Search results for: multilevel fuzzy decision support model

Authors: Raúl González Muñoz, Essam Shehab, Martin Weinitzke, Chris Fowler, Paul Baguley

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Software applications have become crucial to the aerospace industry, providing a wide range of functionalities and capabilities used during the design, manufacturing and support of aircraft. However, as this criticality increases, so too does the risk for business operations when facing a software failure. Hence, there is a need for new methodologies to be developed to support aerospace companies in effectively managing their software portfolios, avoiding the hazards of business disruption and additional costs. This paper aims to provide a definition of operational software maturity, and how this can be used to assess software operational behaviour, as well as a view on the different aspects that drive software maturity within the aerospace industry. The key research question addressed is, how can operational software maturity monitoring assist the aerospace industry in effectively managing large software portfolios? This question has been addressed by conducting an in depth review of current literature, by working closely with aerospace professionals and by running an industry case study within a major aircraft manufacturer. The results are a software maturity model composed of a set of drivers and a prototype tool used for the testing and validation of the research findings. By utilising these methodologies to assess the operational maturity of software applications in aerospace, benefits in maintenance activities and operations disruption avoidance have been observed, supporting business cases for system improvement.

Keywords: Aerospace, capability maturity model, software maturity, software lifecycle.

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Authors: G. Dottavio, M. F. Andrade, F. Renard, V. Cheutet, A.-L. L. S. Vercraene, P. Hoang, S. Briet, R. Dachicourt, Y. Baizet

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An important number of technological equipment and high-skilled workers over long periods of time have to be mobilized during nuclear decommissioning processes. The related operations generate complex flows of waste and high inventory levels, associated to information flows of heterogeneous types. Taking into account that more than 10 decommissioning operations are on-going in France and about 50 are expected toward 2025: A big challenge is addressed today. The management of decommissioning and dismantling of nuclear installations represents an important part of the nuclear-based energy lifecycle, since it has an environmental impact as well as an important influence on the electricity cost and therefore the price for end-users. Bringing new technologies and new solutions into decommissioning methodologies is thus mandatory to improve the quality, cost and delay efficiency of these operations. The purpose of our project is to improve decommissioning management efficiency by developing a decision-support framework dedicated to plan nuclear facility decommissioning operations and to optimize waste evacuation by means of a logistic approach. The target is to create an easy-to-handle tool capable of i) predicting waste flows and proposing the best decommissioning logistics scenario and ii) managing information during all the steps of the process and following the progress: planning, resources, delays, authorizations, saturation zones, waste volume, etc. In this article we present our results from waste nuclear flows simulation during decommissioning process, including discrete-event simulation supported by FLEXSIM 3-D software. This approach was successfully tested and our works confirms its ability to improve this type of industrial process by identifying the critical points of the chain and optimizing it by identifying improvement actions. This type of simulation, executed before the start of the process operations on the basis of a first conception, allow ‘what-if’ process evaluation and help to ensure quality of the process in an uncertain context. The simulation of nuclear waste flows before evacuation from the site will help reducing the cost and duration of the decommissioning process by optimizing the planning and the use of resources, transitional storage and expensive radioactive waste containers. Additional benefits are expected for the governance system of the waste evacuation since it will enable a shared responsibility of the waste flows.

Keywords: Nuclear decommissioning, logistical optimization, decision-support framework, waste management.

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: Client classification, loan suitability, risk rating, CART analysis, decision tree.

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Authors: Dev Ras Pandey, Bharat Mishra, S. K. Tripathi

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Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.

Keywords: Virtualization, OS based virtualization, container and hypervisor based virtualization.

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Authors: Kanthida Kusonmano, Michael Netzer, Bernhard Pfeifer, Christian Baumgartner, Klaus R. Liedl, Armin Graber

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Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.

Keywords: Classification, High dimensional data, Machine learning

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studied with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studies with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: A.Guntida, K. Suriye, S. Kunjara Na Ayudhya, J. Panpranot, P. Praserthdam

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Propylene self-metathesis to ethylene and butene was studied over WOx/SiO2 catalysts at 450oC and atmospheric pressure. The WOx/SiO2 catalysts were prepared by incipient wetness impregnation of ammonium metatungstate aqueous solution. It was found that, adding nano-sized extra supports (SiO2 and TiO2) by physical mixing with the WOx/SiO2 enhanced propylene conversion. The UV-Vis and FT-Raman results revealed that WOx could migrate from the original silica support to the extra support, leading to a better dispersion of WOx. The ICP-OES results also indicate that WOx existed on the extra support. Coke formation was investigated on the catalysts after 10 h time-on-stream by TPO. However, adding nano-sized extra supports led to higher coke formation which may be related to acidity as characterized by NH3-TPD.

