• Journal of the Korean Recycled Construction Resources Institute
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• v.12 no.2
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• pp.128-134
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• 2024

Asphalt-based waterproofing materials for bridge decks face issues such as softening or liquefaction of the material during the process of pouring hot asphalt concrete on top of the waterproofing layer. This leads to instability and reduced thickness of the waterproofing layer. To address these problems, new solutions beyond the existing materials, including the development and adoption of new materials, are required. Therefore, this study investigates the properties of MMA(Methyl Methacrylate) coating waterproofing material, which meets the basic physical properties for bridge deck waterproofing. We examined the overall quality standards in a system where the substrate concrete, waterproofing material, and paving layer are integrated. The study confirmed the applicability of MMA coating waterproofing material on bridge decks. The results indicate that a stable application of MMA coating waterproofing material for civil engineering structures' bridge decks can be achieved with a mix ratio of hard MMA resin : soft MMA resin : powder = 6 : 34 : 60. Additionally, when using emulsified asphalt with hardening characteristics for the adhesion between the dissimilar materials of MMA waterproofing and asphalt concrete, it is expected to meet the minimum quality standards of the Ministry of Land, Infrastructure, and Transport's 'Guidelines for Asphalt Concrete Pavement Construction (2021.07)'.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1124-1139
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• 2021

The objective assessment of atrophy and the measurement of brain volume is important in the early diagnosis of dementia and neurodegenerative diseases. Recently, several MR-based volumetry software have been developed. For their clinical application, several issues arise, including the standardization of image acquisition and their validation of software. Additionally, it is important to highlight the diagnostic performance of the volumetry software based on expert opinions. We instituted a task force within the Korean Society of Neuroradiology to develop guidelines for the clinical use of MR-based brain volumetry software. In this review, we introduce the commercially available software and compare their diagnostic performances. We suggest the need for a standard protocol for image acquisition, the validation of the software, and evaluations of the limitations of the software related to clinical practice. We present recommendations for the clinical applications of commercially available software for volumetry based on the expert opinions of the Korean Society of Neuroradiology.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.539-549
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• 2024

This study employed text mining techniques like frequency analysis, word clouds, and LDA topic modeling to assess consumer satisfaction and dissatisfaction with Hyundai Motor Company in the Chinese market, compared to brands such as Toyota, Volkswagen, Buick, and Geely. Focusing on compact vehicles from these brands between 2021 and 2023, this study analyzed customer reviews. The results indicated Hyundai Avante's positive factors, including a long wheelbase. However, it also highlighted dissatisfaction aspects like Manipulate, engine performance, trunk space, chassis and suspension, safety features, quantity and brand of audio speakers, music membership service, separation band, screen reflection, CarLife, and map services. Addressing these issues could significantly enhance Hyundai's competitiveness in the Chinese market. Previous studies mainly focused on literature research and surveys, which only revealed consumer perceptions limited to the variables set by the researchers. This study, through text mining and comparing various car brands, aims to gain a deeper understanding of market trends and consumer preferences, providing useful information for marketing strategies of Hyundai and other brands in the Chinese market.

• Korean Journal of Optics and Photonics
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• v.35 no.4
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• pp.175-183
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• 2024

Research into optical signal processing using photonic integrated circuits (PICs) has been actively pursued in various fields, including optical communication, optical sensors, and quantum optics. Among the materials used in PIC fabrication, polymers have attracted significant interest due to their unique characteristics. To fabricate polymer-based PICs, establishing an accurate manufacturing process for the cross-sectional structure of an optical waveguide is crucial. For stable device performance and high yield in mass production, a process with high reproducibility and a wide tolerance for variation is necessary. This study proposes an efficient method for fabricating polymer optical-waveguide devices by introducing the atomic layer deposition (ALD) process. Compared to conventional photoresist or metal-film deposition methods, the ALD process enables more precise fabrication of the optical waveguide's core structure. Polyimide optical waveguides with a core size of 1.8 × 1.6 ㎛² are fabricated using the ALD process, and their propagation losses are measured. Additionally, a multimode interference (MMI) optical-waveguide power-splitter device is fabricated and characterized. Throughout the fabrication, no cracking issues are observed in the etching-mask layer, the vertical profiles of the waveguide patterns are excellent, and the propagation loss is below 1.5 dB/cm. These results confirm that the ALD process is a suitable method for the mass production of high-quality polymer photonic devices.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.335-347
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• 2024

