Search results for: Template Knowledge Models

Authors: Yogesh Aggarwal

Abstract:

The paper discusses the results obtained to predict reinforcement in singly reinforced beam using Neural Net (NN), Support Vector Machines (SVM-s) and Tree Based Models. Major advantage of SVM-s over NN is of minimizing a bound on the generalization error of model rather than minimizing a bound on mean square error over the data set as done in NN. Tree Based approach divides the problem into a small number of sub problems to reach at a conclusion. Number of data was created for different parameters of beam to calculate the reinforcement using limit state method for creation of models and validation. The results from this study suggest a remarkably good performance of tree based and SVM-s models. Further, this study found that these two techniques work well and even better than Neural Network methods. A comparison of predicted values with actual values suggests a very good correlation coefficient with all four techniques.

Keywords: Linear Regression, M5 Model Tree, Neural Network, Support Vector Machines.

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Authors: A. El-Domiaty, H. M. Abd El-Hafez, M. A. Shaker

Abstract:

Drilling of glass sheets with different thicknesses have been carried out by Abrasive Jet Machining process (AJM) in order to determine its machinability under different controlling parameters of the AJM process. The present study has been introduced a mathematical model and the obtained results have been compared with that obtained from other models published earlier [1-6]. The experimental results of the present work are used to discuss the validity of the proposed model as well as the other models.

Keywords: Abrasive Jet Machining, Erosion rate, Glass, Mathematical model.

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Authors: Rohitash Chandra, Christian W. Omlin

Abstract:

We present a hybrid architecture of recurrent neural networks (RNNs) inspired by hidden Markov models (HMMs). We train the hybrid architecture using genetic algorithms to learn and represent dynamical systems. We train the hybrid architecture on a set of deterministic finite-state automata strings and observe the generalization performance of the hybrid architecture when presented with a new set of strings which were not present in the training data set. In this way, we show that the hybrid system of HMM and RNN can learn and represent deterministic finite-state automata. We ran experiments with different sets of population sizes in the genetic algorithm; we also ran experiments to find out which weight initializations were best for training the hybrid architecture. The results show that the hybrid architecture of recurrent neural networks inspired by hidden Markov models can train and represent dynamical systems. The best training and generalization performance is achieved when the hybrid architecture is initialized with random real weight values of range -15 to 15.

Keywords: Deterministic finite-state automata, genetic algorithm, hidden Markov models, hybrid systems and recurrent neural networks.

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Authors: Sourabh Agrawal, Ashok K. Jain

Abstract:

Stick models are widely used in studying the behaviour of straight as well as skew bridges and viaducts subjected to earthquakes while carrying out preliminary studies. The application of such models to highly curved bridges continues to pose challenging problems. A viaduct proposed in the foothills of the Himalayas in Northern India is chosen for the study. It is having 8 simply supported spans @ 30 m c/c. It is doubly curved in horizontal plane with 20 m radius. It is inclined in vertical plane as well. The superstructure consists of a box section. Three models have been used: a conventional stick model, an improved stick model and a 3D finite element model. The improved stick model is employed by making use of body constraints in order to study its capabilities. The first 8 frequencies are about 9.71% away in the latter two models. Later the difference increases to 80% in 50th mode. The viaduct was subjected to all three components of the El Centro earthquake of May 1940. The numerical integration was carried out using the Hilber- Hughes-Taylor method as implemented in SAP2000. Axial forces and moments in the bridge piers as well as lateral displacements at the bearing levels are compared for the three models. The maximum difference in the axial forces and bending moments and displacements vary by 25% between the improved and finite element model. Whereas, the maximum difference in the axial forces, moments, and displacements in various sections vary by 35% between the improved stick model and equivalent straight stick model. The difference for torsional moment was as high as 75%. It is concluded that the stick model with body constraints to model the bearings and expansion joints is not desirable in very sharp S curved viaducts even for preliminary analysis. This model can be used only to determine first 10 frequency and mode shapes but not for member forces. A 3D finite element analysis must be carried out for meaningful results.

Keywords: Bearing, body constraint, box girder, curved viaduct, expansion joint, finite element, link element, seismic, stick model, time history analysis.

