosting approach for building ensembles of QSVM models and assess performance improvement across multiple datasets. This approach is derived from the best ensemble building practices that worked well in traditional machine learning and thus should push the limits of quantum model performance even further. We find that in some cases, a single QSVM model with tuned hyperparameters is sufficient to simulate the data, while in others - an ensemble of QSVMs that are forced to do exploration of the feature space via proposed method is beneficial.

Keywords: QSVM, Quantum Support Vector Machines, quantum kernel, boosting, ensemble.

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Authors: Abdelaziz Guerrouat, Harald Richter

Abstract:

In this paper test generation methods and appropriate fault models for testing and analysis of embedded systems described as (extended) finite state machines ((E)FSMs) are presented. Compared to simple FSMs, EFSMs specify not only the control flow but also the data flow. Thus, we define a two-level fault model to cover both aspects. The goal of this paper is to reuse well-known FSM-based test generation methods for automation of embedded system testing. These methods have been widely used in testing and validation of protocols and communicating systems. In particular, (E)FSMs-based specification and testing is more advantageous because (E)FSMs support the formal semantic of already standardised formal description techniques (FDTs) despite of their popularity in the design of hardware and software systems.

Keywords: Formal methods, testing and validation, finite state machines, formal description techniques.

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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.

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Authors: Justice K. Owusu-Ansah, Kevin O'Connor

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This research explores the links between physical development and transportation infrastructure around Kumasi, Ghana. It utilizes census data as well as fieldwork and interviews carried out during July and December 2005. The results suggest that there is a weak association between transportation investments and physical development, and that recent housing has generally occurred in poorly accessible locations. Road investments have generally followed physical expansion rather than the reverse. Hence policies designed to manage the fast growth now occurring around Ghanaian cities should not focus exclusively on improving transportation infrastructure but also strengthening the underlying the traditional land management structures and the official land administrative institutions that operate within those structures.

Keywords: Housing, Kumasi, population, physical development, transportation, villages.

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Authors: Kamaruddin Malik Mohamad, Sapiee Haji Jamel, Mustafa Mat Deris

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Memory forensic is important in digital investigation. The forensic is based on the data stored in physical memory that involve memory management and processing time. However, the current forensic tools do not consider the efficiency in terms of storage management and the processing time. This paper shows the high redundancy of data found in the physical memory that cause inefficiency in processing time and memory management. The experiment is done using Borland C compiler on Windows XP with 512 MB of physical memory.

Keywords: Digital Evidence, Memory Forensics.

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Authors: M. Erol Genevois, D. Celik, H. Z. Ulukan

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Automated Teller Machines (ATMs) can be considered among one of the most important service facilities in the banking industry. The investment in ATMs and the impact on the banking industry is growing steadily in every part of the world. The banks take into consideration many factors like safety, convenience, visibility, and cost in order to determine the optimum locations of ATMs. Today, ATMs are not only available in bank branches but also at retail locations. Another important factor is the cash management in ATMs. A cash demand model for every ATM is needed in order to have an efficient cash management system. This forecasting model is based on historical cash demand data which is highly related to the ATMs location. So, the location and the cash management problem should be considered together. This paper provides a general review on studies, efforts and development in ATMs location and cash management problem.

Keywords: ATM location problem, cash management problem, ATM cash replenishment problem, literature review in ATMs.

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Authors: Sh. Khoshemehry, M. J. Pourvaghar, M. E. Bahram

Abstract:

In modern life, daily physical activity is relatively reduced, which is why the incidence of some diseases associated with overweight and obesity, such as hypertension, diabetes and other chronic illnesses, even in young people are observed. Obesity and overweight is one of the most common metabolic disorders in industrialized countries and in developing countries. One consequence of pathological obesity is cardiovascular disease and metabolic syndrome. In the past, it was believed that adipose tissue was ineffective and served only for storing triglycerides. In this review article, it was tried to refer to the esteemed scientific sources about physical activity and responses of leptin.

Keywords: Disease, leptin, obesity, physical activity.

