Search results for: ANP (analytic network process)

Authors: Byung – wan Jo, Jung – hoon Park, Jang - wook Kim

Abstract:

Recently, as information industry and mobile communication technology are developing, this study is conducted on the new concept of intelligent structures and maintenance techniques that applied wireless sensor network, USN (Ubiquitous Sensor Network), to social infrastructures such as civil and architectural structures on the basis of the concept of Ubiquitous Computing that invisibly provides human life with computing, along with mutually cooperating, compromising and connecting networks each other by having computers within all objects around us. Therefore, the purpose of this study is to investigate the capability of wireless communication of sensor node embedded in reinforced concrete structure with a basic experiment on an electric wave permeability of sensor node by fabricating molding with variables of concrete thickness and steel bars that are mostly used in constructing structures to determine the feasibility of application to constructing structures with USN. At this time, with putting the pitches of steel bars, the thickness of concrete placed, and the intensity of RF signal of a transmitter-receiver as variables and when wireless communication module was installed inside, the possible communication distance of plain concrete and the possible communication distance by the pitches of steel bars was measured in the horizontal and vertical direction respectively. Besides, for the precise measurement of diminution of an electric wave, the magnitude of an electric wave in the range of used frequencies was measured by using Spectrum Analyzer. The phenomenon of diminution of an electric wave was numerically analyzed and the effect of the length of wavelength of frequencies was analyzed by the properties of a frequency band area. As a result of studying the feasibility of an application to constructing structures with wireless sensor, in case of plain concrete, it shows 45cm for the depth of permeability and in case of reinforced concrete with the pitches of 5cm, it shows 37cm and 45cm for the pitches of 15cm.

Keywords: Ubiquitous, Concrete, Permeability, Wireless, Sensor

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Authors: Karel Frajtak, Miroslav Bures, Ivan Jelinek

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Software testing is important stage of software development cycle. Current testing process involves tester and electronic documents with test case scenarios. In this paper we focus on new approach to testing process using automated test case generation and tester guidance through the system based on the model of the system. Test case generation and model-based testing is not possible without proper system model. We aim on providing better feedback from the testing process thus eliminating the unnecessary paper work.

Keywords: Model based testing, test automation, test generating, tester support.

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Authors: K.Manimala, Dr.K.Selvi, R.Ahila

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Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.

Keywords: back propagation network, power quality, probabilistic neural network, radial basis function support vector machine

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Authors: A. Diaf, H. Aburideh, Z.Tigrine, D. Tassalit, F.Alaoui

Abstract:

The tray/multi-tray distillation process is a topic that has been investigated to great detail over the last decade by many teams such as Jubran et al. [1], Adhikari et al. [2], Mowla et al. [3], Shatat et al. [4] and Fath [5] to name a few. A significant amount of work and effort was spent focusing on modeling and/simulation of specific distillation hardware designs. In this work, we have focused our efforts on investigating and gathering experimental data on several engineering and design variables to quantify their influence on the yield of the multi-tray distillation process. Our goals are to generate experimental performance data to bridge some existing gaps in the design, engineering, optimization and theoretical modeling aspects of the multi-tray distillation process.

Keywords: Distillation, Desalination, Multi-Stage still, Solar Energy

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Authors: H. Shareef, A. Mohamed, S. A. Khalid, Aziah Khamis

Abstract:

This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation. 

Keywords: Artificial intelligence, Power tracing, Artificial neural network, ANFIS, Power system deregulation.

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Authors: Touba Amirazodi

Abstract:

Rapid process of urbanism development has increased the demand for some infrastructures such as supplying potable water, electricity network and transportation facilities and etc. Nonefficiency of the existing system with parallel managements of urban traffic management has increased the gap between supply and demand of traffic facilities. A sustainable transport system requires some activities more important than air pollution control, traffic or fuel consumption reduction and the studies show that there is no unique solution for solving complicated transportation problems and solving such a problem needs a comprehensive, dynamic and reliable mechanism. Sustainable transport management considers the effects of transportation development on economic efficiency, environmental issues, resources consumption, land use and social justice and helps reduction of environmental effects, increase of transportation system efficiency as well as improvement of social life and aims to enhance efficiency, goods transportation, provide services with minimum access problems that cannot be realized without reorganization of strategies, policies and plans.