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Authors: Gilberto Gonzalez-A

Abstract:

Bond graph models of an electrical transformer including the nonlinear saturation are presented. These models determine the relation between self and mutual inductances, and the leakage and magnetizing inductances of power transformers with two and three windings using the properties of a bond graph. The modelling and analysis using this methodology to three phase power transformers or transformers with internal incipient faults can be extended.

Keywords: Bond graph, electrical transformer, nonlinear saturation

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

Abstract:

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, Prediction modeling, rail track degradation.

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Authors: Cesar Hernández, Diego Giral, Fernando Santa

Abstract:

Spectrum handover is a significant topic in the cognitive radio networks to assure an efficient data transmission in the cognitive radio user’s communications. This paper proposes a comparison between three spectrum handover models: VIKOR, SAW and MEW. Four evaluation metrics are used. These metrics are, accumulative average of failed handover, accumulative average of handover performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three spectrum handover models was validated with captured data of spectrum occupancy in experiments performed at the GSM frequency band (824 MHz - 849 MHz). These data represent the actual behavior of the licensed users for this wireless frequency band. The results of the comparison show that VIKOR Algorithm provides a 15.8% performance improvement compared to SAW Algorithm and, it is 12.1% better than the MEW Algorithm.

Keywords: Cognitive radio, decision making, MEW, SAW, spectrum handover, VIKOR.

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Authors: Khawla Ben Abderrahim

Abstract:

In order to define a new model of Tunisian foot sizes and for building the most comfortable shoes, Tunisian industrialists must be able to offer for their customers products able to put on and adjust the majority of the target population concerned. Moreover, the use of models of shoes, mainly from others country, causes a mismatch between the foot and comfort of the Tunisian shoes. But every foot is unique; these models become uncomfortable for the Tunisian foot. We have a set of measures produced from a 3D scan of the feet of a diverse population (women, men ...) and we try to analyze this data to define a model of foot specific to the Tunisian footwear design. In this paper we propose tow new approaches to modeling a new foot sizes model. We used, indeed, the neural networks, and specially the Kohonen network. Next, we combine neural networks with the concept of half-foot size to improve the models already found. Finally, it was necessary to compare the results obtained by applying each approach and we decide what-s the best approach that give us the most model of foot improving more comfortable shoes.

Keywords: Morphology of the foot, foot size, half foot size, neural network, Kohonen network, model of foot size.

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Authors: L. Novakova-Marcincinova, J. Novak-Marcincin

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Paper presents knowledge about types of test in area of materials properties of selected methods of rapid prototyping technologies. In today used rapid prototyping technologies for production of models and final parts are used materials in initial state as solid, liquid or powder material structure. In solid state are used various forms such as pellets, wire or laminates. Basic range materials include paper, nylon, wax, resins, metals and ceramics. In Fused Deposition Modeling (FDM) rapid prototyping technology are mainly used as basic materials ABS (Acrylonitrile Butadiene Styrene), polyamide, polycarbonate, polyethylene and polypropylene. For advanced FDM applications are used special materials as silicon nitrate, PZT (Piezoceramic Material - Lead Zirconate Titanate), aluminium oxide, hydroxypatite and stainless steel.

Keywords: Rapid prototyping, materials, testing of materials.

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Authors: Monika Chuchro

Abstract:

This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain subgroups of time series data with normal distribution from the inflow into wastewater treatment plant data, composed of several groups differing by mean value. Two simple algorithms, K-mean and EM, were chosen as a clustering method. The Rand index was used to measure the similarity. After simple meta-clustering, a regression model was performed for each subgroups. The final model was a sum of the subgroups models. The quality of the obtained model was compared with the regression model made using the same explanatory variables, but with no clustering of data. Results were compared using determination coefficient (R2), measure of prediction accuracy- mean absolute percentage error (MAPE) and comparison on a linear chart. Preliminary results allow us to foresee the potential of the presented technique.

Keywords: Clustering, Data analysis, Data mining, Predictive models.

