• Journal of Digital Convergence
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• v.17 no.11
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• pp.149-156
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• 2019

With widespread use of information technologies, information privacy issues have been gaining more attention by not only the public but also researchers. The number of studies on the issues has been increasing exponentially, which makes incomprehensible the whole picture of research outcome. Thus, it is necessary to conduct a systematic examination of past research. This study developed two competing models with four essential constructs in information privacy research and empirically tested the models with data obtained from previous studies. This study employed a quantitative meta-analysis method called TSSEM. It is one of MASEM methods in which structural equation modeling and meta-analysis are integrated. The analysis results indicated that risk-centric model exhibited much better model fits than those of concern-centric model. This study implies that traditional concern-centric model should be questioned it's explanatory power of the model and researchers may consider alternative risk-centric model to explain user's intention to provide privacy information.

• Journal of Information Technology Applications and Management
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• v.15 no.3
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• pp.195-208
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• 2008

In an emergency management case, evaluating the economic value of information technology investments is a challenging problem due to the effects of decision making, uncertainty of disasters, and difficulty of measurements. Risk assessment and recovery process, one of the major functions in emergency management, consists of (1) measurement of damages or losses, (2) recovery planning, (3) reporting and approving budgets, (4) auctioning off recovery projects to constructors, and (5) construction for the recovery. Specifically and of our interest, measurement of damages or losses is often a costly and time-consuming process because the wide range of field surveys should be performed by a limited pool of trained agents. Managers, therefore, have to balance accuracy of the field survey against the total time to complete the survey. Using information technologies to support field survey and reporting has great potential to reduce errors and lowers the cost of the process. However, existing cost benefit analysis framework may be problematic to evaluate and justify the IT investment because the cost benefit analysis often include the long-run benefit of IT that is difficult to quantify and overlook the impact of managerial control upon the investment outcomes. Therefore, we present an alternative cost-centric control model that conservatively quantifies all cost savings to replace benefits in cost benefit analysis and incorporate the managerial control. The model provides a framework to examine how managerial decision making and uncertainty of disaster affect the economic value of IT investments. The current project in Emergency Agency in South Korea is introduced as a case to apply the cost-centric control model. Our work helps managers to better evaluate and justify IT-related investment alternatives in emergency management.

• The Journal of Information Systems
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• v.32 no.4
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• pp.119-136
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• 2023

Purpose We introduce a groundbreaking surveillance system explicitly designed to overcome the limitations typically associated with conventional surveillance systems, which often focus primarily on object-centric behavior analysis. Design/methodology/approach The study introduces an innovative approach to risk assessment in surveillance, employing image captioning to generate descriptive captions that effectively encapsulate the interactions among objects, actions, and spatial elements within observed scenes. To support our methodology, we developed a distinctive dataset comprising pairs of [image-caption-danger score] for training purposes. We fine-tuned the BLIP-2 model using this dataset and utilized BERT to decipher the semantic content of the generated captions for assessing risk levels. Findings In a series of experiments conducted with our self-constructed datasets, we illustrate that these datasets offer a wealth of information for risk assessment and display outstanding performance in this area. In comparison to models pre-trained on established datasets, our generated captions thoroughly encompass the necessary object attributes, behaviors, and spatial context crucial for the surveillance system. Additionally, they showcase adaptability to novel sentence structures, ensuring their versatility across a range of contexts.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.415-422
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• 2024

Recent advances in construction automation have led to increased use of deep learning-based computer vision technology for construction monitoring. However, monitoring systems based on supervised learning struggle with recognizing complex risk factors in construction environments, highlighting the need for adaptable solutions. Large multimodal models, pretrained on extensive image-text datasets, present a promising solution with their capability to recognize diverse objects and extract semantic information. This paper proposes a methodology that generates training data for multimodal models, including safety-centric descriptions using GPT-4V, and fine-tunes the LLaVA model using the LoRA method. Experimental results from seven construction site hazard scenarios show that the fine-tuned model accurately assesses safety status in images. These findings underscore the proposed approach's effectiveness in enhancing construction site safety monitoring and illustrate the potential of large multimodal models to tackle domain-specific challenges.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.287-299
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• 2024

