Search results for: Machine Vision

Authors: Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain

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This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.

Keywords: Dynamic voltage collapse, prediction, artificial neural network, support vector machines

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Authors: J. Barati, A. Saeedian, S. S. Mortazavi

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The main objective of this paper is a comparative investigate in enhancement of damping power system oscillation via coordinated design of the power system stabilizer (PSS) and static synchronous series compensator (SSSC) and static synchronous compensator (STATCOM). The design problem of FACTS-based stabilizers is formulated as a GA based optimization problem. In this paper eigenvalue analysis method is used on small signal stability of single machine infinite bus (SMIB) system installed with SSSC and STATCOM. The generator is equipped with a PSS. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. This aim is to enhance both rotor angle and power system stability. The eigenvalue analysis and non-linear simulation results are presented to show the effects of these FACTS-based stabilizers and reveal that SSSC exhibits the best effectiveness on damping power system oscillation.

Keywords: Power system stability, PSS, SSSC, STATCOM, Coordination, Optimization, Damping Oscillations.

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Authors: G. Zazzaro, F.M. Pisano, G. Romano

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During last decades, worldwide researchers dedicated efforts to develop machine-based seismic Early Warning systems, aiming at reducing the huge human losses and economic damages. The elaboration time of seismic waveforms is to be reduced in order to increase the time interval available for the activation of safety measures. This paper suggests a Data Mining model able to correctly and quickly estimate dangerousness of the running seismic event. Several thousand seismic recordings of Japanese and Italian earthquakes were analyzed and a model was obtained by means of a Bayesian Network (BN), which was tested just over the first recordings of seismic events in order to reduce the decision time and the test results were very satisfactory. The model was integrated within an Early Warning System prototype able to collect and elaborate data from a seismic sensor network, estimate the dangerousness of the running earthquake and take the decision of activating the warning promptly.

Keywords: Bayesian Networks, Decision Support System, Magnitude Classification, Seismic Early Warning System

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched direction to the digital world. The domain of politics, as one of the hottest topics of opinion mining research, merged together with the behavior analysis for affiliation determination in texts, which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 were constituted by Linguistic Inquiry and Word Count (LIWC) features were tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that the “Decision Tree”, “Rule Induction” and “M5 Rule” classifiers when used with “SVM” and “IGR” feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “Function”, as an aggregate feature of the linguistic category, was found as the most differentiating feature among the 68 features with the accuracy of 81% in classifying articles either as Republican or Democrat.

Keywords: Politics, machine learning, feature selection, LIWC.

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Authors: Vivian Guo, Yan Qu

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Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising.

Keywords: Cannibalization, machine learning, online marketplace, revenue optimization, yield optimization.

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Authors: C. Galasso

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The ancestral relationship between humans and geographical environment has long been at the center of an interdisciplinary dialogue, which sees one of its main research nodes in the relationship between memory and places. Given its deep complexity, this symbiotic connection continues to look for a proper definition that appears increasingly negotiated by different disciplines. Numerous fields of knowledge are involved, from anthropology to semiotics of space, from photography to architecture, up to subjects traditionally far from these reasonings. This is the case of Design of Communication, a young discipline, now confident in itself and its objectives, aimed at finding and investigating original forms of visualization and representation, between sedimented knowledge and new technologies. In particular, Design of Communication for the Territory offers an alternative perspective to the debate, encouraging the reactivation and reconstruction of the memory of places. Recognizing mnemotopes as a cultural object of vertical interpretation of the memory-place relationship, design can become a real mediator of the territorial fixation of memories, making them increasingly accessible and perceptible, contributing to build a topography of memory. According to a mnemotopic vision, Communication Design can support the passage from a memory in which the observer participates only as an individual to a collective form of memory. A mnemotopic form of Communication Design can, through geolocation and content map-based systems, make chronology a topography rooted in the territory and practicable; it can be useful to understand how the perception of the memory of places changes over time, considering how to insert them in the contemporary world. Mnemotopes can be materialized in different format of translation, editing and narration and then involved in complex systems of communication. The memory of places, therefore, if stabilized by the tools offered by Communication Design, can make visible ruins and territorial stratifications, illuminating them with new communicative interests that can be shared and participated.

