• Land and Housing Review
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• v.13 no.2
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• pp.125-139
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• 2022

For construction companies to become competitive innovative, cost management as well as process improvement are required. Activity-based costing (ABC), which uses cost information to support long-term decision-making, is a tool that enhances a company's competitiveness. In this study, we compare and analyze tradition-based costing (TBC) and ABC to confirm the adequacy of performance-based costing. In addition, we will empirically examine the relationship between the impact of the reward system using ABC on employee satisfaction and involvement. In research results, the influence of the reward system on employee involvement appeared in the order of intrinsic reward (𝛽 = 0.338) and extrinsic reward (𝛽 = 0.308). In addition, the reward system showed positive (+) effects on employee satisfaction, with influence appearing in the order of intrinsic reward (𝛽 = 0.360) and extrinsic reward (𝛽 = 0.337). And employee satisfaction (𝛽 = 0.225) had a positive effect on involvement. We were able to confirm that it is necessary to build a reward system consisting of intrinsic and extrinsic rewards to increase employee satisfaction and involvement.

• Journal of Korean Society on Water Environment
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• v.38 no.4
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• pp.177-189
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• 2022

Nature-based Solutions (NbS) are defined as practical and technical approaches to restoring functioning ecosystems and biodiversity as a means to address socio-environmental challenges and provide human-nature co-benefits. This study reviews NbS-related literature to identify its key characteristics, techniques, and challenges for its application in climate-adaptive water management. The review finds that NbS has been commonly used as an umbrella term incorporating a wide range of existing ecosystem-based approaches such as low-impact development (LID), best management practices (BMP), forest landscape restoration (FLR), and blue-green infrastructure (BGI), rather than being a uniquely-situated practice. Its technical form and operation can vary significantly depending on the spatial scale (small versus large), objective (mitigation, adaptation, naturalization), and problem (water supply, quality, flooding). Commonly cited techniques include green spaces, permeable surfaces, wetlands, infiltration ponds, and riparian buffers in urban sites, while afforestation, floodplain restoration, and reed beds appear common in non- and less-urban settings. There is a greater lack of operational clarity for large-scale NbS than for small-scale NbS in urban areas. NbS can be a powerful tool that enables an integrated and coordinated action embracing not only water management, but also microclimate moderation, ecosystem conservation, and emissions reduction. This study points out the importance of developing decision-making guidelines that can inform practitioners of the selection, operation, and evaluation of NbS for specific sites. The absence of this framework is one of the obstacles to mainstreaming NbS for water management. More case studies are needed for empirical assessment of NbS.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.762-769
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• 2022

Genetic testing in prenatal diagnosis is a precious tool providing valuable information in clinical management and parental decision-making. For the last year, cytogenetic testing methods, such as G-banding karyotype analysis, fluorescent in situ hybridization, chromosomal microarray, and gene panels have evolved to become part of routine laboratory testing. However, the limitations of each of these methods demonstrate the need for a revolutionary technology that can alleviate the need for multiple technologies. The recent introduction of new genomic technologies based on next-generation sequencing has changed the current practice of prenatal testing. The promise of these innovations lies in the fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy for prenatal diagnosis. Here, we review the current state of sequencing-based pediatric diagnostics, associated challenges, as well as future prospects.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.21-29
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• 2022

Productivity estimation of construction operations is crucial to successful project delivery. Especially in the preconstruction phase, the adequacy and effectiveness of plans directly affect the actual performance of operations. Currently, productivity estimation is conducted by referring to existing references such as the Construction Standard Production Rate. However, it is difficult to promptly apply changing conditions of operations when using such references. Moreover, it is difficult to deduce the optimal combination of construction machinery for the given condition. This paper presents a simple simulation model that can be used to generate productivity data that considers site conditions and construction equipment combination. The suggested method is expected to be used as a decision making assisting tool for practitioners who rely on estimations using the Construction Standard Production Rate when establishing construction plans using heavy machinery such as backhoes, loaders and dumptrucks.

• Informatization Policy
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• v.29 no.3
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• pp.61-81
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• 2022

This paper explores how the provided information from Enterprise Architecture (EA) affects the activities to for performing IT projects. The IT projects analyzed in this paper are projects to for developing and maintaining Korea Customs' UNI-PASS. This research was conducted based on surveys to demonstrate the effects of the information from EA on activities for IT projects. Information from EA is categorized into propriety, sufficiency and consistency. Activities for IT projects are defined as management, participation, communication, requirement management and human resource. Correlational analysis is used to measure the effects of the inf ormation on the defined activities. The analysis, which verifies the provided information by EA, does not have meaningful correlation with project management nor human resource. For public officials in charge, Sufficiency of the information produces a positive effect on decision making. For operation company, consistency of the information encourages utilization of the resources required for the project. This research suggests that strategies for performing IT projects with EA information that can support the verification of characteristic environments of each project and performance of vital activities required by the participants' roles will ensure the success of government IT projects.

• The Korean Journal of Archival Studies
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• no.75
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• pp.135-176
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• 2023

The business management system is a standardized system commonly used by government agencies and is a major tool that remains in the work process and decision-making of government agencies. Production of document-type records is mainly focused on approval documents, so it does not cover various types of records. In a changing environment such as the production of new digital types of records, the emergence of various collaboration software and work systems, and remote work, it is necessary to think about the direction the government work management system will go. This study investigates business systems and utilization methods of competitive companies, analyzes the usage status of On-Nara 2.0, a government business management system, and conducts interviews with business managers. And the purpose is to analyze what kind of difference there is. In addition, we would like to suggest improvement functions and policy directions to respond to digital innovation and improve job accountability and efficiency.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.99-109
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• 2010

Existing research about automated wafer transport management strategy for semiconductor manufacturing equipment was mainly focused on dispatching rules which is optimized to specific system layout, process environment or transfer patterns. But these methods can cause problem as like requiring additional rules or changing whole transport management strategy when applied to new type of process or system. In addition, a lack of consideration for interconnectedness of the added rules can cause unexpected deadlock. In this study, in order to improve these problems, propose dynamic priority based transfer job decision making algorithm which is applicable with regardless of system lay out and transfer patterns. Also, extra rule handling part proposed to support special transfer requirement which is available without damage to generality for maintaining a consistent scheduling policies and minimize loss of stability due to expansion and lead to improve productivity at the same time. Simulation environment of Twin-slot type semiconductor equipment was built In order to measure performance and examine validity about proposed wafer scheduling algorithm.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.255-265
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• 2009

Because the current disaster management task is being processed by using a separated operation system and an insufficient information system by each division, construction facilities suffer great damage by disaster. This research has classified the disaster management business phase and information by each phase through analyzing the existing disaster management information and business process. Then, it has built a disaster management information breakdown structure for integrating of individual disaster information, and also established a standard XML (eXtensible Markup Language) schema of disaster management electronic documents for a real-time utilization. The suggested methods in this research are verified by developing a manual system of electronic type. The disaster management IETM developed in this study provides the consistency for processing the disaster management tasks and the prevention of information omission. And it can be used as a electronic decision making tool for providing of integrated disaster management information.