Search results for: minimum root mean square (RMS) error matching algorithm

Authors: Neda Navidi, Rene Jr. Landry

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Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.

Keywords: Driving behavior, integration, IMU, GNSS, monitoring, tracking.

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Authors: Hamdi. A. Awad, Mohamed A. Al-Zorkany

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Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular region. The submodels then are combined in a unified structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the proposed LMN reflect the soundness of the proposed scheme.

Keywords: Mobile Robot Navigation, Neural Networks, Local Model Networks

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Authors: Anja Kurz, Marc Flynn, Tobias Good, Marco Caversaccio, Martin Kompis

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Bone Anchored Hearing Implants (BAHI) are  routinely used in patients with conductive or mixed hearing loss, e.g.  if conventional air conduction hearing aids cannot be used. New  sound processors and new fitting software now allow the adjustment  of parameters such as loudness compression ratios or maximum  power output separately. Today it is unclear, how the choice of these  parameters influences aided speech understanding in BAHI users.  In this prospective experimental study, the effect of varying the  compression ratio and lowering the maximum power output in a  BAHI were investigated.  Twelve experienced adult subjects with a mixed hearing loss  participated in this study. Four different compression ratios (1.0; 1.3;  1.6; 2.0) were tested along with two different maximum power output  settings, resulting in a total of eight different programs. Each  participant tested each program during two weeks. A blinded Latin  square design was used to minimize bias.  For each of the eight programs, speech understanding in quiet and  in noise was assessed. For speech in quiet, the Freiburg number test  and the Freiburg monosyllabic word test at 50, 65, and 80 dB SPL  were used. For speech in noise, the Oldenburg sentence test was  administered.  Speech understanding in quiet and in noise was improved  significantly in the aided condition in any program, when compared  to the unaided condition. However, no significant differences were  found between any of the eight programs. In contrast, on a subjective  level there was a significant preference for medium compression  ratios of 1.3 to 1.6 and higher maximum power output.

 

Keywords: Bone Anchored Hearing Implant, Compression, Maximum Power Output, Speech understanding.

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Authors: M. Byzedi, B. Saghafian

Abstract:

Droughts are complex, natural hazards that, to a varying degree, affect some parts of the world every year. The range of drought impacts is related to drought occurring in different stages of the hydrological cycle and usually different types of droughts, such as meteorological, agricultural, hydrological, and socioeconomical are distinguished. Streamflow drought was analyzed by the method of truncation level (at 70% level) on daily discharges measured in 54 hydrometric stations in southwestern Iran. Frequency analysis was carried out for annual maximum series (AMS) of drought deficit volume and duration series. Some factors including physiographic, climatic, geologic, and vegetation cover were studied as influential factors in the regional analysis. According to the results of factor analysis, six most effective factors were identified as area, rainfall from December to February, the percent of area with Normalized Difference Vegetation Index (NDVI) <0.1, the percent of convex area, drainage density and the minimum of watershed elevation that explained 90.9% of variance. The homogenous regions were determined by cluster analysis and discriminate function analysis. Suitable multivariate regression models were evaluated for streamflow drought deficit volume with 2 years return period. The significance level of regression models was 0.01. The results showed that the watershed area is the most effective factor with high correlation with deficit volume. Also, drought duration was not a suitable drought index for regional analysis.

Keywords: Iran, Streamflow drought, truncation level method, regional analysis.

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Authors: Nicholas D. Assimakis

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In linear estimation, the traditional Kalman filter uses the Kalman filter gain in order to produce estimation and prediction of the n-dimensional state vector using the m-dimensional measurement vector. The computation of the Kalman filter gain requires the inversion of an m x m matrix in every iteration. In this paper, a variation of the Kalman filter eliminating the Kalman filter gain is proposed. In the time varying case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix and the inversion of an m x m matrix in every iteration. In the time invariant case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix in every iteration. The proposed Kalman filter gain elimination algorithm may be faster than the conventional Kalman filter, depending on the model dimensions.

Keywords: Discrete time, linear estimation, Kalman filter, Kalman filter gain.

