• 한국물환경학회지
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• 제37권5호
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Journal of Acupuncture Research
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• 제36권1호
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• pp.1-11
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• 2019

Models of traditional Chinese medicine (TCM) are still difficult to grasp from the view of a Western-cultural background. For proper integration into science and clinical research, it is vital to think "out of the box" of classical sciences. Modern sciences, such as quantum physics, system theory, and information theory offer new models, that reveal TCM as a method to process information. For this purpose, we apply concepts of information theory to propose a "Chinese black box model," that allows for a non-deterministic, bottom-up approach. Considering a patient as an undeterminable complex system, the process of getting information about an individual in Chinese diagnostics is compared to the input-process-output principle of information theory and quantum physics, which is further illustrated by Wheeler's "surprise 20 questions." In TCM, an observer uses a decision-making algorithm to qualify diagnostic information by the binary polarities of "yang" (latin activity) and "yin" (latin structivity) according to the so called "8 principles" (latin 8 guiding criteria). A systematic reconstruction of ancient Chinese terms and concepts illuminates a scattered scientific method, which is specified in a medical context by Latin terminology of the sinologist Porkert [definitions of the Latin terms are presented in Porkert's appendix [1] (cf. Limitations)].

• 대한원격탐사학회지
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• 제35권1호
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.413-421
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• 2021

This research empirically analyzed whether the foods which came from a non-Muslim majority country, such as South Korea, could play an important role in affecting the consumer intention in a predominantly Muslim country. Online survey methods were used to investigate the proposed hypothesis. 318 responses were used for further analysis. Forty-six reflective constructs were adapted from literature and designed by using a five-point Likert scale to facilitate measurement. Estimation models and structural models were examined through SEM-PLS analysis techniques using SmartPLS 3.0 application as the data processing tool. The results showed that religiosity and halal awareness had a positive and significant effect on attitude toward halal labels, including the mediating effect from consumer attitudes towards halal labels which had a positive but insignificant effect on purchase intention. Halal awareness plays an important role for Muslims in the decision-making process for purchasing food. In contrast to the initial hypothesis, the country of origin actually did not have a positive effect on attitudes towards the halal label. In a Muslim-majority country like Indonesia, findings halal food is not difficult, so this research basically is a reminder to marketers to follow those halal principles in implementing their marketing strategies.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.419-430
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• 2021

The study aims to analyze the factors that shape patient loyalty, namely, by involving the service quality factor (SERVQUAL), hospital image, patient value, and patient satisfaction in private hospitals. This study was conducted in Makassar City, Indonesia, with a sample of 296 eligible samples from private hospitals. The sample criteria were patients with outpatient and hospitalization status. Then, this study developed 23 hypotheses to test the statistical relationship between direct, intervening and multiple-effect models. Problem-solving and research focus are carried out using a quantitative method approach with a PLS-SEM-based testing tool. The bootstrapping method is being used with the constant bootstrapping step to demonstrate the results of hypothesis testing; we find that the overall hypothesis has a positive and significant effect. The combination of testing models involving several variables shows that a patient's loyalty can be formed if a patient's satisfaction has been realized. Satisfaction can be realized if the value-customer has been felt by the patients. Therefore, the hospital image must be directly proportional to service quality. Service quality is the essence of service that directly affects customers; service quality is also the reason that shapes consumer perceptions in increasing rationalization and solid customer (patient's) decision-making.

• 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.