• Journal of Korean Academy of Nursing
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• v.49 no.3
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• pp.349-360
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• 2019

Purpose: This study was conducted to derive a substantive theory on lived experiences of elderly cancer patients. Methods: The data were collected from February to March 2018 through in-depth personal interviews with 14 elderly cancer patients. The collected data were analyzed based on Corbin and Strauss's grounded theory. Results: The core category was "the journey to find balance in daily lives as a cancer patient by recovering disturbed ego integrity." The core phenomenon was "shattered by suffering from cancer," and the causal conditions were "physical change" and "limitations in daily life." The contextual conditions were "decreased self-esteem," "feelings of guilt toward the family," and the sense of "economic burden." The participants' action and interaction strategies were "maintaining or avoiding social relations," "seeking meaning of the illness," "falling into despair," and "strengthening the willingness to battle the cancer." The intervening conditions were "support from health care providers and family," "dissatisfaction with health care providers," "spiritual help from religion," and "the improvement or worsening of health conditions." The consequences were "having a new insight for life," "living positively along with cancer illness," and "the loss of willingness to live." A summary of the series of processes includes the "crisis stage," "reorganizing stage," and the "ego integration stage." Conclusion: This study explored the holistic process of ego integrity impairment and the recovery experience of elderly cancer patients. This study is expected to be used as a basis for the development of nursing interventions that can support patients when coping with all stages of their cancer illness trajectory.

• Journal of IKEEE
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• v.24 no.4
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• pp.1176-1179
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• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Journal of Science Education
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• v.44 no.3
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• pp.318-330
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• 2020

This study has been conducted to identify the types and longitudinal changes in the career values of students in science core school. Data collected from 174 students in science core school were analyzed using 11 career value items. First, this study found that there are three types of career value shown in students in science core schools. Second, the career value of students in science core school did not differ by their track. Third, many students of science core high schools have little or no change in their career value values depending on the time of collecting data. However, some students show rapidly changing career values. These findings suggest the need for individualized career education based on the changing trend of students' career values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1770-1776
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on hand movement are used together. In this paper, we proposed a hand expression recognition method that recognizes symbols based on the trajectory of a hand movement on a virtual blackboard. In order to recognize a sign drawn by hand on a virtual blackboard, not only a method of recognizing a sign from a hand movement, but also hand pose recognition for finding the start and end of data input is also required. In this paper, MediaPipe was used to recognize hand pose, and LSTM(Long Short Term Memory), a type of recurrent neural network, was used to recognize hand gesture from time series data. To verify the effectiveness of the proposed method, it was applied to the recognition of numbers written on a virtual blackboard, and a recognition rate of about 94% was obtained.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.340-352
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• 2022

This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.

• Journal of Environmental Science International
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• v.32 no.8
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• pp.521-531
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• 2023

This study analyzed the impact of recirculation on high-concentration PM_2.5 in the coastal area. Through the analysis of observational data, it was observed that the development of sea breeze led to an increase in PM_2.5 and SO₄^2- concentrations. Hysplit backward trajectory analysis confirmed the occurrence of air mass recirculation. Results from WRF and CMAQ numerical simulations indicated that pollutants transported from land to sea during the night were re-transported to the land by daytime sea breeze, leading to high-concentration PM_2.5 in Busan. To quantitatively investigate the recirculation a recirculation factor (RF) was calculated, showing an increase in RF values during high-concentration PM_2.5 episodes. However, the RF values varied slightly depending on the time resolution of meteorological data used for the calculations. This variation was attributed to the terrain characteristics at observation sites. Additionally, during long-range transported days leading to nationwide high-concentration PM_2.5 events, synoptic-scale circulation dominated, resulting in weaker correlation between PM_2.5 concentration and RF values. This study enhances the understanding of the influence of recirculation on air pollution. However, it is important to consider the impact of temporal resolution and terrain characteristics when using RF for evaluating recirculation during episodes of air pollution.