Keywords: Sustainable Urban Transport, Environment, Social Justice, Air Pollution

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Authors: B. Sohbi, M. Meakaff, M. Emtir, M. Elgarni

Abstract:

Natural gas is defined as gas obtained from a natural underground reservoir. It generally contains a large quantity of methane along with heavier hydrocarbons such as ethane, propane, isobutene, normal butane; also in the raw state it often contains a considerable amount of non hydrocarbons, such as nitrogen and the acid gases (carbon dioxide and hydrogen sulfide). The acid gases must be removed from natural gas before use. One of the processes witch are use in the industry to remove the acid gases from natural gas is the use of alkanolamine process. In this present paper, a simulation study for an industrial gas sweetening plant has been investigated. The aim of the study is to investigate the effect of using mixing amines as solvent on the gas treatment process using the software Hysys.

Keywords: Natural gas, alkanolamine process, gas sweetening plant, simulation, mixing amines.

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Authors: Miodrag T. Manić, Dejan I. Tanikić, Miloš S. Stojković, Dalibor M. ðenadić

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The design of technological procedures for manufacturing certain products demands the definition and optimization of technological process parameters. Their determination depends on the model of the process itself and its complexity. Certain processes do not have an adequate mathematical model, thus they are modeled using heuristic methods. First part of this paper presents a state of the art of using soft computing techniques in manufacturing processes from the perspective of applicability in modern CAx systems. Methods of artificial intelligence which can be used for this purpose are analyzed. The second part of this paper shows some of the developed models of certain processes, as well as their applicability in the actual calculation of parameters of some technological processes within the design system from the viewpoint of productivity.

Keywords: fuzzy logic, manufacturing, neural networks

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Authors: Deepak Goswami, Padma Lochan Hazarika, Kandarpa Kumar Sarma

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The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.

Keywords: Industrial, Health, Classification, ANN, MLP, MSE.

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Authors: R. Bhuvenesh, M.H. Norizaman, M.S. Abdul Manan

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Industrial surveys shows that manufacturing companies define the qualities of thermal removing process based on the dimension and physical appearance of the cutting material surface. Therefore, the roughness of the surface area of the material cut by the plasma arc cutting process and the rate of the removed material by the manual plasma arc cutting machine was importantly considered. Plasma arc cutter Selco Genesis 90 was used to cut Standard AISI 1017 Steel of 200 mm x100 mm x 6 mm manually based on the selected parameters setting. The material removal rate (MRR) was measured by determining the weight of the specimens before and after the cutting process. The surface roughness (SR) analysis was conducted using Mitutoyo CS-3100 to determine the average roughness value (Ra). Taguchi method was utilized to achieve optimum condition for both outputs studied. The microstructure analysis in the region of the cutting surface is performed using SEM. The results reveal that the SR values are inversely proportional to the MRR values. The quality of the surface roughness depends on the dross peak that occurred after the cutting process.

Keywords: Material removal rate, plasma arc cutting, surface roughness, Taguchi method

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Authors: C. Bisu, M. Zapciu, A. Gérard

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This paper proposes a method to vibration analysis in order to on-line monitoring and predictive maintenance during the milling process. Adapting envelope method to diagnostics and the analysis for milling tool materials is an important contribution to the qualitative and quantitative characterization of milling capacity and a step by modeling the three-dimensional cutting process. An experimental protocol was designed and developed for the acquisition, processing and analyzing three-dimensional signal. The vibration envelope analysis is proposed to detect the cutting capacity of the tool with the optimization application of cutting parameters. The research is focused on Hilbert transform optimization to evaluate the dynamic behavior of the machine/ tool/workpiece.

Keywords: diagnostics, envelope, milling, vibration

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Authors: Teodora Bakardjieva, Galya Gercheva

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In this paper an open agent-based modular framework for personalized and adaptive curriculum generation in e-learning environment is proposed. Agent-based approaches offer several potential advantages over alternative approaches. Agent-based systems exhibit high levels of flexibility and robustness in dynamic or unpredictable environments by virtue of their intrinsic autonomy. The presented framework enables integration of different types of expert agents, various kinds of learning objects and user modeling techniques. It creates possibilities for adaptive e-learning process. The KM e-learning system is in a process of implementation in Varna Free University and will be used for supporting the educational process at the University.

