Search results for: knowledge domain

Authors: Guolong Zhong, Venkatesh Chennam Vijay, Ilias Oraifige

Abstract:

In the last 20 years, Knowledge Based Engineering (KBE) has shown its advantages in product development in different engineering areas such as automation, mechanical, civil and aerospace engineering in terms of digital design automation and cost reduction by automating repetitive design tasks through capturing, integrating, utilising and reusing the existing knowledge required in various aspects of the product design. However, in primary design stages, the descriptive information of a product is discrete and unorganized while knowledge is in various forms instead of pure data. Thus, it is crucial to have an integrated product model which can represent the entire product information and its associated knowledge at the beginning of the product design. One of the shortcomings of the existing product models is a lack of required knowledge representation in various aspects of product design and its mapping to an interoperable schema. To overcome the limitation of the existing product model and methodologies, two key factors are considered. First, the product model must have well-defined classes that can represent the entire product information and its associated knowledge. Second, the product model needs to be represented in an interoperable schema to ensure a steady data exchange between different product modelling platforms and CAD software. This paper introduced a method to provide a general product model as a generative representation of a product, which consists of the geometry information and non-geometry information, through a product modelling framework. The proposed method for capturing the knowledge from the designers through a knowledge file provides a simple and efficient way of collecting and transferring knowledge. Further, the knowledge schema provides a clear view and format on the data that needed to be gathered in order to achieve a unified knowledge exchange between different platforms. This study used a game-based platform to make product modelling environment accessible for non-engineers. Further the paper goes on to test use case based on the proposed game-based product modelling environment to validate the effectiveness among non-engineers.

Keywords: Game-based learning, knowledge based engineering, product modelling, design automation.

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Authors: Ahmed Alaoui

Abstract:

This paper contributes to the ongoing debate as to the relevance of translation studies to professional practitioners. It exposes the various misconceptions permeating the links between theory and practice in the translation landscape in the Arab World. It is a thesis of this paper that specialization in translation should be redefined; taking account of the fact, that specialized knowledge alone is neither crucial nor sufficient in technical translation. It should be tested against the readability of the translated text, the appropriateness of its style and the usability of its content by endusers to carry out their intended tasks. The paper also proposes a preliminary model to establish a working link between theory and practice from the perspective of professional trainers and practitioners, calling for the latter to participate in the production of knowledge in a systematic fashion. While this proposal is driven by a rather intuitive conviction, a research line is needed to specify the methodological moves to establish the mediation strategies that would relate the components in the model of knowledge transfer proposed in this paper. 

Keywords: Knowledge transfer, misconceptions, specialized texts, translation theory, translation practice.

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Authors: V. I. Thajudin Ahamed, P. Dhanasekaran, Paul Joseph K.

Abstract:

Analysis of heart rate variability (HRV) has become a popular non-invasive tool for assessing the activities of autonomic nervous system. Most of the methods were hired from techniques used for time series analysis. Currently used methods are time domain, frequency domain, geometrical and fractal methods. A new technique, which searches for pattern repeatability in a time series, is proposed for quantifying heart rate (HR) time series. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are able to distinguish HR data clearly from noise and electroencephalogram (EEG). The results of analysis using these measures give an insight into the fundamental difference between the composition of HR time series with respect to EEG and noise.

Keywords: Approximate entropy, heart rate variability, noise, pattern repeatability, and sample entropy.

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Lexicon, sentiment analysis, stock movement prediction., computational finance.

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Authors: A. Johannet, B. Vayssade, D. Bertin

Abstract:

Neural networks are well known for their ability to model non linear functions, but as statistical methods usually does, they use a no parametric approach thus, a priori knowledge is not obvious to be taken into account no more than the a posteriori knowledge. In order to deal with these problematics, an original way to encode the knowledge inside the architecture is proposed. This method is applied to the problem of the evapotranspiration inside karstic aquifer which is a problem of huge utility in order to deal with water resource.

Keywords: Neural-Networks, Hydrology, Evapotranpiration, Hidden Function Modeling.

