• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.60-75
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• 2024

Research on health outcomes of environmental factors has been implemented by multiple and interacting factors, including environmental, socio-demographic, economic, and traffic aspects. There are still significant challenges and limitations in constructing databases for the connections between contributing factors and an integrated approach to environmental health research even though there has been a dramatic increase in data availability and incredible technological advance in data storage and processing. This study emphasizes the necessity of establishing a geospatial data pipeline to analyze the impact of environmental factors on health. It also highlights the difficulties and solutions related to the construction and utilization of a geospatial database. Key challenges include diverse data sources and formats, different spatio-temporal data structures, and coordinate system inconsistencies over time within the same geospatial data. To address these issues, a data pipeline was constructed with pre-processing and post-processing for the data, resulting in refined datasets that could be used for calculating geographic variables. In addition, an AWS-based relational database and shared platform were established to provide an efficient environment for data storage and analysis. Guidelines for each step of the process, including data management and analysis, were developed to enable future researchers to effectively use the data pipeline.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.484-499
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• 2024

Purpose: Recently, due to the aging of safety facilities in national industrial complexes, there has been an increase in the frequency and scale of safety accidents, highlighting the need for a shift toward a prevention-centered disaster management paradigm and the establishment of a digital safety network. In response, this study aims to provide an information system that supports more rapid and precise decision-making during disasters by utilizing digital twin-based integrated control technology to predict the spread of hazardous substances, trace the origin of accidents, and offer safe evacuation routes. Method: We considered various simulation results, such as surface diffusion, upper-level diffusion, and combined diffusion, based on the actual characteristics of hazardous substances and weather conditions, addressing the limitations of previous studies. Additionally, we designed an integrated management system to minimize the limitations of spatiotemporal monitoring by utilizing an IoT sensor-based backtracking model to predict leakage points of hazardous substances in spatiotemporal blind spots. Results: We selected two pilot companies in the Gumi Industrial Complex and installed IoT sensors. Then, we operated a living lab by establishing an integrated management system that provides services such as prediction of hazardous substance dispersion, traceback, AI-based leakage prediction, and evacuation information guidance, all based on digital twin technology within the industrial complex. Conclusion: Taking into account the limitations of previous research, we used digital twin-based AI analysis to predict hazardous chemical leaks, detect leakage accidents, and forecast three-dimensional compound dispersion and traceback diffusion.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.449-457
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• 2024

AIoT, the intelligent Internet of Things, refers to a technology that collects data measured by IoT devices and applies machine learning technology to create and utilize predictive models. Existing research on AIoT technology education focused on building an educational AIoT platform and teaching how to use it. However, there was a lack of case studies that taught the process of automatically creating and utilizing machine learning models from data measured by IoT devices. In this paper, we developed a case study using a machine learning model for AIoT technology education. The case developed in this paper consists of the following steps: data collection from AIoT devices, data preprocessing, automatic creation of machine learning models, calculation of accuracy for each model, determination of valid models, and data prediction using the valid models. In this paper, we considered that sensors in AIoT devices measure different ranges of values, and presented an example of data preprocessing accordingly. In addition, we developed a case where AIoT devices automatically determine what information they can predict by automatically generating several machine learning models and determining effective models with high accuracy among these models. By applying the developed cases, a variety of educational contents using AIoT, such as prediction-based object control using AIoT, can be developed.

• Information Systems Review
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• v.26 no.2
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• pp.23-43
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• 2024

Nowadays, online reviews have become a common word of mouth that many users produce and consume. Posting online reviews is a kind of job that consumers do themselves. Since posting online reviews is not mandatory, it entirely relies on the consumer's voluntary willingness. In this respect, this study aims to describe the motivation for posting online reviews and their behavior processes, such as why online reviewers generate reviews and what types of reviews they create. In this study, we have conducted an in-depth study with 18 participants who have experience in posting reviews. By analyzing interview manuscripts from the grounded theory method approach, we have ultimately presented motivating factors for review posting (mutual reciprocity, material rewards), determinants of review browsing (trust toward review contents, preference for review format), and shadow work (a job that must be done, voluntary data production, consumer's share). We have also proposed the dynamics between core dimensions for theorizing a cycle process of review production and consumption. Our findings could bridge the gap in the existing online review research and offer practical implications for platform companies that need review management.