Open-vocabulary 3D point cloud instance segmentation (OV-3DIS) is a challenging visual task to segment a 3D scene point cloud into object instances of both base and novel classes. In this paper, we propose a novel model Open3DME for OV-3DIS to address important design issues and overcome limitations of the existing approaches. First, in order to improve the quality of class-agnostic 3D masks, our model makes use of T3DIS, an advanced Transformer-based 3D point cloud instance segmentation model, as mask proposal module. Second, in order to obtain semantically text-aligned visual features of each point cloud segment, our model extracts both 2D and 3D features from the point cloud and the corresponding multi-view RGB images by using pretrained CLIP and OpenSeg encoders respectively. Last, to effectively make use of both 2D and 3D visual features of each point cloud segment during label assignment, our model adopts a unique feature ensemble method. To validate our model, we conducted both quantitative and qualitative experiments on ScanNet-V2 benchmark dataset, demonstrating significant performance gains.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.43-54
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• 2024

This study employs text mining techniques to compare user responses across various Over-The-Top (OTT) service platforms. The primary objective of the research is to understand user satisfaction with OTT service platforms and contribute to the formulation of more effective review strategies. The key questions addressed in this study involve identifying prominent topics and keywords in user reviews of different OTT services and comprehending platform-specific user reactions. TF-IDF is utilized to extract significant words from positive and negative reviews, while BERTopic, an advanced topic modeling technique, is employed for a more nuanced and comprehensive analysis of intricate user reviews. The results from TF-IDF analysis reveal that positive app reviews exhibit a high frequency of content-related words, whereas negative reviews display a high frequency of words associated with potential issues during app usage. Through the utilization of BERTopic, we were able to extract keywords related to content diversity, app performance components, payment, and compatibility, by associating them with content attributes. This enabled us to verify that the distinguishing attributes of the platforms vary among themselves. The findings of this study offer significant insights into user behavior and preferences, which OTT service providers can leverage to improve user experience and satisfaction. We also anticipate that researchers exploring deep learning models will find our study results valuable for conducting analyses on user review text data.

• The Journal of Bigdata
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• v.9 no.1
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• pp.75-88
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• 2024

Recently, there have been cases reported in the news of individuals experiencing symptoms of food poisoning after consuming raw beef purchased from online platforms, or reviews claiming that cherry tomatoes tasted bitter. This suggests the potential for analyzing food reviews on online platforms to detect food hazards, enabling government agencies, food manufacturers, and distributors to manage consumer food safety risks. This study proposes a classification model that uses sentiment analysis and large language models to analyze food reviews and detect negative ones, multi-labeling key food safety hazards (food poisoning, spoilage, chemical odors, foreign objects). The sentiment analysis model effectively minimized the misclassification of negative reviews with a low False Positive rate using a 'funnel' model. The multi-labeling model for food safety hazards showed high performance with both recall and accuracy over 96% when using GPT-4 Turbo compared to GPT-3.5. Government agencies, food manufacturers, and distributors can use the proposed model to monitor consumer reviews in real-time, detect potential food safety issues early, and manage risks. Such a system can protect corporate brand reputation, enhance consumer protection, and ultimately improve consumer health and safety.

• Information Systems Review
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• v.26 no.2
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• pp.45-57
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• 2024

This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

• Information Systems Review
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• v.26 no.1
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• pp.73-91
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• 2024

Recently, e-learning platforms are rapidly growing by innovating the education industry by applying various IT technologies. Because student participation in the online environment is considered a prerequisite for learning, low participation rates are considered one of the most important issues determining the performance of e-learning platforms. Gamification has grown rapidly over the past decades and is highly valued for its applicability in education because it is expected to enhance learning motivation. However, despite the interest of researchers, previous studies have reported conflicting results on the effect of gamification on participation rates in the context of e-learning platforms, and have mainly studied structural gamification, but have not sufficiently addressed the effects of content gamification. In this context, this study aims to analyze the effect of content gamification on e-learning platform engagement and to explore the boundary conditions moderating this effect. For empirical analysis, 5,017 data registered from February 11, 2022 to May 31, 2022 were analyzed for the education platform entry (https://playentry.org). The propensity score matching method and Poisson multilevel regression model were applied as analysis methods. As a result of the analysis, content gamification had a statistically significant effect on engagement, and the interaction effects of interaction and content difficulty were statistically significant.