• 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.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.73-82
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• 2010

Productivity improvement is considered as one of hot potato topics in international shipyards by the increasing amount of orders. In order to improve productivity of lines, shipbuilders have been researching and developing new work method, process automation, advanced planning and scheduling and so on. An optimization approach was accomplished on short-term scheduling of subassembly lines in this research. The problem of subassembly line scheduling turned out to be a non-deterministic polynomial time problem with regard to SKID pattern’s sequence and worker assignment to each station. The problem was applied by simulated annealing algorithm, one of meta-heuristic methods. The algorithm was aimed to avoid local minimum value by changing results with probability function. The optimization result was compared with discrete-event simulation's to propose what pros and cons were. This paper will help planners work on scheduling and decision-making to complete their task by evaluation.

• 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.

• Architectural research
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• v.25 no.3
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• pp.41-51
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• 2023

Building restoration is a complex process with a high level of uncertainty. Restoration professionals can significantly benefit from the well-established discipline of project management to achieve their targets; however, available evidence shows that the use of the project management body of knowledge in restoration projects is far from the desired level. Several historical organisations have since been established with the goal of preserving and governing cultural identity, and numerous studies have supported the need of preserving architectural heritage. Many owners, investors, academics, and developers believe that it would be considerably more expensive to renovate and restore an old building than to create a new one. Although the project management process is generally recognised, the concept of project management for architectural heritage projects differs due to the uniqueness of each project. It differs from many construction projects in terms of the need for research-based practices to define scope, planning, scheduling, supervision,decision-making,and also performance. The Greco-Roman Museum in Alexandria's planning, design, and building phases are being studied with the aim of identifying and analysing the variables that contribute to project delays. Three project management pillars were established as a result of gathering this data from the project's stakeholders: the first pillar addresses time management for the existing phase and how it will be incorporated into the new extension phase; the second pillar addresses performance in relation to project management issues in the delivery of the best quality of a construction project; and the third pillar addresses the scope of the new extension because it will significantly impact the other two pillars. This paper argues that a contemporary perspective which utilizes project management tools and techniques can contribute to the conservation of architectural heritage in line with the conservation principles.

• Steel and Composite Structures
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• v.45 no.3
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• pp.389-408
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• 2022

Residual capacity is defined as the load carrying capacity of an RC column after undergoing severe damage. Evaluation of residual capacity of RC columns is necessary to avoid damage initiation in RC structures. The central aspect of the current research is to propose an empirical formula to estimate the residual capacity of RC columns after undergoing severe damage. This formula facilitates decision making of whether a replacement or a repair of the damaged column is adequate for further use. Available literature mainly focused on the simulation of explosion loads by using simplified pressure time histories to develop residual capacity of RC columns and rarely simulated the actual explosive. Therefore, there is a gap in the literature concerning general relation between blast damage of columns with different explosive loading conditions for a reliable and quick evaluation of column behavior subjected to blast loading. In this paper, the Arbitrary Lagrangian Eulerian (ALE) technique is implemented to simulate high fidelity blast pressure propagations. LS-DYNA software is utilized to solve the finite element (FE) model. The FE model is validated against the practical blast tests, and outcomes are in good agreement with test results. Multivariate linear regression (MLR) method is utilized to derive an analytical formula. The analytical formula predicts the residual capacity of RC columns as functions of structural element parameters. Based on intensive numerical simulation data, it is found that column depth, longitudinal reinforcement ratio, concrete strength and column width have significant effects on the residual axial load carrying capacity of reinforced concrete column under blast loads. Increasing column depth and longitudinal reinforcement ratio that provides better confinement to concrete are very effective in the residual capacity of RC column subjected to blast loads. Data obtained with this study can broaden the knowledge of structural response to blast and improve FE models to simulate the blast performance of concrete structures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.328-329
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• 2021

Newly, the fourth industrial revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's a fusion of advances in AI (artificial intelligence), robotics, the IoT (Internet of Things), 3d printing, genetic engineering, quantum computing, and other technologies. At the world economic forum in Davos, switzerland, in january 2016, chairman professor klaus schwab proposed the fourth industrial revolution for the first time. In order to apply the AHP (analytic hierarchy process) analysis method, the first stage factors were designed as Natural, Water, Earth and Atom energy. In addition, the second stage factors were organized into 9 detailed energies presented in the conceptual model. Thus, we present the theoretical and practical implications of these results.

