Search results for: data safety.
7296 MIBiClus: Mutual Information based Biclustering Algorithm
Authors: Neelima Gupta, Seema Aggarwal
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Most of the biclustering/projected clustering algorithms are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However, in many applications, like gene expression data and word-document data, non linear relationships may exist between the objects. Mutual Information between two variables provides a more general criterion to investigate dependencies amongst variables. In this paper, we improve upon our previous algorithm that uses mutual information for biclustering in terms of computation time and also the type of clusters identified. The algorithm is able to find biclusters with mixed relationships and is faster than the previous one. To the best of our knowledge, none of the other existing algorithms for biclustering have used mutual information as a similarity measure. We present the experimental results on synthetic data as well as on the yeast expression data. Biclusters on the yeast data were found to be biologically and statistically significant using GO Tool Box and FuncAssociate.
Keywords: Biclustering, mutual information.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16167295 Spatio-Temporal Data Mining with Association Rules for Lake Van
Authors: T. Aydin, M. F. Alaeddinoglu
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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatiotemporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newlyformed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.Keywords: Apriori algorithm, association rules, data mining, spatio-temporal data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13957294 Data Extraction of XML Files using Searching and Indexing Techniques
Authors: Sushma Satpute, Vaishali Katkar, Nilesh Sahare
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XML files contain data which is in well formatted manner. By studying the format or semantics of the grammar it will be helpful for fast retrieval of the data. There are many algorithms which describes about searching the data from XML files. There are no. of approaches which uses data structure or are related to the contents of the document. In these cases user must know about the structure of the document and information retrieval techniques using NLPs is related to content of the document. Hence the result may be irrelevant or not so successful and may take more time to search.. This paper presents fast XML retrieval techniques by using new indexing technique and the concept of RXML. When indexing an XML document, the system takes into account both the document content and the document structure and assigns the value to each tag from file. To query the system, a user is not constrained about fixed format of query.
Keywords: XML Retrieval, Indexed Search, Information Retrieval.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17697293 GeNS: a Biological Data Integration Platform
Authors: Joel Arrais, João E. Pereira, João Fernandes, José Luís Oliveira
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The scientific achievements coming from molecular biology depend greatly on the capability of computational applications to analyze the laboratorial results. A comprehensive analysis of an experiment requires typically the simultaneous study of the obtained dataset with data that is available in several distinct public databases. Nevertheless, developing a centralized access to these distributed databases rises up a set of challenges such as: what is the best integration strategy, how to solve nomenclature clashes, how to solve database overlapping data and how to deal with huge datasets. In this paper we present GeNS, a system that uses a simple and yet innovative approach to address several biological data integration issues. Compared with existing systems, the main advantages of GeNS are related to its maintenance simplicity and to its coverage and scalability, in terms of number of supported databases and data types. To support our claims we present the current use of GeNS in two concrete applications. GeNS currently contains more than 140 million of biological relations and it can be publicly downloaded or remotely access through SOAP web services.Keywords: Data integration, biological databases
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16267292 A Modified Run Length Coding Technique for Test Data Compression Based on Multi-Level Selective Huffman Coding
Authors: C. Kalamani, K. Paramasivam
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Test data compression is an efficient method for reducing the test application cost. The problem of reducing test data has been addressed by researchers in three different aspects: Test Data Compression, Built-in-Self-Test (BIST) and Test set compaction. The latter two methods are capable of enhancing fault coverage with cost of hardware overhead. The drawback of the conventional methods is that they are capable of reducing the test storage and test power but when test data have redundant length of runs, no additional compression method is followed. This paper presents a modified Run Length Coding (RLC) technique with Multilevel Selective Huffman Coding (MLSHC) technique to reduce test data volume, test pattern delivery time and power dissipation in scan test applications where redundant length of runs is encountered then the preceding run symbol is replaced with tiny codeword. Experimental results show that the presented method not only improves the test data compression but also reduces the overall test data volume compared to recent schemes. Experiments for the six largest ISCAS-98 benchmarks show that our method outperforms most known techniques.