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Authors: A. Ahmed Haza’a, M. Ali Odhah, S. Ahmed Al-Ahdal, A. Saleh Al-Jaradi, G. Ghaleb Alrubaiee

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Patient safety in hospitals is an essential professional indicator that should be noticed. The threat of fires is potentially the most dangerous risk that could harm patients and personnel. The aim of the study is to assess the knowledge of operating room (OR) staff toward prevention and control sources of fires. Data collection was done between March 1 and March 30, 2022. A descriptive cross-sectional study was conducted. The sample of the study consisted of 89 OR staff from different governmental hospitals. Convenient sampling was applied to select the sample size. Official approvals were obtained from selected settings for start collection data. Data were collected using a close-ended questionnaire and tested for knowledge. This study was conducted in four governmental hospitals in Sana'a, Yemen. Most of the OR staff were male. Of these, 50.6% of them were operation technician professionals. More than two-thirds of OR staff have less than ten years of experience; 93% of OR staff had inadequate knowledge of sources of fires, and inadequate knowledge toward control and prevention of fires (73%, 79.8%), respectively; 77.5% of OR staff had inadequate knowledge of prevention and control sources of fires. The study concluded that most of OR staff had inadequate knowledge of sources, controls, and prevention of fires, while 22.5% of them had adequate knowledge of prevention and control sources of fires. We recommended the implementation of training programs toward sources, controls, and prevention of fires or related workshops in their educational planning for OR staff of hospitals.

Keywords: Staff, fire source, operation room safety.

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Authors: Khababa Abdallah, Touahria Mohamed, Frécon Louis

Abstract:

The school / university orientation interests a broad and often badly informed public. Technically, it is an important multicriterion decision problem, which supposes the combination of much academic professional and/or lawful knowledge, which in turn justifies software resorting to the techniques of Artificial Intelligence. CORUS is an expert system of the "Conseil et ORientation Universitaire et Scolaire", based on a knowledge representation language (KRL) with rules and objects, called/ known as Ibn Rochd. CORUS was developed thanks to DéGSE, a workshop of cognitive engineering which supports this LRC. CORUS works out many acceptable solutions for the case considered, and retains the most satisfactory among them. Several versions of CORUS have extended its services gradually.

Keywords: Kknowledge Engineering, Multicriterion Decision, Knowledge-Based Systems.

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Computational finance, sentiment analysis, sentiment lexicon, stock movement prediction.

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Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan

Abstract:

This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed by pruning of the data set. The refined data set is then processed through various Rough Set operators which enable discovery of parameter relationships and interdependencies. The discovered knowledge is automatically transformed into a rule base expressed in Fuzzy terms. Two exemplary cancer repository datasets (for Breast and Lung Cancer) have been used to test and implement the proposed framework.

Keywords: Knowledge Extraction, Fuzzy Sets, Rough Sets, Neuro–Fuzzy Systems, Databases

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Authors: Shagufta Tabassum, V. P. Pawar

Abstract:

The study of static dielectric properties in a binary mixture of 1,2 dichloroethane (DE) and n,n dimethylformamide (DMF) polar liquids has been carried out in the frequency range of 10 MHz to 30 GHz for 11 different concentration using time domain reflectometry technique at 10ºC temperature. The dielectric relaxation study of solute-solvent mixture at microwave frequencies gives information regarding the creation of monomers and multimers as well as interaction between the molecules of the binary mixture. The least squares fit method is used to determine the values of dielectric parameters such as static dielectric constant (ε₀), dielectric constant at high frequency (ε_∞) and relaxation time (τ).

Keywords: Excess parameters, relaxation time, static dielectric constant, time domain reflectometry.