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Authors: Kailash C. Madan

Abstract:

We study four models of a three server queueing system with Bernoulli schedule optional server vacations. Customers arriving at the system one by one in a Poisson process are provided identical exponential service by three parallel servers according to a first-come, first served queue discipline. In model A, all three servers may be allowed a vacation at one time, in Model B at the most two of the three servers may be allowed a vacation at one time, in model C at the most one server is allowed a vacation, and in model D no server is allowed a vacation. We study steady the state behavior of the four models and obtain steady state probability generating functions for the queue size at a random point of time for all states of the system. In model D, a known result for a three server queueing system without server vacations is derived.

Keywords: A three server queue, Bernoulli schedule server vacations, queue size distribution at a random epoch, steady state.

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Authors: S. H. Sabzpoushan

Abstract:

In this research we show that the dynamics of an action potential in a cell can be modeled with a linear combination of the dynamics of the gating state variables. It is shown that the modeling error is negligible. Our findings can be used for simplifying cell models and reduction of computational burden i.e. it is useful for simulating action potential propagation in large scale computations like tissue modeling. We have verified our finding with the use of several cell models.

Keywords: Linear model, Action potential, gating dynamics.

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Authors: Yogi Satrya Aryadinata, Anne Laurent, Michel Sala

Abstract:

Gradual patterns have been studied for many years as they contain precious information. They have been integrated in many expert systems and rule-based systems, for instance to reason on knowledge such as “the greater the number of turns, the greater the number of car crashes”. In many cases, this knowledge has been considered as a rule “the greater the number of turns → the greater the number of car crashes” Historically, works have thus been focused on the representation of such rules, studying how implication could be defined, especially fuzzy implication. These rules were defined by experts who were in charge to describe the systems they were working on in order to turn them to operate automatically. More recently, approaches have been proposed in order to mine databases for automatically discovering such knowledge. Several approaches have been studied, the main scientific topics being: how to determine what is an relevant gradual pattern, and how to discover them as efficiently as possible (in terms of both memory and CPU usage). However, in some cases, end-users are not interested in raw level knowledge, and are rather interested in trends. Moreover, it may be the case that no relevant pattern can be discovered at a low level of granularity (e.g. city), whereas some can be discovered at a higher level (e.g. county). In this paper, we thus extend gradual pattern approaches in order to consider multiple level gradual patterns. For this purpose, we consider two aggregation policies, namely horizontal and vertical.

Keywords: Gradual Pattern.

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Authors: P. Ruttanee, K-N. Areerak, K-L. Areerak

Abstract:

Dynamic models of power converters are normally time-varying because of their switching actions. Several approaches are applied to analyze the power converters to achieve the timeinvariant models suitable for system analysis and design via the classical control theory. The paper presents how to derive dynamic models of the power system consisting of a three-phase controlled rectifier feeding an uncontrolled buck converter by using the combination between the well known techniques called the DQ and the generalized state-space averaging methods. The intensive timedomain simulations of the exact topology model are used to support the accuracies of the reported model. The results show that the proposed model can provide good accuracies in both transient and steady-state responses.

Keywords: DQ method, Generalized state-space averaging method, Three-phase controlled rectifier, Uncontrolled buck converter, Averaging model, Modeling, Simulation.

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Authors: Ghanima Al-Sharrah, Haitham M. Lababidi, Yusuf I. Ali

Abstract:

The main objective of the current work is to introduce sustainability factors in optimizing the supply chain model for process industries. The supply chain models are normally based on purely economic considerations related to costs and profits. To account for sustainability, two additional factors have been introduced; environment and risk. A supply chain for an entire petroleum organization has been considered for implementing and testing the proposed optimization models. The environmental and risk factors were introduced as indicators reflecting the anticipated impact of the optimal production scenarios on sustainability. The aggregation method used in extending the single objective function to multi-objective function is proven to be quite effective in balancing the contribution of each objective term. The results indicate that introducing sustainability factor would slightly reduce the economic benefit while improving the environmental and risk reduction performances of the process industries.

Keywords: Supply chain, optimization, LP models, risk, environmental indicators, multi-objective.