Keywords: Modified run length coding, multilevel selective Huffman coding, built-in-self-test modified selective Huffman coding, automatic test equipment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12597291 EEIA: Energy Efficient Indexed Aggregation in Smart Wireless Sensor Networks
Authors: Mohamed Watfa, William Daher, Hisham Al Azar
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The main idea behind in network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Existing sensor network data aggregation techniques assume that the nodes are preprogrammed and send data to a central sink for offline querying and analysis. This approach faces two major drawbacks. First, the system behavior is preprogrammed and cannot be modified on the fly. Second, the increased energy wastage due to the communication overhead will result in decreasing the overall system lifetime. Thus, energy conservation is of prime consideration in sensor network protocols in order to maximize the network-s operational lifetime. In this paper, we give an energy efficient approach to query processing by implementing new optimization techniques applied to in-network aggregation. We first discuss earlier approaches in sensors data management and highlight their disadvantages. We then present our approach “Energy Efficient Indexed Aggregation" (EEIA) and evaluate it through several simulations to prove its efficiency, competence and effectiveness.Keywords: Sensor Networks, Data Base, Data Fusion, Aggregation, Indexing, Energy Efficiency
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17787290 Comparison of Different Methods to Produce Fuzzy Tolerance Relations for Rainfall Data Classification in the Region of Central Greece
Authors: N. Samarinas, C. Evangelides, C. Vrekos
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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.
Keywords: Classification, fuzzy logic, tolerance relations, rainfall data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10107289 Granularity Analysis for Spatio-Temporal Web Sensors
Authors: Shun Hattori
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In recent years, many researches to mine the exploding Web world, especially User Generated Content (UGC) such as weblogs, for knowledge about various phenomena and events in the physical world have been done actively, and also Web services with the Web-mined knowledge have begun to be developed for the public. However, there are few detailed investigations on how accurately Web-mined data reflect physical-world data. It must be problematic to idolatrously utilize the Web-mined data in public Web services without ensuring their accuracy sufficiently. Therefore, this paper introduces the simplest Web Sensor and spatiotemporallynormalized Web Sensor to extract spatiotemporal data about a target phenomenon from weblogs searched by keyword(s) representing the target phenomenon, and tries to validate the potential and reliability of the Web-sensed spatiotemporal data by four kinds of granularity analyses of coefficient correlation with temperature, rainfall, snowfall, and earthquake statistics per day by region of Japan Meteorological Agency as physical-world data: spatial granularity (region-s population density), temporal granularity (time period, e.g., per day vs. per week), representation granularity (e.g., “rain" vs. “heavy rain"), and media granularity (weblogs vs. microblogs such as Tweets).Keywords: Granularity analysis, knowledge extraction, spatiotemporal data mining, Web credibility, Web mining, Web sensor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18727288 The Estimation Method of Stress Distribution for Beam Structures Using the Terrestrial Laser Scanning
Authors: Sang Wook Park, Jun Su Park, Byung Kwan Oh, Yousok Kim, Hyo Seon Park
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This study suggests the estimation method of stress distribution for the beam structures based on TLS (Terrestrial Laser Scanning). The main components of method are the creation of the lattices of raw data from TLS to satisfy the suitable condition and application of CSSI (Cubic Smoothing Spline Interpolation) for estimating stress distribution. Estimation of stress distribution for the structural member or the whole structure is one of the important factors for safety evaluation of the structure. Existing sensors which include ESG (Electric strain gauge) and LVDT (Linear Variable Differential Transformer) can be categorized as contact type sensor which should be installed on the structural members and also there are various limitations such as the need of separate space where the network cables are installed and the difficulty of access for sensor installation in real buildings. To overcome these problems inherent in the contact type sensors, TLS system of LiDAR (light detection and ranging), which can measure the displacement of a target in a long range without the influence of surrounding environment and also get the whole shape of the structure, has been applied to the field of structural health monitoring. The important characteristic of TLS measuring is a formation of point clouds which has many points including the local coordinate. Point clouds are not linear distribution but dispersed shape. Thus, to analyze point clouds, the interpolation is needed vitally. Through formation of averaged lattices and CSSI for the raw data, the method which can estimate the displacement of simple beam was developed. Also, the developed method can be extended to calculate the strain and finally applicable to estimate a stress distribution of a structural member. To verify the validity of the method, the loading test on a simple beam was conducted and TLS measured it. Through a comparison of the estimated stress and reference stress, the validity of the method is confirmed.Keywords: Structural health monitoring, terrestrial laser scanning, estimation of stress distribution, coordinate transformation, cubic smoothing spline interpolation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27227287 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 16817286 Concurrent Approach to Data Parallel Model using Java
