• Annual Conference of KIPS
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• 2022.11a
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• pp.55-56
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• 2022

Hypertension is a severe health problem and increases the risk of other health issues, such as heart disease, heart attack, and stroke. In this research, we propose a machine learning-based prediction method for the risk of chronic hypertension. The proposed method consists of four main modules. In the first module, the linear interpolation method fills missing values of the integration of gas and meteorological datasets. In the second module, the OrdinalEncoder-based normalization is followed by the Decision tree algorithm to select important features. The prediction analysis module builds three models based on k-Nearest Neighbors, Decision Tree, and Random Forest to predict hypertension levels. Finally, the features used in the prediction model are explained by the DeepSHAP approach. The proposed method is evaluated by integrating the Korean meteorological agency dataset, natural gas leakage dataset, and Korean National Health and Nutrition Examination Survey dataset. The experimental results showed important global features for the hypertension of the entire population and local components for particular patients. Based on the local explanation results for a randomly selected 65-year-old male, the effect of hypertension increased from 0.694 to 1.249 when age increased by 0.37 and gas loss increased by 0.17. Therefore, it is concluded that gas loss is the cause of high blood pressure.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.31-35
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• 2006

The delta-formulation of the Navier-Stokes equations has been popularly used in the aerodynamics area. Implicit algorithm can be easily implemented in that by using Taylor series expansion. This formulation is extended for an unsteady analysis by using a dual-time integration. In the meanwhile, the incompressible flows with heat transfers which occur in the area of thermo-hydraulics have been solved by a segregated algorithm such as the SIMPLE method, where each equation is discretised by using an under-relaxed deferred correction method and solved sequentially. In this study, the dual-time delta formulation is implemented in the segregated Navier-Stokes solver which is based on the collocated cell-centerd scheme with un unstructured mesh FVM. The pressure correction equation is derived by the SIMPLE method. From this study, it was found that the Euler dual-time method in the delta formulation can be combined with the SIMPLE method.

• Journal of the Korean Society of Industry Convergence
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• v.11 no.3
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• pp.127-134
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• 2008

This study is to develop the structure analysis and optimization algorithm of the strut supported temporary structure for underground constructions. Developed algorithm performs the analysis and the optimization of each strut, wale, and H pile of temporary structures separately. The design variables of nonlinear optimization consist of the cross-sections of temporary structures such as strut, wale, and H pile and the solution of the nonlinear programming is searched using for the method of successive unconstranint minimization technique. The weight of the structure is used for the object function of nonlinear programming. the constraints are derived from the specification of the temporary structures as compressive axial, bending, shear, composite stress and serviceability. The structural analysis is performed based on the elastoplastic beam theory. This developed program can be used to evaluate the applicability, convergence, and effectiveness of the temporary structures.

• Interaction and multiscale mechanics
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• v.6 no.2
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• pp.237-255
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• 2013

In this paper, a meshfree-enriched finite element method (ME-FEM) is introduced for the large deformation analysis of nonlinear path-dependent problems involving contact. In linear ME-FEM, the element formulation is established by introducing a meshfree convex approximation into the linear triangular element in 2D and linear tetrahedron element in 3D along with an enriched meshfree node. In nonlinear formulation, the area-weighted smoothing scheme for deformation gradient is then developed in conjunction with the meshfree-enriched element interpolation functions to yield a discrete divergence-free property at the integration points, which is essential to enhance the stress calculation in the stage of plastic deformation. A modified variational formulation using the smoothed deformation gradient is developed for path-dependent material analysis. In the industrial metal forming problems, the mortar contact algorithm is implemented in the explicit formulation. Since the meshfree-enriched element shape functions are constructed using the meshfree convex approximation, they pose the desired Kronecker-delta property at the element edge thus requires no special treatments in the enforcement of essential boundary condition as well as the contact conditions. As a result, this approach can be easily incorporated into a conventional displacement-based finite element code. Two elasto-plastic problems are studied and the numerical results indicated that ME-FEM is capable of delivering a volumetric locking-free and pressure oscillation-free solutions for the large deformation problems in metal forming analysis.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.127-134
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• 2004