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Authors: T. C. Machaba, S. M. Mahlo

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The current study investigates the antifungal properties of crude plant extracts from selected medicinal plant species. Eight plant species used by the traditional healers and local people to treat fungal infections were selected for further phytochemical analysis and biological assay. The selected plant species were extracted with solvent of various polarities such as acetone, methanol, ethanol, hexane, dichloromethane, ethyl acetate and water. Leaf, roots and bark extracts of Maerua juncea Pax, Albuca seineri (Engl & K. Krause) J.C Manning & Goldblatt, Senna italica Mill., Elephantorrhiza elephantina (Burch.) Skeels, Indigofera circinata Benth., Schinus molle L., Asparagus buchananii Bak., were screened for antifungal activity against three animal fungal pathogens (Candida albicans, Aspergillus fumigatus and Cryptococcus neoformans). All plant extracts were active against the tested microorganisms. Acetone, dichloromethane, hexane and ethanol extracts of Senna italica and Elephantorrhiza elephantine had excellent activity against Candida albicans and A. fumigatus with the lowest MIC value of 0.02 mg/ml. Bioautography assay was used to determine the number of antifungal compounds presence in the plant extracts. No active compounds were observed in plant extracts of Indigofera circinnata, Schinus molle and Pentarrhinum insipidum with good antifungal activity against C. albicans and A. fumigatus indicating possible synergism between separated metabolites.

Keywords: Antifungal activity, minimum inhibitory concentration, bioautography.

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Authors: M. Sadeghzadeh, M. Teshnehlab

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Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.

Keywords: Ant colony optimization, Classification, Datamining, Feature selection.

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Authors: Lv Meibo, Naqvi Najam Abbas, Li YanJun

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Attitude Determination (AD) of a spacecraft using the phase measurements of the Global Navigation Satellite System (GNSS) is an active area of research. Various attitude determination algorithms have been developed in yester years for spacecrafts using different sensors but the last two decades have witnessed a phenomenal increase in research related with GPS receivers as a stand-alone sensor for determining the attitude of satellite using the phase measurements of the signals from GNSS. The GNSS-based Attitude determination algorithms have been experimented in many real missions. The problem of AD algorithms using GNSS phase measurements has two important parts; the ambiguity resolution and the determining of attitude. Ambiguity resolution is the widely addressed topic in literature for implementing the AD algorithm using GNSS phase measurements for achieving the accuracy of millimeter level. This paper broadly overviews the different techniques for resolving the integer ambiguities encountered in AD using GNSS phase measurements.

Keywords: Attitude Determination, Ambiguity Resolution, GNSS, LAMBDA Method, Satellite.

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Authors: Md. Masum Billah, Mohiuddin Ahmed, Soheli Farhana

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In modern day disaster recovery mission has become one of the top priorities in any natural disaster management regime. Smart autonomous robots may play a significant role in such missions, including search for life under earth quake hit rubbles, Tsunami hit islands, de-mining in war affected areas and many other such situations. In this paper current state of many walking robots are compared and advantages of hexapod systems against wheeled robots are described. In our research we have selected a hexapod spider robot; we are developing focusing mainly on efficient navigation method in different terrain using apposite gait of locomotion, which will make it faster and at the same time energy efficient to navigate and negotiate difficult terrain. This paper describes the method of terrain negotiation navigation in a hazardous field.

Keywords: Walking robots, locomotion, hexapod robot, gait, hazardous field.

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Authors: M. Kashfi, K. Jahani

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Sound attenuation in absorptive silencers has been analyzed in this paper. The structure of such devices is as follows. When the rigid duct of an expansion chamber has been lined by a packed absorptive material under a perforated membrane, incident sound waves will be dissipated by the absorptive liners. This kind of silencer, usually are applicable for medium to high frequency ranges. Several conditions for different absorptive materials, variety in their thicknesses, and different shapes of the expansion chambers have been studied in this paper. Also, graphs of sound attenuation have been compared between empty expansion chamber and duct of silencer with applying liner. Plane waves have been assumed in inlet and outlet regions of the silencer. Presented results that have been achieved by applying finite element method (FEM), have shown the dependence of the sound attenuation spectrum to flow resistivity and the thicknesses of the absorptive materials, and geometries of the cross section (configuration of the silencer). As flow resistivity and thickness of absorptive materials increase, sound attenuation improves. In this paper, diagrams of the transmission loss (TL) for absorptive silencers in five different cross sections (rectangle, circle, ellipse, square, and rounded rectangle as the main geometry) have been presented. Also, TL graphs for silencers using different absorptive material (glass wool, wood fiber, and kind of spongy materials) as liner with three different thicknesses of 5 mm, 15 mm, and 30 mm for glass wool liner have been exhibited. At first, the effect of substances of the absorptive materials with the specific flow resistivity and densities on the TL spectrum, then the effect of the thicknesses of the glass wool, and at last the efficacy of the shape of the cross section of the silencer have been investigated.