Authors: Bala Dhandayuthapani Veerasamy
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Parallel programming models exist as an abstraction of hardware and memory architectures. There are several parallel programming models in commonly use; they are shared memory model, thread model, message passing model, data parallel model, hybrid model, Flynn-s models, embarrassingly parallel computations model, pipelined computations model. These models are not specific to a particular type of machine or memory architecture. This paper expresses the model program for concurrent approach to data parallel model through java programming.Keywords: Concurrent, Data Parallel, JDK, Parallel, Thread
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20817285 Adjusted Ratio and Regression Type Estimators for Estimation of Population Mean when some Observations are missing
Authors: Nuanpan Nangsue
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Ratio and regression type estimators have been used by previous authors to estimate a population mean for the principal variable from samples in which both auxiliary x and principal y variable data are available. However, missing data are a common problem in statistical analyses with real data. Ratio and regression type estimators have also been used for imputing values of missing y data. In this paper, six new ratio and regression type estimators are proposed for imputing values for any missing y data and estimating a population mean for y from samples with missing x and/or y data. A simulation study has been conducted to compare the six ratio and regression type estimators with a previous estimator of Rueda. Two population sizes N = 1,000 and 5,000 have been considered with sample sizes of 10% and 30% and with correlation coefficients between population variables X and Y of 0.5 and 0.8. In the simulations, 10 and 40 percent of sample y values and 10 and 40 percent of sample x values were randomly designated as missing. The new ratio and regression type estimators give similar mean absolute percentage errors that are smaller than the Rueda estimator for all cases. The new estimators give a large reduction in errors for the case of 40% missing y values and sampling fraction of 30%.
Keywords: Auxiliary variable, missing data, ratio and regression type estimators.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17217284 Efficient Implementation of Serial and Parallel Support Vector Machine Training with a Multi-Parameter Kernel for Large-Scale Data Mining
Authors: Tatjana Eitrich, Bruno Lang
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This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.
Keywords: Support Vector Machines, Shared Memory Parallel Computing, Large Data
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15707283 Failure Analysis of Pipe System at a Hydroelectric Power Plant
Authors: Ali Göksenli, Barlas Eryürek
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In this study, failure analysis of pipe system at a micro hydroelectric power plant is investigated. Failure occurred at the pipe system in the powerhouse during shut down operation of the water flow by a valve. This locking had caused a sudden shock wave, also called “Water-hammer effect”, resulting in noise and inside pressure increase. After visual investigation of the effect of the shock wave on the system, a circumference crack was observed at the pipe flange weld region. To establish the reason for crack formation, calculations of pressure and stress values at pipe, flange and welding seams were carried out and concluded that safety factor was high (2.2), indicating that no faulty design existed. By further analysis, pipe system and hydroelectric power plant was examined. After observations it is determined that the plant did not include a ventilation nozzle (air trap), that prevents the system of sudden pressure increase inside the pipes which is caused by water-hammer effect. Analyses were carried out to identify the influence of water-hammer effect on inside pressure increase and it was concluded that, according Jowkowsky’s equation, shut down time is effective on inside pressure increase. The valve closing time was uncertain but by a shut down time of even one minute, inside pressure would increase by 7.6 bar (working pressure was 34.6 bar). Detailed investigations were also carried out on the assembly of the pipe-flange system by considering technical drawings. It was concluded that the pipe-flange system was not installed according to the instructions. Two of five weld seams were not applied and one weld was carried out faulty. This incorrect and inadequate weld seams resulted in; insufficient connection of the pipe to the flange constituting a strong notch effect at weld seam regions, increase in stress values and the decrease of strength and safety factor.Keywords: Failure analysis, hydroelectric plant, water-hammer, crack, welding seam.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27197282 Software Test Data Generation using Ant Colony Optimization
Authors: Huaizhong Li, C.Peng Lam
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State-based testing is frequently used in software testing. Test data generation is one of the key issues in software testing. A properly generated test suite may not only locate the errors in a software system, but also help in reducing the high cost associated with software testing. It is often desired that test data in the form of test sequences within a test suite can be automatically generated to achieve required test coverage. This paper proposes an Ant Colony Optimization approach to test data generation for the state-based software testing.
