International Journal of Computational Systems Engineering (8 papers in press)
Need for RADAR System Utilization for Maritime Traffic Management: A case of Congo River Basin
by Habib Ullah Khan, Oduniyi Ayotunde Adediji
Abstract: Maritime traffic management has emerged as a new challenge along with the developments going on in the world. It has always been a task to maintain the productivity in the ports along with the maintenance of safety and security. The present study concentrated on such measures for the maritime traffic management on the Congo River Basin with the help of RADAR technology. It aimed to know the intensity of the issue as well as necessity for such system in lieu of old procedures to meet the growing mishaps and traffic. The study collected the opinion of the personnel of maritime department as primary data and the secondary data is collected from the records of CICOS and SCTP departments. The data is analysed using the analysis of variance technique to know whether there is increase in the untoward incidents and the traffic among the three countries connected to this basin. The results of the analysis showed the role of human errors in the occurrence of the mishaps and the personnel also opined to install the RADAR system to control this. The results of ANOVA are tested for the p-value at 5% level of significance and showed a significant increase among the accidents and deaths on the Congo River Basin from the years 2008 to 2012. The data of the traffic of passengers and the goods for the years 2010 to 2012 also showed similar trend highlighting the necessity for the efficient measures by employing strict rules and by installing new systems with the help of RADAR technology.
Keywords: Democratic Republic of Congo (DRC); Central Africa Republic (CAR); Congo Republic (RC); International Maritime Organization (IMO); Commission Internationale du Bassin Congo-Oubangui-Sangha (CICOS); Societe Commerciale des Transports et des ports (SCTP).
Finding the Best Bug Fixing Rate and Bug Fixing Time Using Software Reliability Modelling
by Rama Rao
Abstract: This article is mainly focused on finding the best possible way to rectify Bug Fixing Rate (BFR) and Bug Fixing Time (BFT). Further, versatile software projects have been verified when materializing the bug fixing rate. To increase the bug fixing rate, bug traceability is reduced by virtue of version tag in each and every component of a software deliverable. Software build release time is optimized by using mathematical optimization techniques such as software reliability growth and non-homogeneous poisson process models. This is very much essential in present market scenario. The build inconsistency and automation are also rectified in this erudite research work. The developed software is free from defects and improves the software quality by increasing bug fixing rate.
Keywords: Bug Fixing Rate; Bug Fixing Time; Bug Traceability Time; Software Build Automation; Software Reliability; Version Tag; Software Risk and Version Control System.
Evolutionary Optimisation to Minimise Material Waste in Construction
by Andy Connor, Wilson Siringoringo
Abstract: This paper describes the development and evaluation of a range of metaheuristic search algorithms applied to the optimal design of two-dimensional layout problems, with the particular application on residential building construction. Results are presented to allow the performance of the different algorithms to be compared in the pareto-optimal solution space, with resulting solutions identified and analysed in the objective space. These results show that all of the algorithms investigated have the potential to be applied to optimise material layout and improve the design processes used during building construction.
Keywords: Metaheuristic algorithms; Evolutionary computation; Layout optimisation; Residential construction.
Special Issue on: Data Analysis for Enabling Technological and Computational Enhancement in Design and Optimisation in Various Engineering Domains
Design of PID Controller for Magnetic Leviation System using Modified Gravitational Search Algorithm
by Ankush Rathore, Harish Sharma, Manisha Bhandari
Abstract: Gravitational Search Algorithm (GSA) is a swarm intelligence based algorithmrnwhich is inspired from the law of motion and law of gravity. GSA leads to the lossrnof the exploitation capability. To find a trade-off between exploration and exploitation capabilities of GSA, a modified gravitational search algorithm is proposed namely Exponent Inertia Weight based GSA (EIWGSA). The proposed algorithm maintains a proper balance between the exploitation and exploration skills of GSA by introducing an exponent inertia weight(EIW) parameter. The proposed algorithm is implemented over 15 benchmark functions and compared with basic GSA, BBO and PSO algorithm. Then, the MGSA algorithm is applied to design of PID controller for the magnetic leviation system over a wide difference operating air gap as 3mm, 10mm and 17mm.
