• 정보교육학회논문지
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• 제26권5호
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• pp.467-480
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• 2022

이 논문은 IT 인문학 관련 연구의 동향을 탐색함으로써 IT 인문학이 어떤 개념으로 활용되고 있는지 알아보고자 하였다. 디지털 과학기술 IT과 인문학 조합의 가능성에 주목하여 꾸준히 수행되어온 국내외의 문헌을 통해, IT 인문학의 기원과 배경, 유사 개념을 바탕으로 연구 동향을 알아보고 IT 인문학의 의미에 대해서 고찰하였다. 그리고 'IT 인문학' 및 'IT humanities' 검색어를 활용하여, 2001년 이후 발간 된 학술논문 중 주제어 정보를 제공하는 KCI급 1,566편, SCI급 64편을 대상으로 주제어의 네트워크 토픽 분석을 실시하였다. IT 인문학이라는 용어가 등장한 논문에서의 IT 인문학의 의미는 다양한 분야의 IT 정보기술이 인문학의 관점에서 생각할 수 있는 역량과 관련이 있었다. 토픽모델링 결과는 IT 인문학과 융합하는 분야 대상, 적용되는 형태, 문학·문화와의 연관, IT 인문학의 창출의 네 가지 군집으로 형성되었다. IT와 인문학의 융합은 한 쪽이 다른 한쪽을 도구화하거나 일방적으로 수렴하는 구조가 아닌, 상호 존중에 기초한 협업의 자세로 새로운 사유를 창출하도록 해야할 것이다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.329-329
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• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• 한국환경과학회지
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• 제32권9호
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• pp.635-645
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• 2023

The purpose of this study is to establish a linkage between local forest human resources policies and the analysis of forest resource conditions in Gyeongsangbuk-do. In particular, the study aims to gather insights from students enrolled local formal education institutions through a demand survey and their opinions. These findings would serve as basic data for the formulation of medium- and long-term policies. According to the results of the analysis, all surveyed groups expressed a desire to pursue careers, entrepreneurship, or further school education based on their forestry majors. Among, the most important needs identified for local human resources, receiving training related to field practice and access to information emerged as paramount. In addition, it was observed that educational programs were conducted on weekends and during school vacations, with integration into the school curriculum to ensure participants not only benefit from self-development but also receive administrative support. A notable observation in the survey results was the absence of a network among forest professionals, signifying a key weakness within the forest sector in Gyeongsangbuk-do. The results of this study hold significant value in terms of analyzing and sharing the educational preferences of forest human resources in Gyeongsangbuk-do, thereby serving as basic research data for proposing policies. In the future, by expanding the scope to include case studies and forest human resource preference analyses through cooperation with other local governments and institutions, the research can contribute to the establishment of national-level policies for forest human resources on a broader scale.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• 한국IT서비스학회지
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• 제23권1호
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• pp.1-10
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• 2024

The IT environment is changing due to the acceleration of digital transformation in enterprises and organizations. This expansion of the digital space makes centralized cybersecurity controls more difficult. For this reason, cyberattacks are increasing in frequency and severity and are becoming more sophisticated, such as ransomware and digital supply chain attacks. Even in large organizations with numerous security personnel and systems, security incidents continue to occur due to unmanaged and unknown threats and vulnerabilities to IT assets. It's time to move beyond the current focus on detecting and responding to security threats to managing the full range of cyber risks. This requires the implementation of asset Inventory for comprehensive management by collecting and integrating all IT assets of the enterprise and organization in a wide range. IT Asset Management(ITAM) systems exist to identify and manage various assets from a financial and administrative perspective. However, the asset information managed in this way is not complete, and there are problems with duplication of data. Also, it is insufficient to update of data-set, including Network Infrastructure, Active Directory, Virtualization Management, and Cloud Platforms. In this study, we, the researcher group propose a new framework for automated 'Comprehensive IT Asset Management(CITAM)' required for security operations by designing a process to automatically collect asset data-set. Such as the Hostname, IP, MAC address, Serial, OS, installed software information, last seen time, those are already distributed and stored in operating IT security systems. CITAM framwork could classify them into unique device units through analysis processes in term of aggregation, normalization, deduplication, validation, and integration.

• Journal of Ecology and Environment
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• 제47권4호
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• pp.264-271
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• 2023

Environmental crises caused by climate change and human-induced disturbances have become urgent challenges to the sustainability of human beings. These issues can be addressed based on a data-driven understanding and forecasting of ecosystem responses to environmental changes. In this study, we introduce a long-term ecological monitoring system in Korean Long-Term Ecological Research (KLTER), and a plan for the Korean Ecological Observatory Network (KEON). KLTER has been conducted since 2004 and has yielded valuable scientific results. However, the KLTER approach has limitations in data integration and coordinated observations. To overcome these limitations, we developed a KEON plan focused on multidisciplinary monitoring of the physiochemical, meteorological, and biological components of ecosystems to deepen process-based understanding of ecosystem functions and detect changes. KEON aims to answer nationwide and long-term ecological questions by using a standardized monitoring approach. We are preparing three types of observatories: two supersites depending on the climate-vegetation zones, three local sites depending on the ecosystem types, and two mobile deployment platforms to act on urgent ecological issues. The main observation topics were species diversity, population dynamics, biogeochemistry (carbon, methane, and water cycles), phenology, and remote sensing. We believe that KEON can address environmental challenges and play an important role in ecological observations through partnerships with international observatories.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• International Journal of Quality Innovation
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• 제8권3호
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• pp.156-172
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• 2007

The high-tech industry is the economic lifeline for Taiwan. Its characteristics are short product life cycle, rapid changes in the market, and a high obsolescence rate for new products. Under globalization, the high-tech industry has adopted Information Technology (IT) to shorten the manufacturing process, reduce costs and conduct product research and development (R&D) to increase the core competence of enterprises and achieve the goal of sustainable operations. Enterprises should actively strengthen their integration with internal and external resources and lead in R&D management to increase industrial operating performance. Effectively managing operations and R&D management evaluation in Taiwan's High-tech Industry has become a critical subject. This study adopted 4 major Balanced Scorecard (BSC) perspectives to establish the Total Performance Evaluation Indicators for the R&D management department in Taiwan's High-tech Industry. The Analytic Network Process (ANP) was applied to evaluate the overall performance of the R&D management department. The research framework is divided into 2 phases. The first phase is combined with the 4 major perspectives, Financial, Customer, Internal Business Process and Learning and Growth, as the related indicators for each measurement perspective. The Key Performance Indicators (KPI) were selected using Factor Analysis to identify the key factor from the complicated indicators. The relationship between the characteristics of each BSC's evaluation perspective is dependence and feedback. This study applied ANP to conduct the calculation and adjustment of correlation between each KPI, and determine on their relative weights for the objective KPI. The "Financial Perspective" for R&D management department in Taiwan's High-tech Industry focused on the budget achievement rate of R&D management. The weight indicator value is (0.05863). The "Customer Perspective" focused on problem-solving satisfaction. The weight value of this indicator is (0.17549). The "Internal Business Process Perspective" focused on the quantity and quality of R&D. The weight value of this indicator is (0.13506). The "Learning and Growth Perspective" focused on improving competence in the research personnel's professional techniques. The weight value of this indicator is (0.02789). From the total weighting indicators, the order of the Performance Indicators for the R&D management department in Taiwan's High-tech Industry is: (1) Customer Perspective; (2) Internal Business Process Perspective; (3) Financial Perspective; and (4) Learning and Growth Perspective.