Keywords: Memory of places, design of communication, territory, mnemotope, topography of memory.

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Authors: Sandra Muizniece-Brasava, Lija Dukalska, Solvita Kampuse, Irisa Murniece, Martins Sabovics, IlonaDabina-Bicka, Emils Kozlinskis, Svetlana Sarvi

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The research object was apple-black currant marmalade candies. Experiments were carried out at the Faculty of Food Technology of the Latvia University of Agriculture. An active packaging in combination with modified atmosphere (MAP, CO2 100%) was examined and compared with traditional packaging in air ambiance. Polymer Multibarrier 60 and paper bags were used. Influence of iron based oxygen absorber in sachets of 500 cc obtained from Mitsubishi Gas Chemical Europe Ageless® was tested on the quality during the shelf of marmalade. Samples of 80±5 g were packaged in polymer pouches (110 mm x 110 mm), hermetically sealed by MULTIVAC C300 vacuum chamber machine, and stored in room temperature +20.0±1.0 °C. The physiochemical properties – weight losses, moisture content, hardness, aw, pH, colour, changes of atmosphere content (CO2 and O2) in headspace of packs, and microbial conditions were analysed before packaging and in the 1st, 3rd , 5th, 8th, 11th and 15th weeks of storage.

Keywords: Active packaging, marmalade candies, shelf life

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Authors: Walid M. Adel, Liang Guo-Zhu

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To investigate the characterization of the mechanical properties of composite solid propellant (CSP) based on hydroxyl-terminated polybutadiene (HTPB) at different temperatures and strain rates, uniaxial tensile tests were conducted over a range of temperatures -60 °C to +76 °C and strain rates 0.000164 to 0.328084 s^-1 using a conventional universal testing machine. From the experimental data, it can be noted that the mechanical properties of AP/HTPB propellant are mainly dependent on the applied strain rate and the temperature condition. The stress-strain responses exhibited an initial yielding followed by the viscoelastic phase, which was strongly affected by the strain rate and temperature. It was found that the mechanical properties increased with both increasing strain rate and decreasing temperature. Based on the experimental tests, the master curves of the tensile properties are drawn using predetermined shift factor and the results were discussed. This work is a first step in preliminary investigation the nonlinear viscoelasticity behavior of CSP.

Keywords: AP/HTPB composite solid propellant, mechanical behavior, nonlinear viscoelastic, tensile test, master curves.

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: Mohammad D. AL-Tahat, Shahnaz M. Alkhalil

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International markets driven forces are changing continuously, therefore companies need to gain a competitive edge in such markets. Improving the company's products, processes and practices is no longer auxiliary. Lean production is a production management philosophy that consolidates work tasks with minimum waste resulting in improved productivity. Lean production practices can be mapped into many production areas. One of these is Manufacturing Equipment and Technology (MET). Many lean production practices can be implemented in MET, namely, specific equipment configurations, total preventive maintenance, visual control, new equipment/ technologies, production process reengineering and shared vision of perfection.The purpose of this paper is to investigate the implementation level of these six practices in Jordanian industries. To achieve that a questionnaire survey has been designed according to five-point Likert scale. The questionnaire is validated through pilot study and through experts review. A sample of 350 Jordanian companies were surveyed, the response rate was 83%. The respondents were asked to rate the extent of implementation for each of practices. A relationship conceptual model is developed, hypotheses are proposed, and consequently the essential statistical analyses are then performed. An assessment tool that enables management to monitor the progress and the effectiveness of lean practices implementation is designed and presented. Consequently, the results show that the average implementation level of lean practices in MET is 77%, Jordanian companies are implementing successfully the considered lean production practices, and the presented model has Cronbach-s alpha value of 0.87 which is good evidence on model consistency and results validation.

