• Steel and Composite Structures
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• v.47 no.2
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• pp.167-184
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• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.

• The Journal of Engineering Geology
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• v.33 no.3
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• pp.489-502
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• 2023

Urban floods pose significant challenges to cities worldwide, driven by the interplay between urbanization and climate change. This review examines recent studies of urban floods to understand their causes, impacts, and potential mitigation strategies. Urbanization, with its increase in impermeable surfaces and altered drainage patterns, disrupts natural water flow, exacerbating surface runoff during intense rainfall events. The impacts of urban floods are far-reaching, affecting lives, infrastructure, the economy, and the environment. Loss of life, property damage, disruptions to critical services, and environmental consequences underscore the urgency of effective urban flood management. To mitigate urban floods, integrated flood management strategies are crucial. Sustainable urban planning, green infrastructure, and improved drainage systems play pivotal roles in reducing flood vulnerabilities. Early warning systems, emergency response planning, and community engagement are essential components of flood preparedness and resilience. Looking to the future, climate change projections indicate increased flood risks, necessitating resilience and adaptation measures. Advances in research, data collection, and modeling techniques will enable more accurate flood predictions, thus guiding decision-making. In conclusion, urban flooding demands urgent attention and comprehensive strategies to protect lives, infrastructure, and the economy.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.400-408
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• 2009

Simulation applications for analyzing the productivity of construction operations at operation level and project schedules at project level are crucial methods in project management. The application at two different levels should be very tightly linked to each other in practice. However, appropriate integration at the levels is not achieved in that existing systems do not support to integrate operation models into a schedule model. This paper presents a new approach named to Discrete Event Simulation-Nesting modeling approach, which supports not only productivity analysis at operation level but also schedule management at a project level. The system developed by the authors allows creating operation models at the operation level, maintaining them in operation model library, executing sensitivity analysis to find the behaviors of the operation models when different combination of resources are used as existing DES systems do. On top of the conventional functions, the new system facilitates to find the optimum solution of resource combinations which satisfy the user's interest by computing the hourly productivity and the hourly cost of the operation. By drag-and-dropping an operation model kept in the operation model library, the operation models are integrated into an activity of the schedule model. When a complete schedule model is established by nesting operation models into the schedule model, stochastic simulation based scheduling is executed. A case study is presented to demonstrate the new simulation system and verify the validity of the system.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.1-8
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• 2009

Enterprise security management system proposed to properly manage heterogeneous security products is the security management infrastructure designed to avoid needless duplications of management tasks and inter-operate those security products effectively. In this paper, we defined the security policies using Z-Notation and the detection algorithm of policy conflict for managing heterogeneous firewall systems. It is designed to help security management build invulnerable security policies that can unify various existing management infrastructures of security policies. Its goal is not only to improve security strength and increase the management efficiency and convenience but also to make it possible to include different security management infrastructures while building security policies. With the process of the detection and resolution for policy conflict, it is possible to integrate heterogeneous security policies and guarantee the integrity of them by avoiding conflicts or duplications among security policies. And further, it provides convenience to manage many security products existing in large networks.

• Molecules and Cells
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• v.46 no.8
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• pp.513-525
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• 2023

Orange carotenoid protein (OCP) of photosynthetic cyanobacteria binds to ketocarotenoids noncovalently and absorbs excess light to protect the host organism from light-induced oxidative damage. Herein, we found that mutating valine 40 in the α3 helix of Gloeocapsa sp. PCC 7513 (GlOCP1) resulted in blue- or red-shifts of 6-20 nm in the absorption maxima of the lit forms. We analyzed the origins of absorption maxima shifts by integrating X-ray crystallography, homology modeling, molecular dynamics simulations, and hybrid quantum mechanics/molecular mechanics calculations. Our analysis suggested that the single residue mutations alter the polar environment surrounding the bound canthaxanthin, thereby modulating the degree of charge transfer in the photoexcited state of the chromophore. Our integrated investigations reveal the mechanism of color adaptation specific to OCPs and suggest a design principle for color-specific photoswitches.

• Smart Media Journal
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• v.12 no.4
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• pp.27-40
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• 2023

Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.

• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.693-700
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• 2017

The mines in our country are complex in geography and shape and because of its small scale, accurate surveying performance and 3D modeling are necessary for mine development and management and mining plans. However, due to the data acquisition and processing technology and economy, the existing methods are currently used. The structure, mining, and mining area of the mine are recorded and managed based on the 2D drawings. As a result, it is true that there is risk of accidents caused by problems of accuracy as well as waste of personnel and time. In recent years, research data on geology and geospatial information on mines have been integrated into a database in foreign countries, and they are used for mine management and mining planning. In this study, we tried to construct spatial information for mining management and mining plan using laser scanner. Through research, spatial information about the mine was effectively obtained and produced data modeled through data processing. The 3D model for mining mines is expected to be a valuable tool for establishing and operating a safe mining plan for mines.

• Journal of the Korean Society for information Management
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• v.40 no.2
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• pp.137-156
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• 2023

As social and political paradigms change, public institution tasks and structures are constantly created, integrated, or abolished. From an effective record management perspective, it is necessary to review whether the previously established record classification schemes reflect these changes and remain relevant to current tasks. However, in most institutions, the restructuring process relies on manual labor and the experiential judgment of practitioners or institutional record managers, making it difficult to reflect changes in a timely manner or comprehensively understand the overall context. To address these issues and improve the efficiency of record management, this study proposes an approach using automation and intelligence technologies to restructure the classification schemes, ensuring records are filed within an appropriate context. Furthermore, the proposed approach was applied to the target institution, its results were used as the basis for interviews with the practitioners to verify the effectiveness and limitations of the approach. It is, aiming to enhance the accuracy and reliability of the restructured record classification schemes and promote the standardization of record management.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.