Earticle

The COVID-19 pandemic has affected various sectors of society, including politics. The political changes have had both positive and negative impacts on people's lives. Different public discussions happened during that situation on social media. It is essential to understand those discussions to prepare for the same kind of situation in future. Therefore, this study aims to identify the topics discussed on Twitter regarding this influence. During March 2020 and December 2021, 10,658 Tweets were gathered through the Twitter application programming interface and preprocessed using Python libraries. After feature extraction using the bag-of-words method, both probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) were used as topic modeling methods. As a result of the analysis, 15 topics by LDA and 25 topics by PLSA were extracted during the study and then grouped into five key themes: Government responses for managing the COVID-19 Pandemic, Government decisions for COVID-19, Public response to government measures for COVID-19, Social influence, and Vaccination. Through a comparative evaluation of the LDA and PLSA topic modeling techniques, the research identifies LDA as the superior method, providing more accurate and coherent results.

This study investigates how digital discourse surrounding Yuseong-gu public libraries is structured and how it informs evidence-based policy architecture. Online text data were collected from major Korean portals (Naver and Daum) between July 2022 and June 2025 using "Yuseong-gu public libraries" as the core search query. The corpus was analyzed using text mining, keyword network analysis, and latent Dirichlet allocation (LDA) topic modeling. Word frequency and TF-IDF results indicate that place-anchored identifiers (e.g., Yuseong-gu, Daejeon) and culture-related vocabulary constitute the discourse backbone, while managerial and operational terms such as integration, support, and homepage signal demand for coordinated governance and enhanced digital accessibility. N-gram analysis further emphasizes the demand for an integrated information and participation portal, most clearly reflected in the recurrent sequence "Yuseong-gu-integrated-library-homepage." Network analysis reveals a high-density structure with a short average path length, confirming strong thematic interconnectedness; the node "library" functions as the primary hub and is directly linked to "culture," indicating the library's discursive positioning as a cultural platform. The findings support strategic policy directions, including a hub-satellite spatial system embedded across neighborhood life zones, cross-sectional programming integrating education, culture, and community participation, a mobile-first integrated digital portal, and institutionalized partnerships with schools and local cultural institutions.

Research in multi-dimensional magnetotelluric (MT) modeling and inversion, particularly within anisotropic media, is gaining substantial interest. One-dimensional (1-D) modeling is utilized as boundary conditions necessary for accurate 2-D or 3-D anisotropic modeling. Based on Maxwell’s equations and interface boundary conditions, we developed a new algorithm to calculate electromag- netic fields for both 1-D isotropic and anisotropic layered models. However, this algorithm encounters stability issues at considerable depths or within exceptionally thick layers. To address these challenges, we introduced two stabilization strategies. The first strategy alters the exponential terms in the plane-wave solutions to mitigate instabilities, while the second evaluates each layer’s contribution to numerical instability, replacing problematic thick layers with an infinite half-space. Through numerical tests on a four-layer anisotropic model, these strategies proved effective, ensuring stable computations of electromagnetic fields at any depth and in configurations with thick layers, thereby enhancing the model’s applicability for in-depth anisotropic analyses.

Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.

In this paper a new modeling method for an eddy current sensor is presented using geometric and electromagnetic data of a sensor and a measuring target. It can predict not only sensor output but also medium behavior related to sensor output. The geometric data of a sensor coil and the eddy current generated on a measuring target are simplified to an array of circular loops. And to perform computations of the network circuit between sensor coil loops and eddy current loops using the geometric and electromagnetic data in order to consider all possible interactions, the equivalent network circuit of eddy current sensor's behavior has been drawn. Because the sensor's initial value, medium behavior, and final value can be shown quantitatively by the proposed modeling method as the geometric and electromagnetic data varies, it can precisely predict the sensor output depending on the measuring goal and application field. Thus the model can be utilized to improve accuracy, eliminate the need for calibration before use, and produce the best design for any given purpose.

Recently, vehicle simulators are widely used to evaluate driver s responses and driver assistance systems. It needs much effort to construct the virtual driving environment for a vehicle simulator. In this study, it is described how to make effectively the roads and the driving environment for a vehicle simulator. GIS (Geographic Information System) is used to construct the roads and the environment effectively. Because the GIS is the integrated system of geographical data, it contains useful data to make virtual driving environment. First, boundaries and centerlines of roads are extracted from the GIS. From boundaries, the road width is calculated. Using centerlines, mesh models of roads are constructed. The final graphic model of roads is constructed by mapping road images to those mesh models considering the number of lanes and the kind of surface. Data of buildings from the GIS are extracted. Each shape and height of building is determined considering the kind of building to construct the final graphic model of buildings. Then, the graphic model of roadside trees is constructed to decide their locations. Finally, the driving environment for driving simulator is constructed by converting the three graphic models with the graphic format of Direct-X and by joining the three graphic models.

