• Korea Science and Art Forum
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• v.30
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• pp.41-56
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• 2017

This study is an intensive study on Tae Rin Lee's research results. A linear system for Estimating the Strength of Super Personal Conflict (ESSPC) in animations is proposed. Tae Rin Lee has extracted the Super Personal Conflict (SPC) shots of animations, and obtained the strength through the experts' psychological test experiment. The purpose of this study is to find a model that automatically computes the superpersonal conflict intensity value (ESSPC). By utilizing these results, 1) 20 image feature vectors are suggested for analyzing the SPC, and 2) a linear system is found for auto-calculating ESSPC by using the pseudo inverse matrix. The proposed system shows 9.25% root mean square error and the effectiveness is proven.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Korean Journal of Exercise Nutrition
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• v.16 no.1
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• pp.43-50
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• 2012

This study was designed to investigate the effect of vibratory stimulation on recovery of muscle function from delayed onset muscle soreness (DOMS). Volunteers performed 3 set of 70 % maximal voluntary eccentric muscle contraction and induced DOMS. volunteers were allocated to one of three treatment group after DOMS : group I (control), group II (ultrasound), group III (vibration). Maximal Voluntary Isometric Contraction (MVIC), Visual Analog Scale (VAS), Range Of Motion (ROM), Root Mean Square (RMS), Median frequency (MDF), Blood Serum Creatine Kinase (CK), Lactic dehydrogenase (LDH) were recorded at baseline, and 24, 48, 72 hours post-exercise. In MVIC measurement, there was a statistically significant difference in group III compared to group I (p < .05). In VAS measurements, there were a statistically significant difference in group II and III compared to group I (p < .05). In ROM measurement, there was a statistically difference in group II and III compared to group I (p < .05). In Muscle Volume with Ultrasonography measurement, there was no statistically significant difference in any groups (p > .05). In RMS and MDF measurement, there were a statistically significant difference in group II and III compared to group I (p < .05). In Blood samples of CK and LDH measurements, There were no statistically significant difference in any groups (p > .05). From the above result, Vibratory stimulation had a positive effect on recovery of muscle function from delayed onset muscle soreness. Further studies should be undertaken to ascertain the more effectiveness of vibratory stimulation and may be a promising treatment modality.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1151-1156
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• 2023

In this paper, we propose a deep learning model that utilizes charge/discharge data from initial lithium-ion batteries to predict the remaining useful life of lithium-ion batteries. We build the DMP using the PNP model. To demonstrate the performance of DMP, we organize DML using the LSTM model and compare the remaining useful life prediction performance of lithium-ion batteries between DMP and DML. We utilize the RMSE and RMSPE error measurement methods to evaluate the performance of DMP and DML models using test data. The results reveal that the RMSE difference between DMP and DML is 144.62 [Cycle], and the RMSPE difference is 3.37 [%]. These results indicate that the DMP model has a lower error rate than DML. Based on the results of our analysis, we have showcased the superior performance of DMP over DML. This demonstrates that in the field of lithium-ion batteries, the PNP model outperforms the LSTM model.

• Journal of Korean Academy of Nursing
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• v.53 no.6
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• pp.652-665
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• 2023

Purpose: This study aimed to develop a nursing clinical judgment scale (NCJS) and verify its validity and reliability in assessing the clinical judgment of nurses. Methods: A preliminary instrument of the NCJS comprising 38 items was first developed from attributes and indicators derived from a literature review and an in-depth/focus interview with 12 clinical nurses. The preliminary tool was finalized after 7 experts conducted a content validity test based on a data from a preliminary survey of 30 hospital nurses in Korea. Data were collected from 443 ward, intensive care unit, emergency room nurses who voluntarily participated in the survey through offline and online for the verification of the construct validity and reliability of the scale. Results: The final scale comprised 23 items scored on a 5-point Likert scale. Six factors - integrated data analysis, evaluation and reflection on interventions, evidence on interventions, collaboration among health professionals, patient-centered nursing, and collaboration among nurse colleagues - accounted for 64.9% of the total variance. Confirmatory factor analysis supported the fit of the measurement model, comprising six factors (root mean square error of approximation = .07, standardized root mean square residual = .04, comparative fit index = .90). Cronbach's α for all the items was .92. Conclusion: The NCJS is a valid and reliable tool that fully reflects the characteristics of clinical practice, and it can be used effectively to evaluate the clinical judgment of Korean nurses. Future research should reflect the variables influencing clinical judgment and develop an action plan to improve it.