Keywords: agents, e-Learning, knowledge management, knowledge sharing, artificial intelligence

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Authors: Dimitar Georgiev, Bogdan Bogdanov, Yancho Hristov, Irena Markovska

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In this study the adsorption of Cu (II) ions from aqueous solutions on synthetic zeolite NaA was evaluated. The effect of solution temperature and the determination of the kinetic parameters of adsorption of Cu(II) from aqueous solution on zeolite NaA is important in understanding the adsorption mechanism. Variables of the system include adsorption time, temperature (293- 328K), initial solution concentration and pH for the system. The sorption kinetics of the copper ions were found to be strongly dependent on pH (the optimum pH 3-5), solute ion concentration and temperature (293 – 328 K). It was found, the pseudo-second-order model was the best choice among all the kinetic models to describe the adsorption behavior of Cu(II) onto ziolite NaA, suggesting that the adsorption mechanism might be a chemisorptions process The activation energy of adsorption (Ea) was determined as Cu(II) 13.5 kJ mol-1. The low value of Ea shows that Cu(II) adsorption process by zeolite NaA may be an activated chemical adsorption. The thermodynamic parameters (ΔG0, ΔH0, and ΔS0) were also determined from the temperature dependence. The results show that the process of adsorption Cu(II) is spontaneous and endothermic process and rise in temperature favors the adsorption.

Keywords: Zeolite NaA, adsorption, adsorption capacity, kinetic sorption

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Authors: Nikhil, Bestamin Özkaya, Ari Visa, Chiu-Yue Lin, Jaakko A. Puhakka, Olli Yli-Harja

Abstract:

The performance of a sucrose-based H2 production in a completely stirred tank reactor (CSTR) was modeled by neural network back-propagation (BP) algorithm. The H2 production was monitored over a period of 450 days at 35±1 ºC. The proposed model predicts H2 production rates based on hydraulic retention time (HRT), recycle ratio, sucrose concentration and degradation, biomass concentrations, pH, alkalinity, oxidation-reduction potential (ORP), acids and alcohols concentrations. Artificial neural networks (ANNs) have an ability to capture non-linear information very efficiently. In this study, a predictive controller was proposed for management and operation of large scale H2-fermenting systems. The relevant control strategies can be activated by this method. BP based ANNs modeling results was very successful and an excellent match was obtained between the measured and the predicted rates. The efficient H2 production and system control can be provided by predictive control method combined with the robust BP based ANN modeling tool.

Keywords: Back-propagation, biohydrogen, bioprocessmodeling, neural networks.

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Authors: S. Thirumurugan, D. R. Prince Williams

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Software Reliability is one of the key factors in the software development process. Software Reliability is estimated using reliability models based on Non Homogenous Poisson Process. In most of the literature the Software Reliability is predicted only in testing phase. So it leads to wrong decision-making concept. In this paper, two Software Reliability concepts, testing and operational phase are studied in detail. Using S-Shaped Software Reliability Growth Model (SRGM) and Exponential SRGM, the testing and operational reliability values are obtained. Finally two reliability values are compared and optimal release time is investigated.

Keywords: Error Detection Rate, Estimation of Parameters, Instantaneous Failure Rate, Mean Value Function, Non Homogenous Poisson Process (NHPP), Software Reliability.

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Authors: Nahid Ardalani, Ahmadreza Khoogar, H. Roohi

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In this paper we compare the response of linear and nonlinear neural network-based prediction schemes in prediction of received Signal-to-Interference Power Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The nonlinear predictor is Multilayer Perceptron MLP and the linear predictor is an Adaptive Linear (Adaline) predictor. We solve the problem of complexity by using the Minimum Mean Squared Error (MMSE) principle to select the optimal predictors. The optimized Adaline predictor is compared to optimized MLP by employing noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an urban environment. The results show that the Adaline predictor can estimates SIR with the same error as MLP when the user has the velocity of 5 km/h and 60 km/h but by increasing the velocity up-to 120 km/h the mean squared error of MLP is two times more than Adaline predictor. This makes the Adaline predictor (with lower complexity) more suitable than MLP for closed-loop power control where efficient and accurate identification of the time-varying inverse dynamics of the multi path fading channel is required.

Keywords: Power control, neural networks, DS/CDMA mobilecommunication systems.

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Authors: Roland S. Nattermann, Reiner Anderl

Abstract:

Existing proceeding-models for the development of mechatronic systems provide a largely parallel action in the detailed development. This parallel approach is to take place also largely independent of one another in the various disciplines involved. An approach for a new proceeding-model provides a further development of existing models to use for the development of Adaptronic Systems. This approach is based on an intermediate integration and an abstract modeling of the adaptronic system. Based on this system-model a simulation of the global system behavior, due to external and internal factors or Forces is developed. For the intermediate integration a special data management system is used. According to the presented approach this data management system has a number of functions that are not part of the "normal" PDM functionality. Therefore a concept for a new data management system for the development of Adaptive system is presented in this paper. This concept divides the functions into six layers. In the first layer a system model is created, which divides the adaptronic system based on its components and the various technical disciplines. Moreover, the parameters and properties of the system are modeled and linked together with the requirements and the system model. The modeled parameters and properties result in a network which is analyzed in the second layer. From this analysis necessary adjustments to individual components for specific manipulation of the system behavior can be determined. The third layer contains an automatic abstract simulation of the system behavior. This simulation is a precursor for network analysis and serves as a filter. By the network analysis and simulation changes to system components are examined and necessary adjustments to other components are calculated. The other layers of the concept treat the automatic calculation of system reliability, the "normal" PDM-functionality and the integration of discipline-specific data into the system model. A prototypical implementation of an appropriate data management with the addition of an automatic system development is being implemented using the data management system ENOVIA SmarTeam V5 and the simulation system MATLAB.