Data modeling in data warehousing is a multifaceted process crucial for effective data management and analysis. Through techniques like conceptual, logical, and physical modeling, data architects organize complex data structures. The iterative nature of modeling allows adaptation to evolving business needs. The analysis shows that most organizations used the hybrid approach to create the data modeling in the data warehouse because this approach helps to create a good and efficient model of data in the data warehouse. But on the other hand, when we talk about the anomalies, researchers used an isolation forest and an LSTM algorithm based on net earnings by month. This is the way that helps to remove maximum anomalies from the data warehouse during storing data from sources. In this era, mostly people use Big data concept for data model for data warehousing but this technique is much complex for storing data and retrieval of data in this technique just single thing is missed for the data modeling named as reusability in future working of big data if we majorly emphasize on reusability of data the this tech can be most efficient as compare to present.

Bankruptcy prediction has been studied to develop predictive models based on financial variables. Using only financial variables may be insufficient in bankruptcy prediction modeling because they do not reflect the latest information, essentially when using past corporate accounting information. Thus, exploiting qualitative information with quantitative information is required to supplement the limited accounting information. Among big data analytics techniques, text mining is used for processing qualitative information. In this study, we propose an integrated approach for bankruptcy prediction using market sentiment extracted from economic news as qualitative information and financial variables as quantitative information for bankruptcy prediction. Unlike previous sentiment analysis approaches, consideration of topics extracted from economic news in sentiment analysis is included to mitigate the ambiguity of capturing the sentiment for single terms. This study validates the effectiveness of incorporating topic-based market sentiment into the conventional bankruptcy prediction model using financial variables in terms of predictive performance.

산업 전 분야에 4차 산업혁명의 신기술인 IoT(Internet of Things), AI(Artificial Intelligence), Bigdata 등이 융합되어 새로운 가치를 창출하는 초연결 지능정보사회가 도래되고 있다. 모든 것이 네트워크에 연결되어 데이터가 폭발적으로 증가하고, 인공지능이 스스로 학습하여 지적 판단 기능까지도 가능하다. 특히 사물인터넷은 언제 어디서나 어느 것과도 연결될 수 있는 새로운 통신환경을 제공함에 따라 모든 것들이 연결되는 초 연결을 가능케 하고 있다. 인공지능 기술은 인간이 가진 지각, 학습, 추론, 자연어처리 등의 능력을 컴퓨터가 실행할 수 있도록 구현되고 있다. 인공지능은 기계학습, 딥러닝(Deep leearning), 자연어처리, 음성인식, 시각인식 등 첨단기술을 개발하는 방향으로 발전되고 있으며, 안전, 의료, 국방, 금융, 복지 등의 다양한 응용 분야에 특화된 소프트웨어와 머신러닝(Machine learning), 클라우드(Cloud) 기술을 포함하고 있다. 이를 통해 인간의 편의와 새로운 가치를 제공하기 위해 산업 전반의 다양한 분야에 활용된다. 하지만, 이와는 반대로 지능적이고 정교해진 사이버 위협들이 증가하고 신기술의 기술적 안전성 확보와 같은 잠재적 역기능들을 동반함에 따라 이에 대한 대응이 필요한 시점이다. 본 논문에서는 이러한 역기능을 해결하기 위한 하나의 방안으로 인공지능기술을 활용하여 IoT 통합보안관제 가능하도록 새로운 데이터모델링(Data modelling) 방안을 제안하였다.

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

이 논문의 목적은 개념적인 데이터 모델링이 기존의 정보 검색(IR) 시스템을 어떤 식으로 보다 향상시킬 수 있는지를 보여주는 것이다. 개념적인 데이터베이스 디자인은 1)개체들간의 관계에 기반하여 새로운 지식을 발견해 내는 데이터 마이닝 능력과 2)기존의 개별적으로 분리된 데이터베이스를 하나의 정보검색 시스템 안으로의 결합을 위해 사용된다 (예: ISI 인용, 시소러스, 서지 데이터베이스를 하나의 정보검색 시스템 안에 결집시킴). 더 나아가서, 개념적인 모델링은 수정을 용이하게 하므로, 새로운 이용자의 요구가 가미될 때마다, 개념적인 데이터 모델링에 기반한 정보검색 시스템을 수정하는 것은 기존의 정보검색 시스템 상에서보다 훨씬 수월해질 수 있다. 보다 향상된 개체-관계(Entity-Relationship) 모델이 이 논문에서 다룬 정보검색 데이터의 개념적 스키마를 개발하는데 사용되었다.