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Authors: A. Golshanraz, L. Hakemi, L. Pourkazemi, E. Dadgostar, F. Moradzandi, R. Tabatabaee, F. Moradi, K. Hosseinihajiagha, N. Jazayeri, H. Abedifar, R. Fouladi, M. Khooban, H. Saboori, M. Kiani, M. Sajedi, E. Karooninejad, S.Moeen, M.Ghavam, F.Beiranvand, S.Mansoori, F.Gheisari, H.Barzegari

Abstract:

Supplement use is common in athletes. Besides their cost, they may have side effects on health and performance. 250 questionnaires were distributed among female athletes (mean age 27.08 years). The questionnaire aimed to explore the frequency, type, believes, attitudes and knowledge regarding dietary supplements. Knowledge was good in 30.3%, fair in 60.2%, and poor in 9.1% of respondents. 65.3% of athletes did not use supplements regularly. The most widely used supplements were vitamins (48.4%), minerals (42.9%), energy supplements (21.3%), and herbals (20.9%). 68.9% of athletes believed in their efficacy. 34.4% experienced performance enhancement and 6.8% of reported side effects. 68.2% reported little knowledge and 60.9% were eager to learn more. In conclusion, many of the female athletes believe in the efficacy of supplements and think they are an unavoidable part of competitive sports. However, their information is not sufficient. We have to stress on education, consulting sessions, and rational prescription.

Keywords: athlete, female, sports, supplement

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Authors: Mohammadhosein Hasanbeig, Lacra Pavel

Abstract:

In this paper, we study a distributed control algorithm for the problem of unknown area coverage by a network of robots. The coverage objective is to locate a set of targets in the area and to minimize the robots’ energy consumption. The robots have no prior knowledge about the location and also about the number of the targets in the area. One efficient approach that can be used to relax the robots’ lack of knowledge is to incorporate an auxiliary learning algorithm into the control scheme. A learning algorithm actually allows the robots to explore and study the unknown environment and to eventually overcome their lack of knowledge. The control algorithm itself is modeled based on game theory where the network of the robots use their collective information to play a non-cooperative potential game. The algorithm is tested via simulations to verify its performance and adaptability.

Keywords: Distributed control, game theory, multi-agent learning, reinforcement learning.

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Authors: Enrique Claver Cortés, Bartolomé Marco Lajara, Eduardo Sánchez García, Pedro Seva Larrosa, Encarnación Manresa Marhuenda, Lorena Ruiz Fernández, Esther Poveda Pareja

Abstract:

In recent decades, the analysis of the effects of clustering as an essential factor for the development of innovations and the competitiveness of enterprises has raised great interest in different areas. Nowadays, companies have access to almost all tangible and intangible resources located and/or developed in any country in the world. However, despite the obvious advantages that this situation entails for companies, their geographical location has shown itself, increasingly clearly, to be a fundamental factor that positively influences their innovative performance and competitiveness. Industrial clusters could represent a unique level of analysis, positioned between the individual company and the industry, which makes them an ideal unit of analysis to determine the effects derived from company membership of a cluster. Also, the absorptive capacity (hereinafter 'AC') can mediate the process of innovation development by companies located in a cluster. The transformation and exploitation of knowledge could have a mediating effect between knowledge acquisition and innovative performance. The main objective of this work is to determine the key factors that affect the degree of generation and use of knowledge from the environment by companies and, consequently, their innovative performance and competitiveness. The elements analyzed are the companies' membership of a cluster and the AC. To this end, 30 most relevant papers published on this subject in the "Web of Science" database have been reviewed. Our findings show that, within a cluster, the knowledge coming from the companies' environment can significantly influence their innovative performance and competitiveness, although in this relationship, the degree of access and exploitation of the companies to this knowledge plays a fundamental role, which depends on a series of elements both internal and external to the company.

Keywords: Absorptive capacity, clusters, innovation, knowledge.