• Journal of Korean Critical Care Nursing
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• v.16 no.2
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• pp.42-53
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• 2023

Purpose : This retrospective mixed-methods study aimed to explore key considerations for designing effective simulated education in nursing, focusing specifically on the recognition and response to deteriorating patients. Methods : Quantitative and qualitative data were analyzed to assess the clinical judgment and performance of the nursing students. Descriptive statistics were used to analyze quantitative data related to prior knowledge, simulation satisfaction, clinical judgment, and nursing performance during deteriorating patient simulations. Qualitative content analysis was conducted for the reflective journal entries of the participants. Results : Quantitative analysis showed that most participants demonstrated a "being skillful" level of clinical judgment (33.1%) in effective response. At the beginner level, clinical judgment varied across effective noticing(39.7-82.8%), effective interpretating(77.6-82.8%), effective responding(3.4-86.2%), and effective reflecting(90.0-95.4%). Nursing performance in assessing patient respiration or SpO2 after request from a physician ranged from 46.6-48.3%. Qualitative analysis indicated that 48.5% of the participants anticipated a deteriorating condition and initiated appropriate actions, while 70% noticed patient unresponsiveness for the first time. Conclusion : To design an effective simulation program for identifying and addressing deteriorating patient care, a framework for observation and interpretation is essential, along with regular simulated training. It is important to design and assess simulation programs and to conduct thorough interviews with nursing students to gain insight into their clinical decision-making.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.343-352
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• 2010

Recently, many researchers have been carried out to estimate more controlled service life and long-term performance of carbonated concrete structures. Durability analysis and design based on probability have been induced to new concrete structures for design. This paper provides a carbonation prediction model based on the Fick's 1st law of diffusion using statistic data of carbonated concrete structures and the probabilistic analysis of the durability performance has been carried out by using a Bayes' theorem. The influence of concerned design parameters such as $CO_2$ diffusion coefficient, atmospheric $CO_2$ concentration, absorption quantity of $CO_2$ and the degree of hydration was investigated. Using a monitoring data, this model which was based on probabilistic approach was predicted a carbonation depth and a remaining service life at a variety of environmental concrete structures. Form the result, the application method using a realistic carbonation prediction model can be to estimate erosion-open-time, controlled durability and to determine a making decision for suitable repair and maintenance of carbonated concrete structures.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1220-1220
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• 2022

Constructionarium Ltd is a not-for-profit organisation which delivers a residential, experiential, immersive learning opportunity to university students from across the built environment education sector. Since 2002, the Constructionarium education model has been available to students in engineering, construction management and architecture at a purpose built, 19-acre multi-disciplinary training facility in Bircham Newton, England simulating real site life and reflecting site processes, practices and health and safety requirements. The unique approach of Constructionarium puts experiential learning and sustainability at the heart of everything. In a week, students develop a practical understanding of the construction process, develop transferable skills, build a team and are exposed to the latest in sustainable technologies. Experiential learning is what differentiates a Constructionarium project from regular field trips or site visits. At Constructionarium the focus is on learning by participation rather than learning through theory or watching a demonstration. The projects cannot be replicated in a classroom or on campus. Using the hands-on construction of scaled down versions of iconic structures from around the world, students learn that it requires the involvement of the whole construction team to successfully complete their project. Skills such as communication, planning, budgeting, time management and decision making are woven into a week-long interrelationship with industry professionals, academic mentors and trades workers. Working together to enhance transferable skills brings the educational environment into the reality of completing an actual construction project handled by the students. Constructionarium has used this transformational learning model to educate thousands of students from all over the United Kingdom, Europe and Asia. Texas A&M University in the United States has sent multiple teams of students from its Department of Construction Science every operational year since 2016.