• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.731-736
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• 2001

Nowadays various simulation tools are widely used for the design and the analysis of power electronic converters. From the engineering point of view it is rather difficult to parameterize power semiconductor device models without the knowledge of basic physical parameters. In recent years some data sheet driven behavioral models or so called 'wizard' tools have been introduced to solve this problem. In this contribution some experiences with some user-friendly power semiconductor models will be discussed. Using special simulation test circuits it is possible to get information on the static and dynamic behavior of the parameterized models before they are applied in more complex schemes. These results can be compared with data sheets or with measurements. The application of these models for power loss analysis of inverter type arc welding power sources will be described.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.215-222
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• 1995

The objective of this paper is to develop a finite element structural analysis program using VB(Visual Basic) which has been recently getting popular as development tools of application program for Windows. VB provides several functions to develop an application program easily by supporting event-driven programming method and graphic object as control data type. This system is an integrated processor including preprocessor, solver and postprocessor. Automatic mesh generation is available at preprocess stage, and graphic presentation of deformation and stress is also represented at postprocess one.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.91-97
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• 1998

Design methodology of Interactive Measuring Part Program Generating Tools(IMPPGT) realized on the FANUC 15MA using touch trigger probes and interactive macro functions of the CNC was described in this paper. Measuring G codes were designed according to geometric form, precision attributes, relations between parts, datum hierarchies, and relevant technological data by using measuring arguments. Menu driven measuring and inspection functions of the IMPPGT were studied and implemented on the CNC through the macro executor and ROM writer. Using the developed measuring G code system on the machine tool, untended measurement and inspection operation was able to be realized in precision FMS lines.

• The Mathematical Education
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• v.63 no.2
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• pp.233-254
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• 2024

This paper delves into the symbiotic integration of coding and mathematics education, aimed at cultivating computational thinking and enriching mathematical problem-solving proficiencies. We have identified a corpus of scholarly articles (n=38) disseminated within the preceding two decades, subsequently culling a portion thereof, ultimately engendering a contemplative analysis of the extant remnants. In a swiftly evolving society driven by the Fourth Industrial Revolution and the ascendancy of Artificial Intelligence (AI), understanding the synergy between these domains has become paramount. Mathematics education stands at the crossroads of this transformation, witnessing a profound influence of AI. This paper explores the evolving landscape of mathematical cognition propelled by AI, accentuating how AI empowers advanced analytical and problem-solving capabilities, particularly in the realm of big data-driven scenarios. Given this shifting paradigm, it becomes imperative to investigate and assess AI's impact on mathematics education, a pivotal endeavor in forging an education system aligned with the future. The symbiosis of AI and human cognition doesn't merely amplify AI-centric thinking but also fosters personalized cognitive processes by facilitating interaction with AI and encouraging critical contemplation of AI's algorithmic underpinnings. This necessitates a broader conception of educational tools, encompassing AI as a catalyst for mathematical cognition, transcending conventional linguistic and symbolic instruments.

• The Journal of Information Systems
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• v.20 no.3
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.154-162
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• 2022

Digital marketing tools are actively used in managing the development of park and recreation complexes to familiarize the population with the objects of natural heritage. This article aims to empirically evaluate digital marketing tools for popularizing the park and recreational complexes. The methodology was based on the concept of ecosystem value of park and recreation complexes as a natural heritage site. These methods included: identifying and selecting websites with information about park and recreation complexes in Slovakia and Ukraine. structural analysis of the main channels of online details about natural parks. Assessing the current state of online identity of the studied sites from the perspective of Internet users. The results indicate that to manage the development of park and recreational complexes developed their driven official websites in the Internet space, on which sections structure the information with the allocation of data on tourism and recreational potential. The article identifies additional digital marketing tools for managing the development of park and recreation complexes, particularly social networks and tourist websites. There is a sufficient amount of information about tourist recreation sites within these natural parks and tourist routes. Among the main problems of the websites: the information on the websites is entirely textual, there is a lack of sufficient data on social networks, despite the created official pages, there is no video content, which was more attracted tourists and visitors, allowing a visual assessment of the tourist potential; there is a problem of many communication channels to present the natural heritage of the countries. The research proves that the website is the primary and most common digital marketing tool for natural heritage, structuring information about tourism potential and recreation.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• Asian review of World Histories
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• v.4 no.1
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1075-1078
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• 2015

Three kinds of typical practices to reflect the values of Agile Development Methodology were selected from a previous study. Those were Communicate using Web 2.0 collaboration tools, test-driven development (TDD, Test-Driven Development) method is adopted, and refactoring. In this study, we set up a hypothesis that the adoption of TDD project will make user satisfaction is higher. Select 100 sample projects from SourceForge(sourceforge.net), the most popular open source hosting site, the criteria is we can be determined whether operate in the project (developer least 7 people, bugs can occur more than 100, created the project since 2000). To determine whether the use of automated development tools xUnit of TDD through the CVS and SVN log analysis. Using data from the FLOSSmole and to evaluate the user experience of the project. User satisfaction of each project Rating, bug fix cycle, downloads and pageviews. Through this study, correlates of whether TDD adoption and user satisfaction, we will suggest a reflected the Agile practices new open source development methodology. As a result, it contributes to increase the maturity of the open source community.