Keywords: Gravitational Search Algorithm; Swarm Intelligence; Inertia Weight; Magnetic Leviation System.
CloudCampus: building an ubiquitous Cloud with classroom PCs at an university campus
by Andre Monteiro, Claudio Teixeira, Joaquim Sousa Pinto
Abstract: While Cloud Computing is still a developing paradigm, many of the existing challenges point to new research trends, as resource and power saving. Current datacentres are being used more efficiently, new hardware tries to comply with energy saving and software helps to fulfil these goals. On the other hand, the resource optimization can also be undertaken by maximizing the existing resources, even if not intended for cloud purposes or have state-of-the-art hardware. This paper investigated how to integrate common desktop PCs, with a wide cardinality inside a university campus, on a Cloud infrastructure to lower cost efforts, and how to deliver appropriate services to researchers. We propose a model to categorize applications, show how to build the infrastructure and present performance and consumption results.
Keywords: Distributed applications; resource management; scheduling; performance evaluation.
Fast and Effective Image Retrieval using Color and Texture Features with Self Organizing Map
by Vibhav Prakash Singh, Ashim Gupta, Rajeev Srivastava
Abstract: Content based image retrieval is an emerging area in computer vision, in which we retrieve similar images from the huge set of database on the basis of their own visual content. Most of the image retrieval systems are still, incapable of providing better retrieval results in less searching time. In this paper, we introduce Self Organizing Map (SOM) clustering approach with fusion of features. Using SOM, system performances are improved by the learning and searching capability of the neural network. Here, first we extract color moment, color histogram, local binary pattern, color percentile, and wavelet transform based color and texture features. All these features are computationally light weighted, speedup the process of image indexing. Hereafter, all these features sets are fused together with equal weight. Then, these hybrid features are fed to SOM which generates clusters of images, having similar visual content. SOM produces different clusters with their centers. Further, query image content are matched with all cluster representative to find closest cluster. Finally, images are retrieved from this closest cluster using similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Experiments on benchmark database show that the proposed clustering with hybrid features performs significantly encouraging.
Keywords: Feature Extraction; Self Organizing Map; Content Based Image Retrieval; Searching; Similarity Measure.
TripletDS: A prototype of dataspace system based on triple data model
by Mrityunjay Singh, S.K. Jain
Abstract: A dataspace system provides a powerful mechanism for searching and querying the structured, semi-structured, and unstructured data in an integrated manner. This paper aims to build a prototype called as Triplet Dataspace System (TripletDS) to provide an on-demand large scale data integration solution with less effort. The TripletDS is a prototype of dataspace system based on triple model. The triple model is a simple and flexible data model which supports the Subject-Predicate-Object (SPO) query language. The proposed prototype has the ability to efficiently bridge the gaps between syntactic and structural heterogeneity among data. The performance of TripletDS has been verified on the data sets including personal data and relational data.
Keywords: TripletDS; Dataspace; TripletDSpace; Triple Model; DSP Tool; Transformation Rules.
A Fireworks Algorithm for Solving Traveling Salesman Problem
by Zoubair Taidi, Lamia Benameur, Jihane Alami Chentoufi
Abstract: In this paper, a novel swarm intelligence algorithm inspired by observing fireworks explosions, called Fireworks Algorithm (FW), is proposed for solving the traveling salesman problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem. The problem is easy to state, but hard to solve. Many real-world problems can be formulated as instances of the TSP, for example, computer wiring, vehicle routing, crystallography, robot control, drilling of printed circuit boards and chronological sequencing. The proposed algorithm has been performed on TSP instances taken from TSPLIB library and has been compared with other methods in the literature. Computational results showed that the proposed firework algorithm is competitive in terms of quality of the solutions compared to other techniques.
Keywords: Meta-Heuristic; Fireworks Algorithm; Optimization; Swarm Intelligence; Traveling salesman problem.