Keywords: Lean Production, SME applications, Visual Control, New equipment/technologies, Specific equipment configurations, Jordan

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Authors: Ahmad Attiq Al-Ogaibi, Ahmad Sharieh, Moh’d Belal Al-Zoubi, R. Bremananth

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In pattern clustering, nearest neighborhood point computation is a challenging issue for many applications in the area of research such as Remote Sensing, Computer Vision, Pattern Recognition and Statistical Imaging. Nearest neighborhood computation is an essential computation for providing sufficient classification among the volume of pixels (voxels) in order to localize the active-region-of-interests (AROI). Furthermore, it is needed to compute spatial metric relationships of diverse area of imaging based on the applications of pattern recognition. In this paper, we propose a new methodology for finding the nearest neighbor point, depending on making a virtually grid of a hexagon cells, then locate every point beneath them. An algorithm is suggested for minimizing the computation and increasing the turnaround time of the process. The nearest neighbor query points Φ are fetched by seeking fashion of hexagon holistic. Seeking will be repeated until an AROI Φ is to be expected. If any point Υ is located then searching starts in the nearest hexagons in a circular way. The First hexagon is considered be level 0 (L0) and the surrounded hexagons is level 1 (L1). If Υ is located in L1, then search starts in the next level (L2) to ensure that Υ is the nearest neighbor for Φ. Based on the result and experimental results, we found that the proposed method has an advantage over the traditional methods in terms of minimizing the time complexity required for searching the neighbors, in turn, efficiency of classification will be improved sufficiently.

Keywords: Hexagon cells, k-nearest neighbors, Nearest Neighbor, Pattern recognition, Query pattern, Virtually grid

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

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Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: K. D. Ro, J. T. Park, J. H. Kim

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This experiment was conducted in attempt of improving hydrodynamic efficiency of the propulsion mechanism by installing a spring to the wing so that the opening angle of the wing in one stroke can be changed automatically, compared to the existing method of fixed maximum opening angle in Weis-Fogh type ship propulsion mechanism. Average thrust coefficient was almost fixed with all velocity ratio with the prototype, but with the spring type, thrust coefficient increased sharply as velocity ratio increased. Average propulsive efficiency was larger with bigger opening angle in the prototype, but in the spring type, the one with smaller spring coefficient had larger value. In the range over 1.0 in velocity ratio where big thrust can be generated, spring type had more than twice of propulsive efficiency increase compared to the prototype.

Keywords: Hydraulic Machine, Propulsion Mechanism, FluidForce, Elastic Wing.

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: Identification, Neural networks, Predictive control, Transient stability, UPFC.

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Authors: Ziad. Sh. Al Sarraf

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Ultrasonic metal welding has been the subject of ongoing research and development, most recently concentrating on metal joining in miniature devices, for example to allow solder-free wire bonding. As well as at the small scale, there are also opportunities to research the joining of thicker sheet metals and to widen the range of similar and dissimilar materials that can be successfully joined using this technology. This study presents the design, characterisation and test of a lateral-drive ultrasonic metal spot welding device. The ultrasonic metal spot welding horn is modelled using finite element analysis (FEA) and its vibration behaviour is characterised experimentally to ensure ultrasonic energy is delivered effectively to the weld coupon. The welding stack and fixtures are then designed and mounted on a test machine to allow a series of experiments to be conducted for various welding and ultrasonic parameters. Weld strength is subsequently analysed using tensile-shear tests. The results show how the weld strength is particularly sensitive to the combination of clamping force and ultrasonic vibration amplitude of the welding tip, but there are optimal combinations of these and also limits that must be clearly identified.

Keywords: Ultrasonic welding, vibration amplitude, welding force, weld strength.

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Authors: Stephen Akuma, Timothy Ndera

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Information seekers get “lost in hyperspace” due to the voluminous documents updated daily on the internet. Adaptive Hypermedia Systems (AHS) are used to direct learners to their target goals. One of the most common AHS designed to help information seekers to overcome the problem of information overload is the Adaptive Education Hypermedia System (AEHS). However, this paper focuses on AEHS that adopts the learning preference of high school students and deliver learning content according to this preference throughout their learning experience. The research developed a prototype system for predicting students’ learning preference from the Visual, Aural, Read-Write and Kinesthetic (VARK) learning style model and adopting the learning content suitable to their preference. The predicting strength of several classifiers was compared and we found Support Vector Machine (SVM) to be more accurate in predicting learning style based on users’ preferences.

