Search results for: Network Interface

Authors: S. Chokri

Abstract:

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: Neural network, Load Forecasting, Fuzzy inference, Machine learning, Fuzzy modeling and rule extraction, Support Vector Regression.

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Authors: Frank Emmert Streib, Matthias Dehmer, Jing Liu, Max Mühlhauser

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In this paper we present a method for gene ranking from DNA microarray data. More precisely, we calculate the correlation networks, which are unweighted and undirected graphs, from microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to progression of the tumor. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth and, hence, indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, DNA microarray data, cancer.

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Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

Abstract:

Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: Breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration (FNA).

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Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

Abstract:

Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.

Keywords: Hybrid MAC protocol, data integrity, lightweight encryption, Neighbor based key sharing, Sensor node data processing, Z-MAC.

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Authors: Dimitrios Koukopoulos

Abstract:

In this work, we study the impact of dynamically changing link slowdowns on the stability properties of packetswitched networks under the Adversarial Queueing Theory framework. Especially, we consider the Adversarial, Quasi-Static Slowdown Queueing Theory model, where each link slowdown may take on values in the two-valued set of integers {1, D} with D > 1 which remain fixed for a long time, under a (w, p)-adversary. In this framework, we present an innovative systematic construction for the estimation of adversarial injection rate lower bounds, which, if exceeded, cause instability in networks that use the LIS (Longest-in- System) protocol for contention-resolution. In addition, we show that a network that uses the LIS protocol for contention-resolution may result in dropping its instability bound at injection rates p > 0 when the network size and the high slowdown D take large values. This is the best ever known instability lower bound for LIS networks.

Keywords: Parallel computing, network stability, adversarial queuing theory, greedy scheduling protocols.

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Authors: Prakash G L, Thejaswini M, S H Manjula, K R Venugopal, L M Patnaik

Abstract:

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. We propose Tree on DAG (ToD), a semistructured approach that uses Dynamic Forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node Mica2- based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.

Keywords: Aggregation, Packet Merging, Query Processing.

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Authors: Amir Abolfazl Suratgar, Mohammad Bagher Tavakoli, Abbas Hoseinabadi

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In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method.

Keywords: Levenberg-Marquardt, modification, neural network, variable learning rate.

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Authors: Babiga Birregah, Prosper K. Doh, Kondo H. Adjallah

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In this work we present some matrix operators named circulant operators and their action on square matrices. This study on square matrices provides new insights into the structure of the space of square matrices. Moreover it can be useful in various fields as in agents networking on Grid or large-scale distributed self-organizing grid systems.

Keywords: Pascal matrices, Binomial Recursion, Circulant Operators, Square Matrix Bipartition, Local Network, Parallel networks of agents.

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Authors: Tariq Aljuneidi, Akif Asil Bulgak

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An efficient remanufacturing network lead to an efficient design of sustainable manufacturing enterprise. In remanufacturing network, products are collected from the customer zone, disassembled and remanufactured at a suitable remanufacturing facility. In this respect, another issue to consider is how the returned product to be remanufactured, in other words, what is the best layout for such facility. In order to achieve a sustainable manufacturing system, Cellular Manufacturing System (CMS) designs are highly recommended, CMSs combine high throughput rates of line layouts with the flexibility offered by functional layouts (job shop). Introducing the CMS while designing a remanufacturing network will benefit the utilization of such a network. This paper presents and analyzes a comprehensive mathematical model for the design of Dynamic Cellular Remanufacturing Systems (DCRSs). In this paper, the proposed model is the first one to date that considers CMS and remanufacturing system simultaneously. The proposed DCRS model considers several manufacturing attributes such as multi period production planning, dynamic system reconfiguration, duplicate machines, machine capacity, available time for workers, worker assignments, and machine procurement, where the demand is totally satisfied from a returned product. A numerical example is presented to illustrate the proposed model.

Keywords: Cellular Manufacturing System, Remanufacturing, Mathematical Programming, Sustainability.

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Authors: Amin Sahraeian

Abstract:

One of the criteria in production scheduling is Make Span, minimizing this criteria causes more efficiently use of the resources specially machinery and manpower. By assigning some budget to some of the operations the operation time of these activities reduces and affects the total completion time of all the operations (Make Span). In this paper this issue is practiced in parallel flow shops. At first we convert parallel flow shop to a network model and by using a linear programming approach it is identified in order to minimize make span (the completion time of the network) which activities (operations) are better to absorb the predetermined and limited budget. Minimizing the total completion time of all the activities in the network is equivalent to minimizing make span in production scheduling.

Keywords: parallel flow shop, make span, linear programming, budget

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Authors: Amitabh Wahi, Sundaramurthy S.

