• Journal of Hospice and Palliative Care
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• 제26권1호
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• pp.22-25
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• 2023

Although most patients prefer dying at home, patients whose condition rapidly becomes critical need care in the intensive care unit (ICU), and it is rare for them to die at home with their families. Therefore, interest in hospice and palliative care for patients in the ICU is increasing. Hospice and palliative care (PC) is necessary for all patients with life-threatening diseases. The following patients need palliative care in the ICU: patients with chronic critical illnesses who need tracheostomy, percutaneous gastrostomy tube, and extracorporeal life support; patients aged 80 years or older; stage 4 cancer patients; patients with specific acute diseases with a poor prognosis (e.g., anoxic brain injury and intracerebral hemorrhage requiring mechanical ventilation); and patients for whom the attending physician expects a poor prognosis. There are two PC models-a consultative model and an integrative model-in the ICU setting. Since these two models have advantages and disadvantages, it is necessary to apply the model that best fits each hospital's circumstances. Furthermore, interdisciplinary decision-making between the ICU care team and PC specialists should be strengthened to increase the provision of hospice and palliative care services for patients expected to have poor outcomes and their families.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1085-1092
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• 2022

Travel distance is a parameter mainly used in the objective function of Construction Site Layout Planning (CSLP) automation models. To obtain travel distance, common approaches, such as linear distance, shortest-distance algorithm, visibility graph, and access road path, concentrate only on identifying the shortest path. However, humans do not necessarily follow one shortest path but can choose a safer and more comfortable path according to their situation within a reasonable range. Thus, paths generated by these approaches may be different from the actual paths of the workers, which may cause a decrease in the reliability of the optimized construction site layout. To solve this problem, this paper adopts reinforcement learning (RL) inspired by various concepts of cognitive science and behavioral psychology to generate a realistic path that mimics the decision-making and behavioral processes of wayfinding of workers on the construction site. To do so, in this paper, the collection of human wayfinding tendencies and the characteristics of the walking environment of construction sites are investigated and the importance of taking these into account in simulating the actual path of workers is emphasized. Furthermore, a simulation developed by mapping the identified tendencies to the reward design shows that the RL agent behaves like a real construction worker. Based on the research findings, some opportunities and challenges were proposed. This study contributes to simulating the potential path of workers based on deep RL, which can be utilized to calculate the travel distance of CSLP automation models, contributing to providing more reliable solutions.

• 청정기술
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• 제29권1호
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• pp.59-70
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• 2023

This study aims to introduce a biowaste processing system that uses spent coffee grounds and implement a real options method to evaluate the proposed process. Energy systems based on eco-friendly fuels lack sufficient data, and thus along with conventional approaches, they lack the techno-economic assessment required for great input qualities. On the other hand, real options analysis can estimate the different costs of options, such as continuing or abandoning a project, by considering uncertainties, which can lead to better decision-making. This study investigated the feasibility of a biowaste processing method using spent coffee grounds to produce biofuel and considered three different valuation models, which were the net present value using discounted cash flow, the Black-Scholes and binomial models. The suggested biowaste processing system consumes 200 kg/h of spent coffee grounds. The system utilizes a tilted-slide pyrolysis reactor integrated with a heat exchanger to warm the air, a combustor to generate a primary heat source, and a series of condensers to harness the biofuel. The result of the net present value is South Korean Won (KRW) -225 million, the result of the binomial model is KRW 172 million, and the result of the Black-Scholes model is KRW 1,301 million. These results reveal that a spent coffee ground-related biowaste processing system is worthy of investment from a real options valuation perspective.

• 농촌계획
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• 제28권4호
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.4296-4309
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• 2022

Mesh and turbulence model sensitivity analyses have been performed on computational fluid dynamics simulations executed with Hydra and ANSYS Fluent for a single CANadian Deuterium Uranium (CANDU) 37M nuclear fuel bundle placed within a standard pressure tube. The goal of this work was to perform a methodical analysis to objectively determine an appropriate mesh and to gauge the sensitivity of different turbulence models for CANDU subchannel flow under isothermal conditions. The boundary conditions and material properties are representative of normal operating conditions in a high-powered channel of the Darlington Nuclear Generating Station. Four meshes were generated with ANSYS Workbench Meshing, ranging from 22 to 84 million cells, and analyzed here to determine an appropriate level of mesh resolution and quality. Five turbulence models were compared in the turbulence model sensitivity analysis: standard k - ε, RNG k - ε, realizable k - ε, SST k - ω, and the Reynolds Stress Model. The intent of this work was to gain confidence in mesh generation and turbulence model selection of a single bundle to inform the decision making of subsequent investigations of an entire fuel channel containing a string of twelve bundles.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.30-38
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• 2023

Building Information Models(BIM) provides rich geometric and attribute information throughout the entire life cycle of a building and infrastructure object, while Geographic Information System(GIS) enables the detail analysis of urban issues based on the geo-spatial information in support of decision-making. The Integration of BIM and GIS data makes it possible to create a digital twin of the land in order to effectively manage smart cities. In the perspective of integrating BIM data into GIS systems, this study performs literature reviews on georeferencing techniques and identifies limitations in carrying out the georeferencing process using attribute information associated with absolute coordinates probided by Industry Foundation Classes(IFC) as a BIM standard. To address these limitations, an automated georeferencing process is proposed as a pilot study to position a IFC model with the Local Coordinate System(LCS) in GIS environments with the Reference Coordinate System(RCS). An evaluation of the proposed approach over a BIM model demonstrates that the proposed method is expected to be a great help for automatically georeferencing complex BIM models in a GIS environment, and thus provides benefits for efficient and reliable BIM and GIS integration in practice.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.176-181
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• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• International Journal of Advanced Culture Technology
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• 제2권2호
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• pp.15-18
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• 2014

This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.