The purpose of this paper is to show how conceptual data modeling can enhance current information retrieval (IR) systems. The conceptual database design provides for: 1) data mining capability to discover new knowledge based on the relationships between entities, and 2) integrating current separate databases into one IR system (e.g., integrating ISI Citation, a thesaurus, and bibliographic databases into one retrieval system) . Further, as new user requirements are unfolded, modifications of IR systems based on conceptual data modeling will be much easier to make than they were in the current IR systems because conceptual modeling facilitates flexible modifications. The enhanced Entity-Relationship (ER) model was employed in this study to develop conceptual schemas of IR data.

This research presents an streamflow modeling approach in a data-scarce estuary reservoir watershed which has been suffered from high salinity irrigation water problem after completion of land reclamation project in South Korea. Since limited hydrology data was available on the Iwon estuary reservoir watershed, water balance relation of the reservoir was used to estimate runoff from upstream of the reservoir. Water balance components in the reservoir consists precipitation, inflow from upstream, discharge through sluice, and evaporation. Estimated daily inflow data, which is stream discharge from upstream, shows a good consistency with the observed water level data in the reservoir in terms of EI (0.93) and $R^2$ (0.94), and were used as observed flow data for the streamflow modeling. HSPF (Hydrological Simulation Program - Fortran) was used to simulate hydrologic response of upstream of the reservoir. The model was calibrated and validated for the periods of 2006 to 2007 and 2008 to 2009, respectively, showing that values of EI and $R^2$ were 0.89 and 0.91 for calibration period, 0.71 and 0.84 for validation period.

본 연구에서는 개인정보 비식별화 데이터의 통계적 유용성에 대한 품질 측정 방안에 대하여 통계 모형화에 따른 예측 정확도 측면에서 고찰하였다. 4차 산업혁명 시대에서 정보통신기술을 통한 혁신에는 반드시 빅데이터의 효과적인 활용이 필수적이지만, 개인정보 이슈는 적극적인 빅데이터 활용에 제약이 되고 있다. 이를 해결하기 위해 비식별화 가이드라인이 제정되었으며 다양한 개인정보 비식별화 방법이 활용되면서 개인정보의 실질적인 재식별 가능성은 매우 낮아졌다. 반면에 강력한 비식별화는 데이터의 유용성을 떨어뜨리는 부작용이 나타날 수 있다. 그 동안은 재식별 불가능한 비식별화 방법이 연구의 주를 이루어 왔다면 본 연구에서는 대표적인 비식별 방법인 KLT 모형에 의한 비식별화 데이터에 대한 통계적 유용성 측면의 품질 측정에 대하여 연구하였다. 비식별화 데이터에 대한 통계적 예측모형의 정확도에 기반하여 비식별화 된 데이터의 통계적 유용성이 어느 정도 훼손되는지에 대하여 사례분석을 수행하였다. 또한, 비식별 자료에 어느 정도의 비식별화 되지 않은 자료가 추가되어야 예측모형의 정확도를 회복하는 지를 살펴봄으로써 비식별화된 자료의 데이터 유용성 정도에 대한 새로운 측정지표를 제안하였다.

In this study, the method of quality measurement for the statistical usefulness of de-identified data was examined in terms of prediction accuracy by statistical modeling. In the era of the 4th industrial revolution, effective use of big data is essential to innovation through information and communication technology, but personal information issues are constrained to actively utilize big data. In order to solve this problem, de-identification guidelines have been established and the possibility of actual re-identification of personal information has become very low due to the utilization of various de-identification methods. On the other hand, strong de-identification can have side effects that degrade the usefulness of the data. We have studied the quality of statistical usefulness of the de-identified data by KLT model which is a representative de-identification method, A case study was conducted to see how statistical accuracy of prediction is degraded by de-identification. We also proposed a new measure of data usefulness of the de-identified data by quantifying how much data is added to the de-identified data to restore the accuracy of the predictive model.