Volume 8, Issue 1, July 2011


A Flexible security Process for SPL construction [Full Text]

Sami OUALI, Naoufel KRAIEM and Henda BEN GHEZALA

Software product line (SPL) engineering is an approach that develops and maintains families of products while taking advantage of their common aspects and predicted variabilities. Numerous SPL construction approaches are proposed. Different in nature, these approaches have nevertheless some common disadvantages. We have proceeded to an in-depth analysis of existing approaches for the construction of SPL within a comparison framework in order to identify their drawbacks. We suggest overcoming these drawbacks by an improvement of the tool support for these approaches and for their interactivity with their users. We propose an amelioration of this framework. We present also a flexible process to obtain a flexible SPL. This process deals with goal models to fulfill stakeholder goals, behavioral specifications, architectures and business processes. Our process is based on goal modeling, feature modeling and metamodels. It is possible to reconcile flexibility and efficiency during the SPL construction and product derivation.

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Issues and Challenges in Clustering Tech-niques for Wireless Mesh Networks
[Full Text]

Adebanjo Adekiigbe, Kamalrulnizam Abu Bakar and Ogunnusi Olumide Simeon


The implementation of wireless mesh networks (WMNs) for large scale use is not very easy because the topologies
in these networks are multihop and highly dynamic while the bandwidth issue is also a great limitation. Despite the huge advan-tages  that  this  technology  portends  its  implementation  has  been  seriously  affected  by  myriad  of  challenges,  thus  clustering schemes  are  proposed  by  various  authors  to  confront  these  challenges.  In  this  paper,  some  existing  clustering  technique schemes are perused with the mindset to reveal the huge benefits that such scheme offers when implemented while we also in-tend to point out some of its weaknesses for the research community to find a way of fine-tuning it for its betterment. Clustering techniques were classified in this paper according to their implementation criteria such as heuristic, weighted, emergent and hie-rarchical while the advantages and weaknesses are technically highlighted.

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MCAIM: Modified CAIM Discretization [Full Text]



Shivani V. Vora and Rupa G. Mehta 


Discretization is a process of dividing a continuous attribute into a finite set of intervals to generate an attribute with small number of distinct values, by associating discrete numerical value with each of the generated intervals. It produces a concise summarization of continuous attributes and leads to make learning, more accurate and faster.  Discretization is usually performed prior to the learning process and has played an important role in data mining and knowledge discovery. The results of CAIM are not satisfactory in some cases, led us to modify the algorithm. The Modified CAIM (MCAIM) results are compared with other discretization techniques for classification accuracy and generated the outperforming results.

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ABC Algorithm to Thermal Units Maintenance 
[Full Text]

R. Anandhakumar, S. Subramanian and S. Ganesan

The  goal  of  an  optimal  Generator  Maintenance  Scheduling  (GMS)  is  to  evolve  optimal  preventive  maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. In this paper a novel bio-inspired search technique namely; Artificial Bee Colony (ABC) algorithm is applied to solve the GMS optimization problem efficiently. The efficacy of the proposed algorithm is illustrated with  three different power systems. The  test  system  1  consists  of  21  generating  units,  test  system  2  is  a  practical  Nigerian  grid  system  consists  of  49 generating units and test system 3 consists of 13 generating units. The simulation results are compared with Discrete Particle Swarm  Optimization  (DPSO),  Modified  Discrete  Particle  Swarm  Optimization  (MDPSO)  and  Multiple  Swarms  -  Modified Discrete Particle Swarm Optimization (MS- MDPSO) which is also population based heuristic search algorithms.  The simulation results indicate that ABC algorithm can efficiently be used for GMS.

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Application of Fuzzy Logic Controller for a Pressure Process
[Full Text]

V.Kalaichelvi and Srijan Bhatnagar

Pressure is a key process variable in process control industries because pressure provides a critical condition for boiling, chemical reaction, distillation, vacuuming, and air conditioning. Pressure Process rig is a pneumatic control system which allows study of the principles of pressure as the process variable to be controlled. The aim of the present work is to apply different control strategies to a pressure control process. Controllers help in bringing the output of a process to settle at a specified set point. Conventional Controllers like the Proportional, Integral and Derivative (PID) is used to control the pressure process under consideration. The Internal Model Control method (IMC) of tuning is used to determine the parameters of the controller.Since pressure is a nonlinear process, intelligent control strategies like Fuzzy Logic Controller (FLC) work better for the pressure process. The fuzzy rule based system helps in the design aspect of the FLC. The performance of two controllers are compared to  understand which controller is better suited for the process under consideration. Simulation results using MATLAB are shown to carry out the design and control aspects of the process.

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Application of VRML and PNML for Feature Recognition of Mechanical Parts
[Full Text]

B.V.Sudheer Kumar and C.S.P.Rao


Recognition  of  manufacturing  features  forms  the  basis  for  the  computer  aided  process  planning  (CAPP)  and  plays  a key  role in integration  of  computer aided  design  (CAD) and  computer  aided  manufacturing  (CAM).  It  is  the process  of  converting CAD data of a part into a model of the manufacturing activities required to create the part. This paper presents feature recognition method  using  Virtual  Reality  Modeling  Language  (VRML)  The  geometric  information  of  the  part  is  translated  into  manufacturing information  through  VRML.  A  Java  program  in  Netbeans  environment  is  used  to  recognize  the  features,  where  geometric information  of  the  part  is  extracted  from  VRML  file.  By  using  this  data,  an  output  file  in  the  form  of  Petri  Net  Markup  Language (PNML) is obtained, which is given as input to the Petri net generation software, P3. The resulting output file gives the number of machinable  features  present  in  the  part  in  the  form  of  places  and  transitions.  This  process  has  been  tested  on  a  part  and successfully extracted all the features.