Keywords: Transmission loss, absorptive material, flow resistivity, thickness, frequency.

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Authors: P. Georgieva, S. Feyo de Azevedo

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This paper is focused on issues of process modeling and two model based control strategies of a fed-batch sugar crystallization process applying the concept of artificial neural networks (ANNs). The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. Two control alternatives are considered – model predictive control (MPC) and feedback linearizing control (FLC). Adequate ANN process models are first built as part of the controller structures. MPC algorithm outperforms the FLC approach with respect to satisfactory reference tracking and smooth control action. However, the MPC is computationally much more involved since it requires an online numerical optimization, while for the FLC an analytical control solution was determined.

Keywords: artificial neural networks, nonlinear model control, process identification, crystallization process

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Authors: H. Shayeghi, H. Hosseini, A. Shabani, M. Mahdavi

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In this paper, optimal generation expansion planning (GEP) is investigated considering purchase prices, profits of independent power producers (IPPs) and reliability criteria using a new method based on hybrid coded Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this approach, optimal purchase price of each IPP is obtained by HCGA and reliability criteria are calculated by PSO technique. It should be noted that reliability criteria and the rate of carbon dioxide (CO2) emission have been considered as constraints of the GEP problem. Finally, the proposed method has been tested on the case study system. The results evaluation show that the proposed method can simply obtain optimal purchase prices of IPPs and is a fast method for calculation of reliability criteria in expansion planning. Also, considering the optimal purchase prices and profits of IPPs in generation expansion planning are caused that the expansion costs are decreased and the problem is solved more exactly.

Keywords: GEP Problem, IPPs, Reliability Criteria, GA, PSO.

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Authors: S. Sarumathi, N. Shanthi, P. Ranjetha

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Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.

Keywords: Cluster Ensemble, Consensus Function, CSPA, Diversity, HGPA, MCLA.

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Authors: A.L. Seetharam, B.L. Udaya Simha

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The Bangalore City is facing the acute problem of pollution in the atmosphere due to the heavy increase in the traffic and developmental activities in recent years. The present study is an attempt in the direction to assess trend of the ambient air quality status of three stations, viz., AMCO Batteries Factory, Mysore Road, GRAPHITE INDIA FACTORY, KHB Industrial Area, Whitefield and Ananda Rao Circle, Gandhinagar with respect to some of the major criteria pollutants such as Total Suspended particular matter (SPM), Oxides of nitrogen (NOx), and Oxides of sulphur (SO2). The sites are representative of various kinds of growths viz., commercial, residential and industrial, prevailing in Bangalore, which are contributing to air pollution. The concentration of Sulphur Dioxide (SO2) at all locations showed a falling trend due to use of refined petrol and diesel in the recent years. The concentration of Oxides of nitrogen (NOx) showed an increasing trend but was within the permissible limits. The concentration of the Suspended particular matter (SPM) showed the mixed trend. The correlation between model and observed values is found to vary from 0.4 to 0.7 for SO2, 0.45 to 0.65 for NOx and 0.4 to 0.6 for SPM. About 80% of data is observed to fall within the error band of ±50%. Forecast test for the best fit models showed the same trend as actual values in most of the cases. However, the deviation observed in few cases could be attributed to change in quality of petro products, increase in the volume of traffic, introduction of LPG as fuel in many types of automobiles, poor condition of roads, prevailing meteorological conditions, etc.

Keywords: Bangalore, urban air pollution, time series analysis.

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Authors: Reza Ghaemi , Md. Nasir Sulaiman , Hamidah Ibrahim , Norwati Mustapha

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The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.

Keywords: Clustering Ensembles, Combinational Algorithm, Consensus Function, Unsupervised Classification.

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Authors: Siyuan Jing, Kun She

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Virtualization-based server consolidation has been proven to be an ideal technique to solve the server sprawl problem by consolidating multiple virtualized servers onto a few physical servers leading to improved resource utilization and return on investment. In this paper, we solve this problem by using existing servers, which are heterogeneous and diversely preferred by IT managers. Five practical consolidation rules are introduced, and a decision model is proposed to optimally allocate source services to physical target servers while maximizing the average resource utilization and preference value. Our model can be regarded as a multi-objective multi-dimension bin-packing (MOMDBP) problem with constraints, which is strongly NP-hard. An improved grouping generic algorithm (GGA) is introduced for the problem. Extensive simulations were performed and the results are given.