Keywords: Hypermedia, adaptive education, learning style, lesson content, user profile, prediction, feedback, adaptive hypermedia, learning style.

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modeling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: Sentiment Analysis, Social Media, Twitter, Amazon, Data Mining, Machine Learning, Text Mining.

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Authors: R.K. Behera, S.Behera

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In a wind power generator using doubly fed induction generator (DFIG), the three-phase pulse width modulation (PWM) voltage source converter (VSC) is used as grid side converter (GSC) and rotor side converter (RSC). The standard linear control laws proposed for GSC provides not only instablity against comparatively large-signal disturbances, but also the problem of stability due to uncertainty of load and variations in parameters. In this paper, a nonlinear controller is designed for grid side converter (GSC) of a DFIG for wind power application. The nonlinear controller is designed based on the input-output feedback linearization control method. The resulting closed-loop system ensures a sufficient stability region, make robust to variations in circuit parameters and also exhibits good transient response. Computer simulations and experimental results are presented to confirm the effectiveness of the proposed control strategy.

Keywords: Doubly fed Induction Generator, grid side converter, machine side converter, dc link, feedback linearization.

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: Anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines.

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Authors: R. Usubamatov

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The velocity of a moving point in a general path is the vector quantity, which has both magnitude and direction. The magnitude or the direction of the velocity vector can change over time as a result of acceleration that the time rate of velocity changes. Acceleration analysis is important because inertial forces and inertial torques are proportional to rectilinear and angular accelerations accordingly. The loads must be determined in advance to ensure that a machine is adequately designed to handle these dynamic loads. For planar motion, the vector direction of acceleration is commonly separated into two elements: tangential and centripetal or radial components of a point on a rotating body. All textbooks in physics, kinematics and dynamics of machinery consider the magnitude of a radial acceleration at condition when a point rotates with a constant angular velocity and it means without acceleration. The magnitude of the tangential acceleration considered on a basis of acceleration for a rotating point. Such condition of presentation of magnitudes for two components of acceleration logically and mathematically is not correct and may cause further confusion in calculation. This paper presents new analytical expressions of the radial and absolute accelerations of a rotating point with acceleration and covers the gap in theoretical study of acceleration analysis.

Keywords: acceleration analysis, kinematics of mechanisms.

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Authors: M. Shadmand

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The curing process of insulation system for electrical machines plays a determinative role for its durability and reliability. Polar structure of insulating resin molecules and used filler of insulation system can be taken as an occasion to leverage it to enhance overall characteristics of insulation system, mechanically and electrically. The curing process regime for insulating system plays an important role for its mechanical and electrical characteristics by arranging the polymerization of chain structure for resin. In this research, the effect of electrical field application on in-curing insulating system for Global Vacuum Pressurized Impregnation (GVPI) system for traction motor was considered by performing the dissipation factor, polarization and de-polarization current (PDC) and voltage endurance (aging) measurements on sample test objects. Outcome results depicted obvious improvement in mechanical strength of the insulation system as well as higher electrical characteristics with routing and long-time (aging) electrical tests. Coming together, polarization of insulation system during curing process would enhance the machine life time. 

Keywords: Insulation system, GVPI, PDC, aging.

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Authors: Galina Mihaleva, C. Koh

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Fashion, like many other design fields, undergoes numerous evolutions throughout the ages. This paper aims to recognize and evaluate the significance of advance technology in fashion design and examine how it changes the role of modern fashion designers by modifying the creation process. It also touches on how modern culture is involved in such developments and how it affects fashion design in terms of conceptualizing and fabrication. The methodology used is through surveying the various examples of technological applications to fashion design and drawing parallels between what was achievable then and what is achievable now. By comparing case studies, existing fashion design examples and crafting method experimentations; we then spot patterns in which to predict the direction of future developments in the field. A breakdown on the elements of technology in fashion design helps us understand the driving force behind such a trend. The results from explorations in the paper have shown that there is an observed pattern of a distinct increase in interest and progress in the field of fashion technology, which leads to the birth of hybrid crafting methods. In conclusion, it is shown that as fashion technology continues to evolve, their role in clothing crafting becomes more prominent and grows far beyond the humble sewing machine.

