Search results for: Spatial data analysis
13581 Analysis of Diverse Clustering Tools in Data Mining
Authors: S. Sarumathi, N. Shanthi, M. Sharmila
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Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.
Keywords: Cluster Analysis, Clustering Algorithms, Clustering Techniques, Association, Visualization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 219513580 Application of Multi-Dimensional Principal Component Analysis to Medical Data
Authors: Naoki Yamamoto, Jun Murakami, Chiharu Okuma, Yutaro Shigeto, Satoko Saito, Takashi Izumi, Nozomi Hayashida
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Multi-dimensional principal component analysis (PCA) is the extension of the PCA, which is used widely as the dimensionality reduction technique in multivariate data analysis, to handle multi-dimensional data. To calculate the PCA the singular value decomposition (SVD) is commonly employed by the reason of its numerical stability. The multi-dimensional PCA can be calculated by using the higher-order SVD (HOSVD), which is proposed by Lathauwer et al., similarly with the case of ordinary PCA. In this paper, we apply the multi-dimensional PCA to the multi-dimensional medical data including the functional independence measure (FIM) score, and describe the results of experimental analysis.Keywords: multi-dimensional principal component analysis, higher-order SVD (HOSVD), functional independence measure (FIM), medical data, tensor decomposition
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 249413579 Mean Shift-based Preprocessing Methodology for Improved 3D Buildings Reconstruction
Authors: Nikolaos Vassilas, Theocharis Tsenoglou, Djamchid Ghazanfarpour
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In this work, we explore the capability of the mean shift algorithm as a powerful preprocessing tool for improving the quality of spatial data, acquired from airborne scanners, from densely built urban areas. On one hand, high resolution image data corrupted by noise caused by lossy compression techniques are appropriately smoothed while at the same time preserving the optical edges and, on the other, low resolution LiDAR data in the form of normalized Digital Surface Map (nDSM) is upsampled through the joint mean shift algorithm. Experiments on both the edge-preserving smoothing and upsampling capabilities using synthetic RGB-z data show that the mean shift algorithm is superior to bilateral filtering as well as to other classical smoothing and upsampling algorithms. Application of the proposed methodology for 3D reconstruction of buildings of a pilot region of Athens, Greece results in a significant visual improvement of the 3D building block model.Keywords: 3D buildings reconstruction, data fusion, data upsampling, mean shift.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 199813578 The Use of Network Theory in Heritage Cities
Authors: J. L. Oliver, T. Agryzkov, L. Tortosa, J. Vicent, J. Santacruz
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This paper aims to demonstrate how the use of Network Theory can be applied to a very interesting and complex urban situation: The parts of a city which may have some patrimonial value, but because of their lack of relevant architectural elements, they are not considered to be historic in a conventional sense. In this paper, we use the suburb of La Villaflora in the city of Quito, Ecuador as our case study. We first propose a system of indicators as a tool to characterize and quantify the historic value of a geographic area. Then, we apply these indicators to the suburb of La Villaflora and use Network Theory to understand and propose actions.Keywords: Data visualization, historic value, spatial analysis, urban networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 109913577 Non-negative Principal Component Analysis for Face Recognition
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Principle component analysis is often combined with the state-of-art classification algorithms to recognize human faces. However, principle component analysis can only capture these features contributing to the global characteristics of data because it is a global feature selection algorithm. It misses those features contributing to the local characteristics of data because each principal component only contains some levels of global characteristics of data. In this study, we present a novel face recognition approach using non-negative principal component analysis which is added with the constraint of non-negative to improve data locality and contribute to elucidating latent data structures. Experiments are performed on the Cambridge ORL face database. We demonstrate the strong performances of the algorithm in recognizing human faces in comparison with PCA and NREMF approaches.Keywords: classification, face recognition, non-negativeprinciple component analysis (NPCA)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 169013576 Development of Single Layer of WO3 on Large Spatial Resolution by Atomic Layer Deposition Technique
Authors: S. Zhuiykov, Zh. Hai, H. Xu, C. Xue
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Unique and distinctive properties could be obtained on such two-dimensional (2D) semiconductor as tungsten trioxide (WO3) when the reduction from multi-layer to one fundamental layer thickness takes place. This transition without damaging single-layer on a large spatial resolution remained elusive until the atomic layer deposition (ALD) technique was utilized. Here we report the ALD-enabled atomic-layer-precision development of a single layer WO3 with thickness of 0.77±0.07 nm on a large spatial resolution by using (tBuN)2W(NMe2)2 as tungsten precursor and H2O as oxygen precursor, without affecting the underlying SiO2/Si substrate. Versatility of ALD is in tuning recipe in order to achieve the complete WO3 with desired number of WO3 layers including monolayer. Governed by self-limiting surface reactions, the ALD-enabled approach is versatile, scalable and applicable for a broader range of 2D semiconductors and various device applications.Keywords: Atomic layer deposition, tungsten oxide, WO3, two-dimensional semiconductors, single fundamental layer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 161513575 Development of Road Maintenance Management System Based on WebGIS
Authors: Feng Xiao, Zhou Hongyu, YuCaixia
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Based on an analysis of the current research and application of Road maintenance, geographic information system (WebGIS) and ArcGIS Server, the platform overhead construction for Road maintenance development is studied and the key issues are presented, including the organization and design of spatial data on the basis of the geodatabase technology, middleware technology, tiles cache index technology and dynamic segmentation of WebGIS. Road maintenance geographic information platform is put forward through the researching ideas of analysis of the system design. The design and application of WebGIS system are discussed on the basis of a case study of BaNan district of Chongqing highway maintenance management .The feasibility of the theories and methods are validated through the system.