Keywords: Extra support, nanomaterial, propylene self-metathesis, tungsten oxide.

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Authors: Y. T. Ku, S. W. Chen, J. R. Wang, C. Shih, Y. F. Chang

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In order to respond the policy decision of non-nuclear homes, Tai Power Company (TPC) will provide the decommissioning project of Kuosheng Nuclear power plant (KSNPP) to meet the regulatory requirement in near future. In this study, the computational fluid dynamics (CFD) methodology has been employed to develop a flow prediction model for boiling water reactor (BWR) with upper pool under decommissioning stage. The model can be utilized to investigate the flow behavior as the vessel combined with upper pool and continuity cooling system. At normal operating condition, different parameters are obtained for the full fluid area, including velocity, mass flow, and mixing phenomenon in the reactor pressure vessel (RPV) and upper pool. Through the efforts of the study, an integrated simulation model will be developed for flow field analysis of decommissioning KSNPP under normal operating condition. It can be expected that a basis result for future analysis application of TPC can be provide from this study.

Keywords: CFD, BWR, decommissioning, upper pool.

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Authors: John Hadden, Ashutosh Tiwari, Rajkumar Roy, Dymitr Ruta

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The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn.

Keywords: Churn, Decision Trees, Neural Networks, Regression.

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Authors: József Valyon, Gábor Horváth

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Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS–SVM) has been introduced. In this paper we present an extended view of the Least Squares Support Vector Regression (LS–SVR), which enables us to develop new formulations and algorithms to this regression technique. Based on manipulating the linear equation set -which embodies all information about the regression in the learning process- some new methods are introduced to simplify the formulations, speed up the calculations and/or provide better results.

Keywords: Function estimation, Least–Squares Support VectorMachines, Regression, System Modeling

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Authors: W. Hou, X. Shan, X. Ye

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Bidding is a very important business function to find latent contractors of construction projects. Moreover, bid markup is one of the most important decisions for a bidder to gain a reasonable profit. Since the bidding system is a complex adaptive system, bidding agent need a learning process to get more valuable knowledge for a bid, especially from past public bidding information. In this paper, we proposed an iterative agent leaning model for bidders to make markup decisions. A classifier for public bidding information named PIBS is developed to make full use of history data for classifying new bidding information. The simulation and experimental study is performed to show the validity of the proposed classifier. Some factors that affect the validity of PIBS are also analyzed at the end of this work.

Keywords: bidding markup, decision making, agent learning, information similarity.

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Authors: K. Allaw, J. Adjizian Gerard, M. Chehayeb, A. Raad, W. Fahs, A. Badran, A. Fakherdin, H. Madi, N. Badaro Saliba

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Population estimation using Geographic Information System (GIS) and remote sensing faces many obstacles such as the determination of permanent residents. A permanent resident is an individual who stays and works during all four seasons in his village. So, all those who move towards other cities or villages are excluded from this category. The aim of this study is to identify the factors affecting the percentage of permanent residents in a village and to determine the attributed weight to each factor. To do so, six factors have been chosen (slope, precipitation, temperature, number of services, time to Central Business District (CBD) and the proximity to conflict zones) and each one of those factors has been evaluated using one of the following data: the contour lines map of 50 m, the precipitation map, four temperature maps and data collected through surveys. The weighting procedure has been done using decision tree method. As a result of this procedure, temperature (50.8%) and percentage of precipitation (46.5%) are the most influencing factors.

Keywords: Remote sensing and GIS, permanent residence, decision tree, Lebanon.

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Authors: Enas M. F. El Houby, Marwa S. Hassan

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Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.

Keywords: Associative Classification, Data mining, Decision tree, HCV, interferon.

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Authors: M. Raseen, A.Assi, P.W. C. Prasad, A. Harb

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Binary Decision Diagrams (BDDs) are useful data structures for symbolic Boolean manipulations. BDDs are used in many tasks in VLSI/CAD, such as equivalence checking, property checking, logic synthesis, and false paths. In this paper we describe a new approach for the realization of a BDD package. To perform manipulations of Boolean functions, the proposed approach does not depend on the recursive synthesis operation of the IF-Then-Else (ITE). Instead of using the ITE operation, the basic synthesis algorithm is done using Boolean NOR operation.