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Authors: Syed Salman Ahmed, Zahid Halim, Rauf Baig, Shariq Bashir

Abstract:

With the rapid growth in business size, today's businesses orient towards electronic technologies. Amazon.com and e-bay.com are some of the major stakeholders in this regard. Unfortunately the enormous size and hugely unstructured data on the web, even for a single commodity, has become a cause of ambiguity for consumers. Extracting valuable information from such an everincreasing data is an extremely tedious task and is fast becoming critical towards the success of businesses. Web content mining can play a major role in solving these issues. It involves using efficient algorithmic techniques to search and retrieve the desired information from a seemingly impossible to search unstructured data on the Internet. Application of web content mining can be very encouraging in the areas of Customer Relations Modeling, billing records, logistics investigations, product cataloguing and quality management. In this paper we present a review of some very interesting, efficient yet implementable techniques from the field of web content mining and study their impact in the area specific to business user needs focusing both on the customer as well as the producer. The techniques we would be reviewing include, mining by developing a knowledge-base repository of the domain, iterative refinement of user queries for personalized search, using a graphbased approach for the development of a web-crawler and filtering information for personalized search using website captions. These techniques have been analyzed and compared on the basis of their execution time and relevance of the result they produced against a particular search.

Keywords: Data mining, web mining, search engines, knowledge discovery.

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Authors: Peng He

Abstract:

Imprecision is a long-standing problem in CAD design and high accuracy image-based reconstruction applications. The visual hull which is the closed silhouette equivalent shape of the objects of interest is an important concept in image-based reconstruction. We extend the domain-theoretic framework, which is a robust and imprecision capturing geometric model, to analyze the imprecision in the output shape when the input vertices are given with imprecision. Under this framework, we show an efficient algorithm to generate the 2D partial visual hull which represents the exact information of the visual hull with only basic imprecision assumptions. We also show how the visual hull from polyhedra problem can be efficiently solved in the context of imprecise input.

Keywords: Geometric Domain, Computer Vision, Computational Geometry, Visual Hull, Image-Based reconstruction, Imprecise Input, CAD object

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Authors: Nedeljko Cvejic, Tapio Sepp

Abstract:

A study of the obtainable watermark data rate for information hiding algorithms is presented in this paper. As the perceptual entropy for wideband monophonic audio signals is in the range of four to five bits per sample, a significant amount of additional information can be inserted into signal without causing any perceptual distortion. Experimental results showed that transform domain watermark embedding outperforms considerably watermark embedding in time domain and that signal decompositions with a high gain of transform coding, like the wavelet transform, are the most suitable for high data rate information hiding. Keywords?Digital watermarking, information hiding, audio watermarking, watermark data rate.

Keywords: Digital watermarking, information hiding, audio watermarking, watermark data rate.

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Authors: Siti Waringin Oon, Rashidah Shuib, Siti Hawa Ali, Nik Hazlina Nik Hussain, Juwita Shaaban, Harmy Mohd Yusoff

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This paper explores the knowledge and attitude of women and men in decision making on pap smear screening. This qualitative study recruited 52 respondents with 44 women and 8 men, using the purposive sampling with snowballing technique through indepth interviews. This study demonstrates several key findings: Female respondents have better knowledge compared to male. Most of the women perceived that pap smear screening is beneficial and important, but to proceed with the test is still doubtful. Male respondents were supportive in terms of sending their spouses to the health facilities or give more freedom to their wives to choose and making decision on their own health due to prominent reason that women know best on their own health. It is expected that the results from this study will provide useful guideline for healthcare providers to prepare any action/intervention to provide an extensive education to improve people-s knowledge and attitude towards pap smear.

Keywords: Attitude, decision making, knowledge, pap smearscreening..

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

Abstract:

This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: Data mining, digital libraries, digital preservation, file format.

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Authors: Aqeel-ur-Rehman, Zubair A. Shaikh

Abstract:

This paper is to present context-aware sensor grid framework for agriculture and its design challenges. Use of sensor networks in the domain of agriculture is not new. However, due to the unavailability of any common framework, solutions that are developed in this domain are location, environment and problem dependent. Keeping the need of common framework for agriculture, Context-Aware Sensor Grid Framework is proposed. It will be helpful in developing solutions for majority of the problems related to irrigation, pesticides spray, use of fertilizers, regular monitoring of plot and yield etc. due to the capability of adjusting according to location and environment. The proposed framework is composed of three layer architecture including context-aware application layer, grid middleware layer and sensor network layer.

