• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.23-28
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• 2001

There has been several meaning full efforts to save costs on system development and expand the life-time of a system in changeful IT circumstance. It was a module-based architecture that empower productivity at structured programming era. But it couldn't grow nor evolve, but could raise only calling frequency of module. But OOP or OO-method overcome limit of structured programing by class inheritance and/or overloading and/or over-riding. A component centric architecture, what is mixture of distributed systems, like CORBA or DCOM with OOP, can support not only high reusability or expansion of life-time but also Plug-&-Play between them. To assemble these component to build a new system in easy way, the well-formed specification of a component is highly required. At this study, the enhanced referential component architecture and its UML specification will be suggested.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.85-90
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• 2023

Data analysis methodology in the healthcare field is shifting from traditional statistics-oriented research methods to predictive research using machine learning. In this study, we survey various machine learning tools, and compare several programming models, which utilize R and Spark, for applying R, a statistical tool widely used in the health care field, to machine learning. In addition, we compare the performance of linear regression model using scala, which is the basic languages of Spark and R. As a result of the experiment, the learning execution time when using SparkR increased by 10 to 20% compared to Scala. Considering the presented performance degradation, SparkR's distributed processing was confirmed as useful in R as the traditional statistical analysis tool that could be used as it is.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.79-84
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• 2023

Apache Spark, which provides the fastest processing speed among recent distributed and parallel processing technologies, provides real-time functions and machine learning functions. Although official documentation guides for these functions are provided, a method for fusion of functions to predict a specific value in real time is not provided. Therefore, in this paper, we conducted a study to predict the value of data in real time by fusion of these functions. The overall configuration is collected by downloading stock price data provided by the Python programming language. And it creates a model of regression analysis through the machine learning function, and predicts the adjusted closing price among the stock price data in real time by fusing the real-time streaming function with the machine learning function.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.33-42
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• 2012

Rapid evolution of wired and wireless technologies results in various types of embedded systems, and the software to be embedded into those devices now needs the flexibility rather than the fixedness which was well-known property for the embedded software in the past. Mobile agent is one of the useful distributed technologies of reducing network load and latency because of its disconnected operations and high asynchrony. In this paper, a component-based mobile agent framework, called EmHUMAN, is designed and implemented for structural embedding into the devices showing different functions and resource constraints. It consists of 3 layers of components. Based on those components, a structural embedding, considering resource constraints of required functions, amount of storage space, computing power, network bandwidth, ${\ldots} $ etc can be performed. The components in each layer can be extended with addition of new components, removing some components and modifying components. EmHUMAN plays the role of a framework for developing mobile agent based distributed systems. It is also a mobile agent system by itself. EmHUMAN provides several utilities as built-in API's, and thus high effectiveness in programming mobile agents can be achieved.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.107-114
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• 2001

This paper describes Connected Digit Recognition with Considering Acoustic Feature in Korea. The recognition rate of connected digit is usually lower than word recognition. Therefore, speech feature parameter and acoustic feature are employed to make robust model for digit, and we could confirm the effect of Considering. Acoustic Feature throughout the experience of recognition. We used KLE 4 connected digit as database and 19 continuous distributed HMM as PLUs(Phoneme Like Units) using phonetical rules. For recognition experience, we have tested two cases. The first case, we used usual method like using Mel-Cepstrum and Regressive Coefficient for constructing phoneme model. The second case, we used expanded feature parameter and acoustic feature for constructing phoneme model. In both case, we employed OPDP(One Pass Dynamic Programming) and FSA(Finite State Automata) for recognition tests. When appling FSN for recognition, we applied various acoustic features. As the result, we could get 55.4% recognition rate for Mel-Cepstrum, and 67.4% for Mel-Cepstrum and Regressive Coefficient. Also, we could get 74.3% recognition rate for expanded feature parameter, and 75.4% for applying acoustic feature. Since, the case of applying acoustic feature got better result than former method, we could make certain that suggested method is effective for connected digit recognition in korean.