Keywords: Binary Decision Diagram (BDD), ITE Operation, Boolean Function, NOR operation.

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Authors: P. Jomsri

Abstract:

Currently, web usage make a huge data from a lot of user attention. In general, proxy server is a system to support web usage from user and can manage system by using hit rates. This research tries to improve hit rates in proxy system by applying data mining technique. The data set are collected from proxy servers in the university and are investigated relationship based on several features. The model is used to predict the future access websites. Association rule technique is applied to get the relation among Date, Time, Main Group web, Sub Group web, and Domain name for created model. The results showed that this technique can predict web content for the next day, moreover the future accesses of websites increased from 38.15% to 85.57 %. This model can predict web page access which tends to increase the efficient of proxy servers as a result. In additional, the performance of internet access will be improved and help to reduce traffic in networks.

Keywords: Association rule, proxy server, data mining.

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Authors: Mikel Alonso López

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In the first decade of the twenty-first century, advanced imaging techniques began to be applied for neuroscience research. The Functional Magnetic Resonance Imaging (fMRI) is one of the most important and most used research techniques for the investigation of emotions, because of its ease to observe the brain areas that oxygenate when performing certain tasks. In this research, we make a review about the main research carried out on the influence of the emotions in the decision-making process that is exposed by using the fMRI.

Keywords: Decision making, emotions, fMRI.

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Authors: Mohammed Abdalla Osman Mukhtar, Azween Abdullah, Alan Giffin Downe

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Model mapping and transformation are important processes in high level system abstractions, and form the cornerstone of model-driven architecture (MDA) techniques. Considerable research in this field has devoted attention to static system abstraction, despite the fact that most systems are dynamic with high frequency changes in behavior. In this paper we provide an overview of work that has been done with regard to behavior model mapping and transformation, based on: (1) the completeness of the platform independent model (PIM); (2) semantics of behavioral models; (3) languages supporting behavior model transformation processes; and (4) an evaluation of model composition to effect the best approach to describing large systems with high complexity.

Keywords: MDA; PIM, PSM, QVT, Model Transformation

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Authors: Leila Mohammadi

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This paper presents the impact of persuasive communication implemented by fertility clinics websites, and how this information influences women at their decision-making for undertaking this procedure. The influential factors for women decisions to do social egg freezing (SEF) are analyzed from a framing theory perspective, with a specific focus on the impact of persuasive information on women’s decision making. This study follows a quantitative approach. A two-phase survey has been conducted to examine the interest rate to undertake SEF. In the first phase, a questionnaire was available during a month (May 2015) to women to answer whether or not they knew enough information of this process, with a total of 230 answers. The second phase took place in the two last weeks of July 2015. All the respondents were invited to a seminars called ‘All about egg freezing’ and afretwards they were requested to answer the second questionnaire. After the seminar, in which they were given an extensive amount of information about egg freezing, a total of 115 women replied the questionnaire. The collected data during this process were analyzed using descriptive statistics. Most of the respondents changed their opinion in the second questionaire which was after receiving information. Although in the first questionnaire their self-evaluation of having knowledge about this process and the implemented technologies was very high, they realized that they still need to access more information from different sources in order to be able to make a decision. The study reached the conclusion that persuasive and framed information by clinics would affect the decisions of these women. Despite the reasons women have to do egg freezing and their motivations behind it, providing people necessary information and unprejudiced data about this process (such as its positive and negative aspects, requirements, suppositions, possibilities and consequences) would help them to make a more precise and reasonable decision about what they are buying.

Keywords: Decision making, fertility clinics, framing theory, persuasive information, social egg freezing.