Keywords: GGA-based Heuristics, Preference, Real-worldConstraints, Resource Utilization, Server Consolidation

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Authors: E T. Rakgati, E. Matlotse

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In this paper the principle, basic torque theory and design optimisation of a six-phase reluctance dc machine are considered. A trapezoidal phase current waveform for the machine drive is proposed and evaluated to minimise ripple torque. Low cost normal laminated salient-pole rotors with and without slits and chamfered poles are investigated. The six-phase machine is optimised in multi-dimensions by linking the finite-element analysis method directly with an optimisation algorithm; the objective function is to maximise the torque per copper losses of the machine. The armature reaction effect is investigated in detail and found to be severe. The measured and calculated torque performances of a 35 kW optimum designed six-phase reluctance dc machine drive are presented.

Keywords: Reluctance dc machine, current waveform, design optimisation, finite element analysis, armature reaction effect.

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Authors: Tim Dunne, Jiaxiang Ren, Lei Zhao, Peng Cheng, Yi Song, Yu Liu, Wenhan Yue, Xiongwen Yang

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Two decades of the Shale Revolution has transforming transformed the global energy market, in part by the adaption of multi-stage dissolvable frac plugs. Magnesium has been favored for the bulk of plugs, requiring development of materials to suit specific field requirements. Herein, the mechanical and dissolution results from equal channel angular pressing (ECAP) of two cast dissolvable magnesium alloy are described. ECAP was selected as a route to increase the mechanical properties of two formulations of dissolvable magnesium, as solutionizing failed. In this study, 1” square cross section samples cast Mg alloys formulations containing rare earth were processed at temperatures ranging from 200 to 350 °C, at a rate of 0.005”/s, with a backpressure from 0 to 70 MPa, in a brass, or brass + graphite sheet. Generally, the yield and ultimate tensile strength (UTS) doubled for all. For formulation DM-2, the yield increased from 100 MPa to 250 MPa; UTS from 175 MPa to 325 MPa, but the strain fell from 2 to 1%. Formulation DM-3 yield increased from 75 MPa to 200 MPa, UTS from 150 MPa to 275 MPa, with strain increasing from 1 to 3%. Meanwhile, ECAP has also been found to reduce the dissolution rate significantly. A microstructural analysis showed grain refinement of the alloy and the movement of secondary phases away from the grain boundary. It is believed that reconfiguration of the grain boundary phases increased the mechanical properties and decreased the dissolution rate. ECAP processing of dissolvable high rare earth content magnesium is possible despite the brittleness of the material. ECAP is a possible processing route to increase mechanical properties for dissolvable aluminum alloys that do not extrude.

Keywords: Equal channel angular processing, dissolvable magnesium, frac plug, mechanical properties.

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Authors: S. Kannimuthu, K. Premalatha

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This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.

Keywords: Data mining, Knowledge as a Service, service oriented computing, utility mining.

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Authors: P. Jomsri

Abstract:

Nowadays social media are important tools for web resource discovery. The performance and capabilities of web searches are vital, especially search results from social research paper bookmarking. This paper proposes a new algorithm for ranking method that is a combination of similarity ranking with paper posted time or CSTRank. The paper posted time is static ranking for improving search results. For this particular study, the paper posted time is combined with similarity ranking to produce a better ranking than other methods such as similarity ranking or SimRank. The retrieval performance of combination rankings is evaluated using mean values of NDCG. The evaluation in the experiments implies that the chosen CSTRank ranking by using weight score at ratio 90:10 can improve the efficiency of research paper searching on social bookmarking websites.

Keywords: combination ranking, information retrieval, time, similarity ranking, static ranking, weight score

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Authors: Salvatore Marra, Francesco C. Morabito

Abstract:

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

Keywords: Elman neural networks, sunspot, solar activity, time series prediction.

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Authors: Swadhin Kumar Mishra, Sidhartha Panda, C. Ardil

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Multiple-Input-Multiple-Output (MIMO) is one of the most important communication techniques that allow wireless systems to achieve higher data rate. To overcome the practical difficulties in implementing Dirty Paper Coding (DPC), various suboptimal MIMO Broadcast (MIMO-BC) scheduling algorithms are employed which choose the best set of users among all the users. In this paper we discuss such a sub-optimal MIMO-BC scheduling algorithm which employs antenna selection at the receiver side. The channels for the users considered here are not Identical and Independent Distributed (IID) so that users at the receiver side do not get equal opportunity for communication. So we introduce a method of applying weights to channels of the users which are not IID in such a way that each of the users gets equal opportunity for communication. The effect of weights on overall sum-rate achieved by the system has been investigated and presented.