• Korean Chemical Engineering Research
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• v.50 no.3
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• pp.471-478
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• 2012

A carbon capture and storage (CCS) plays a very important role to reduce $CO_2$ dramatically in $CO_2$ emission sources which are distributed throughout various areas. Numerous research works have been undertaken to analyze the techno-economic feasibility of planning the CCS infrastructure. However, uncertainties such as $CO_2$ emissions, $CO_2$ reduction costs, and carbon taxes may exist in various impact factors of the CCS infrastructure. However, few research works have adopted these uncertainties in designing the CCS infrastructure. In this study, a two-stage stochastic programming model is developed for planning the CCS infrastructure under uncertain operating costs and carbon taxes. It can help determine where and how much $CO_2$ to capture, store or transport for the purpose of minimizing the total annual $CO_2$ reduction cost in handling the uncertainties while meeting the $CO_2$ mitigation target. The capability of the proposed model to provide correct decisions despite changing the operating costs and carbon taxes is tested by applying it to a real case study based on Korea. The results will help to determine planning of a CCS infrastructure under uncertain environments.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.608-616
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• 2016

The Smith-Watrman (SW) algorithm is a local alignment algorithm which is one of important operations in DNA sequence analysis. The SW algorithm finds the optimal local alignment with respect to the scoring system being used, but it has a problem to demand long execution time. To solve the problem of SW, some methods to perform SW in distributed and parallel manner have been proposed. The ADAM which is a distributed and parallel processing framework for DNA sequence has parallel SW. However, the parallel SW of the ADAM does not consider that the SW is a dynamic programming method, so the parallel SW of the ADAM has the limit of its performance. In this paper, we propose a method to enhance the parallel SW of ADAM. The proposed parallel SW (PSW) is performed in two phases. In the first phase, the PSW splits a DNA sequence into the number of partitions and assigns them to multiple nodes. Then, the original Smith-Waterman algorithm is performed in parallel at each node. In the second phase, the PSW estimates the portion of data sequence that should be recalculated, and the recalculation is performed on the portions in parallel at each node. In the experiment, we compare the proposed PSW to the parallel SW of the ADAM to show the superiority of the PSW.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.265-274
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• 2018

The increasing use of curved beams in buildings, vehicles, ships, and aircraft has prompted studies directed toward the development of an accurate method for analyzing the dynamic behavior of such structures. The stability behavior of elastic curved beams has been the subject of a large number of investigations. Solutions of the relevant differential equations have been obtained traditionally using standard finite difference or finite element methods. These techniques require a great deal of computer time as the number of discrete nodes becomes relatively large under the conditions of complex geometry and loading. One of the efficient procedures for the solution of partial differential equations is the method of differential quadrature. The differential quadrature method (DQM) has been applied to a large number of cases to overcome the difficulties of the complex algorithms of programming for the computer, as well as the excessive use of storage due to the conditions of complex geometry and loading. The in-plane buckling of curved beams considering the extensibility of the arch axis was analyzed under uniformly distributed radial loads using the DQM. The critical loads were calculated for the member with various parameter ratios, boundary conditions, and opening angles. The results were compared with the precise results by other methods for cases, in which they were available. The DQM, using only a limited number of grid points, provided results that agreed very well (less than 0.3%) with the exact ones. New results according to diverse variations were obtained, showing the important roles in the buckling behavior of curved beams, and can be used in comparisons with other numerical solutions or with experimental test data.

• Spatial Information Research
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• v.5 no.2
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• pp.147-160
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• 1997

"The "Map Algebra", beeing recognized as a viable theoretical framework for GIS (Geographica Infonnation System), models map layers as "operands" which are the basic unit of geo-processing, and a variety of GIS commands as "operators." In this paper, we attempt at lifting some limitations of map algebras proposed in GIS literature. First, we model map layer as "function" such that we may employ the notion of meta operator (or, higher-order funtion) available in the functional programming paradigm. This approach provides map algebraic language with "programmability" needed in GIS user language. Second, we extend the semantics of, and improve on the sytactic structure of map algebraic language. Mer the data model and language associated with map algebra are formalized, we proceed to design and implement a prototype of map algebraic processor. The parser of the language in our prototype plays the role of transforming the native and heterogeneous user language of current GISs into a canonical map algebraic language. The prototype, named "MapSee" is a proof-of-concept system for the ideas we propsed in this paper. We believe that the uniform interface based on the map algebraic language will make promising infrastructure to support "Internet GIS." This is because the uniform but powerful interface through the Web clients allow access to both geo-data and geo-processing resources distributed over the network.to both geo-data and geo-processing resources distributed over the network.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.975-987
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• 2013

The Hadoop system was developed by the Apache foundation based on GFS and MapReduce technologies of Google. Many modern systems for managing and processing the big data have been developing based on the Hadoop because the Hadoop was designed for scalability and distributed computing. The R software has been considered as a well-suited analytic tool in the Hadoop based systems because the R is flexible to other languages and has many libraries for complex analyses. We introduced Rhipe which is a R package supporting MapReduce programming easily under the Hadoop system, and implemented a MapReduce program using Rhipe for multiple regression especially. In addition, we compared the computing speeds of our program with the other packages (ff and bigmemory) for processing the large data. The simulation results showed that our program was more fast than ff and bigmemory as the size of data increases.