Keywords: Software testing, ant colony optimization, UML.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34417281 Natural Language News Generation from Big Data
Authors: Bastian Haarmann, Lukas Sikorski
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In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts from big data. The resulting fully automatic generated news stories have a high resemblance to the style in which the human writer would draw up such a story. Topics include soccer games, stock exchange market reports, and weather forecasts. Each generated text is unique. Readyto-publish stories written by a computer application can help humans to quickly grasp the outcomes of big data analyses, save timeconsuming pre-formulations for journalists and cater to rather small audiences by offering stories that would otherwise not exist.
Keywords: Big data, natural language generation, publishing, robotic journalism.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16687280 Yield Prediction Using Support Vectors Based Under-Sampling in Semiconductor Process
Authors: Sae-Rom Pak, Seung Hwan Park, Jeong Ho Cho, Daewoong An, Cheong-Sool Park, Jun Seok Kim, Jun-Geol Baek
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It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.
Keywords: Yield Prediction, Semiconductor Test Process, Support Vector Machine, Under Sampling
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23837279 A New Model for Discovering XML Association Rules from XML Documents
Authors: R. AliMohammadzadeh, M. Rahgozar, A. Zarnani
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The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. We explain our model within multi-steps from data preparation to rule generation.Keywords: XML, Data Mining, Association Rule Mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16217278 Modelling Silica Optical Fibre Reliability: A Software Application
Authors: I. Severin, M. Caramihai, R. El Abdi, M. Poulain, A. Avadanii
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In order to assess optical fiber reliability in different environmental and stress conditions series of testing are performed simulating overlapping of chemical and mechanical controlled varying factors. Each series of testing may be compared using statistical processing: i.e. Weibull plots. Due to the numerous data to treat, a software application has appeared useful to interpret selected series of experiments in function of envisaged factors. The current paper presents a software application used in the storage, modelling and interpretation of experimental data gathered from optical fibre testing. The present paper strictly deals with the software part of the project (regarding the modelling, storage and processing of user supplied data).
Keywords: Optical fibres, computer aided analysis, data models, data processing, graphical user interfaces.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18077277 The Role of Synthetic Data in Aerial Object Detection
Authors: Ava Dodd, Jonathan Adams
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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represent another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.
Keywords: computer vision, machine learning, synthetic data, YOLOv4
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8267276 Unsupervised Text Mining Approach to Early Warning System
Authors: Ichihan Tai, Bill Olson, Paul Blessner
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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.
Keywords: Early Warning System, Knowledge Management, Topic Modeling, Market Prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19087275 An Implementation of Data Reusable MPEG Video Coding Scheme
Authors: Vasily G. Moshnyaga
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This paper presents an optimized MPEG2 video codec implementation, which drastically reduces the number of computations and memory accesses required for video compression. Unlike traditional scheme, we reuse data stored in frame memory to omit unnecessary coding operations and memory read/writes for unchanged macroblocks. Due to dynamic memory sharing among reference frames, data-driven macroblock characterization and selective macroblock processing, we perform less than 15% of the total operations required by a conventional coder while maintaining high picture quality.
Keywords: Data reuse, adaptive processing, video coding, MPEG
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12557274 A Hybrid Scheme for on-Line Diagnostic Decision Making Using Optimal Data Representation and Filtering Technique
Authors: Hyun-Woo Cho
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The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.
