• Knowledge Management Research
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• v.24 no.4
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• pp.23-42
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• 2023

This study originated from recognition of importance of firefighters' decision-making in fire response, coupled with existing gap in research. By utilizing Q-methodology, the study aimed to categorize firefighters' subjectivity in fire response decision-making. Through this categorization, the study sought to highlight insights into the current technological and data limitations, as well as potential directions for future R&D in the field of firefighting. The findings of the study revealed that firefighters' subjectivity could be classified into three factors: "emphasis on direct information related to rescue," "emphasis on information related to the target property," and "emphasis on information related to command and coordination." The study theoretically confirmed that the subjectivity of firefighters' decision-making in fire response is partially influenced by their experiences and job. Additionally, the study's significance lay in its approach of collecting specific decision-making factors in fire response, moving beyond general theoretical models. Furthermore, from a policy perspective, the typification of decision-making factors contributed to connecting the identified data-based administrative needs from prior studies. Insights from the study emphasized the importance of leveraging on-site experience in Korea to aid decision-making, calling for the development of equipment and data collection methods that can rapidly and accurately assess on-site conditions.

• Information Systems Review
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• v.22 no.4
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• pp.75-92
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• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.647-659
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• 2023

Objective: The study was conducted to investigate the effect of correct occlusion of the left atrial appendage (LAA) on intracardiac blood flow and thrombus formation in patients with atrial fibrillation (AF) using four-dimensional (4D) flow magnetic resonance imaging (MRI) and three-dimensional (3D)-printed phantoms. Materials and Methods: Three life-sized 3D-printed left atrium (LA) phantoms, including a pre-occlusion (i.e., before the occlusion procedure) model and correctly and incorrectly occluded post-procedural models, were constructed based on cardiac computed tomography images from an 86-year-old male with long-standing persistent AF. A custom-made closed-loop flow circuit was set up, and pulsatile simulated pulmonary venous flow was delivered by a pump. 4D flow MRI was performed using a 3T scanner, and the images were analyzed using MATLAB-based software (R2020b; Mathworks). Flow metrics associated with blood stasis and thrombogenicity, such as the volume of stasis defined by the velocity threshold ($\left|\vec{V}\right|$ < 3 cm/s), surface-and-time-averaged wall shear stress (WSS), and endothelial cell activation potential (ECAP), were analyzed and compared among the three LA phantom models. Results: Different spatial distributions, orientations, and magnitudes of LA flow were directly visualized within the three LA phantoms using 4D flow MRI. The time-averaged volume and its ratio to the corresponding entire volume of LA flow stasis were consistently reduced in the correctly occluded model (70.82 mL and 39.0%, respectively), followed by the incorrectly occluded (73.17 mL and 39.0%, respectively) and pre-occlusion (79.11 mL and 39.7%, respectively) models. The surfaceand-time-averaged WSS and ECAP were also lowest in the correctly occluded model (0.048 Pa and 4.004 Pa^-1, respectively), followed by the incorrectly occluded (0.059 Pa and 4.792 Pa^-1, respectively) and pre-occlusion (0.072 Pa and 5.861 Pa^-1, respectively) models. Conclusion: These findings suggest that a correctly occluded LAA leads to the greatest reduction in LA flow stasis and thrombogenicity, presenting a tentative procedural goal to maximize clinical benefits in patients with AF.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.137-148
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• 2024

The purpose of this study is to determine how the use of retailtech technology affects consumers' purchase intention. Furthermore, this study aims to investigate the mediating effects of technology usefulness and ease of use on this influence relationship and whether experiential marketing moderates consumers' purchase intention. The survey was conducted from August 1, 2023 to September 30, 2023, and a total of 257 people participated in the study. For statistical analysis, hierarchical regression analysis, three-stage mediation regression analysis, and hierarchical three-stage controlled regression analysis were conducted to test the hypothesis. The results of the study are as follows. First, it was confirmed that big data-AI utilization, mobile-SNS utilization, live commerce utilization, and IoT utilization affect purchase intention in retail technology utilization. Second, technology usefulness has a mediating effect on IoT utilization, mobile-SNS utilization, and big data-AI utilization. Third, perceived ease of use of technology mediated the effects of IoT utilization, mobile-SNS utilization, live-commerce utilization, and big data-AI utilization. Fourth, escapist experience has a moderating effect on mobile SNS utilization and live commerce utilization. Fifth, esthetic experience has a moderating effect on mobile-SNS utilization and big data-AI utilization. Through this study, we hope that the domestic distribution industry will contribute to national competitiveness by securing the competitive advantage of companies by utilizing new technologies in entering the global market.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Information Systems Review
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• v.23 no.4
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• pp.183-198
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• 2021