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Natural outdoor scene classification is active and promising research area around the globe. In this study, the classification is carried out in two phases. In the first phase, the features are extracted from the images by wavelet decomposition method and stored in a database as feature vectors. In the second phase, the neural classifiers such as back-propagation neural network (BPNN) and resilient back-propagation neural network (RPNN) are employed for the classification of scenes. Four hundred color images are considered from MIT database of two classes as forest and street. A comparative study has been carried out on the performance of the two neural classifiers BPNN and RPNN on the increasing number of test samples. RPNN showed better classification results compared to BPNN on the large test samples.

Keywords: BPNN, Classification, Feature extraction, RPNN, Wavelet.

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Authors: Ghassan A. Abed, Mahamod Ismail, Kasmiran Jumari

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Long Term Evolution (LTE) is a 4G wireless broadband technology developed by the Third Generation Partnership Project (3GPP) release 8, and it's represent the competitiveness of Universal Mobile Telecommunications System (UMTS) for the next 10 years and beyond. The concepts for LTE systems have been introduced in 3GPP release 8, with objective of high-data-rate, low-latency and packet-optimized radio access technology. In this paper, performance of different TCP variants during LTE network investigated. The performance of TCP over LTE is affected mostly by the links of the wired network and total bandwidth available at the serving base station. This paper describes an NS-2 based simulation analysis of TCP-Vegas, TCP-Tahoe, TCPReno, TCP-Newreno, TCP-SACK, and TCP-FACK, with full modeling of all traffics of LTE system. The Evaluation of the network performance with all TCP variants is mainly based on throughput, average delay and lost packet. The analysis of TCP performance over LTE ensures that all TCP's have a similar throughput and the best performance return to TCP-Vegas than other variants.

Keywords: LTE; EUTRAN; 3GPPP, SAE; TCP Variants; NS-2

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Authors: Anamika Jain, A. S. Thoke, R. N. Patel

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This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.

Keywords: Double circuit transmission line, Fault detection and classification, High impedance fault and Artificial Neural Network.

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Authors: D. A. Farinde

Abstract:

One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.

Keywords: Bank, Bankruptcy, Financial Ratios, Neural Network, Multilayer Perceptron, Predictive Model

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Authors: Mianheng Lai, Johnny Ching Ming Ho, Hoat Joen Pam

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Concrete-filled-steel-tube (CFST) columns are becoming increasingly popular owing to the superior behavior contributed by the composite action. However, this composite action cannot be fully developed because of different dilation properties between steel tube and concrete. During initial compression, there will be de-bonding between the constitutive materials. As a result, the strength, initial stiffness and ductility of CFST columns reduce significantly. To resolve this problem, external confinement in the form of spirals is proposed to improve the interface bonding. In this paper, a total of 14CFST columns with high-strength as well as ultra-high-strength concrete in-filled were fabricated and tested under uni-axial compression. From the experimental results, it can be concluded that the proposed spirals can improve the strength, initial stiffness, ductility and the interface bonding condition of CFST columns by restraining the lateral expansion of steel tube and core concrete. Moreover, the failure modes of confined core concrete change due to the strong confinement provided by spirals.

Keywords: Concrete-filled-steel-tube, confinement, failure mode, high-strength concrete, spirals.

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Authors: Marco Bötel, Tobias Gelau, Wendelin Gross

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Supply chain networks are frequently hit by unplanned events which lead to disruptions and cause operational and financial consequences. It is neither possible to avoid disruption risk entirely, nor are network members able to prepare for every possible disruptive event. Therefore a continuity planning should be set up which supports effective operational responses in supply chain networks in times of emergencies. In this research network related degrees of freedom which determine the options for responsive actions are derived from interview data. The findings are further embedded into a common risk management process. The paper provides support for researchers and practitioners to identify the network related options for responsive actions and to determine the need for improving the reaction capabilities.

Keywords: Supply Chain Risk Management, Business Continuity Planning, Degrees of Freedom, Risk Management Process, Mitigation Measures.

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Authors: Ganesan V.

Abstract:

Application of Expert System in the area of agriculture would take the form of Integrated Crop Management decision aids and would encompass water management, fertilizer management, crop protection systems and identification of implements. In order to remain competitive, the modern farmer often relies on agricultural specialists and advisors to provide information for decision-making. An expert system normally composed of a knowledge base (information, heuristics, etc.), inference engine (analyzes knowledge base), and end user interface (accepting inputs, generating outputs). Software named 'CROP-9-DSS' incorporating all modern features like, graphics, photos, video clippings etc. has been developed. This package will aid as a decision support system for identification of pest and diseases with control measures, fertilizer recommendation system, water management system and identification of farm implements for leading crops of Kerala (India) namely Coconut, Rice, Cashew, Pepper, Banana, four vegetables like Amaranthus, Bhindi, Brinjal and Cucurbits. 'CROP-9-DSS' will act as an expert system to agricultural officers, scientists in the field of agriculture and extension workers for decision-making and help them in suggesting suitable recommendations.