Keywords: Fashion design, functional aesthetics, smart textiles, 3D printing.

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Authors: R. Babisaraswathi, N. Shanthi, S. S. Kiruthika

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Web search engines are designed to retrieve and extract the information in the web databases and to return dynamic web pages. The Semantic Web is an extension of the current web in which it includes semantic content in web pages. The main goal of semantic web is to promote the quality of the current web by changing its contents into machine understandable form. Therefore, the milestone of semantic web is to have semantic level information in the web. Nowadays, people use different keyword- based search engines to find the relevant information they need from the web. But many of the words are polysemous. When these words are used to query a search engine, it displays the Search Result Records (SRRs) with different meanings. The SRRs with similar meanings are grouped together based on Word Sense Disambiguation (WSD). In addition to that semantic annotation is also performed to improve the efficiency of search result records. Semantic Annotation is the process of adding the semantic metadata to web resources. Thus the grouped SRRs are annotated and generate a summary which describes the information in SRRs. But the automatic semantic annotation is a significant challenge in the semantic web. Here ontology and knowledge based representation are used to annotate the web pages.

Keywords: Ontology, Semantic Web, WordNet, Word Sense Disambiguation.

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Authors: A.M. Vural, K.C. Bayindir

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Back-to-back static synchronous compensator (BtBSTATCOM) consists of two back-to-back voltage-source converters (VSC) with a common DC link in a substation. This configuration extends the capabilities of conventional STATCOM that bidirectional active power transfer from one bus to another is possible. In this paper, VSCs are designed in quasi multi-pulse form in which GTOs are triggered only once per cycle in PSCAD/EMTDC. The design details of VSCs as well as gate switching circuits and controllers are fully represented. Regulation modes of BtBSTATCOM are verified and tested on a multi-machine power system through different simulation cases. The results presented in the form of typical time responses show that practical PI controllers are almost robust and stable in case of start-up, set-point change, and line faults.

Keywords: Flexible AC Transmission Systems (FACTS), Backto-Back Static Synchronous Compensator (BtB-STATCOM), quasi multi-pulse voltage source converter, active power transfer; voltage control.

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Authors: Parvinder S. Sandhu, Sunil Khullar, Satpreet Singh, Simranjit K. Bains, Manpreet Kaur, Gurvinder Singh

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Fault-proneness of a software module is the probability that the module contains faults. To predict faultproneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed study is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C Programming language datasets of NASA software projects. The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model.

Keywords: Genetic Algorithm, Fault Proneness, Software Faultand Software Quality.

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Authors: K. P. Oyeduntan, K. Oshinubi

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Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the sparkplug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria.

Keywords: Economy, GDP, maritime transport, port, regression.

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Authors: Mian Tahir Aftab, Muhammad Umer, Riaz Ahmad

Abstract:

This article proposes an Ant Colony Optimization (ACO) metaheuristic to minimize total makespan for scheduling a set of jobs and assign workers for uniformly related parallel machines. An algorithm based on ACO has been developed and coded on a computer program Matlab®, to solve this problem. The paper explains various steps to apply Ant Colony approach to the problem of minimizing makespan for the worker assignment & jobs scheduling problem in a parallel machine model and is aimed at evaluating the strength of ACO as compared to other conventional approaches. One data set containing 100 problems (12 Jobs, 03 machines and 10 workers) which is available on internet, has been taken and solved through this ACO algorithm. The results of our ACO based algorithm has shown drastically improved results, especially, in terms of negligible computational effort of CPU, to reach the optimal solution. In our case, the time taken to solve all 100 problems is even lesser than the average time taken to solve one problem in the data set by other conventional approaches like GA algorithm and SPT-A/LMC heuristics.

Keywords: Ant Colony Optimization (ACO), Genetic algorithms (GA), Makespan, SPT-A/LMC heuristic.

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

Abstract:

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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