Keywords: Antenna selection, Identical and Independent Distributed (IID), Sum-rate capacity, Weighted sum rate.

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Authors: Margaret F. Shipley

Abstract:

Fuzzy logic can be used when knowledge is incomplete or when ambiguity of data exists. The purpose of this paper is to propose a proactive fuzzy set- based model for reacting to the risk inherent in investment activities relative to a complete view of portfolio management. Fuzzy rules are given where, depending on the antecedents, the portfolio size may be slightly or significantly decreased or increased. The decision maker considers acceptable bounds on the proportion of acceptable risk and return. The Fuzzy Controller model allows learning to be achieved as 1) the firing strength of each rule is measured, 2) fuzzy output allows rules to be updated, and 3) new actions are recommended as the system continues to loop. An extension is given to the fuzzy controller that evaluates potential financial loss before adjusting the portfolio. An application is presented that illustrates the algorithm and extension developed in the paper.

Keywords: Portfolio Management, Financial Market Monitoring, Fuzzy Controller, Fuzzy Logic,

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Authors: Motonobu Hattori

Abstract:

In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.

Keywords: catastrophic forgetting, chaotic neural network, complementary learning systems, dual-network

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Authors: Marios Poulos, Sozon Papavlasopoulos, V. S. Belesiotis

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In the proposed method for Web page-ranking, a novel theoretic model is introduced and tested by examples of order relationships among IP addresses. Ranking is induced using a convexity feature, which is learned according to these examples using a self-organizing procedure. We consider the problem of selforganizing learning from IP data to be represented by a semi-random convex polygon procedure, in which the vertices correspond to IP addresses. Based on recent developments in our regularization theory for convex polygons and corresponding Euclidean distance based methods for classification, we develop an algorithmic framework for learning ranking functions based on a Computational Geometric Theory. We show that our algorithm is generic, and present experimental results explaining the potential of our approach. In addition, we explain the generality of our approach by showing its possible use as a visualization tool for data obtained from diverse domains, such as Public Administration and Education.

Keywords: Computational Geometry, Education, e-Governance, Semantic Web.

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Authors: F. E. Cherati, T. Naij , A. Amraei, Y. Hosseinpor

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In order to study the influence of different methods of controlling weeds such as mechanical weeding and mechanical weeder efficiency analysis in mechanical cultivation conditions, in farming year of 2011 an experiment was done in a farm in coupling and development of technology center in Haraz,Iran. The treatments consisted of (I) control treatment: where no weeding was done, (II) use of mechanical weeding without engine and (III) power mechanical weeding. Results showed that experimental treatments had significantly different effects (p=0.05) on yield traits and number of filled grains per panicle, while treatments had the significant effects on grain weight and dry weight of weeds in the first, second and third weeding methods at 1% of confidence level. Treatment (II) had its most significant effect on number of filled grains per panicle and yield performance standpoint, which was 3705.97 kg ha-1 in its highest peak. Treatment (III) was ranked as second influential with 3559.8 kg ha-1. In addition, under (I) treatments, 2364.73 kg ha-1 of yield produced. The minimum dry weights of weeds in all weeding methods were related to the treatment (II), (III) and (I), respectively. The correlation coefficient analysis showed that total yield had a significant positive correlation with the panicle grain yield per plant (r= 0.55*) and the number of grains per panicle-1 (r= 0.57*) and the number of filled grains (r= 0.63*). Total rice yield also had negative correlation of r= -0. 64* with weed dry weight at second weed sampling time (17 DAT). The weed dry weight at third and fourth sampling times (24 and 40 DAT) had negative correlations of -0.65** and r=-0.61* with rice yield, respectively.

Keywords: Dry weight, without engine mechanical weeder, power mechanical weeder, yield rice.

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Authors: K. Sreelakshmi, P. Ramakanthkumar

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Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. The values of these parameters are obtained from a nearest weather station and are used to train various forms of neural networks. The trained model of neural networks is validated using a similar set of data. The model is then used to predict the wind speed, using the same meteorological information. This paper reports an Artificial Neural Network model for short term wind speed prediction, which uses back propagation algorithm.

