• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.73-82
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• 2020

The main purpose of this study was to analyze the general informatization situation of North Korean defectors and to study the characteristics and needs of the learners in order to provide the directions of information education for them. The results of the study showed the following characteristics of the North Korean defectors: They are slow learners due to the fear of new devices, have difficulty in learning due to the unfamiliar language of information and English, and indifferent when the situation is not related to themselves. Based on these learner characteristics and needs, this study suggests the strategies of step-by-step repetition, use of North and South Korean dictionary of the information terminology, apply job-centered and communication abilities, and suggested a four-element STEP model. Raising the level of informatization of North Korean defectors will help establish a successful settlement to South Korea. This will be a valuable foundation and a stepping stone for the future unification of Korea.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1082-1092
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• 1996

The design of distrbuted systems is difficult to achieve as the execution patterns of distrbuted systems are typically more complex than those of non- distributed systems. Thus, research toward the development of design methods for distributed systems is quitely needed. As object-oriented systems and distrbuted systems share similar properties, the combination of these two is somehow natural. In this work, a design of distributed systems is introduced. The goal of the method in this paper is to provide assistance to the process of specifying a formal object- oriented specification from graphical representation specification inputs such as data flow diagrams, state transition diagrams and Petri nets. It addresses the extraction of objects, operations and reationshipsfrom the problem domain with emphasis on the specification of the characteristics of distributed systems. This object identification method is supported by a knowledge base that provides for the automated analysis and reasoning about objects and their relationsships. The final object model is represented in a format which provides a formal mechanism for reprsenting the object information.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7665-7671
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• 2014

Objectives: The association between body mass index (BMI) and ovarian cancer risk is unclear and requires further investigation. The present meta-analysis was conducted to assess the effect of overweight and obesity on ovarian cancer risk in the premenopausal and postmenopausal periods. Data sources: Major electronic databases were searched until February 2014 including Medline and Scopus. Reference lists and relevant conference databases were searched and the authors were contacted for additional unpublished references. Review Methods: All cohort and case-control studies addressing the effect of BMI on ovarian cancer were included, irrespective of publication date and language. The effect measure of choice was risk ratio (RR) for cohort studies and odds ratio (OR) for case-control studies. The results were reported using a random effects model with 95% confidence intervals (CIs). Results: Of 3,776 retrieved studies, 19 were ultimately analyzed including 10 cohort studies involving 29,237,219 person-years and 9 case-control studies involving 96,965 people. The results of both cohort and case-control studies showed being overweight and obesity increased the risk of ovarian cancer compared to women with normal weight during both premenopausal and postmenopausal periods: RR=1.08 (95%CI: 0.97, 1.19) and OR=1.26 (95%CI: 0.97, 1.63) for overweight and RR=1.27 (95%CI: 1.16, 1.38) and OR=1.26 (95%CI: 1.06, 1.50) for obesity. Conclusions: There is sufficient evidence that an increase in BMI can increase the risk of ovarian cancer regardless of the menopausal status, mimicking a dose-response relationship although the association is not very strong.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.17-24
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• 2019

The biggest problem in the AI field, which has become a hot topic in recent years, is how to deal with the lack of training data. Since manual data construction takes a lot of time and efforts, it is non-trivial for an individual to easily build the necessary data. On the other hand, automatic data construction needs to handle data quality issue. In this paper, we introduce a method to automatically extract the data required to develop Korean speech command recognizer from the web and to automatically select the data that can be used for training data. In particular, we propose a modified ResNet model that shows modest performance for the automatically constructed Korean speech command data. We conducted an experiment to show the applicability of the command set of the health and daily life domain. In a series of experiments using only automatically constructed data, the accuracy of the health domain was 89.5% in ResNet15 and 82% in ResNet8 in the daily lives domain, respectively.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.2
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• pp.87-93
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• 2018

This Study Propose A New Approach To Impliment A Intelligent Robot Control Based on Voice Recognition For Smart Factory Automation Since human usually communicate each other by voices, it is very convenient if voice is used to command humanoid robots or the other type robot system. A lot of researches has been performed about voice recognition systems for this purpose. Hidden Markov Model is a robust statistical methodology for efficient voice recognition in noise environments. It has being tested in a wide range of applications. A prediction approach traditionally applied for the text compression and coding, Prediction by Partial Matching which is a finite-context statistical modeling technique and can predict the next characters based on the context, has shown a great potential in developing novel solutions to several language modeling problems in speech recognition. It was illustrated the reliability of voice recognition by experiments for humanoid robot with 26 joints as the purpose of application to the manufacturing process.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.75-83
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• 2007