• The Korean Journal of Nuclear Medicine Technology
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• v.27 no.2
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• pp.95-98
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• 2023

Purpose: The precision error of a bone density meter reflects the equipment and reproducibility of results by an examiner. Precision error values can be expressed as coefficient of variation (CV), CV%, and root mean square-SD (RMS-SD). The International Society for Clinical Densitometry (ISCD) currently recommends using RMS-SD as the precision error value. When a 95% confidence interval is applied, the least significant change (LSC) value is calculated by multiplying the precision error value by 2.77. Exceeding the LSC value reflects a significant difference in measured bone density. Therefore, the LSC value of a bone density equipment is an essential factor for accurately determining a patient's bone density. Accordingly, we aimed to calculate the LSC value of a bone density meter (Lunar iDXA, GE) and compare it with the value recommended by the ISCD. We also assessed whether the value measured by the iDXA equipment was below the LSC value recommended by ISCD. Material and Methods: The bone densities of the lumbar spine and thighs of 30 participants were measured twice, and the LSC values were calculated using the precision calculation tool provided by the ISCD (http://www.iscd.org). To check the reproducibility of the measurement, patients were asked to completely dismount from the equipment after the first measurement; the patient was then repositioned before proceeding with the second measurement. Results: The LSC values derived using the CV% values recommended by the ISCD were 5.3% for the lumbar spine and 5.0% for the thigh. The LSC values measured using our bone density equipment were 2.47% for the lumbar spine and 1.61% for the thigh. The LSC value using RMS-SD was 0.031 g/cm² for the lumbar spine and 0.017 g/cm² for the thigh. Conclusion: that the findings confirm that the CV% value measured using our bone density meter and the LSC value using RMS-SD were maintained very stably. This can be helpful for obtaining accurate measurements during bone density follow-up examinations.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.73-80
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• 2024

In this paper, we proposes a method to improve the accuracy of predicting the Korea Composite Stock Price Index (KOSPI) by combining topic modeling and Long Short-Term Memory (LSTM) neural networks. In this paper, we use the Latent Dirichlet Allocation (LDA) technique to extract ten major topics related to interest rate increases and decreases from financial news data. The extracted topics, along with historical KOSPI index data, are input into an LSTM model to predict the KOSPI index. The proposed model has the characteristic of predicting the KOSPI index by combining the time series prediction method by inputting the historical KOSPI index into the LSTM model and the topic modeling method by inputting news data. To verify the performance of the proposed model, this paper designs four models (LSTM_K model, LSTM_KNS model, LDA_K model, LDA_KNS model) based on the types of input data for the LSTM and presents the predictive performance of each model. The comparison of prediction performance results shows that the LSTM model (LDA_K model), which uses financial news topic data and historical KOSPI index data as inputs, recorded the lowest RMSE (Root Mean Square Error), demonstrating the best predictive performance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.2
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• pp.115-125
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• 2024

Wind is a meteorological factor that has a significant impact on agriculture. Gust cause damage such as fruit drop and damage to facilities. In this study, low-altitude wind speed prediction was performed by applying physical models to Local Ensemble Prediction System (LENS). Logarithmic Law (LOG) and Power Law (POW) were used as the physical models, and Korea Ministry of Environment indicators and Moderate Resolution Imaging Spectroradiometer (MODIS) data were applied as indicator variables. We collected and verified wind and gust data at 3m altitude in 2022 operated by the Rural Development Administration, and presented the results in scatter plot, correlation coefficient, Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Threat Score (TS). The LOG-applied model showed better results in wind speed, and the POW-applied model showed better results in gust.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.383-390
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• 2024

When measuring the radiated noise of an underwater vehicle, the range information between the vehicle and the receiver is an important factor, but since Global Positioning System (GPS) is not available in underwater, an alternative method is needed. As an alternative, the range is measured by estimating the arrival time, arrival time difference, and arrival frequency difference using a separate acoustic signal. However, errors occur due to the channel environment, and these outliers become obstacles in continuously measuring range. In this paper, we propose a method to reduce errors by curve fitting with a function in the form of a V-curve as a post-processing to remove outliers that occurred in the process of measuring range information. Simulation, lake and sea trials were conducted to verify the performance of the proposed method. In the results of the lake trial, the range estimation error was reduced by about 85 % from the Root Mean Square Error (RMSE) point of view.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.