Keywords: Agriculture, Context-Awareness, Grid Computing, and Sensor Grid.

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Authors: Norsiah Abdul Hamid, Halimah Badioze Zaman

Abstract:

This paper outlines the research conducted to propose na framework of 'Knowledge Society' (KS) in the Malaysian context. It is important to highlight that the emergence of KS is a result of the rapid growth in knowledge and information. However, the discussion of KS should not only be limited to the importance of knowledge, but a holistic KS is also determined by other imperative dimensions. This article discusses the results of a study conducted previously in Malaysia in order to identify the essential dimensions of KS, and consequently propose a KS framework in the Malaysian context. Two methods were employed, namely the Delphi technique and semi-structured interviews. The modified Delphi involved five rounds with ten experts, while the interviews were conducted with two prominent figures in Malaysia. The results support the proposed framework which contains seven major dimensions in order for Malaysia to become a KS in the future. The dimensions which are crucial for a holistic Malaysian KS are human capital, spirituality, economy, social, institutional, sustainability, and driven by the ICT.

Keywords: Malaysia, Knowledge Society, Framework, Delphi Technique, Interview.

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Authors: María Jesús Luengo-Valderrey, Mónica Moso-Díez

Abstract:

In the knowledge-based economy, innovation is considered essential in order to achieve survival and growth in organizations. On the other hand, knowledge management is currently understood as one of the keys to innovation process. Both factors are generally admitted as generators of competitive advantage in organizations. Specifically, activities on R&D&I and those that generate internal knowledge have a positive influence in innovation results. This paper examines this effect and if it is similar or not is what we aimed to quantify in this paper. We focus on the impact that proportion of knowledge workers, the R&D&I investment, the amounts destined for ICTs and training for innovation have on the variation of tangible and intangibles returns for the sector of high and medium technology in Spain. To do this, we have performed an empirical analysis on the results of questionnaires about innovation in enterprises in Spain, collected by the National Statistics Institute. First, using clusters methodology, the behavior of these enterprises regarding knowledge management is identified. Then, using SEM methodology, we performed, for each cluster, the study about cause-effect relationships among constructs defined through variables, setting its type and quantification. The cluster analysis results in four groups in which cluster number 1 and 3 presents the best performance in innovation with differentiating nuances among them, while clusters 2 and 4 obtained divergent results to a similar innovative effort. However, the results of SEM analysis for each cluster show that, in all cases, knowledge workers are those that affect innovation performance most, regardless of the level of investment, and that there is a strong correlation between knowledge workers and investment in knowledge generation. The main findings reached is that Spanish high and medium technology companies improve their innovation performance investing in internal knowledge generation measures, specially, in terms of R&D activities, and underinvest in external ones. This, and the strong correlation between knowledge workers and the set of activities that promote the knowledge generation, should be taken into account by managers of companies, when making decisions about their investments for innovation, since they are key for improving their opportunities in the global market.

Keywords: High and medium technology sector, innovation, knowledge management, Spanish companies.

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Authors: Joumana Farah, François Marx, Clovis Francis

Abstract:

The aim of this paper is to emphasize and alleviate the effect of phase noise due to imperfect local oscillators on the performances of a Multi-Carrier CDMA system. After the cancellation of Common Phase Error (CPE), an iterative approach is introduced which iteratively estimates Inter-Carrier Interference (ICI) components in the frequency domain and cancels their contribution in the time domain. Simulation are conducted in order to investigate the achievable performances for several parameters, such as the spreading factor, the modulation order, the phase noise power and the transmission Signal-to-Noise Ratio.

Keywords: Inter-carrier Interference, Multi-Carrier Code DivisionMultiple Access, Orthogonal Frequency Division Multiplexing, Phase noise.