Xiaohongshu, a community-centric social media platform, has pioneered a unique e-commerce model known as 'buyer commerce,' leveraging user-generated content (UGC). Distinctively, Xiaohongshu Live Commerce focuses on fostering deep user relationships and providing superior product and information services, crucial for sustained consumer engagement. This study investigates consumer behavior in purchasing health functional foods via Xiaohongshu Live Commerce, aiming to understand the determinants of continuous usage intention. A novel theoretical framework was devised by integrating the Expectation Confirmation Model (ECM) and the Task-Technology Fit (TTF) model. The research model scrutinizes the impact of Xiaohongshu Live Commerce characteristics, such as perceived usefulness and perceived online intimacy, on task-technology fit. Additionally, it examines the moderating role of perceived risk specific to health functional foods and the influence of expectation confirmation on perceived usefulness, online intimacy, and task-technology fit, alongside their effects on satisfaction and continuous usage intention. The findings reveal that expectation confirmation positively influences perceived usefulness, online intimacy, and task-technology fit. Perceived usefulness significantly enhances task-technology fit, while perceived online intimacy and risk do not significantly affect task-technology fit. Moreover, perceived usefulness and intimacy positively impact consumer satisfaction and continuous usage intention, with task-technology fit playing a pivotal role. Perceived risk moderates the relationship between perceived usefulness and task-technology fit. These insights suggest that companies can augment consumer satisfaction and continuous usage intentions by enhancing the perceived usefulness of technology, effectively managing perceived risks, and continually improving user experience

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.100-110
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• 2020

Recently, the frequency of typhoon and torrential rain due to climate change is increasing. In addition, the upsurge in the complexity of urban sewer network and impervious surfaces area aggravates the inland flooding damage. In response to these worsening situations, the central and local governments are conducting R&D tasks related to predict and mitigate the flood risk. Researches on the analysis of inundation in urban areas have been implemented through various ways, and the common features were to evaluate the accuracy and justification of the model by comparing the model results with the actual inundation data. However, the evaluation procesure using available urban flooding data are not consistent, and if there are no quantitative urban inundation data, verification has to be performed by using press releases, public complaints, or photos of inundation occurring through 'CCTV'. Because theses materials are not quantitative, there is a problem of low reliability. Therefore, this study intends to develop a comparative analysis procedure on the quantitative degree and applicability of the verifiable inundation data, and a systematic framework for the performance assessment of urban flood analysis model was proposed. This would contribute to the standardization of the evaluation and verification procedure for urban flooding modelling.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.205-215
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• 2022

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• The Journal of Korean Academy of Prosthodontics
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• v.32 no.4
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• pp.484-514
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• 1994

Cantilever bridge is widely used by mny clinicians, but its worst mechanical character, so called Class I lever system, makes dentists hesitate to restore the missing tooth with it. Therefore it is important to study stress of the cantilever bridge. In this study, two models of cantilever bridges that restores the missing mandibular second molar with two abutment teeth were constructed. One model was a type of cantilever bridge supported by a normal alveolar bone, the other one was supported by an alveolar bone resorbed to its 1/3 of root length. Maximum bite force(550N) and funtional maximum bite force(300N) were vertically applied to the distal end of the pontic, distal 1/3, and distal half of the pontic. And each force was also applied to centric occlusal contacts as a distributed force. Total 16 loading cases were compared and analyzed with 3-dimensional finite element method. The results were as follows: 1. The stress was concentrated on the joint of the pontic and the retainer, grooves, and distal cervical margin of the posterior retainer. 2. In case of maximum bite force(550N) at the end of the pontic, the risk of fracture at the joint of the pontic and the retainer was high. 3. In case of distributed force in centric occlusion and functional maximum bite force(300N), the stresses were less than the yield strength of the type VI gold for any loading cases. 4. In case of alveolar bone resorption, the occlusal force to the cantilever pontic caused more stress on the root apex and less stress on the alveolar crest region of the distal surface of the posterior abutment. 5. In case of alveolar bone resorption, the displacement was larger than that of normal alveolar bone in all loading cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2C
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• pp.206-216
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• 2012

Today's war fields can be characterized by net-centric wars where a variety of independent weapon systems are operated in connection with each other via networks. As such, weapon systems become dramatically advanced in terms of complexity, functionality, precision and so on. It is then obvious that the defense R&D of those requires systematic and efficient development tools enabling the effective management of the complexity, budget/cost, development time, and risk all together. One viable approach is known to be the development methods based on systems engineering, which is already proved to successful in U.S. In this paper, a systems engineering approach is studied to be used in the conceptual design of advanced weapon systems. The approach is utilizing some graphical models in the design phase. As a target system, an unmanned aerial vehicle system is considered and the standard SysML is also used as a modeling language to create models. The generated models have several known merits such as ease of understanding and communication. The interrelationships between the models and the design artifacts are identified, which should be useful in the generation of some design documents that are required in the defense R&D. The result reported here could be utilized in the further study that can eventually lead to a full-scale model-based systems engineering method.