Multithreaded model is an effective parallel system in that it can reduce the long memory reference latency time and solve the synchronization problems. When compiling the non-strict functional programs for the multithreaded parallel machine, the most important thing is to find an set of sequentially executable instructions and to partitions them into threads. The existing partitioning algorithm partitions the condition of conditional expression, true expression and false expression into the basic blocks and apply local partitioning to these basic blocks. We can do the better partitioning if we modify the definition of the thread and allow the branching within the thread. The branching within the thread do not reduce the parallelism, do not increase the number of synchronization and do not violate the basic rule of the thread partitioning. On the contrary, it can lengthen the thread and reduce the number of synchronization. In the paper, we enhance the method of the partition of threads by combining the three basic blocks into one of two blocks.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.3
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• pp.209-217
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• 2013

This paper presents an optimal design of a magnetorheological(MR) fluid-based mount(MR mount) that can be used for to vibration control in diesel engines of ships. In this work, a mount that uses mixed-modes(squeeze mode, flow mode, and shear mode) is proposed and designed. To determine the actuating damping force of the MR mount required for efficient vibration control, the excitation force from a diesel engine is analyzed. In this analysis, a model of a V-type engine is considered. The relationship between the velocity and pressure of gas in terms of the torque acting on the piston is derived. Subsequently, by integrating the field-dependent rheological properties of commercially available MR fluid with the excitation force, the appropriate size of the MR mount is designed. In addition, to achieve the maximum actuating force under geometric constraints, design optimization is undertaken using the ANSYS parametric design language software. Through magnetic density analysis, optimal design parameters such as the bottom gap and radius of coil are determined.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1077-1094
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• 2016

Apache Spark is a fast and general-purpose cluster computing package. It provides a new abstraction named resilient distributed dataset, which is capable of support for fault tolerance while keeping data in memory. This type of abstraction results in a significant speedup compared to legacy large-scale data framework, MapReduce. In particular, Spark framework is suitable for iterative machine learning applications such as logistic regression and K-means clustering, and interactive data querying. Spark also supports high level libraries for various applications such as machine learning, streaming data processing, database querying and graph data mining thanks to its versatility. In this work, we introduce the concept and programming model of Spark as well as show some implementations of simple statistical computing applications. We also review the machine learning package MLlib, and the R language interface SparkR.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1279-1290
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• 2017

R's {shiny} package provides an environment for creating web applications with only R scripts. Shiny does not require knowledge of a separate web programming language and its development is very easy and straightforward. In addition, Shiny has a variety of extensibility, and its functions are expanding day by day. Therefore, the presentation of high-quality results is an excellent tool for R-based analysts. In this paper, we present actual cases of large data analysis using Shiny. First, geological anomaly zone is extracted by analyzing topographical data expressed in the form of contour lines by analysis related to spatial data. Next, we will construct a model to predict major diseases by 16 cities and provinces nationwide using weather, environment, and social media information. In this process, we want to show that Shiny is very effective for data visualization and analysis.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.1
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• pp.83-91
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• 2000

This paper presents a new technique to reduce a large interconnect circuit with tens of thousands of elements into the one that is small enough to be analyzed by circuit simulators such as SPICE. This technique takes a fundamentally different approach form the conventional methods based on the interconnect circuit structure analysis and several rules based on the Elmore time constant. The time moments are computed form the circuit consisting of the interconnect circuit and the CMOS gate driver model computed by the AWE technique. Then, the equivalent RC circuit is synthesized from those moments. The characteristics of the driving CMOS gate can be reflected with the high degree of accuracy and the size of the compressed circuit is determined by the number of output nodes regardless of the size of the original interconnect circuits. This technique has been implemented in C language, applied to several interconnect circuits driven by a 0.5${\mu}m$ CMOS gate and the equivalent RC circuits with more than 99% reduction ratio and accuracy with 1 ~ 10% error in therms of propagation delays were obtained.