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Authors: Anastasia K. Eleftheriadou, Athanasios Sfetsos, Nikolaos Gounaris

Abstract:

The current paper presents an extensive bottom-up framework for assessing building sector-specific vulnerability to climate change: energy supply and demand. The research focuses on the application of downscaled seasonal models for estimating energy performance of buildings in Greece. The ARW-WRF model has been set-up and suitably parameterized to produce downscaled climatological fields for Greece, forced by the output of the CFSv2 model. The outer domain, D01/Europe, included 345 x 345 cells of horizontal resolution 20 x 20 km2 and the inner domain, D02/Greece, comprised 180 x 180 cells of 5 x 5 km2 horizontal resolution. The model run has been setup for a period with a forecast horizon of 6 months, storing outputs on a six hourly basis.

Keywords: Urban environment, vulnerability, climate change, energy performance, seasonal forecast models.

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Authors: Philippe Jorrand

Abstract:

Research in quantum computation is looking for the consequences of having information encoding, processing and communication exploit the laws of quantum physics, i.e. the laws which govern the ultimate knowledge that we have, today, of the foreign world of elementary particles, as described by quantum mechanics. This paper starts with a short survey of the principles which underlie quantum computing, and of some of the major breakthroughs brought by the first ten to fifteen years of research in this domain; quantum algorithms and quantum teleportation are very biefly presented. The next sections are devoted to one among the many directions of current research in the quantum computation paradigm, namely quantum programming languages and their semantics. A few other hot topics and open problems in quantum information processing and communication are mentionned in few words in the concluding remarks, the most difficult of them being the physical implementation of a quantum computer. The interested reader will find a list of useful references at the end of the paper.

Keywords: Quantum information processing, quantum algorithms, quantum programming languages.

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Authors: Mohamed Abdelwahed

Abstract:

The aeration process via injectors is used to combat the lack of oxygen in lakes due to eutrophication. A 3D numerical simulation of the resulting flow using a simplified model is presented. In order to generate the best dynamic in the fluid with respect to the aeration purpose, the optimization of the injectors location is considered. We propose to adapt to this problem the topological sensitivity analysis method which gives the variation of a criterion with respect to the creation of a small hole in the domain. The main idea is to derive the topological sensitivity analysis of the physical model with respect to the insertion of an injector in the fluid flow domain. We propose in this work a topological optimization algorithm based on the studied asymptotic expansion. Finally we present some numerical results, showing the efficiency of our approach

Keywords: Quasi Stokes equations, Numerical simulation, topological optimization, sensitivity analysis.

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Authors: Mario Gianni, Federico Nardi, Federico Ferri, Filippo Cantucci, Manuel A. Ruiz Garcia, Karthik Pushparaj, Fiora Pirri

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In this paper, we describe a Mixed-Initiative Operational Model (MIOM) which directly intervenes on the state of the functionalities embedded into a robot for Urban Search&Rescue (USAR) domain applications. MIOM extends the reasoning capabilities of the vehicle, i.e. mapping, path planning, visual perception and trajectory tracking, with operator knowledge. Especially in USAR scenarios, this coupled initiative has the main advantage of enhancing the overall performance of a rescue mission. In-field experiments with rescue responders have been carried out to evaluate the effectiveness of this operational model.

Keywords: Actively articulated tracked vehicles, mixed-initiative planning interfeces, robot planning, urban search and rescue.

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studied with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studies with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Hazem M. El-Bakry, Qiangfu Zhao

Abstract:

Here, a new idea to speed up the operation of complex valued time delay neural networks is presented. The whole data are collected together in a long vector and then tested as a one input pattern. The proposed fast complex valued time delay neural networks uses cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically that the number of computation steps required for the presented fast complex valued time delay neural networks is less than that needed by classical time delay neural networks. Simulation results using MATLAB confirm the theoretical computations.