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Authors: George Eleftherakis, Petros Kefalas, Anna Sotiriadou, Evangelos Kehris

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Recent advances in both the testing and verification of software based on formal specifications of the system to be built have reached a point where the ideas can be applied in a powerful way in the design of agent-based systems. The software engineering research has highlighted a number of important issues: the importance of the type of modeling technique used; the careful design of the model to enable powerful testing techniques to be used; the automated verification of the behavioural properties of the system; the need to provide a mechanism for translating the formal models into executable software in a simple and transparent way. This paper introduces the use of the X-machine formalism as a tool for modeling biology inspired agents proposing the use of the techniques built around X-machine models for the construction of effective, and reliable agent-based software systems.

Keywords: Biology inspired agent, formal methods, x-machines.

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Authors: Aline Dessupoio Chaves, Carla N. Luguetti, Michele Viviene Carbinatto

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The aim of this study was to establish the relationship between the principles of Educational Sport and the objectives of Physical Education in two brasilian laws: National Curriculum Guidelines (PCNs) for the Elementary and Middle School Levels and the Guidelines and Basis Legislation (LDB). The method used was the survey analysis in order to determine the practices present in, or the opinions of, a specific population. The instrument used in this research was a questionnaire. After a broad review of the bibliography and according to the methodological procedures, the aim was to set the relationships between the Principles of Educational Sport and the objectives of Physical Education, according to the Brazilian Law (LDB) and National Curriculum Guidelines (PCNs) in a table made under the analysis of a group of specialists. As the relation between the principles of Educational Sport and the objectives of School Physical Education have shown, we can state that School Physical Education has gained pedagogical security for the potential use of Educational Sport as part of its contents.

Keywords: Educational sport, physical education, education.

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Authors: Surinder Deswal, D. V. S. Verma

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Aeration by a plunging water jet is an energetically attractive way to effect oxygen-transfer than conventional oxygenation systems. In the present study, a new type of conical shaped plunging aeration device is fabricated to generate hollow inclined ined plunging jets (jet plunge angle of π/3 ) to investigate its oxygen transfer capacity. The results suggest that the volumetric oxygen-transfer coefficient and oxygen-transfer efficiency of the conical plunging jet aerator are competitive with other types of aeration systems. Relationships of volumetric oxygen-transfer coefficient with jet power per unit volume and jet parameters are also proposed. The suggested relationships predict the volumetric oxygentransfer coefficient within a scatter of ± 15% . Further, the application of Support Vector Machines on the experimental data revealed its utility in the prediction of volumetric oxygen-transfer coefficient and development of conical plunging jet aerators.

Keywords: Conical plunging jet, oxygen-transfer efficiency, support vector machines, volumetric oxygen-transfer coefficient.

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Authors: A. Perolini

Abstract:

Feature and model selection are in the center of attention of many researches because of their impact on classifiers- performance. Both selections are usually performed separately but recent developments suggest using a combined GA-SVM approach to perform them simultaneously. This approach improves the performance of the classifier identifying the best subset of variables and the optimal parameters- values. Although GA-SVM is an effective method it is computationally expensive, thus a rough method can be considered. The paper investigates a joined approach of Genetic Algorithm and kernel matrix criteria to perform simultaneously feature and model selection for SVM classification problem. The purpose of this research is to improve the classification performance of SVM through an efficient approach, the Kernel Matrix Genetic Algorithm method (KMGA).

Keywords: Feature and model selection, Genetic Algorithms, Support Vector Machines, kernel matrix.

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Authors: Ellie Abdi, Susana Juniu

Abstract:

The purpose of this presentation is to describe an interdisciplinary teaching program that integrates physical education concepts using a philosophical approach. The presentation includes a review of: a) the philosophy of American education, b) the philosophy of sports and physical education, c) the interdisciplinary physical education program, d) professional development programs, (e) the Success of this physical education program, f) future of physical education. This unique interdisciplinary program has been implemented in an urban school physical education discipline in East Orange, New Jersey for over 10 years.

During the program the students realize that the bodies go through different experiences. The body becomes a place where a child can recognize in an enjoyable way to express and perceive particular feelings or mental states. Children may distinguish themselves to have high abilities in the social or other domains but low abilities in the field of athletics.

The goal of this program for the individuals is to discover new skills, develop and demonstrate age appropriate mastery level at different tasks, therefore the program consists of 9 to 12 sports, including many game. Each successful experience increases the awareness ability. Engaging in sports and physical activities are social movements involving groups of children in situations such as teams, friends, and recreational settings, which serve as a primary socializing agent for teaching interpersonal skills. As a result of this presentation the audience will reflect and explore how to structure a physical education program to integrate interdisciplinary subjects with philosophical concepts.