• Geomechanics and Engineering
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• v.32 no.1
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• pp.49-68
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• 2023

The three-dimensional failure criterion is essential for maintaining wellbore stability and sand production problem. The convenient factor for a stable wellbore is mud weight and borehole orientation, i.e., mud window design and selection of borehole trajectory. This study proposes a new three-dimensional failure criterion with linear relation of three in-situ principal stresses. The number of failure criteria executed to understand the phenomenon of rock failure under in-situ stresses is the Mohr-Coulomb criterion, Hoek-Brown criterion, Mogi-Coulomb criterion, and many more. A new failure criterion is the extended Mohr-Coulomb failure criterion with the influence of intermediate principal stress (σ₂). The influence of intermediate principal stress is considered as a weighting of (σ₂) on the mean effective stress. The triaxial compression test data for eleven rock types are taken from the literature for calibration of material constant and validation of failure prediction. The predictions on rock samples using new criteria are the best fit with the triaxial compression test data points. Here, Drucker-Prager and the Mogi-Coulomb criterion are also implemented to predict the failure for eleven different rock types. It has been observed that the Drucker-Prager criterion gave over prediction of rock failure. On the contrary, the Mogi-Coulomb criterion gave an equally good prediction of rock failure as our proposed new 3D failure criterion. Based on the yield surface of a new 3D linear criterion it gave the safest prediction for the failure of the rock. A new linear failure criterion is recommended for the unique solution as a linear relation of the principal stresses rather than the dual solution by the Mogi-Coulomb criterion.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.1-15
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• 2002

Atmospheric aerosols interact with sunlight and affect the global radiation balance that can cause climate change through direct and indirect radiative forcing. Because of the spatial and temporal uncertainty of aerosols in atmosphere, aerosol characteristics are not considered through GCMs (General Circulation Model). Therefor it is important physical and optical characteristics should be evaluated to assess climate change and radiative effect by atmospheric aerosols. In this study GMS-5 satellite data and surface measurement data were analyzed using a radiative transfer model for the Yellow Sand event of April 7~8, 2000 in order to investigate the atmospheric radiative effects of Yellow Sand aerosols, MODTRAN3 simulation results enable to inform the relation between satellite channel albedo and aerosol optical thickness(AOT). From this relation AOT was retreived from GMS-5 visible channel. The variance observations of satellite images enable remote sensing of the Yellow Sand particles. Back trajectory analysis was performed to track the air mass from the Gobi desert passing through Korean peninsular with high AOT value measured by ground based measurement. The comparison GMS-5 AOT to ground measured RSR aerosol optical depth(AOD) show that for Yellow Sand aerosols, the albedo measured over ocean surfaces can be used to obtain the aerosol optical thickness using appropriate aerosol model within an error of about 10%. In addition, LIDAR network measurements and backward trajectory model showed characteristics and appearance of Yellow Sand during Yellow Sand events. These data will be good supporting for monitoring of Yellow Sand aerosols.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.155-164
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• 2008

It is critical to develop a core ITS technology such as real-time travel time estimation in order that the efficient use of the ITS implementation can be achieved as the ITS infrastructure and relevant facilities are broadly installed in recent years. The provision of travel time information in real-time allows travellers to make informed decisions and hence not only the traveller's travel utilities but also the road utilization can be maximized. In this paper, a hybrid model is proposed to combine VDS and AVI which have different characteristics in terms of space and time dimensions. The proposed model can incorporate the immediacy of VDS data and the reality of AVI data into one single framework simultaneously. In addition, the solution algorithm is made to have no significant computational burden so that the model can be deployable in real world. A set of real field data is used to analyze the reliability and applicability of the proposed model. The analysis results suggest that the proposed model is very efficient computationally and improves the accuracy of the information provided, which demonstrates the real-time applicability of the proposed model. In particular, the data fusion methodology developed in this paper is expected to be used more widely when a new type of traffic data becomes available.