Keywords: Diagnostic, inference engine, knowledge base and user interface.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Bayar Shahab

Abstract:

The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems and issues of this new era. The Brain-Computer Interface (BCI) has opened the door to several new research areas and have been able to provide solutions to critical and vital issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair. This review presents the state-of-the-art methods and improvements of canonical correlation analyses (CCA), an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said differently, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand the most state-of-the-art methods available in this field, their pros and cons, and their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the main methods used in this field in a hierarchical way, (2) explaining the pros and cons of each method and their performance, (3) presenting the gaps that exist at the end of each method that can improve the understanding and open doors to new researches or improvements. 

Keywords: BCI, CCA, SSVEP, EEG

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Authors: João Paulo Teixeira, Paula Odete Fernandes

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The authors have been developing several models based on artificial neural networks, linear regression models, Box- Jenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels" of one region. Several comparisons between the different type models have been experimented as well as the features used at the entrance of the models. The Artificial Neural Network (ANN) models have always had their performance at the top of the best models. Usually the feed-forward architecture was used due to their huge application and results. In this paper the author made a comparison between different architectures of the ANNs using simply the same input. Therefore, the traditional feed-forward architecture, the cascade forwards, a recurrent Elman architecture and a radial based architecture were discussed and compared based on the task of predicting the mentioned time series.

Keywords: Artificial Neural Network Architectures, time series forecast, tourism.

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Authors: R. Srinivasa Rao, S. V. L. Narasimham

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This paper presents a novel approach for optimal reconfiguration of radial distribution systems. Optimal reconfiguration involves the selection of the best set of branches to be opened, one each from each loop, such that the resulting radial distribution system gets the desired performance. In this paper an algorithm is proposed based on simple heuristic rules and identified an effective switch status configuration of distribution system for the minimum loss reduction. This proposed algorithm consists of two parts; one is to determine the best switching combinations in all loops with minimum computational effort and the other is simple optimum power loss calculation of the best switching combination found in part one by load flows. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 33-bus system. The results show that the performance of the proposed method is better than that of the other methods.

Keywords: Distribution system, network reconfiguration, powerloss reduction, radial network, heuristic technique.

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Authors: Supriya Pal, Kalyan Adhikari, Somnath Mukherjee, Sudipta Ghosh

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This study presents the application of artificial neural network for modeling the phenolic compound migration through vertical soil column. A three layered feed forward neural network with back propagation training algorithm was developed using forty eight experimental data sets obtained from laboratory fixed bed vertical column tests. The input parameters used in the model were the influent concentration of phenol(mg/L) on the top end of the soil column, depth of the soil column (cm), elapsed time after phenol injection (hr), percentage of clay (%), percentage of silt (%) in soils. The output of the ANN was the effluent phenol concentration (mg/L) from the bottom end of the soil columns. The ANN predicted results were compared with the experimental results of the laboratory tests and the accuracy of the ANN model was evaluated.

Keywords: Modeling, Neural Networks, Phenol, Soil media

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Authors: Sudhir Agashe, Ashok Ghatol, Sujata Agashe

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In this paper the development of a heat exchanger as a pilot plant for educational purpose is discussed and the use of neural network for controlling the process is being presented. The aim of the study is to highlight the need of a specific Pseudo Random Binary Sequence (PRBS) to excite a process under control. As the neural network is a data driven technique, the method for data generation plays an important role. In light of this a careful experimentation procedure for data generation was crucial task. Heat exchange is a complex process, which has a capacity and a time lag as process elements. The proposed system is a typical pipe-in- pipe type heat exchanger. The complexity of the system demands careful selection, proper installation and commissioning. The temperature, flow, and pressure sensors play a vital role in the control performance. The final control element used is a pneumatically operated control valve. While carrying out the experimentation on heat exchanger a welldrafted procedure is followed giving utmost attention towards safety of the system. The results obtained are encouraging and revealing the fact that if the process details are known completely as far as process parameters are concerned and utilities are well stabilized then feedback systems are suitable, whereas neural network control paradigm is useful for the processes with nonlinearity and less knowledge about process. The implementation of NN control reinforces the concepts of process control and NN control paradigm. The result also underlined the importance of excitation signal typically for that process. Data acquisition, processing, and presentation in a typical format are the most important parameters while validating the results.

Keywords: Process identification, neural network, heat exchanger.