Keywords: Fast Complex Valued Time Delay Neural Networks, Cross Correlation, Frequency Domain

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Authors: Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar

Abstract:

In present scenario, cardiovascular problems are growing challenge for researchers and physiologists. As heart disease have no geographic, gender or socioeconomic specific reasons; detecting cardiac irregularities at early stage followed by quick and correct treatment is very important. Electrocardiogram is the finest tool for continuous monitoring of heart activity. Heart rate variability (HRV) is used to measure naturally occurring oscillations between consecutive cardiac cycles. Analysis of this variability is carried out using time domain, frequency domain and non-linear parameters. This paper presents HRV analysis of the online dataset for normal sinus rhythm (taken as healthy subject) and sudden cardiac death (SCD subject) using all three methods computing values for parameters like standard deviation of node to node intervals (SDNN), square root of mean of the sequences of difference between adjacent RR intervals (RMSSD), mean of R to R intervals (mean RR) in time domain, very low-frequency (VLF), low-frequency (LF), high frequency (HF) and ratio of low to high frequency (LF/HF ratio) in frequency domain and Poincare plot for non linear analysis. To differentiate HRV of healthy subject from subject died with SCD, k –nearest neighbor (k-NN) classifier has been used because of its high accuracy. Results show highly reduced values for all stated parameters for SCD subjects as compared to healthy ones. As the dataset used for SCD patients is recording of their ECG signal one hour prior to their death, it is therefore, verified with an accuracy of 95% that proposed algorithm can identify mortality risk of a patient one hour before its death. The identification of a patient’s mortality risk at such an early stage may prevent him/her meeting sudden death if in-time and right treatment is given by the doctor.

Keywords: Early stage prediction, heart rate variability, linear and non linear analysis, sudden cardiac death.

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Authors: Chiung-Ju Huang

Abstract:

This study utilizes a frequency domain approach over the period of 1996 to 2013 to examine the causal relationship between governance and economic growth in ten Asian countries, which have different levels of democracy; classified as “Free”, “Partly Free”, and “Not Free” countries. The empirical results show that there is no Granger causality running from governance to economic growth in “Not Free” countries and “Partly Free” countries with the exception of Singapore. As for “Free” countries such as South Korea and Taiwan, there is a one-way causality running from governance to economic growth. The findings of this study indicate that policy makers in South Korea, Taiwan, and Singapore could use governance index to improve their predictions of the future economic growth.

Keywords: Economic growth, frequency domain, governance, Granger causality.

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Authors: Minh Vuong Pham, Frédéric Plourde, Son Doan Kim

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A strip domain decomposition parallel algorithm for fast direct Poisson solver is presented on a 3D Cartesian staggered grid. The parallel algorithm follows the principles of sequential algorithm for fast direct Poisson solver. Both Dirichlet and Neumann boundary conditions are addressed. Several test cases are likewise addressed in order to shed light on accuracy and efficiency in the strip domain parallelization algorithm. Actually the current implementation shows a very high efficiency when dealing with a large grid mesh up to 3.6 * 10⁹ under massive parallel approach, which explicitly demonstrates that the proposed algorithm is ready for massive parallel computing.

Keywords: Strip-decomposition, parallelization, fast directpoisson solver.

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Authors: Faisal Amin, Abdul Mueed, Xu Jiadong

Abstract:

This paper describes the development of a fully automated measurement software for antenna radiation pattern measurements in a Compact Antenna Test Range (CATR). The CATR has a frequency range from 2-40 GHz and the measurement hardware includes a Network Analyzer for transmitting and Receiving the microwave signal and a Positioner controller to control the motion of the Styrofoam column. The measurement process includes Calibration of CATR with a Standard Gain Horn (SGH) antenna followed by Gain versus angle measurement of the Antenna under test (AUT). The software is designed to control a variety of microwave transmitter / receiver and two axis Positioner controllers through the standard General Purpose interface bus (GPIB) interface. Addition of new Network Analyzers is supported through a slight modification of hardware control module. Time-domain gating is implemented to remove the unwanted signals and get the isolated response of AUT. The gated response of the AUT is compared with the calibration data in the frequency domain to obtain the desired results. The data acquisition and processing is implemented in Agilent VEE and Matlab. A variety of experimental measurements with SGH antennas were performed to validate the accuracy of software. A comparison of results with existing commercial softwares is presented and the measured results are found to be within .2 dBm.

Keywords: Antenna measurement, calibration, time-domain gating, VNA, Positioner controller

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