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Authors: Ashiquer Rahman

Abstract:

The cutting edge knowledge mediation system called ‘ePLANETe’ provides a framework for building knowledge, tools, and methods for education, research, and sustainable practices, as well as the deliberative assessment support for Higher Education, Research Institutions, and elsewhere e.g., the collaborative learning and research on sustainability and biodiversity issues of territorial development sectors. The paper is to present the analytical perspective of the ‘ePLANETe’ concept and functionalities as an experimental platform for contributing to sustainability assessment. Now the ‘ePLANETe’ can be seen as experimentation of the challenges of “ICT for Green”. The digital technologies of ‘ePLANETe’ are exploited (i) to facilitate collaborative research, learning tools, and knowledge for sustainability challenges, and (ii) as deliberation support tools in pursuing of sustainability performance and practices in territorial governance, public policy, and business strategy, as well as in the higher education sectors itself. The paper investigates the dealing capacity of qualitative and quantitative assessment of agriculture sustainability through the stakeholder-based integrated assessment. Specifically, this paper focuses on integrating system methodologies with Deliberation Support Tools (DST) and INTEGRAAL method for collective assessment and decision-making in implementing regional plans. The report aims to identify the effective knowledge and tools to enable deliberations methodologies regarding practices on the sustainability of agriculture and biodiversity issues, societal responsibilities, and regional planning, concentrating on the question: “How to effectively mobilize resources (knowledge, tools, and methods) from different sources and at different scales regarding on agriculture and biodiversity issues to address sustainability challenges” that will create the scope for qualitative and quantitative assessments of sustainability as a new landmark of the agriculture sector.

Keywords: Biodiversity, Deliberation Support Tools, INTEGRAAL, stakeholder.

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Authors: Iman E. Bouthahab, Badea B. Geador

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Aim of this paper is to explore the prospect of a new approach of mobile phone banking in Libya. This study evaluates customer knowledge on commercial mobile banking in Libya. To examine the relationship between age, occupation and intention for using mobile banking for commercial purpose, a survey was conducted to gather information from one hundred Libyan bank clients. The results indicate that Libyan customers have accepted the new technology and they are ready to use it. There is no significant joint relationship between age and occupation found in intention to use mobile banking in Libya. On the other hand, the customers’ knowledge about mobile banking has a greater relationship with the intention. This study has implications for demographic researches and consumer behaviour disciplines. It also has profitable implications for banks and managers in Libya, as it will assist in better understanding of the Libyan consumers and their activities, when they develop their market strategies and new service.

 

Keywords: Banks in Libya, Customer Knowledge, Intention, Mobile banking.

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Authors: Saber Saati Mohtadi, Ali Payan, Azizallah Kord

Abstract:

One of the most important issues in multi-criteria decision analysis (MCDA) is to determine the weights of criteria so that all alternatives can be compared based on the collective performance of criteria. In this paper, one of popular methods in data envelopment analysis (DEA) known as common weights (CWs) is used to determine the weights in MCDA. Two frontiers named ideal and anti-ideal frontiers, instead of ideal and anti-ideal alternatives, are defined based on two new proposed CWs models. Ideal and antiideal frontiers are more flexible than that of alternatives. According to the optimal solutions of these two models, the distances of an alternative from the ideal and anti-ideal frontiers are derived. Then, a relative distance is introduced to measure the value of each alternative. The suggested models are linear and despite weight restrictions are feasible. An example is presented for explaining the method and for comparing to the existing literature.

Keywords: Anti-ideal frontier, Common weights (CWs), Ideal frontier, Multi-criteria decision analysis (MCDA)

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Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil

Abstract:

Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.

Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

Abstract:

An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: Retrieval, generative, deep learning, response generation, knowledge.

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Authors: S.A. Al-Qallaf, S.A. Al-Mawsawi, A. Haider

Abstract:

Among all FACTS devices, the unified power flow controller (UPFC) is considered to be the most versatile device. This is due to its capability to control all the transmission system parameters (impedance, voltage magnitude, and phase angle). With the growing interest in UPFC, the attention to develop a mathematical model has increased. Several models were introduced for UPFC in literature for different type of studies in power systems. In this paper a novel comparison study between two dynamic models of UPFC with their proposed control strategies.

Keywords: FACTS, UPFC, Dynamic Modeling, PWM, Fundamental Frequency.