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Authors: Farzaneh Ahmadzadeh

Abstract:

Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time. It is very important to manufacturer to determine at what point and which parameters in the past caused the signal. Early warning of process change would expedite the search for the special causes and enhance quality at lower cost. In this paper the quality variables under investigation are assumed to follow a multivariate normal distribution with known means and variance-covariance matrix and the process means after one step change remain at the new level until the special cause is being identified and removed, also it is supposed that only one variable could be changed at the same time. This research applies artificial neural network (ANN) to identify the time the change occurred and the parameter which caused the change or shift. The performance of the approach was assessed through a computer simulation experiment. The results show that neural network performs effectively and equally well for the whole shift magnitude which has been considered.

Keywords: Artificial neural network, change point estimation, monte carlo simulation, multivariate exponentially weighted movingaverage

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Authors: HongIl Ju, JongWook Han

Abstract:

In this paper we describes the authentication for DHCP (Dynamic Host Configuration Protocol) message which provides the efficient key management and reduces the danger replay attack without an additional packet for a replay attack. And the authentication for DHCP message supports mutual authentication and provides both entity authentication and message authentication. We applied the authentication for DHCP message to the home network environments and tested through a home gateway.

Keywords: DHCP, authentication, key management, replayattack, home network.

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Authors: Michal Bidlo, Radek Bidlo, Lukas Sekanina

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A method is presented for the construction of arbitrary even-input sorting networks exhibiting better properties than the networks created using a conventional technique of the same type. The method was discovered by means of a genetic algorithm combined with an application-specific development. Similarly to human inventions in the area of theoretical computer science, the evolved invention was analyzed: its generality was proven and area and time complexities were determined.

Keywords: Development, genetic algorithm, program, sorting network.

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Authors: Jameson McFarlane, Thorne J. McFarlane, Leon Bernard

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Social network sites (SNS) is an emerging phenomenon that is here to stay. The popularity and the ubiquity of the SNS technology are undeniable. Because most SNS are free and easy to use people from all walks of life and from almost any age are attracted to that technology. College age students are by far the largest segment of the population using SNS. Since most SNS have been adapted for mobile devices, not only do you find students using this technology in their study, while working on labs or on projects, a substantial number of students have been found to use SNS even while listening to lectures. This study found that SNS use has a significant negative impact on the grade point average of college students particularly in the first semester. However, this negative impact is greatly diminished by the end of the third semester partly because the students have adjusted satisfactorily to the challenges of college or because they have learned how to adequately manage their time. It was established that the kinds of activities the students are engaged in during the SNS use are the leading factor affecting academic performance. Of those activities, using SNS during a lecture or while studying is the foremost contributing factor to lower academic performance. This is due to “cognitive” or “information” bottleneck, a condition in which the students find it very difficult to multitask or to switch between resources leading to inefficiency in information retention and thus, educational performance.

Keywords: Social network sites, social network analysis, regression coefficient, psychological engagement.

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Authors: Chokri Slim

Abstract:

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.

Keywords: Neuro-fuzzy, Extended Kalman filter, nonlinear systems, financial time series.

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Authors: Efthymios Chondrogiannis, Vassiliki Andronikou, Efstathios Karanastasis, Theodora Varvarigou

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A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries.

Keywords: Ontologies, Relational Databases, SPARQL, Web Interface.

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Authors: Satyanarayana, K.N. Prabhu

Abstract:

Microstructure, wetting behavior and interfacial reactions between Sn–0.7Cu and Sn–0.3Ag–0.7Cu (SAC0307) solders solidified on Ni coated Al substrates were compared and investigated. Microstructure of Sn–0.7Cu alloy exhibited a eutectic matrix composed of primary β-Sn dendrites with a fine dispersion of Cu6Sn5 intermetallics whereas microstructure of SAC0307 alloy exhibited coarser Cu6Sn5 and finer Ag3Sn precipitates of IMCs with decreased tin dendrites. Contact angles ranging from 22° to 26° were obtained for Sn–0.7Cu solder solidified on substrate surface whereas for SAC0307 solder alloy contact angles were found to be in the range of 20° to 22°. Sn–0.7Cu solder/substrate interfacial region exhibited faceted (Cu, Ni)6Sn5 IMCs protruding into the solder matrix and a small amount of (Cu, Ni)3Sn4 intermetallics at the interface. SAC0307 solder/substrate interfacial region showed mainly (Cu, Ni)3Sn4 intermetallics adjacent to the coating layer and (Cu, Ni)6Sn5 IMCs in the solder matrix. The improvement in the wettability of SAC0307 solder alloy on substrate surface is attributed to the formation of cylindrical shape (Cu,Ni)6Sn5 and a layer of (Cu, Ni)3Sn4 IMCs at the interface.

Keywords: Lead-free solder, wetting, contact angle, intermetallics.

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