Keywords: WebGIS, Tile, Road maintenance, dynamic segmentation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 223713574 The Effects on the People's Preference on the Cityscape by the Spatial Characteristics of the Streetscape-Centered on 'Design Seoul Street'-
Authors: Eun-JungKo, Bur-Deul Yoon, Sung-Won Choi, Hong-Kyu Kim
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Jacobs, A.B. (1993) stated that "When I think of a city, the first thing that comes to mind is the street. If the street is interesting, the rest of the city is interesting. If the street is mundane, the city is also mundane." In this statement, he expresses the importance of the streetscape and the street environment. The objective of this paper is to analyze the spatial relationships of the streetscape that affect the general public's preference of the cityscape. Furthermore, this research focuses on the important role that streetscape plays in public perception of the city by the pedestrians who experience it daily. The subject of this paper is eight of the "Design Seoul Street."The analysis and survey results show the preference criteria that affect the streetscape and ultimately the cityscape. This research endeavor shows that differences in physical form, shape, size, color, locations, and context are important.Keywords: Cityscape, Design Seoul Street, street, streetscape.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 128513573 Coalescing Data Marts
Authors: N. Parimala, P. Pahwa
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OLAP uses multidimensional structures, to provide access to data for analysis. Traditionally, OLAP operations are more focused on retrieving data from a single data mart. An exception is the drill across operator. This, however, is restricted to retrieving facts on common dimensions of the multiple data marts. Our concern is to define further operations while retrieving data from multiple data marts. Towards this, we have defined six operations which coalesce data marts. While doing so we consider the common as well as the non-common dimensions of the data marts.Keywords: Data warehouse, Dimension, OLAP, Star Schema.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 155513572 Land Use/Land Cover Mapping Using Landsat 8 and Sentinel-2 in a Mediterranean Landscape
Authors: M. Vogiatzis, K. Perakis
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Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process.
Keywords: land use/land cover, random forest, Landsat-8 OLI, Sentinel-2A MSI, Corine land cover
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32113571 Robust Regression and its Application in Financial Data Analysis
Authors: Mansoor Momeni, Mahmoud Dehghan Nayeri, Ali Faal Ghayoumi, Hoda Ghorbani
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This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.
Keywords: Financial data analysis, Influential data, Outliers, Robust regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 192813570 Multidimensional and Data Mining Analysis for Property Investment Risk Analysis
Authors: Nur Atiqah Rochin Demong, Jie Lu, Farookh Khadeer Hussain
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Property investment in the real estate industry has a high risk due to the uncertainty factors that will affect the decisions made and high cost. Analytic hierarchy process has existed for some time in which referred to an expert-s opinion to measure the uncertainty of the risk factors for the risk analysis. Therefore, different level of experts- experiences will create different opinion and lead to the conflict among the experts in the field. The objective of this paper is to propose a new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach. The propose technique consist of a basic framework which includes four modules: user, technology, end-user access tools and applications. The property investment risk analysis defines as a micro level analysis as the features of the property will be considered in the analysis in this paper.Keywords: Uncertainty factors, data mining, multidimensional data model, risk analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 291213569 Survey on Arabic Sentiment Analysis in Twitter
Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb
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Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.
