• The Korean Journal of Archival Studies
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• no.30
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• pp.57-88
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• 2011

Oral history is the work to make the record of the verbal content recreated by the memories of the survivors. Oral history recording is accomplished through the collaboration of the interviewee, the interviewer, the cameraman, the recorder, the transcriber and etc. Therefore, it is important for the context at the time of the production to be expressed. So planning for the collection of oral records, the collection of oral records, and their preservation and maintenance should be managed systematically. This study, being started from this sense of problem, designed conceptual model of metadata to well reflect the contextual characteristics of the oral records of the women life of among the oral records and extracted the elements through this. The whole process of records management including from planning, production, preservation, management, and leading to use, related to the oral records of the women life, was classified into a hierarchy. It also proposed the system which can express the characteristics of the 'gender' through authority records and subject thesaurus.

• Journal of Creative Information Culture
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• v.7 no.3
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• pp.157-165
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• 2021

As the use of various application services utilizing Web and IoT and the need for large amounts of data management expand accordingly, the importance of efficient data expression and exchange scheme and data query processing is increasing. JSON, characterized by its simplicity, is being used in various fields as a format for data exchange and data storage instead of XML, which is a standard data expression and exchange language on the Web. This means that it is important to develop indexing and query processing techniques to effectively access and search large amounts of data expressed in JSON. Therefore, in this paper, we modeled JSON data with a hierarchical structure in a tree form, and proposed indexing and query processing using the path concept. In particular, we designed an index structure using a suffix array widely used in text search and introduced simple and complex path-based JSON data query processing methods.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.319-326
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• 2010

Effectiveness analysis of weapon system has been accomplished using engagement-level model alone. However, most previous works are prone to errors due to lack of behavioral information about the weapon systems. In order to overcome these limitations, this paper proposes an interoperation approach between the engagement- and engineering-level models. The proposed approach enables the engagement-level model to be supported by the engineering-level model representing the detailed behavior of weapon systems. Our methodology consider a limited combat situation including operational environments, dynamics and operational errors of weapons, and engagement orders. The paper describes a formalization of the system effectiveness analysis and defines an interface for interoperation between engagement- and engineering-level models. Then, we perform an anti-torpedo combat simulation as a case study.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.562-574
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• 2012

Simulation validation techniques which have been employed in most studies are statistical analysis, which validate a model with mean or variance of throughput and resource utilization as an evaluation object. However, these methods have not been able to ensure the reliability of individual elements of the model well. To overcome the problem, the weighted F-measure method was proposed, but this technique also had some limitations. First, it is difficult to apply the technique to complex system environment with numerous values of interarrival time because it assigns a class to an individual value of interarrival time. In addition, due to unbounded weights, the value of weighted F-measure has no lower bound, so it is difficult to determine its threshold. Therefore, this paper propose weighted F-measure technique with cluster analysis to solve these problems. The classes for the technique are defined by each cluster, which reduces considerable number of classes and enables to apply the technique to various systems. Moreover, we improved the validation technique in the way of assigning minimum bounded weights without any lack of objectivity.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.140-160
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• 2003

The purpose of this study is to identify and evaluate the competitiveness of ports in ASEAN(Association of Southeast Asian Nations), which plays a leading role in basing the hub of international logistics strategies as a countermeasure in changes of international logistics environments. This region represents most severe competition among Mega hub ports in the world in terms of container cargo throughput at the onset of the 21st century. The research method in this study accounted for over lapping between attributes, and introduced the HFP method that can perform mathematical operations. The scope of this study was strictly confined to the ports of ASEAN, which cover the top 100 of 350 container ports that were presented in Containerization International Yearbook 2002 wi th reference to container throughput. The results of this study show Singapore in the number one position. Even compared with major ports in Korea (after getting comparative ratings and applying tile same data and evaluation structure), the number one position still goes to Singapore and then Busan(2) and Manila(2), followed by Port Klang(4), Tanjung Priok(5), Tanjung Perak(6), Bangkok(7), Inchon(8), Laem Chabang(9) and Penang(9). In terms of the main contributions of this study, it is the first empirical study to apply the combined at tributes of detailed and representative attributes into the advanced HFP model which was enhanced by the KJ method to evaluate the port competitiveness in ASEAN. Up-to-now, none have comprehensively conducted researches with sophisticated port methodology that has discussed a variety of changes in port development and terminal transfers of major shipping lines. Moreover, through the comparative evaluation among major ports in Korea and ASEAN, the presentation of comparative competitiveness for Korean ports is a great achievement in this study. In order to reinforce this study, it needs further compensative research, including cost factors which could not be applied to modeling the subject ports by lack of consistently qualified data in ASEAN.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.130-142
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• 2008

Different from other ontology languages, TopicMap is capable of integrating numerous amount of heterogenous information resources using the locational information without any information transformation. Although many conventional editors have been developed for topic maps, they are standalone-type only for writing XTM documents. As a result, these tools request too much time for handling large-scale data and provoke practical problems to integrate with legacy systems which are mostly based on relational database. In this paper, we model a large-scale topic map structure based on XTM 1.0 into RDB structure to minimize the processing time and build up the ontology in legacy systems. We implement a topic map platform called X-TOP that can enhance the efficiency of ontology construction and provide interoperability between XTM documents and database. Moreover, we can use conventional SQL tools and other application development tools for topic map construction in X-TOP. The X-TOP is implemented to have 3-tier architecture to support flexible user interfaces and diverse DBMS. This paper shows the usability of X-TOP by means of the comparison with conventional tools and the application to healthcare cancer ontology management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.12-19
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• 2020

The clustered heterogeneous wireless sensor network is comprised of sensor nodes and cluster heads, which are hierarchically organized for different objectives. In the network, we should especially take care of managing node resources to enhance network performance based on memory and battery capacity constraints. For instances, if some interesting events occur frequently in the vicinity of particular sensor nodes, those nodes might receive massive amounts of data. Data congestion can happen due to a memory bottleneck or link disconnection at cluster heads because the remaining memory space is filled with those data. In this paper, we utilize drones as mobile sinks to resolve data congestion and model the network, sensor nodes, and cluster heads. We also design a cost function and a congestion indicator to calculate the degree of congestion. Then we propose a data congestion map index and a data congestion mapping scheme to deploy drones at optimal points. Using control variable, we explore the relationship between the degree of congestion and the number of drones to be deployed, as well as the number of drones that must be below a certain degree of congestion and within communication range. Furthermore, we show that our algorithm outperforms previous work by a minimum of 20% in terms of memory overflow.