Keywords: Adaptronic, Data-Management, LOEWE-CentreAdRIA

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Authors: Hao He, Shaoqian Li

Abstract:

In this paper, we study the cooperative communications where multiple cognitive radio (CR) transmit-receive pairs competitive maximize their own throughputs. In CR networks, the influences of primary users and the spectrum availability are usually different among CR users. Due to the existence of multiple relay nodes and the different spectrum availability, each CR transmit-receive pair should not only select the relay node but also choose the appropriate channel. For this distributed problem, we propose a game theoretic framework to formulate this problem and we apply a regret-matching learning algorithm which is leading to correlated equilibrium. We further formulate a modified regret-matching learning algorithm which is fully distributed and only use the local information of each CR transmit-receive pair. This modified algorithm is more practical and suitable for the cooperative communications in CR network. Simulation results show the algorithm convergence and the modified learning algorithm can achieve comparable performance to the original regretmatching learning algorithm.

Keywords: cognitive radio, cooperative communication, relay selection, channel choice, regret-matching learning, correlated equilibrium.

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Authors: Gianluigi De Mare, Tony Leopoldo Luigi Lenza, Rino Conte

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The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.

Keywords: Impact, compensation for financial loss, depreciation of property, railway network design, genetic algorithms.

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Authors: Elena Chernyshkova

Abstract:

The paper which is dedicated to describing the effect made by the “significant other", presents the new model of interrelation between self-reflection, the “significant other" phenomenon and aggression. Tendencies of direction and type frustration response developments in detail are discussed. New results have been received through designing of the original experiment. It is based on modifications of the “Picture – Frustration Study" test by S. Rosenzweig.

Keywords: Reflection, reflective process, aggression, aggressivereactions, "significant other"

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Authors: U. Bottigli, R.Chiarucci, B. Golosio, G.L. Masala, P. Oliva, S.Stumbo, D.Cascio, F. Fauci, M. Glorioso, M. Iacomi, R. Magro, G. Raso

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Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.

Keywords: Neural Networks, K-Nearest Neighbours, Support Vector Machine, Computer Aided Detection

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Authors: Stephen Akinlabi, Mukul Shukla, Esther Akinlabi, Marwala Tshilidzi

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This paper reports the evolving properties of a 3 mm low carbon steel plate after Laser Beam Forming achieve this objective, the chemical analyse material and the formed components were carried thereafter both were characterized through microhardness profiling microstructural evaluation and tensile testing. showed an increase in the elemental concentration of the component when compared to the as received attributed to the enhancement property of the LBF process Ultimate Tensile Strength (UTS) and the Vickers the formed component shows an increase when compared to the as received material, this was attributed to strain hardening and grain refinement brought about by the LBF process. The microstructure of the as received steel consists of equiaxed ferrit that of the formed component exhibits elongated orming process (LBF). To es of the as received out and compared; profiling, The chemical analyses formed material; this can be process. The microhardness of ferrite and pearlite while grains.

Keywords: Laser beam forming, deformation , deformation, elongated grains

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma

Abstract:

Deep cold rolling (DCR) and low plasticity burnishing (LPB) process are cold working processes, which easily produce a smooth and work-hardened surface by plastic deformation of surface irregularities. The present study focuses on the surface roughness and surface hardness aspects of AISI 4140 work material, using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in order to identify the predominant factors amongst the selected parameters. They were then categorized in order of significance followed by setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. In the present work, the influence of main process parameters (force, feed rate, number of tool passes/overruns, initial roughness of the work piece, ball material, ball diameter and lubricant used) on the surface roughness and the hardness of AISI 4140 steel were studied for both LPB and DCR process and the results are compared. It was observed that by using LPB process surface hardness has been improved by 167% and in DCR process surface hardness has been improved by 442%. It was also found that the force, ball diameter, number of tool passes and initial roughness of the workpiece are the most pronounced parameters, which has a significant effect on the work piece-s surface during deep cold rolling and low plasticity burnishing process.

Keywords: Deep cold rolling, burnishing, surface roughness, surface hardness, design of experiments, AISI4140 steel.