Keywords: Big Data, Social Networks, Sentiment Analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 434313568 Cluster Analysis for the Statistical Modeling of Aesthetic Judgment Data Related to Comics Artists
Authors: George E. Tsekouras, Evi Sampanikou
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We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.Keywords: Aesthetic judgment, comics artists, cluster analysis, categorical data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 162913567 Entropy Based Spatial Design: A Genetic Algorithm Approach (Case Study)
Authors: Abbas Siefi, Mohammad Javad Karimifar
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We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Keywords: Spatial design of experiments, maximum entropy sampling, computer experiments, genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 164713566 Spatial Structure and Process of Arctic Warming and Land Cover Change in the Feedback Systems Framework
Authors: Eric Kojo Wu Aikins
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This paper examines the relationships between and among the various drivers of climate change that have both climatic and ecological consequences for vegetation and land cover change in arctic areas, particularly in arctic Alaska. It discusses the various processes that have created spatial and climatic structures that have facilitated observable vegetation and land cover changes in the Arctic. Also, it indicates that the drivers of both climatic and ecological changes in the Arctic are multi-faceted and operate in a system with both positive and negative feedbacks that largely results in further increases or decreases of the initial drivers of climatic and vegetation change mainly at the local and regional scales. It demonstrates that the impact of arctic warming on land cover change and the Arctic ecosystems is not unidirectional and one dimensional in nature but it represents a multi-directional and multi-dimensional forces operating in a feedback system.Keywords: Arctic Vegetation Change, Climate Change, Feedback System, Spatial Process and Structure.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 173513565 A Monte Carlo Method to Data Stream Analysis
Authors: Kittisak Kerdprasop, Nittaya Kerdprasop, Pairote Sattayatham
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Data stream analysis is the process of computing various summaries and derived values from large amounts of data which are continuously generated at a rapid rate. The nature of a stream does not allow a revisit on each data element. Furthermore, data processing must be fast to produce timely analysis results. These requirements impose constraints on the design of the algorithms to balance correctness against timely responses. Several techniques have been proposed over the past few years to address these challenges. These techniques can be categorized as either dataoriented or task-oriented. The data-oriented approach analyzes a subset of data or a smaller transformed representation, whereas taskoriented scheme solves the problem directly via approximation techniques. We propose a hybrid approach to tackle the data stream analysis problem. The data stream has been both statistically transformed to a smaller size and computationally approximated its characteristics. We adopt a Monte Carlo method in the approximation step. The data reduction has been performed horizontally and vertically through our EMR sampling method. The proposed method is analyzed by a series of experiments. We apply our algorithm on clustering and classification tasks to evaluate the utility of our approach.Keywords: Data Stream, Monte Carlo, Sampling, DensityEstimation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 141313564 Pattern Recognition Using Feature Based Die-Map Clusteringin the Semiconductor Manufacturing Process
Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek
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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.
Keywords: Die-Map Clustering, Feature Extraction, Pattern Recognition, Semiconductor Manufacturing Process.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 314313563 Simulation of Online Communities Using MAS Social and Spatial Organisations
Authors: Maya Rupert, Salima Hassas, Carlos Li, John Sherwood
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Online Communities are an example of sociallyaware, self-organising, complex adaptive computing systems. The multi-agent systems (MAS) paradigm coordinated by self-organisation mechanisms has been used as an effective way for the simulation and modeling of such systems. In this paper, we propose a model for simulating an online health community using a situated multi-agent system approach, governed by the co-evolution of the social and spatial organisations of the agents.Keywords: multi-agent systems, organizations, online communities.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 135913562 Multidimensional Visualization Tools for Analysis of Expression Data
Authors: Urska Cvek, Marjan Trutschl, Randolph Stone II, Zanobia Syed, John L. Clifford, Anita L. Sabichi
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Expression data analysis is based mostly on the statistical approaches that are indispensable for the study of biological systems. Large amounts of multidimensional data resulting from the high-throughput technologies are not completely served by biostatistical techniques and are usually complemented with visual, knowledge discovery and other computational tools. In many cases, in biological systems we only speculate on the processes that are causing the changes, and it is the visual explorative analysis of data during which a hypothesis is formed. We would like to show the usability of multidimensional visualization tools and promote their use in life sciences. We survey and show some of the multidimensional visualization tools in the process of data exploration, such as parallel coordinates and radviz and we extend them by combining them with the self-organizing map algorithm. We use a time course data set of transitional cell carcinoma of the bladder in our examples. Analysis of data with these tools has the potential to uncover additional relationships and non-trivial structures.Keywords: microarrays, visualization, parallel coordinates, radviz, self-organizing maps.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 250213561 Variogram Fitting Based on the Wilcoxon Norm
Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean
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Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.Keywords: Non-Linear Wilcoxon, robust estimation, Variogram estimation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 96413560 Landscape Data Transformation: Categorical Descriptions to Numerical Descriptors
Authors: Dennis A. Apuan
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Categorical data based on description of the agricultural landscape imposed some mathematical and analytical limitations. This problem however can be overcome by data transformation through coding scheme and the use of non-parametric multivariate approach. The present study describes data transformation from qualitative to numerical descriptors. In a collection of 103 random soil samples over a 60 hectare field, categorical data were obtained from the following variables: levels of nitrogen, phosphorus, potassium, pH, hue, chroma, value and data on topography, vegetation type, and the presence of rocks. Categorical data were coded, and Spearman-s rho correlation was then calculated using PAST software ver. 1.78 in which Principal Component Analysis was based. Results revealed successful data transformation, generating 1030 quantitative descriptors. Visualization based on the new set of descriptors showed clear differences among sites, and amount of variation was successfully measured. Possible applications of data transformation are discussed.Keywords: data transformation, numerical descriptors, principalcomponent analysis
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 150013559 A Novel Web Metric for the Evaluation of Internet Trends
Authors: Radek Malinský, Ivan Jelínek
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Web 2.0 (social networking, blogging and online forums) can serve as a data source for social science research because it contains vast amount of information from many different users. The volume of that information has been growing at a very high rate and becoming a network of heterogeneous data; this makes things difficult to find and is therefore not almost useful. We have proposed a novel theoretical model for gathering and processing data from Web 2.0, which would reflect semantic content of web pages in better way. This article deals with the analysis part of the model and its usage for content analysis of blogs. The introductory part of the article describes methodology for the gathering and processing data from blogs. The next part of the article is focused on the evaluation and content analysis of blogs, which write about specific trend.Keywords: Blog, Sentiment Analysis, Web 2.0, Webometrics
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 353713558 Geovisualization of Tourist Activity Travel Patterns Using 3D GIS: An Empirical Study of Tamsui, Taiwan
Authors: Meng-Lung Lin, Chien-Min Chu, Chung-Hung Tsai, Chih-Cheng Chen, Chen-Yuan Chen
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The study of tourist activities and the mapping of their routes in space and time has become an important issue in tourism management. Here we represent space-time paths for the tourism industry by visualizing individual tourist activities and the paths followed using a 3D Geographic Information System (GIS). Considerable attention has been devoted to the measurement of accessibility to shopping, eating, walking and other services at the tourist destination. I turns out that GIS is a useful tool for studying the spatial behaviors of tourists in the area. The value of GIS is especially advantageous for space-time potential path area measures, especially for the accurate visualization of possible paths through existing city road networks. This study seeks to apply space-time concepts with a detailed street network map obtained from Google Maps to measure tourist paths both spatially and temporally. These paths are further determined based on data obtained from map questionnaires regarding the trip activities of 40 individuals. The analysis of the data makes it possible to determining the locations of the more popular paths. The results can be visualized using 3D GIS to show the areas and potential activity opportunities accessible to tourists during their travel time.
Keywords: Tourist activity analysis, space-time path, GIS, geovisualization, activity-travel pattern.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 247413557 Comparative Analysis of the Public Funding for Greek Universities: An Ordinal DEA/MCDM Approach
Authors: Yiannis Smirlis, Dimitris K. Despotis
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This study performs a comparative analysis of the 21 Greek Universities in terms of their public funding, awarded for covering their operating expenditure. First it introduces a DEA/MCDM model that allocates the fund into four expenditure factors in the most favorable way for each university. Then, it presents a common, consensual assessment model to reallocate the amounts, remaining in the same level of total public budget. From the analysis it derives that a number of universities cannot justify the public funding in terms of their size and operational workload. For them, the sufficient reduction of their public funding amount is estimated as a future target. Due to the lack of precise data for a number of expenditure criteria, the analysis is based on a mixed crisp-ordinal data set.Keywords: Data envelopment analysis, Greek universities, operating expenditures, ordinal data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 175613556 Solving Inhomogeneous Wave Equation Cauchy Problems using Homotopy Perturbation Method
Authors: Mohamed M. Mousa, Aidarkhan Kaltayev
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In this paper, He-s homotopy perturbation method (HPM) is applied to spatial one and three spatial dimensional inhomogeneous wave equation Cauchy problems for obtaining exact solutions. HPM is used for analytic handling of these equations. The results reveal that the HPM is a very effective, convenient and quite accurate to such types of partial differential equations (PDEs).