Keywords: Interdisciplinary disciplines, philosophical concepts, physical education.

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Authors: Airoldi G., Bumby J. R., Dominy C., G.L. Ingram, Lim C. H., Mahkamov K., N. L. Brown, A. Mebarki, M. Shanel

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Axial Flux Permanent Magnet (AFPM) Machines require effective cooling due to their high power density. The detrimental effects of overheating such as degradation of the insulation materials, magnets demagnetization, and increase of Joule losses are well known. This paper describes the CFD simulations performed on a test rig model of an air cooled Axial Flux Permanent Magnet (AFPM) generator built at Durham University to identify the temperatures and heat transfer coefficient on the stator. The Reynolds Averaged Navier-Stokes and the Energy equations are solved and the flow pattern and heat transfer developing inside the machine are described. The Nusselt number on the stator surfaces has been found. The dependency of the heat transfer on the flow field is described temperature field obtained. Tests on an experimental are undergoing in order to validate the CFD results.

Keywords: Axial flux permanent magnet machines, thermal modeling, CFD.

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Authors: Z. Q. Zhang, Y. Zhang, D. L. Li

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To overcome the some shortcomings associated with traditional flotation machines and columns in collophanite flotation, a flotation device was designed and fabricated in the laboratory. A multi-impeller pump with same function as a mechanical cell was used instead of the injection sparger and circulation pump in column flotation unit. The influence of main operational parameters of the device like feed flow rate, air flow rate and impellers’ speed on collophanite flotation was analyzed. Experiment results indicate that the influence of the operational parameters were significant on flotation recovery and grade of phosphate concentrate. The best operating conditions of the device were: feed flow rate 0.62 L/min, air flow rate 6.67 L/min and impellers speed 900 rpm. At these conditions, a phosphate concentrate assaying about 30.5% P₂O₅ and 1% MgO with a P₂O₅ recovery of about 81% was obtained from a Yuan'an phosphate ore sample containing about 22.30% P₂O₅ and 3.2% MgO.

Keywords: Collophanite flotation, flotation columns, flotation machines, multi-impeller pump.

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future  storms and hurricanes on coastal communities. Accurate sea level  change prediction and supplement is an important task in determining  constructions and human activities in coastal and oceanic areas. In  this study, support vector machines (SVM) is proposed to predict  daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal  parameter values of kernel function are determined using a genetic  algorithm. The SVM results are compared with the field data and  with back propagation (BP). Among the models, the SVM is superior  to BPNN and has better generalization performance.

 

Keywords: Tides, Prediction, Support Vector Machines, Genetic Algorithm, Back-Propagation Neural Network, Risk, Hazards.

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Authors: Constantin Pehoiu, Cristian Savu, Silviu Badea, Cristian Borida

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The physiological effects of physical exercise on human body are relatively well known in literature, which describes in detail the changes that occur in the cardiovascular system, the respiratory one, in bones and other systems, both during exercise and after its delivery. However, the effects of exercise on mental processes are less treated. From the literature reviews discussed in this study, it can be detached the idea that we can not exactly say that physical exercise has beneficial effects on mental processes, but neither that it would have potentially negative effects. This uncertainty, reflected in the inability to indicate precise and unequivocal meaning, favorable-unfavorable physical effort in acting on mental processes, is a prime reason to undertake a study of the phenomenon influence effort administered physical education classes on the dynamics of mental processes like attention and memory.

Keywords: management, exercise, mental process, lesson.

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Authors: T. C. B. N. Assunção, J. L. Silvino, P. Resende

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The winding hot-spot temperature is one of the most critical parameters that affect the useful life of the power transformers. The winding hot-spot temperature can be calculated as function of the top-oil temperature that can estimated by using the ambient temperature and transformer loading measured data. This paper proposes the estimation of the top-oil temperature by using a method based on Least Squares Support Vector Machines approach. The estimated top-oil temperature is compared with measured data of a power transformer in operation. The results are also compared with methods based on the IEEE Standard C57.91-1995/2000 and Artificial Neural Networks. It is shown that the Least Squares Support Vector Machines approach presents better performance than the methods based in the IEEE Standard C57.91-1995/2000 and artificial neural networks.