Due to the Covid-19 pandemic, the home-training app market is growing rapidly and numerous apps are entering the market. It is becoming more difficult for an app to secure the profitability. In this study, by analyzing actual user data of a reward-based home-training app, we propose a model that predicts cash-cow users of the app. Cash-cow users are the users who watch in-stream ads to watch training videos although they cannot earn any rewards by doing so. Thus, these users make profits for the app yet do not incur any costs. The results of this study show that the users who irregularly watch training videos are more likely to be cash-cow users than the users who regularly watch training videos. This result suggests that, paradoxically, for sustainable profitability, home-training apps may need to find a way to retain the users who watch training videos irregularly so that they can be satisfied with the service and continue use the apps.

• Journal of Adhesion and Interface
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• v.25 no.1
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• pp.169-274
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• 2024

Recent attention has been drawn to materials that undergo reversible expansion and contraction in response to external stimuli, leading to morphological changes. These materials hold potential applications in various fields including soft robotics, sensors, and artificial muscles. In this study, a novel material capable of responding to high temperatures for protection or encapsulation is proposed. To achieve this, liquid crystal elastomer (LCE) with nematic-isotropic transition properties and polyimide (PI) with high mechanical strength and thermal stability were utilized. To utilize a solution process, a dope solution was synthesized and introduced into micro-printing techniques to develop a two-dimensional pattern of LCE/PI bilayer structures with sub-millimeter widths. The honeycomb-patterned LCE/PI bilayer mesh combined the mechanical strength of PI with the high-temperature contraction behavior of LCE, and selective printing of LCE facilitated deformation in desired directions at high temperatures. Consequently, the functionality of selectively and reversibly encapsulating specific high-temperature materials was achieved. This study suggests potential applications in various actuator fields where functionalities can be implemented across different temperature ranges without the need for electrical energy input, contingent upon molecular changes in LCE.

• Information Systems Review
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• v.21 no.1
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• pp.1-22
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• 2019

Mindfulness is the process of actively noticing new things. Today, companies have introduced and run mindfulness programs because the mindfulness has possible applications of productivity and innovation in corporation. However, role of mindfulness has not been clearly investigated in behavior research of Information System. The purpose of this study is to confirm the effects of mindfulness on technology acceptance process. Based on UTAUT Model, we examined how mindfulness in technology acceptance process moderate antecedent factors of acceptance intentions and use behavior. For empirical research, we conducted a survey on acceptance of smart watch of internet of things for employees of companies applying the mindfulness programs. then, we analyzed survey sample in empirical methodologies. Based on the empirical analysis, cognizance of alternative technologies in mindfulness factors increased the impact of performance expectancy on acceptance intention. Novelty seeking in mindfulness factors increased the impact of effort expectancy on acceptance intention. Awareness of local context in mindfulness factors decreased the impact of social influence on acceptance intention. engagement with technology in mindfulness factors increased the impact of facilitating conditions on use behavior. This study suggests academic implications and practical implications based on the results of the research. The implications will help to support and extend the theory of technology acceptance model while providing practical insights for IT acceptance by suggesting ways to utilize mindfulness in corporation.

• Information Systems Review
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• v.19 no.3
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• pp.69-89
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• 2017

In the information age, the rapid development of information technology provides people with an enriching experience yet also causes them harm because of information security (IS) issues. The IS of smartphones faces great challenges. Although many studies on IS awareness have been conducted, most of them have focused on PCs and do not consider the security issues of smartphones. In this study, we focus on those factors that affect IS awareness for both PCs and smartphones. We also analyze the differences in the impacts of certain factors on PCs and smartphones based on the proposed research model. The results are summarized as follows. First, the understanding of security technique, understanding of IS threat, and IS education have significant impacts on IS awareness for PCs and smartphones, while IS intention has a significant impact on IS awareness for PCs but not for smartphones. Moreover, IS policy has no significant impact on IS awareness. Second, PCs and smartphones show no significant differences in IS awareness, IS threat, and IS intention, but show significant differences in understanding of security technique, IS education, and IS policy.