Keywords: Diagnostics, batch process, nonlinear representation, data filtering, multivariate statistical approach
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13077273 Increasing Replica Consistency Performances with Load Balancing Strategy in Data Grid Systems
Authors: Sarra Senhadji, Amar Kateb, Hafida Belbachir
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Data replication in data grid systems is one of the important solutions that improve availability, scalability, and fault tolerance. However, this technique can also bring some involved issues such as maintaining replica consistency. Moreover, as grid environment are very dynamic some nodes can be more uploaded than the others to become eventually a bottleneck. The main idea of our work is to propose a complementary solution between replica consistency maintenance and dynamic load balancing strategy to improve access performances under a simulated grid environment.
Keywords: Consistency, replication, data grid, load balancing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23167272 Nonparametric Control Chart Using Density Weighted Support Vector Data Description
Authors: Myungraee Cha, Jun Seok Kim, Seung Hwan Park, Jun-Geol Baek
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In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.
Keywords: Density estimation, Multivariate control chart, Oneclass classification, Support vector data description (SVDD)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21087271 Model-Based Person Tracking Through Networked Cameras
Authors: Kyoung-Mi Lee, Youn-Mi Lee
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This paper proposes a way to track persons by making use of multiple non-overlapping cameras. Tracking persons on multiple non-overlapping cameras enables data communication among cameras through the network connection between a camera and a computer, while at the same time transferring human feature data captured by a camera to another camera that is connected via the network. To track persons with a camera and send the tracking data to another camera, the proposed system uses a hierarchical human model that comprises a head, a torso, and legs. The feature data of the person being modeled are transferred to the server, after which the server sends the feature data of the human model to the cameras connected over the network. This enables a camera that captures a person's movement entering its vision to keep tracking the recognized person with the use of the feature data transferred from the server.
Keywords: Person tracking, human model, networked cameras, vision-based surveillance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14817270 Slugging Frequency Correlation for Inclined Gas-liquid Flow
Authors: V. Hernandez-Perez, M. Abdulkadir, B. J. Azzopardi
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In this work, new experimental data for slugging frequency in inclined gas-liquid flow are reported, and a new correlation is proposed. Scale experiments were carried out using a mixture of air and water in a 6 m long pipe. Two different pipe diameters were used, namely, 38 and 67 mm. The data were taken with capacitance type sensors at a data acquisition frequency of 200 Hz over an interval of 60 seconds. For the range of flow conditions studied, the liquid superficial velocity is observed to influence the frequency strongly. A comparison of the present data with correlations available in the literature reveals a lack of agreement. A new correlation for slug frequency has been proposed for the inclined flow, which represents the main contribution of this work.Keywords: slug frequency, inclined flow
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31467269 FCA-based Conceptual Knowledge Discovery in Folksonomy
Authors: Yu-Kyung Kang, Suk-Hyung Hwang, Kyoung-Mo Yang
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The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.
Keywords: Folksonomy data mining, formal concept analysis, collaborative tagging, conceptual knowledge discovery, classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20177268 Minimum Data of a Speech Signal as Special Indicators of Identification in Phonoscopy
Authors: Nazaket Gazieva
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Voice biometric data associated with physiological, psychological and other factors are widely used in forensic phonoscopy. There are various methods for identifying and verifying a person by voice. This article explores the minimum speech signal data as individual parameters of a speech signal. Monozygotic twins are believed to be genetically identical. Using the minimum data of the speech signal, we came to the conclusion that the voice imprint of monozygotic twins is individual. According to the conclusion of the experiment, we can conclude that the minimum indicators of the speech signal are more stable and reliable for phonoscopic examinations.
Keywords: Biometric voice prints, fundamental frequency, phonogram, speech signal, temporal characteristics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5447267 An Empirical Evaluation of Performance of Machine Learning Techniques on Imbalanced Software Quality Data
Authors: Ruchika Malhotra, Megha Khanna
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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1507