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Authors: Safaa Al-Ali, Ahmad Shahin, Fadi Chakik

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Image coding based on clustering provides immediate access to targeted features of interest in a high quality decoded image. This approach is useful for intelligent devices, as well as for multimedia content-based description standards. The result of image clustering cannot be precise in some positions especially on pixels with edge information which produce ambiguity among the clusters. Even with a good enhancement operator based on PDE, the quality of the decoded image will highly depend on the clustering process. In this paper, we introduce an ambiguity cluster in image coding to represent pixels with vagueness properties. The presence of such cluster allows preserving some details inherent to edges as well for uncertain pixels. It will also be very useful during the decoding phase in which an anisotropic diffusion operator, such as Perona-Malik, enhances the quality of the restored image. This work also offers a comparative study to demonstrate the effectiveness of a fuzzy clustering technique in detecting the ambiguity cluster without losing lot of the essential image information. Several experiments have been carried out to demonstrate the usefulness of ambiguity concept in image compression. The coding results and the performance of the proposed algorithms are discussed in terms of the peak signal-tonoise ratio and the quantity of ambiguous pixels.

Keywords: Ambiguity Cluster, Anisotropic Diffusion, Fuzzy Clustering, Image Compression.

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Authors: Joseph Ignagni, Rebecca E. Deen

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There are many views on how human decision makers behave. In this work, the Justices of the United States Supreme Court will be viewed in terms of constrained maximization and cognitivecybernetic theory. This paper will integrate research in such fields as law, political science, psychology, economics and decision making theory. It will be argued that due to its heavy workload, the Supreme Court is forced to make decisions in a boundedly rational manner. The ideas and theory put forward here will be tested in the area of the Court’s decisions involving religion. Therefore, the cases involving the U.S. Constitution’s Free Exercise Clause and Establishment Clause will be analyzed. Also, variables such as the U.S. government’s involvement in these cases will be considered. The years to be studied will be 1987-2011.

Keywords: Establishment Clause, Free Exercise Clause, U.S. Constitution, U.S. Supreme Court.

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Authors: N. Haghjou, M. J. Abdekhodaie, Y. L. Cheng, M. Saadatmand

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Intravitreal injection (IVI) is the most common treatment for eye posterior segment diseases such as endopthalmitis, retinitis, age-related macular degeneration, diabetic retinopathy, uveitis, and retinal detachment. Most of the drugs used to treat vitreoretinal diseases, have a narrow concentration range in which they are effective, and may be toxic at higher concentrations. Therefore, it is critical to know the drug distribution within the eye following intravitreal injection. Having knowledge of drug distribution, ophthalmologists can decide on drug injection frequency while minimizing damage to tissues. The goal of this study was to develop a computer model to predict intraocular concentrations and pharmacokinetics of intravitreally injected drugs. A finite volume model was created to predict distribution of two drugs with different physiochemical properties in the rabbit eye. The model parameters were obtained from literature review. To validate this numeric model, the in vivo data of spatial concentration profile from the lens to the retina were compared with the numeric data. The difference was less than 5% between the numerical and experimental data. This validation provides strong support for the numerical methodology and associated assumptions of the current study.

Keywords: Posterior segment, Intravitreal injection (IVI), Pharmacokinetic, Modelling, Finite volume method.

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractive. Thus, this study intends to introducing the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN, and LR to forecast the flavor of grapes juice in real data shows that SVR is more suitable and effective at predicting performance.

Keywords: Flavor forecasting, artificial neural networks, support vector regression, grape juice flavor.

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Authors: Chowdhury Dilruba Shoma

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Gender is about equal rights that both males and females having access to responsibilities and opportunities in decision making is a fundamental human right. It is also a precondition for, and a mark of, sustainable people-oriented development. In Bangladesh, women have fewer opportunities than men do to access credit from banks and financial institutions. Entrenched patriarchal attitudes, unequal inheritance rights, and male-dominated hierarchies in the financial system, plus high interest rates and a lack of security/collateral, make it harder for women to obtain bank loans. Limited access to institutional credit is a serious restraint on the productivity and income of women entrepreneurs, (and the wider economy). These gender-biased and structural barriers inhibit women’s access to fundamental economic rights. Using a liberal feminist theoretical lens, this study provides some useful insights into the relationship between gender inequality and entrepreneurship, leading to a better understanding of women’s entrepreneurship development in Bangladesh. Recently, the Bangladesh Government, the United Nations Capital Development Fund, and Bangladesh Bank opened up the Women Entrepreneur Support Fund (WESF) ‒ Credit Guarantee Scheme (CGS) pilot project to cover collateral shortfalls for women entrepreneurs in the small and medium enterprise sector. The aim is to improve gender equality and advance women’s rights in relation to receiving credit. This article examines the challenges and prospects of the WESF-CGS, and suggests that implementation of measures in WESF-CGS policymaking, coupled with a combination of legislatory and regulatory reforms that implement the fundamental tenets of liberal feminism, can lead to a comprehensive and effective credit policy to boost women’s agency and economic empowerment. This may ultimately lead to more sustainable development in Bangladesh.