Keywords: Artificial Neural Networks, Hot-spot Temperature, Least Squares Support Vector, Top-oil Temperature.

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Authors: Yogesh Aggarwal

Abstract:

The paper discusses the results obtained to predict reinforcement in singly reinforced beam using Neural Net (NN), Support Vector Machines (SVM-s) and Tree Based Models. Major advantage of SVM-s over NN is of minimizing a bound on the generalization error of model rather than minimizing a bound on mean square error over the data set as done in NN. Tree Based approach divides the problem into a small number of sub problems to reach at a conclusion. Number of data was created for different parameters of beam to calculate the reinforcement using limit state method for creation of models and validation. The results from this study suggest a remarkably good performance of tree based and SVM-s models. Further, this study found that these two techniques work well and even better than Neural Network methods. A comparison of predicted values with actual values suggests a very good correlation coefficient with all four techniques.

Keywords: Linear Regression, M5 Model Tree, Neural Network, Support Vector Machines.

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Authors: Arun Kumar Datta, Manisha Dubey, Shailendra Jain

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Electric machines are driven nowadays by static system popularly known as soft starter. This paper describes a thyristor based static frequency converter (SFC) to run a large synchronous machine installed at a short circuit test laboratory. Normally a synchronous machine requires prime mover or some other driving mechanism to run. This machine doesn’t need a prime mover as it operates in dual mode. In the beginning SFC starts this machine as a motor to achieve the full speed. Thereafter whenever required it can be converted to generator mode. This paper begins with the various starting methodology of synchronous machine. Detailed of SFC with different operational modes have been analyzed. Shaft voltage is a very common phenomenon for the machines with static drives. Various causes of shaft voltages in perspective with this machine are the main attraction of this paper.

Keywords: Capacitive coupling, electric discharge machining, inductive coupling, Shaft voltage, static frequency converter.

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Authors: A. Zanj, F. He

Abstract:

In the current paper, a domain independent conduction model compatible for multi-physical system dynamic investigations is suggested. By means of a port-based approach, a classical nonlinear conduction model containing physical states is first represented. A compatible discrete configuration of the thermal domain in line with the elastic domain is then generated through the enhancement of the configuration of the conventional thermal element. The presented simulation results of a sample structure indicate that the suggested conductive model can cover a wide range of dynamic behavior of the thermal domain.

Keywords: Multi-physical domain, conduction model, port-based modeling, dynamic interaction, physical modeling.

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Authors: Florin Leon, Silvia Curteanu

Abstract:

Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: Adaptive sampling, batch bulk methyl methacrylate polymerization, large margin nearest neighbor regression, machine learning.

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Authors: Sandy Daley, Sandra Jones, Don Iverson

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The benefits of physical activity for children are promoted widely and well understood; however factors which impact on children-s beliefs and attitudes towards physical education need to be explored in more detail. The purpose of this study was to evaluate how primary school children value and perceive their involvement in physical education (PE) classes through the use of drawings. While this type of data collection has been used previously to determine a child-s response to specific health education classes, such as drug education, to the best of our knowledge it has not been used in the context of PE. Results from this study showed that kindergarten children found PE classes fun and engaging. Children in Year 4 and Year 6 were less satisfied with PE classes because of the activities offered, the lack of opportunity to play sport, and perception that teachers did not appear to value this area of the curriculum.

Keywords: attitudes, physical education, primary school children, write and draw

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Authors: Watcharapan Sukkerd, Teeradej Wuttipornpun

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This paper aims to present non-population search algorithms called tabu search (TS), simulated annealing (SA) and variable neighborhood search (VNS) to minimize the total cost of capacitated MRP problem in multi-stage assembly flow shop with two alternative machines. There are three main steps for the algorithm. Firstly, an initial sequence of orders is constructed by a simple due date-based dispatching rule. Secondly, the sequence of orders is repeatedly improved to reduce the total cost by applying TS, SA and VNS separately. Finally, the total cost is further reduced by optimizing the start time of each operation using the linear programming (LP) model. Parameters of the algorithm are tuned by using real data from automotive companies. The result shows that VNS significantly outperforms TS, SA and the existing algorithm.

Keywords: Capacitated MRP, non-population search algorithms, linear programming, assembly flow shop.

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