Keywords: Homotopy perturbation method, Exact solution, Cauchy problem, inhomogeneous wave equation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 180213555 Comparison of Imputation Techniques for Efficient Prediction of Software Fault Proneness in Classes
Authors: Geeta Sikka, Arvinder Kaur Takkar, Moin Uddin
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Missing data is a persistent problem in almost all areas of empirical research. The missing data must be treated very carefully, as data plays a fundamental role in every analysis. Improper treatment can distort the analysis or generate biased results. In this paper, we compare and contrast various imputation techniques on missing data sets and make an empirical evaluation of these methods so as to construct quality software models. Our empirical study is based on NASA-s two public dataset. KC4 and KC1. The actual data sets of 125 cases and 2107 cases respectively, without any missing values were considered. The data set is used to create Missing at Random (MAR) data Listwise Deletion(LD), Mean Substitution(MS), Interpolation, Regression with an error term and Expectation-Maximization (EM) approaches were used to compare the effects of the various techniques.Keywords: Missing data, Imputation, Missing Data Techniques.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 166013554 Application of RS and GIS Technique for Identifying Groundwater Potential Zone in Gomukhi Nadhi Sub Basin, South India
Authors: Punitha Periyasamy, Mahalingam Sudalaimuthu, Sachikanta Nanda, Arasu Sundaram
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India holds 17.5% of the world’s population but has only 2% of the total geographical area of the world where 27.35% of the area is categorized as wasteland due to lack of or less groundwater. So there is a demand for excessive groundwater for agricultural and non agricultural activities to balance its growth rate. With this in mind, an attempt is made to find the groundwater potential zone in Gomukhi Nadhi sub basin of Vellar River basin, TamilNadu, India covering an area of 1146.6 Sq.Km consists of 9 blocks from Peddanaickanpalayam to Virudhachalam in the sub basin. The thematic maps such as Geology, Geomorphology, Lineament, Landuse and Landcover and Drainage are prepared for the study area using IRS P6 data. The collateral data includes rainfall, water level, soil map are collected for analysis and inference. The digital elevation model (DEM) is generated using Shuttle Radar Topographic Mission (SRTM) and the slope of the study area is obtained. ArcGIS 10.1 acts as a powerful spatial analysis tool to find out the ground water potential zones in the study area by means of weighted overlay analysis. Each individual parameter of the thematic maps are ranked and weighted in accordance with their influence to increase the water level in the ground. The potential zones in the study area are classified viz., Very Good, Good, Moderate, Poor with its aerial extent of 15.67, 381.06, 575.38, 174.49 Sq.Km respectively.
Keywords: ArcGIS, DEM, Groundwater, Recharge, Weighted Overlay.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 298413553 Kinematic Modeling and Workspace Analysis of a Spatial Cable Suspended Robot as Incompletely Restrained Positioning Mechanism
Authors: Jahanbakhsh Hamedi, Hassan Zohoor
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This article proposes modeling, simulation and kinematic and workspace analysis of a spatial cable suspended robot as incompletely Restrained Positioning Mechanism (IRPM). These types of robots have six cables equal to the number of degrees of freedom. After modeling, the kinds of workspace are defined then an statically reachable combined workspace for different geometric structures of fixed and moving platform is obtained. This workspace is defined as the situations of reference point of the moving platform (center of mass) which under external forces such as weight and with ignorance of inertial effects, the moving platform should be in static equilibrium under conditions that length of all cables must not be exceeded from the maximum value and all of cables must be at tension (they must have non-negative tension forces). Then the effect of various parameters such as the size of moving platform, the size of fixed platform, geometric configuration of robots, magnitude of applied forces and moments to moving platform on workspace of these robots with different geometric configuration are investigated. Obtained results should be effective in employing these robots under different conditions of applied wrench for increasing the workspace volume.Keywords: Kinematic modeling, applied wrench, workspace, cable based robot.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 168913552 Estimating the Life-Distribution Parameters of Weibull-Life PV Systems Utilizing Non-Parametric Analysis
Authors: Saleem Z. Ramadan
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In this paper, a model is proposed to determine the life distribution parameters of the useful life region for the PV system utilizing a combination of non-parametric and linear regression analysis for the failure data of these systems. Results showed that this method is dependable for analyzing failure time data for such reliable systems when the data is scarce.Keywords: Masking, Bathtub model, reliability, non-parametric analysis, useful life.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1838