• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.

• Journal of Preventive Medicine and Public Health
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• v.49 no.4
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• pp.205-219
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• 2016

Objectives: No children-specified review and meta-analysis paper about the short-term effect of fine particulate matter ($PM_{2.5}$) on hospital admissions and emergency department visits for asthma has been published. We calculated more precise pooled effect estimates on this topic and evaluated the variation in effect size according to the differences in study characteristics not considered in previous studies. Methods: Two authors each independently searched PubMed and EMBASE for relevant studies in March, 2016. We conducted random effect meta-analyses and mixed-effect meta-regression analyses using retrieved summary effect estimates and 95% confidence intervals (CIs) and some characteristics of selected studies. The Egger's test and funnel plot were used to check publication bias. All analyses were done using R version 3.1.3. Results: We ultimately retrieved 26 time-series and case-crossover design studies about the short-term effect of $PM_{2.5}$ on children's hospital admissions and emergency department visits for asthma. In the primary meta-analysis, children's hospital admissions and emergency department visits for asthma were positively associated with a short-term $10{\mu}g/m^3$ increase in $PM_{2.5}$ (relative risk, 1.048; 95% CI, 1.028 to 1.067; $I^2=95.7%$). We also found different effect coefficients by region; the value in Asia was estimated to be lower than in North America or Europe. Conclusions: We strengthened the evidence on the short-term effect of $PM_{2.5}$ on children's hospital admissions and emergency department visits for asthma. Further studies from other regions outside North America and Europe regions are needed for more generalizable evidence.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.1
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• pp.65-71
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• 2013

Two digestion trials, one with sheep and another with goats, were conducted to evaluate the long-term bias (LTB) of the indigestible dry matter (iDM), indigestible neutral detergent fiber (iNDF) and indigestible acid detergent fiber (iADF) internal markers. The study used eight Santa In$\hat{e}$s castrated male sheep (average body weight of 16.6 kg) distributed in two $4{\times}4$ Latin squares and eight Saanen castrated male goats (average body weight of 22.6 kg) distributed in two $4{\times}4$ Latin squares. The experiments were conducted simultaneously, and the animals were housed in 1.2 $m^2$ individual pens with wood-battened floors equipped with individual feeders and drinkers. The animals received isonitrogenous diets that were offered ad libitum and contained 14% crude protein and 70% sugar cane (with 0, 0.75, 1.5 or 2.25% CaO, in natural matter percentage), corrected with 1% urea and 30% concentrate. The experiment consisted of four experimental periods of 14 d each, with the feed, leftovers and feces sampled on the last four days of each period. The marker concentrations in the feed, leftovers and fecal samples were estimated by an in situ ruminal incubation procedure with a duration 240 h. The relationship between the intake and excretion of the markers was obtained by adjusting a simple linear regression model, independently from the treatment (diets) fixed effects and Latin squares. For both the sheep and goats, a complete recovery of the iDM and iNDF markers was observed (p>0.05), indicating the absence of LTB for these markers. However, the iADF was not completely recovered, exhibiting an LTB of -9.12% (p<0.05) in the sheep evaluation and -3.02% (p<0.05) in the goat evaluation.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.564-572
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• 2019

Numerical simulation of the sediment by the Delft3d model was conducted to examine the changes in the sediment budget transport caused by long-term wave changes at the Maengbang beach. Representative waves were generated with input reduction tools using NOAA NCEP wave data for about 40 years, i.e., from January 1979 to May 2019. To determine the adequacy of the model, wave and depth changes were compared and verified using wave and depth data observed for about 23 months beginning in March 2017. As a result of the error analysis, the bias was 0.05 and the root mean square error was 0.23, which indicated that the numerical wave results were satisfactory. Also, the observed change in depth and numerical result were similar. In addition, to examine the effect due to long-term changes in the waves, the NOAA wave data classified into each of the representative wave grades, and then the annual trend of the representative wave was analyzed. After deciding the weight of each wave class considering the changed wave environment in 2100, the amounts of sedimentation, deposition, and the sediment transport budget were reviewed for the same period. The results indicated that the sedimentation pattern did not change significantly compared to the current state, and the amount of the local sediment budget shown in the present state was slightly less. And there has been a local increase in the number of sediment budget transport, but there is no significant difference in the net and amount of sediment movements.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.363-372
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• 2017

This study analyzed how management earnings forecasts would have an effect on future earnings quality. The analysis period of study was from 2003 till 2009 (ofrom 2004 till 2011) based on variables of interest (dependent variables) and the annual data from a total of 475 companies that publicly announced manager's operating earnings forecasts among securities listing companies were used for analysis. As a result, first, it appeared that the more optimistic the manager's earnings forecasts were for the current term, the lower the future earnings quality was. Second, it was found that the lower the accuracy of the manager's earnings forecasts was for the current term, the lower the future earnings quality was. Such findings suggest that management earnings forecasts will be used for determining future earnings quality.

• Korean Journal of Materials Research
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• v.10 no.2
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• pp.124-127
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• 2000

A new triode type Ti-silicided Si FEA(field emitter array) was realized by Ti-silicidation of Ti coated Si FEA and its field emission properties were investigated. In the fabricated device, the field emission properties through the unit pixel with $200{\mu\textrm{m}}{\times}200{$\mu\textrm{m}}$ tip array in the area of $1000{\mu\textrm{m}}{\times}1000{$\mu\textrm{m}}$ were as follows : the turn-on voltage was about 70V under high vacuum condition of $10^8Torr$, and the field emission current and steady state current degradation were about 2nA/tip and 0.3%/min under the bias of $V_A=500V\;and\;V_G=150V$. The low turn-on voltage and the high current stability during long term operation of the Ti silicided Si FEA were due to the thermal and chemical stability and the low work function of silicide layer formed at the surface of Si tip.

• Korean Journal of Materials Research
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• v.10 no.4
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• pp.301-304
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• 2000

A new triode type Co-silicided Si FEA(field emitter array) was realized by Co-silicidation of Co coated Si FEA and its field emission properties were investigated. The field emission properties of the fabricated device through the unit pixel with $45{\times}45$ tip array in the area of $250{\mu\textrm{m}}{\times}250{\mu\textrm{m}}$ under high vacuum condition of $10^{-8}Torr$ were as follows : the turn-on voltage was about 35V and the anode current was about $1.2\mu\textrm{A}(0.6㎁/tip)$ at the bias of $V_A=500V\;and\; V_G=55V$. The fabricated device showed the stable electrical characteristics without degradation of field emission current for the long term operation except for the initial transient state. The low turn-on voltage and the high current stability of the Co-silicided Si FEA were due to the thermal and chemical stability and the low work function of silicide layer formed at the surface of Si tip.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.408-414
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• 2019

We developed a surveillance tool for collision avoidance of unmanned aerial vehicles (UAVs) in urban areas. In our tool, users can visualize the safety distance on the actual 3D map of urban area. The estimated positions of UAVs are assumed to be obtained based on the long-term evolution (LTE) signals. The safety distance is defined to include two or more signals with bias. The safety distance calculation method used in this paper enables simulation similar to the actual urban areas where signals are frequently biased due to multipath. In the simulation, the parameters were set based on the measured values, and the change of the safety distance according to the number of faulty signals was simulated. As a result, increasing the number of faulty signals led to a longer safety distance as expected.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.