Keywords: Bangladesh, CGS, liberal feminist theory, women entrepreneur support fund.

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression.

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Authors: Chin-Yin Chen, Chi-Cheng Cheng

Abstract:

This paper aims at to develop a robust optimization methodology for the mechatronic modules of machine tools by considering all important characteristics from all structural and control domains in one single process. The relationship between these two domains is strongly coupled. In order to reduce the disturbance caused by parameters in either one, the mechanical and controller design domains need to be integrated. Therefore, the concurrent integrated design method Design For Control (DFC), will be employed in this paper. In this connect, it is not only applied to achieve minimal power consumption but also enhance structural performance and system response at same time. To investigate the method for integrated optimization, a mechatronic feed drive system of the machine tools is used as a design platform. Pro/Engineer and AnSys are first used to build the 3D model to analyze and design structure parameters such as elastic deformation, nature frequency and component size, based on their effects and sensitivities to the structure. In addition, the robust controller,based on Quantitative Feedback Theory (QFT), will be applied to determine proper control parameters for the controller. Therefore, overall physical properties of the machine tool will be obtained in the initial stage. Finally, the technology of design for control will be carried out to modify the structural and control parameters to achieve overall system performance. Hence, the corresponding productivity is expected to be greatly improved.

Keywords: Machine tools, integrated structure and control design, design for control, multilevel decomposition, quantitative feedback theory.

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Authors: E. Karoui Chaabane, S. Hadouaj, K. Ghedira

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Inter-organizational Workflow (IOW) is commonly used to support the collaboration between heterogeneous and distributed business processes of different autonomous organizations in order to achieve a common goal. E-government is considered as an application field of IOW. The coordination of the different organizations is the fundamental problem in IOW and remains the major cause of failure in e-government projects. In this paper, we introduce a new coordination model for IOW that improves the collaboration between government administrations and that respects IOW requirements applied to e-government. For this purpose, we adopt a Multi-Agent approach, which deals more easily with interorganizational digital government characteristics: distribution, heterogeneity and autonomy. Our model integrates also different technologies to deal with the semantic and technologic interoperability. Moreover, it conserves the existing systems of government administrations by offering a distributed coordination based on interfaces communication. This is especially applied in developing countries, where administrations are not necessary equipped with workflow systems. The use of our coordination techniques allows an easier migration for an e-government solution and with a lower cost. To illustrate the applicability of the proposed model, we present a case study of an identity card creation in Tunisia.

Keywords: E-government, Inter-organizational workflow, Multi-agent systems, Semantic web services.

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Authors: Xiantong Li, Jianzhong Li

Abstract:

Graph has become increasingly important in modeling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. Different from the existing methods, our approach, called VFM (Vertex to Frequent Feature Mapping), makes use of vertices and decision features as the basic indexing feature. VFM constructs two mappings between vertices and frequent features to answer graph queries. The VFM approach not only provides an elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit from data mining, especially frequent pattern mining. The results show that the proposed method not only avoids the enumeration method of getting subgraphs of query graph, but also effectively reduces the subgraph isomorphism tests between the query graph and graphs in candidate answer set in verification stage.

Keywords: Decision Feature, Frequent Feature, Graph Dataset, Graph Query

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Authors: Hassan Baghgar Bostan Abad, Ali Yazdian Varjani, Taheri Asghar

Abstract:

Variable speed drives are growing and varying. Drives expanse depend on progress in different part of science like power system, microelectronic, control methods, and so on. Artificial intelligent contains hard computation and soft computation. Artificial intelligent has found high application in most nonlinear systems same as motors drive. Because it has intelligence like human but there are no sentimental against human like angriness and.... Artificial intelligent is used for various points like approximation, control, and monitoring. Because artificial intelligent techniques can use as controller for any system without requirement to system mathematical model, it has been used in electrical drive control. With this manner, efficiency and reliability of drives increase and volume, weight and cost of them decrease.

Keywords: Artificial intelligent, electrical motor, intelligent drive and control,

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Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER

Abstract:

The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Relevance feedback, bag of words model, probabilistic model, vector space model, image retrieval

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