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Authors: R. Damaševičius

Abstract:

System-level design based on high-level abstractions is becoming increasingly important in hardware and embedded system design. This paper analyzes meta-design techniques oriented at developing meta-programs and meta-models for well-understood domains. Meta-design techniques include meta-programming and meta-modeling. At the programming level of design process, metadesign means developing generic components that are usable in a wider context of application than original domain components. At the modeling level, meta-design means developing design patterns that describe general solutions to the common recurring design problems, and meta-models that describe the relationship between different types of design models and abstractions. The paper describes and evaluates the implementation of meta-design in hardware design domain using object-oriented and meta-programming techniques. The presented ideas are illustrated with a case study.

Keywords: Design patterns, meta-design, meta-modeling, metaprogramming.

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Authors: D Sudheer Reddy, N Gopal Reddy, P V Radhadevi, J Saibaba, Geeta Varadan

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Smoothing or filtering of data is first preprocessing step for noise suppression in many applications involving data analysis. Moving average is the most popular method of smoothing the data, generalization of this led to the development of Savitzky-Golay filter. Many window smoothing methods were developed by convolving the data with different window functions for different applications; most widely used window functions are Gaussian or Kaiser. Function approximation of the data by polynomial regression or Fourier expansion or wavelet expansion also gives a smoothed data. Wavelets also smooth the data to great extent by thresholding the wavelet coefficients. Almost all smoothing methods destroys the peaks and flatten them when the support of the window is increased. In certain applications it is desirable to retain peaks while smoothing the data as much as possible. In this paper we present a methodology called as peak-wise smoothing that will smooth the data to any desired level without losing the major peak features.

Keywords: smoothing, moving average, peakwise smoothing, spatialdensity models, planar shape models, wavelets.

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Authors: Yi-an Cui, Lu Wang, Jian-ping Xiao

Abstract:

This paper presents an automatic feature recognition method based on center-surround difference detecting and fuzzy logic that can be applied in ground-penetrating radar (GPR) image processing. Adopted center-surround difference method, the salient local image regions are extracted from the GPR images as features of detected objects. And fuzzy logic strategy is used to match the detected features and features in template database. This way, the problem of objects detecting, which is the key problem in GPR image processing, can be converted into two steps, feature extracting and matching. The contributions of these skills make the system have the ability to deal with changes in scale, antenna and noises. The results of experiments also prove that the system has higher ratio of features sensing in using GPR to image the subsurface structures.

Keywords: feature recognition, GPR image, matching strategy, salient image

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Authors: W. J. Stępniowski, W. Florkiewicz, M. Norek, M. Michalska-Domańska, E. Kościuczyk, T. Czujko

Abstract:

In this paper, two options of anodic alumina barrier layer thinning have been demonstrated. The approaches varied with the duration of the voltage step. It was found that too long step of the barrier layer thinning process leads to chemical etching of the nanopores on their top. At the bottoms pores are not fully opened what is disadvantageous for further applications in nanofabrication. On the other hand, while the duration of the voltage step is controlled by the current density (value of the current density cannot exceed 75% of the value recorded during previous voltage step) the pores are fully opened. However, pores at the bottom obtained with this procedure have smaller diameter, nevertheless this procedure provides electric contact between the bare aluminum (substrate) and electrolyte, what is suitable for template assisted electrodeposition, one of the most cost-efficient synthesis method in nanotechnology.

Keywords: Anodic aluminum oxide, anodization, barrier layer thinning, nanopores.

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Authors: Jawad Al Khaburi, Abbas A. Dehghani-Sanij, E. Andrea Nelson, Jerry Hutchinson

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Medical compression bandages are widely used in the treatment of chronic venous disorder. In order to design effective compression bandages, researchers have attempted to describe the interface pressure applied by multi-layer bandages using mathematical models. This paper reports on the work carried out to compare and validate the mathematical models used to describe the interface pressure applied by multi-layer bandages. Both analytical and experimental results showed that using simple multiplication of a number of bandage layers with the pressure applied by one layer of bandage or ignoring the increase in the limb radius due to former layers of bandage will result in overestimating the pressure. Experimental results showed that the mathematical models, which take into consideration the increase in the limb radius due to former bandage layers, are more accurate than the one which does not.

Keywords: Compression bandages, FlexiForce, interface pressure, venous ulcer

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Authors: S. Kannimuthu, K. Premalatha

Abstract:

This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.

Keywords: Data mining, Knowledge as a Service, service oriented computing, utility mining.

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