• Tribology and Lubricants
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• v.37 no.6
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• pp.253-260
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• 2021

Metal pipes are used in a wide range of applications, from plumbing systems of large construction sites to small devices such as medical tools. When a liquid is enforced to flow through a metal pipe, a higher flow rate is beneficial for higher efficiency. Using high pressures can enhance the flow rate yet can be harmful for medical applications. Thus, we consider an optimal geometrical design to increase the flow rate in medical devices. In this study, we focus on cannulas, which are widely used small metal pipes for surgical procedures, such as liposuction. We characterize the internal flow rate driven by a negative pressure and explore its dependence on the key design parameters. We quantitatively analyze the suction characteristics for each design variable by conducting computational fluid dynamics simulations. In addition, we build a suction performance measurement system which enables the translational motion of cannulas with pre-programmed velocity for experimental validation. The inner diameter, section geometry, and hole configuration are the design factors to be evaluated. The effect of the inner diameter dominates over that of section geometry and hole configuration. In addition, the circular tube shape provides the maximum flow rate among the elliptical geometries. Once the flow rate exceeds a critical value, the rate becomes independent of the number and width of the suction holes. Finally, we introduce a slippery liquid-infused nanoporous surface (SLIPS) coating using nanoparticles and hydrophobic lubricants that effectively improves the flow rate and antifouling property of cannulas without altering the geometrical design parameter.

• Health Policy and Management
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• v.3 no.2
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• pp.159-176
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• 1993

The utilization of outpatient care services involves two steps of sequential decisions. The first step decision is about whether to initiate the utilization and the second one is about how many more visits to make after the initiation. Presumably, the initiation decision is largely made by the patient and his or her family, while the number of additional visits is decided under a strong influence of the physician. Implication is that the analysis of the outpatient care utilization requires to specify each of the two decisions underlying the utilization as a distinct stochastic process. This paper is concerned with the number of physician visits, which is, by definition, a discrete variable that can take only non-negative integer values. Since the initial visit is considered in the analysis of whether or not having made any physician visit, the focus on the number of visits made in addition to the initial one must be enough. The number of additional visits, being a kind of count data, could be assumed to exhibit a Poisson distribution. However, it is likely that the distribution is over dispersed since the number of physician visits tends to cluster around a few values but still vary widely. A recently reported study of outpatient care utilization employed an analysis based upon the assumption of a negative binomial distribution which is a type of overdispersed Poisson distribution. But there is an indication that the use of Poisson distribution making adjustments for over-dispersion results in less loss of efficiency in parameter estimation compared to the use of a certain type of distribution like a negative binomial distribution. An analysis of the data for outpatient care utilization was performed focusing on an assessment of appropriateness of available techniques. The data used in the analysis were collected by a community survey in Hwachon Gun, Kangwon Do in 1990. It was observed that a Poisson regression with adjustments for over-dispersion is superior to either an ordinary regression or a Poisson regression without adjustments oor over-dispersion. In conclusion, it seems the most approprite to assume that the number of physician visits made in addition to the initial visist exhibits an overdispersed Poisson distribution when outpatient care utilization is studied based upon a model which embodies the two-part character of the decision process uderlying the utilization.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• The Korean Journal of Franchise Management
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• v.4 no.1
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• pp.55-71
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• 2013

The purpose of this study is to analyze the effects of satisfaction or dissatisfaction of a franchise organization training program on its achievements. For the training program variables, it takes a motivation, a role perception, and a customer directivity as independent variables, and satisfaction or dissatistaction as a parameter. Also, it regards operation capability improvements, satisfaction of the job, and a will for a long-time employment as a dependent variable, then it presents the results by using SPSS/PC+ statistics methods. The results shows that satisfaction of training programs affect the long-time employment a lot. Therefore, for a long-run growth of the franchise organization, it is important to carry out proper training programs. The introduction(part I) contains the subject proposal and the purpose of the study, and theological background(part II) shows the definition and quality of a franchise organization, and some expectations from training programs such as motivations, role perceptions, and customer directivity. Also, part III presents the study model, hypothesis, and analyzing methods. For the last, part IV shows the verification of statistic results, then part V presents the conclusion.