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Authors: Kevin McDaid, Simon P. Wilson

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Software developed for a specific customer under contract typically undergoes a period of testing by the customer before acceptance. This is known as user acceptance testing and the process can reveal both defects in the system and requests for changes to the product. This paper uses nonhomogeneous Poisson processes to model a real user acceptance data set from a recently developed system. In particular a split Poisson process is shown to provide an excellent fit to the data. The paper explains how this model can be used to aid the allocation of resources through the accurate prediction of occurrences both during the acceptance testing phase and before this activity begins.

Keywords: User acceptance testing. Software reliability growth modelling. Split Poisson process. Bayesian methods.

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Authors: Masoud Hekmatpanah, Mohammad Sadroddin, Saeid Shahbaz, Farhad Mokhtari, Farahnaz Fadavinia

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The six sigma method is a project-driven management approach to improve the organization-s products, services, and processes by continually reducing defects in the organization. Understanding the key features, obstacles, and shortcomings of the six sigma method allows organizations to better support their strategic directions, and increasing needs for coaching, mentoring, and training. It also provides opportunities to better implement six sigma projects. The purpose of this paper is the survey of six sigma process and its impact on the organizational productivity. So I have studied key concepts , problem solving process of six sigmaas well as the survey of important fields such as: DMAIC, six sigma and productivity applied programme, and other advantages of six sigma. In the end of this paper, present research conclusions. (direct and positive relation between six sigma and productivity)

Keywords: Six sigma, project management, quality, theory, productivity.

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Authors: Ifeyinwa E. Achumba, Djamel Azzi, Rinat Khusainov

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There are three approaches to complete Bayesian Network (BN) model construction: total expert-centred, total datacentred, and semi data-centred. These three approaches constitute the basis of the empirical investigation undertaken and reported in this paper. The objective is to determine, amongst these three approaches, which is the optimal approach for the construction of a BN-based model for the performance assessment of students- laboratory work in a virtual electronic laboratory environment. BN models were constructed using all three approaches, with respect to the focus domain, and compared using a set of optimality criteria. In addition, the impact of the size and source of the training, on the performance of total data-centred and semi data-centred models was investigated. The results of the investigation provide additional insight for BN model constructors and contribute to literature providing supportive evidence for the conceptual feasibility and efficiency of structure and parameter learning from data. In addition, the results highlight other interesting themes.

Keywords: Bayesian networks, model construction, parameterlearning, structure learning, performance index, model comparison.

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Authors: Dimitris Georgoutsos, Petros M. Migiakis

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The concurrent era is characterised by strengthened interactions among financial markets and increased capital mobility globally. In this frames we examine the effects the international financial integration process has on the European bond markets. We perform a comparative study of the interactions of the European and international bond markets and exploit Cointegration analysis results on the elimination of stochastic trends and the decomposition of the underlying long run equilibria and short run causal relations. Our investigation provides evidence on the relation between the European integration process and that of globalisation, viewed through the bond markets- sector. Additionally the structural formulation applied, offers significant implications of the findings. All in all our analysis offers a number of answers on crucial queries towards the European bond markets integration process.

Keywords: financial integration, bond markets, cointegration

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Authors: Hema C. R., Paulraj M. P., S. Yaacob, A. H. Adom, R. Nagarajan

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Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification

Keywords: Motor Imagery, Brain Machine Interfaces, Neural Networks, Particle Swarm Optimization, EEG signal processing.

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Authors: Myint Myint Yi, Khin Sandar Linn, Marlar Kyaw

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This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF.

Keywords: Neural network, Load forecast, Time series, wavelettransform.

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Authors: Jawad Saleem, Abdul Majid, Kent Bertilsson, Torbjörn Carlberg, Nazar Ul Islam

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Resistance spot welding process comprises of electric, thermal and mechanical phenomenon, which makes this process complex and highly non-linear and thus, it becomes difficult to model it. In order to obtain good weld nugget during spot welding, hit and trial welds are usually done which is very costly. Therefore the numerical simulation research has been conducted to understand the whole process. In this paper three different cases were analyzed by varying the tip contact area and it was observed that, with the variation of tip contact area the nugget formation at the faying surface is affected. The tip contact area of the welding electrode becomes large with long welding cycles. Therefore in order to maintain consistency of nugget formation during the welding process, the current compensation in control feedback is required. If the contact area of the welding electrode tip is reduced, a large amount of current flows through the faying surface, as a result of which sputtering occurs.

Keywords: Resistance spot welding, Finite element modeling, Nugget formation, Welding electrode, Numerical method simulation,

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