In this paper, human motor behaving model based sensory motor coordination(SMC) algorithm is implemented on robotic grasping task. Compare to conventional SMC models which connect sensor to motor directly, the proposed method used biologically inspired human behaving system in conjunction with SMC algorithm for fast grasping force control of robot arm. To characterize various grasping objects, pressure sensors on hand gripper were used. Measured sensory data are simultaneously transferred to perceptual mechanism(PM) and long term memory(LTM), and then the sensory information is forwarded to the fastest channel among several information-processing flows in human motor system. In this model, two motor learning routes are proposed. One of the route uses PM and the other uses short term memory(STM) and LTM structure. Through motor learning procedure, successful information is transferred from STM to LTM. Also, LTM data are used for next moor plan as reference information. STM is designed to single layered perception neural network to generate fast motor plan and receive required data which comes from LTM. Experimental results showed that proposed method can control of the grasping force adaptable to various shapes and types of greasing objects, and also it showed quicker grasping-behavior lumining time compare to simple feedback system.

• Advances in aircraft and spacecraft science
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• v.9 no.4
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• pp.335-347
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• 2022

Airport operations are well-known as a bottleneck in the air traffic system, putting growing pressure on the world's busiest airports to schedule arrivals and departures as efficiently as possible. Effective planning and control are essential for increasing airport efficiency and reducing aircraft delays. Many algorithms for controlling the arrival/departure queuing area are handled, considering it as first in first out queues, where any available aircraft can take off regardless of its relative sequence with other aircraft. In the suggested system, this problem was compared to the problem of scheduling n tasks (plane takeoffs and landings) on a multiple machine (runways). The proposed technique decreases delays (via efficient runway allocation or allowing aircraft to be expedited to reach a scheduled time) to enhance runway capacity and decrease delays. The aircraft scheduling problem entails arranging aircraft on available runways and scheduling their landings and departures while considering any operational constraints. The topic of this work is the scheduling of aircraft landings and takeoffs on multiple runways. Each aircraft's takeoff and landing schedules have time windows, as well as minimum separation intervals between landings and takeoffs. We present and evaluate a variety of comprehensive concepts and solutions for scheduling aircraft arrival and departure times, intending to reduce delays relative to scheduled times. When compared to First Come First Serve scheduling algorithm, the suggested strategy is usually successful in reducing the average waiting time and average tardiness while optimizing runway use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2512-2545
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• 2024

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets.

• Geomechanics and Engineering
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• v.38 no.5
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• pp.507-515
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• 2024

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.111-116
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• 2024

These days, the number of patients committing suicide due to depression and stress is rapidly increasing. In addition, if stress and depression persist for a long time, it can cause heart disease, brain disease, and high blood pressure. Arteriosclerosis, known as the main cause of cerebrovascular disease, is a dangerous disease in which cholesterol accumulates in blood vessels, narrowing them. However, no matter how much modern medicine has developed, stroke patients are in a very difficult situation with no special medicine or treatment. In this paper, to solve these problems, we proposed a smart electronic acupuncture device that treats patients' diseases based on electronic acupuncture. In addition, an algorithm for predicting the risk of health status of stroke patients and a posture recognition algorithm were proposed and a computer simulation experiment was performed.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.4
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• pp.431-439
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• 2024

The International GNSS Service (IGS) provides real-time satellite orbit, clock, and bias corrections through its Real-Time Service (RTS). In addition, Galileo has offered the Galileo High Accuracy Service (HAS) since January 24, 2023, further improving positioning accuracy for GPS and Galileo satellites. HAS data are available through Galileo's E6 signal and Ntrip, enhancing positioning accuracy for GPS and Galileo satellites. This study compared Galileo HAS corrections data and IGS RTS corrections data obtained from the BKG Ntrip client (BNC) on February 1 and June 23, 2024. The orbit, clock, and bias corrections of each satellite were analyzed, revealing that HAS and IGS RTS correction values followed similar trends for most satellites. Additionally, satellite position and clock values computed from these corrections were compared with SP3 and CLK data for accuracy. To perform positioning using HAS, a Code-PPP algorithm was developed, and positioning accuracy was evaluated for GPS-only, Galileo-only, and multi-constellation modes using both GPS and Galileo. Tropospheric errors were mitigated using the Global Model of Pressure and Temperature (GPT) with the Global Mapping Function (GMF), and ionospheric corrections were applied using the Global Ionospheric Map (GIM). As a result, sub-meter level positioning accuracy was achieved. Among the correction types, IGS RTS corrections provided better accuracy for GPS-only measurements, while Galileo HAS corrections yielded superior accuracy for Galileo-only positioning.