• Information Systems Review
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• v.26 no.1
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• pp.337-353
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• 2024

The purpose of this study is to investigate the relationship between consumer reviews and managerial responses, aiming to explore the necessity of webcare for efficiently managing consumer reviews. We intend to propose a methodology for effective webcare and to construct a webcare model using machine learning techniques based on an audiobook platform. In this study, we selected four audiobook platforms and conducted data collection and preprocessing for consumer reviews and managerial responses. We utilized techniques such as topic modeling, topic inconsistency analysis, and DBSCAN, along with various machine learning methods for analysis. The experimental results yielded significant findings in clustering managerial responses and predicting responses to consumer reviews, proposing an efficient methodology considering resource constraints and costs. This research provides academic insights by constructing a webcare model through machine learning techniques and practical implications by suggesting an efficient methodology, considering the limited resources and personnel of companies. The proposed webcare model in this study can be utilized as strategic foundational data for consumer engagement and providing useful information, offering both personalized responses and standardized managerial responses.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.83-88
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• 2024

This paper analyzes the historical significance and aesthetic meaning of the emergence of 'artists as theoreticians,' a major aspect of contemporary art criticism practice, focusing on the editorial project of the British monthly visual arts magazine Studio International. The sharing of writings about art through publications confirms the lens of critical perspectives on contemporary art and serves as an opportunity to reflect on broader political and cultural conditions. In particular, this paper can function as a resource for assessing the art historical horizons created by the connection between artists and publications, rather than theorists or critics, on the magazine platform. This paper focuses on the debates formed through Studio International in the late 1960s, examining the magazine's stance on new developments in art, the practice of defining critical terms that accompanied it, and the responses to them. The texts of 'artists as theoreticians' such as Victor Burgin and Joseph Kosuth, published in Studio International, overcame the conventionality of art that relies on formal aspects, and argued that the concept of art as something named by the artist is possible as art that does not require the mediation of objects. The discourse practices of these artists became an important factor in destroying the authority of the historicist critical paradigm, thereby acquiring the art historical value of artists who took the position of theoreticians dealing with art.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.389-396
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• 2024

This is a descriptive study to investigate the association between types of menstrual sanitary products used and menstrual discomfort. The participants included 1,484 women who used either disposable sanitary pads or tampons, out of a total of 1,571 women aged 19-40 years and data collection was conducted from September 2020 to August 2021. The survey was conducted through an online and mobile survey platform, with participants proceeding to take part after clicking the 'agree' button. Data analysis involved 1:4 propensity score matching, descriptive statistics, chi-square tests, t-tests, and hierarchical regression analysis. The results indicated that among the participants, 94.1% used disposable sanitary pads, while 5.9% used tampons. In the final model, significant influencing factors identified were age 30 or older (β=-.157, p=.043), standing for 1-4 hours at work (β=-.131, p=.040), experiencing sleep disorders (β=.337, p<.001), and tampon use (β=.130, p=.005). Therefore, it is essential for nurses to incorporate information about various menstrual sanitary products' characteristics into their menstrual education for women of reproductive age.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.445-452
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• 2024

Through the pandemic, the topography of dining culture is rapidly changing due to the advancement of the food delivery market. Competition in the domestic market is intensifying as Coupang Eats recently surpassed Yogiyo and jumped to second place, and Baedal Minjok(Baemin), the industry's No. 1 company, is also preparing to introduce a subscription system. While the growth of the delivery market is slowing, the use of takeout and pick-up services is increasing due to rising delivery costs and food prices. From Generation Z's perspective, the main factors influencing the active use of app services were identified through prior research as usability and convenience, cost sensitivity, and hedonic motivation. While, they are leading the trend of minimizing spending through 'stepping stone consumption' and delivery pot process instead of choosing a subscription system. Accordingly, we aim to provide customers with a better experience and help strengthen competitiveness by proposing ways to improve and revitalize new delivery apps that reflect the characteristics of Gen.Z. As a result of the expert Delphi survey, we will receive impact evaluation scores in the following order: direct view of accumulated discounts, addition of family benefits, coupon reinforcement, SNS promotion, pick-up walk, in-store promotion, and discount rate display, and review their application to practice. It presents academic and policy implications regarding the food tech market.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.161-182
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• 2024

The study analyzed the rapidly growing food tech startup in South Korea, focusing on industry classification, core technological domains, investment stages, and growth trajectories. Utilizing the ERIS model, two innovative food tech startups, MyChef and CatchTable, were examined as case studies. Results revealed food tech startups are focusing on information technology and smart distribution technology-oriented solutions rather than traditional food production. This study also found that robotics and AI integration were key technology areas. Analyzing the emergence of food tech startups, investment stages, and cumulative investment amounts based on founding years revealed a trend of scaling operations through rounds of funding, especially after securing SERIES A and B funding. The period between 2014 and 2018 saw a dense concentration of food tech startup establishments, likely influenced by favorable conditions for technological innovation amid the Fourth Industrial Revolution. The high rate of strategic mergers and acquisitions and bankruptcy can be interpreted as the complexity inherent in the food tech industry. The case study of MyChef, which grew into HMR manufacturing, and Wad(CatchTable), which expanded into a restaurant reservation platform, derived the entrepreneurs, resources, industry, and strategic factors that served as success factors for food tech startups. This study has practical implications in that it provides entrepreneurs, investors, and policymakers in the food tech industry with insight and direction to develop strategies in line with market trends and technological changes and promote sustainable growth.