Keywords: Short term wind speed prediction, Neural networks, Back propagation.

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Authors: B. O. Diyaolu

Abstract:

The stadium transcends a field of play to cultural heritage of a club especially when there is security of life and property and a conducive environment with exciting media facilities, CCTV and adequate field of play. Football fans love watching their clubs’ matches especially when nothing discourages their presence in the stadium. This study investigated the influence of security on fans’ attendance during Nigeria Professional Football League matches. Descriptive survey research design was used and the population consists of all Nigeria Professional Football League fans. Simple random sampling technique was used to pick a state from the six geo-political zones. 600 respondents comprising male and female fans were sampled from the ten selected vendors’ stands in each selected state. A structured questionnaire on Security and Fan attendance scale (SFAS) was used. The instrument consists of two sections. Section A seeks information on demographic data of the respondents, while section B was used to elicit information on security and fans’ attendance. The modified instrument which consists of 20 items has a reliability coefficient of 0.73. The hypothesis was tested at 0.05 significance level. The completed questionnaire was collated, coded and analyzed using descriptive statistics of frequency counts and percentage and inferential statistics of chi-square (X²). Findings of this study revealed that adequate security significantly influences fan attendance during Nigeria Professional Football League matches. There is no sport that can develop if the facilities in use are inadequate. Improving the condition of the stadium in Nigeria is paramount to the development of the Nigeria Professional Football League. All stakeholders in the organization of the League must put into consideration the need to improve the standard of the stadium as it will help to increase the attendance of fans during matches. Only the standard ones should be used during matches.

Keywords: Adequate Security, fans attendance, football fans, football stadium, Nigeria Professional Football League.

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Authors: W. EL Khoumsi, A. Hammani, M. Kuper, A. Bouaziz

Abstract:

Oases are complex and fragile agro-ecosystems. They have always existed in environments characterized by an arid climate, scarcity of rainfall, high temperatures and high evaporation. These palms have grown up despite the severity of the physical characteristics thanks to the water's existence and irrigation practice. The oases are generally spread along non-perennial rivers (wadis), shallow water table or deep artesian groundwater. However, the sustainability of oasis system is threatened by water scarcity and declining of water table levels particularly in arid areas. Located in the southern east area of Morocco, Tafilalet plain encompasses one of the largest palm groves in the kingdom. In recent years, this area has become increasingly threatened by water shortage and has seen a sharp deterioration under the effect of several combined anthropogenic and climatic factors. The Bayoud disease, successive years of drought, Hassan Addakhil dam construction etc are all factors that have affected both water and phoenicicole heritage of the area. The objective of this study is to understand the interaction between qualitative and quantitative degradation of groundwater resources, and the palm grove dynamics, while reviewing the assumption that groundwater resources contribute in a direct way to the conservation of this oasis agroecosystem. A historical analysis tracing both the oasis dynamics and the groundwater evolution has been established. Data were collected from satellite images, surveys with different actors (farmers, Regional Office for Agricultural Development, Basin agency...). They were complemented by a synthesis of numerous technical reports in the area. The results showed that within 40 years, the thickness of the groundwater table has dropped in 50 %. Along with this, there has been a downsizing of date palm by 50 %. Areas with higher groundwater level were the least affected by the downsizing. So we can say that the shallow groundwater contribute significantly and directly to the water supply of date palm through its root system, and largely ensures the oasis ecosystem sustainability.

Keywords: Oasis dynamics, Arid environments, Groundwater deterioration, Date palm.

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Authors: Inamullah Bhatti, Ahsanullah Baloch, Khadija Qureshi

Abstract:

The objective of this research is to examine the shear thinning behaviour of mixing flow of non-Newtonian fluid like toothpaste in the dissolution container with rotating stirrer. The problem under investigation is related to the chemical industry. Mixing of fluid is performed in a cylindrical container with rotating stirrer, where stirrer is eccentrically placed on the lid of the container. For the simulation purpose the associated motion of the fluid is considered as revolving of the container, with stick stirrer. For numerical prediction, a time-stepping finite element algorithm in a cylindrical polar coordinate system is adopted based on semi-implicit Taylor-Galerkin/pressure-correction scheme. Numerical solutions are obtained for non-Newtonian fluids employing power law model. Variations with power law index have been analysed, with respect to the flow structure and pressure drop.

Keywords: finite element simulation, mixing fluid, rheology, rotating flow, toothpaste

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