• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.182-189
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• 2023

This study investigates the psychological effects of positive/negative valence order in repeated exposure to a suspenseful text. Specifically, the study seeks to understand how the order in which positive and negative elements are presented in a narrative impacts the experience of suspense, arousal, and enjoyment in readers or listeners. Using a suspenseful short story, participants were exposed to narratives with varying valence orders in a repeated-measures design. The study employed self-report questionnaires and psychophysiological measurements to capture participants' psychological responses. The results supported the hypothesis that repeated exposure impacts suspense, with negative valence enhancing suspense and arousal. Moreover, the order of valence influenced participants' psychological responses, indicating that positive valence can mitigate the impact of repeated exposure. However, the influence on enjoyment was not significant. Psychophysiological measures, specifically skin conductance level (SCL), revealed trends of habituation over repeated exposure. The findings underscore the significance of negative valence in heightening suspense and suggest directions for future research in exploring diverse factors that contribute to suspense in both fictional and real-life contexts.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.479-486
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• 2009

This paper presents an integrated real-time monitoring and control framework that facilitates decision making by enabling project managers to take corrective actions right after any deviation happens and mitigate the damage to the ongoing steel projects. The proposed framework employs the High Level Architecture (HLA) as its infrastructure. It is composed of several individual monitoring and control components called "Federates," which cooperate and interact with each other through the Real-time Infrastructure (RTI). Reusability, interoperability and extendibility of federates in the proposed project control system make this a unique system.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.224-229
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• 2009

Digital image recognition has been used for steel bridge surface assessment since late 1990s. However, the non-uniform illumination problems such as shades, shadows, and highlights are still challenges in image processing to date. Therefore, this paper develops a new approach to tackle the non-uniform illumination problem for rust image adjustment. The inhomogeneous illumination problem is divided into shades/shadows and highlights in this paper. The proposed BEMD-morphology approach (BMA) utilizes the bidimensional empirical mode decomposition to mitigate the shade/shadow effect, and the morphological processing to detect and replace the highlight area. Finally, the rust image processed with the BMA will be segmented by the K-Means algorithm, one of the most popular and effective methods, to show the effectiveness of illumination adjustment.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.449-456
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• 2009

Image processing has been applied to steel bridge defect recognition since 1990s. Compare to human visual inspection, image processing provides a more objective and accurate way of assessment. Since shade and shadow may sometimes occur when taking bridge coating images, non-uniform illumination problems should be considered. By means of color image processing, this paper aims to mitigate the illumination effect for bridge coating assessment. Furthermore, the adaptable ellipse method (AEM) is proposed to recognize mild rust colors. Finally, AEM will be compared to the K-Means algorithm, a popular recognition method, to show its advantage.

• BMB Reports
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• v.56 no.8
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• pp.426-438
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• 2023

Muscle wasting in end-stage renal disease (ESRD) is an escalating issue due to the increasing global prevalence of ESRD and its significant clinical impact, including a close association with elevated mortality risk. The phenomenon of muscle wasting in ESRD, which exceeds the rate of muscle loss observed in the normal aging process, arises from multifactorial processes. This review paper aims to provide a comprehensive understanding of muscle wasting in ESRD, covering its epidemiology, underlying molecular mechanisms, and current and emerging therapeutic interventions. It delves into the assessment techniques for muscle mass and function, before exploring the intricate metabolic and molecular pathways that lead to muscle atrophy in ESRD patients. We further discuss various strategies to mitigate muscle wasting, including nutritional, pharmacological, exercise, and physical modalities intervention. This review seeks to provide a solid foundation for future research in this area, fostering a deeper understanding of muscle wasting in ESRD, and paving the way for the development of novel strategies to improve patient outcomes.

• Annual Conference of KIPS
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• 2009.11a
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• pp.679-680
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• 2009

Radio jamming attack is the most effective and easiest Denial-of-Service (DOS) attack in wireless network. In this paper, we proposed a multi-channel MAC protocol to mitigate the jamming attacks by using cognitive radio. The Cognitive Radio (CR) technology supports real-time spectrum sensing and fast channel switching. By using CR technologies, the legitimate nodes can perform periodic spectrum sensing to identify jamming free channels and when the jamming attack is detected, it can switch to un-jammed channel with minimum channel switching delay. In our proposed protocol, these two CR technologies are exploited for thwarting the jamming attacks.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.341-346
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• 2005

After the IMF shock, some major construction companies in Korea have been motivated to avoid and mitigate various risk factors which could be critical and catastrophic events to corporate revenue and organization internally or externally. It means that they are trying to introduce and set up a risk management plan and system suitable to their organization and culture. L construction co. ltd. is one of major construction companies that have been searching methodologies or technologies to manage various risk factors surrounding corporate marketing and project operation. This paper presents an unique approach to develop a model of risk management plan and system suitable to L construction itself focused on the construction phase.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.179-187
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• 2022

The project performances can be measured in terms of meeting the project schedule, budget, and conformance to functional and technical specifications. Numerous studies have been conducted to examine the causes and effects of change orders for both vertical and horizontal construction, respectively. However, these studies mainly focus on a single project type, so this paper examines the impact of change order for cost growth and schedule overruns using four different building types to close the gap in the change order research area. A total of 211 building projects are collected from four building types: healthcare, residential, office, and education. Statistical analyses using ANOVA tests and linear regression models are used to examine the created metric $CO/day on the cost and schedule impacts. The results found that mean $CO/day values were not statistically different among building types, and that the sum of change orders is a statistically significant predictor of $CO/day. The results will help project stakeholders mitigate the negative change orders effects can be a challenge for project managers and researchers alike.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.139-144
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• 2023

Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.