• Smart Structures and Systems
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• v.32 no.1
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• pp.9-21
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• 2023

The parameters of civil engineering structures have time-variant characteristics during their service. When extremely large external excitations, such as earthquake excitation to buildings or overweight vehicles to bridges, apply to structures, sudden or gradual damage may be caused. It is crucially necessary to detect the occurrence time and severity of the damage. The unscented Kalman filter (UKF), as one efficient estimator, is usually used to conduct the recursive identification of parameters. However, the conventional UKF algorithm has a weak tracking ability for time-variant structural parameters. To improve the identification ability of time-variant parameters, an adaptive UKF with forgetting factor (AUKF-FF) algorithm, in which the state covariance, innovation covariance and cross covariance are updated simultaneously with the help of the forgetting factor, is proposed. To verify the effectiveness of the method, this paper conducted two case studies as follows: the identification of time-variant parameters of a simply supported bridge when the vehicle passing, and the model updating of a six-story concrete frame structure with field test during the Yangbi earthquake excitation in Yunnan Province, China. The comparison results of the numerical studies show that the proposed method is superior to the conventional UKF algorithm for the time-variant parameter identification in convergence speed, accuracy and adaptability to the sampling frequency. The field test studies demonstrate that the proposed method can provide suggestions for solving practical problems.

• Earthquakes and Structures
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• v.25 no.2
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• pp.125-134
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• 2023

In the present work, a new global damage index is proposed for the seismic performance and failure analysis of concrete gravity dams. Unlike the existing indices of concrete structures, this index doesn't need scaling with an ultimate or an upper value. For this purpose, the Beni-Haroun dam in north-eastern Algeria, is considered as a case study, for which an average seismic capacity curve is first evaluated by performing several incremental dynamic analyses. The seismic performance point of the dam is then determined using the N2 method, considering multiple modes and taking into account the stiffness degradation. The seismic demand is obtained from the design spectrum of the Algerian seismic regulations. A series of recorded and artificial accelerograms are used as dynamic loads to evaluate the nonlinear responses of the dam. The nonlinear behaviour of the concrete mass is modelled by using continuum damage mechanics, where material damage is represented by a scalar field damage variable. This modelling, which is suitable for cyclic loading, uses only a single damage parameter to describe the stiffness degradation of the concrete. The hydrodynamic and the sediment pressures are included in the analyses. The obtained results show that the proposed damage index faithfully describes the successive brittle failures of the dam which increase with increasing applied ground accelerations. It is found that minor damage can occur for ground accelerations less than 0.3 g, and complete failure can be caused by accelerations greater than 0.45 g.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.450-453
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• 2015

The Construction Engineering Industry (CEI) is construction field based on professional knowledge, staff and information service, and is distinguished by construction activity. The contemporary CEI market has extended globally and diversified construction work classifications. International construction engineering companies now have an important economic and social effect. Over the last five years (2009 to 2013), the top-200 global engineering firms reported global revenue that grew from 54.4 billion to 71.5 billion, about 27% growth (ENR, 2014). Countries such as the U.S.A., Canada, those in Europe (Several developed countries, i.e., United Kingdom, Netherlands, France, Spain, France, Italy, and Spain), Australia, Japan, China, and Korea comprise the bulk of world construction engineering revenue. Although the construction engineering market continues to grow, much of the work is limited to Europe, mid-Asia, and Asia. Additionally, specific construction types are focused on building projects, industrial plants, and refining plants. As such, there are imbalances in the construction engineering market and some market saturation. Further, there is heavy competition and the construction engineering market may shrink in the future. This paper analyzed various factors affecting the construction engineering market, specifically looking at construction classifications and factors related to a global market. To accomplish this, we collected to data from Engineering News Record (ENR) and recast each variable. And we used nonparametric statistics because the number of cases were small, making it difficult to assume a case's population parameter. Then we tested with the Kruskal-Wallis test and drew results. The results indicate that concentration in particular construction types and extending global regional markets will be have a positive effect on the overall global construction engineering market..

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.17-26
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• 2024

In recent times, accidents involving structural elements, formwork, and shore have been persistently occurring during concrete pouring, especially in multi-story reinforced concrete (RC) structures. In previous studies, research on construction load analysis was mainly conducted for cases where the thickness of all slabs is constant. However, when the thickness of some slabs is different, the variation in the stiffness of slab cross-sections can lead to different distributions of construction loads, necessitating further investigation. In this study, the slab thickness was set as a variable, and the analysis of the distribution of construction loads was conducted, taking into account the influence of changes in slab thickness on the concrete stiffness and structure. It was confirmed that not only the concrete material stiffness but also the slab cross-section stiffness should be considered in the estimation of construction loads when the slab thickness changes. As the slab thickness increases, the maximum construction load and maximum damage parameter on the layer with increased thickness significantly increase, and it was observed that a thicker slab results in a higher proportion of construction load.