• Information Systems Review
• /
• v.25 no.4
• /
• pp.89-112
• /
• 2023

In March 2020, as it was declared a COVID-19 pandemic, various quarantine measures were taken. Accordingly, many changes have occurred in the tourism and hospitality industries. In particular, quarantine guidelines, such as the introduction of non-face-to-face services and social distancing, were implemented in the restaurant industry. For decades, research on restaurant attributes has emphasized the importance of three attributes: atmosphere, service quality, and food quality. Nevertheless, to the best of our knowledge, research on restaurant attributes considering the COVID-19 situation is insufficient. To respond to this call, this study attempted an exploratory approach to classify new restaurant attributes based on understanding environmental changes. This study considered 31,115 online reviews registered in Naverplace as an analysis unit, with 475 general restaurants located in Euljiro, Seoul. Further, we attempted to classify restaurant attributes by clustering words within online reviews through TF-IDF and LDA topic modeling techniques. As a result of the analysis, the factors of "prevention of infectious diseases" were derived as new attributes of restaurants in the context of COVID-19 situations, along with the atmosphere, service quality, and food quality. This study is of academic significance by expanding the literature of existing restaurant attributes in that it categorized the three attributes presented by existing restaurant attributes and further presented new attributes. Moreover, the analysis results have led to the formulation of practical recommendations, considering both the operational aspects of restaurants and policy implications.

• Convergence Security Journal
• /
• v.23 no.3
• /
• pp.13-20
• /
• 2023

This study conducted a digital forensic analysis of Signal and Telegram, two secure messengers widely used in the Android environment. As mobile messengers currently play an important role in daily life, data management and security within these apps have become very important issues. Signal and Telegram, among others, are secure messengers that are highly reliable among users, and they safely protect users' personal information based on encryption technology. However, much research is still needed on how to analyze these encrypted data. In order to solve these problems, in this study, an in-depth analysis was conducted on the message encryption of Signal and Telegram and the database structure and encryption method in Android devices. In the case of Signal, we were able to successfully decrypt encrypted messages that are difficult to access from the outside due to complex algorithms and confirm the contents. In addition, the database structure of the two messenger apps was analyzed in detail and the information was organized into a folder structure and file format that could be used at any time. It is expected that more accurate and detailed digital forensic analysis will be possible in the future by applying more advanced technology and methodology based on the analyzed information. It is expected that this research will help increase understanding of secure messengers such as Signal and Telegram, which will open up possibilities for use in various aspects such as personal information protection and crime prevention.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.6
• /
• pp.255-266
• /
• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.6
• /
• pp.543-551
• /
• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Tunnel and Underground Space
• /
• v.33 no.6
• /
• pp.508-518
• /
• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.325-345
• /
• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Information Systems Review
• /
• v.25 no.3
• /
• pp.59-81
• /
• 2023

Elderly suicide problem has become worse in South Korea. With a rapid aging of the population, the trend of suicide among the elderly is expected to accelerate, preventing elderly suicide has been considered an important societal problem. Thus, we aim to investigate various factors that explain suicidal ideation and to develop a predictive model for suicidal ideation in the context of elderly people in South Korea. To this end, this study contributes to addressing the elderly suicide problem. By using seven-year panel data from the Korea Welfare Panel Survey, we extract various potential causal factors for elderly suicidal ideation based on interpersonal theory of suicide and social disorganization theory. Then a panel logit model was employed to assess the impacts of potential factors on suicidal ideation and deep learning and machine learning algorithms were used to develop a predictive model for suicidal ideation of elderly people. The results of our study provide practical implications for preventing elderly suicide by identifying causal factors of suicidal ideation and a high suicidal risk group of the elderly. This study sheds light on synergy of mixed methodology and provides various academic implications.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.12 no.1
• /
• pp.63-71
• /
• 2024

Through the development of construction technology, various construction projects such as redevelopment projects, undergrounding of roads, expansion of subways, and metro railways are being carried out. However, this has led to an increase in the number of construction projects in existing urban centers and neighborhoods, resulting in an increase in the number of damages and disputes between neighboring buildings and residents, as well as an increase in safety accidents due to the aging of existing buildings. In this study, digital data was applied to a graphics program to objectify the progress of cracks by comparing the creation of cracks and the increase in length and width through photographic images and presenting the degree of cracks numerically. Through the application of the program, the error caused by the subjective judgment of crack change, which was mentioned as a shortcoming of the existing field survey, was solved. It is expected that the program can be used universally in the building diagnosis process by improving its reliability if supplemented and improved in the process of use. As a follow-up study, it is necessary to apply the extraction algorithm of the digital graphic data program to calculate the length and width of the crack by itself without human intervention in the preprocessing work and to check the overall change of the building.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.185-197
• /
• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.48 no.3
• /
• pp.34-44
• /
• 2020

In order to suggest performance analysis directions of ecological components based on a vegetation-based LID system model, this study seeks to analyze the statistical significance between monitoring results by using SWMM computer simulation and rainfall and run-off simulation devices and provide basic data required for a preliminary system design. Also, the study aims to comprehensively review a vegetation-based LID system's soil, a vegetation model, and analysis plans, which were less addressed in previous studies, and suggest a performance quantification direction that could act as a substitute device-type LID system. After monitoring artificial rainfall for 40 minutes, the test group zone and the control group zone recorded maximum rainfall intensity of 142.91mm/hr. (n=3, sd=0.34) and 142.24mm/hr. (n=3, sd=0.90), respectively. Compared to a hyetograph, low rainfall intensity was re-produced in 10-minute and 50-minute sections, and high rainfall intensity was confirmed in 20-minute, 30-minute, and 40-minute sections. As for rainwater run-off delay effects, run-off intensity in the test group zone was reduced by 79.8% as it recorded 0.46mm/min at the 50-minute point when the run-off intensity was highest in the control group zone. In the case of computer simulation, run-off intensity in the test group zone was reduced by 99.1% as it recorded 0.05mm/min at the 50-minute point when the run-off intensity was highest. The maximum rainfall run-off intensity in the test group zone (Dv=30.35, NSE=0.36) recorded 0.77mm/min and 1.06mm/min in artificial rainfall monitoring and SWMM computer simulation, respectively, at the 70-minute point in both cases. Likewise, the control group zone (Dv=17.27, NSE=0.78) recorded 2.26mm/min and 2.38mm/min, respectively, at the 50-minutes point. Through statistical assessing the significance between the rainfall & run-off simulating systems and the SWMM computer simulations, this study was able to suggest a preliminary design direction for the rainwater run-off reduction performance of the LID system applied with single vegetation. Also, by comprehensively examining the LID system's soil and vegetation models, and analysis methods, this study was able to compile parameter quantification plans for vegetation and soil sectors that can be aligned with a preliminary design. However, physical variables were caused by the use of a single vegetation-based LID system, and follow-up studies are required on algorithms for calibrating the statistical significance between monitoring and computer simulation results.