• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1407-1419
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• 2020

Long time-series gridded data is crucial for the analyses of Earth environmental changes. Climate reanalysis and satellite images are now used as global-scale periodical and quantitative information for the atmosphere and land surface. This paper examines the feasibility of DCT-PLS (penalized least square regression based on discrete cosine transform) for the spatial gap filling of gridded data through the experiments for multiple variables. Because gap-free data is required for an objective comparison of original with gap-filled data, we used LDAPS (Local Data Assimilation and Prediction System) daily data and MODIS (Moderate Resolution Imaging Spectroradiometer) monthly products. In the experiments for relative humidity, wind speed, LST (land surface temperature), and NDVI (normalized difference vegetation index), we made sure that randomly generated gaps were retrieved very similar to the original data. The correlation coefficients were over 0.95 for the four variables. Because the DCT-PLS method does not require ancillary data and can refer to both spatial and temporal information with a fast computation, it can be applied to operative systems for satellite data processing.

• Science of Emotion and Sensibility
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• v.17 no.3
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• pp.95-106
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• 2014

This research analyzed biosignals associated with the change of emotion from lighting felt by the occupants and task type under various indoor thermal environments and illuminance, and examined the biosignals' impacts on work. To this end, the indoor thermal environment was constructed on the basis of PMV (predicted mean vote) index value, and various indoor environments were created by changing the brightness of LED stands. In this manner, a variety of indoor environments were constructed, and experiments were carried out. This research evaluates the sensibility response to lighting through a questionnaire survey in the given environment and incorporates different types of error searches. In this way, changes were analyzed by measuring electroencephalogram (EEG) and electrocardiograms (ECG). As a result, all biosignals on the task type showed significant differences from the thermal environment change. When PMV index value was 0.8 (temperature: $25^{\circ}C$, humidity: 50 %), concentration and attention were the most activated. However, the biosignals did not show significant differences from the illuminance change. Concentration on an occupant's work capability was confirmed to be closely related to the thermal environment. As for the subjective emotional response to lighting, the occupants felt comfort as illuminance was lower, while they felt discomfort as illuminance was higher. However, there were no significant differences from the thermal environment change.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.11
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• pp.1558-1564
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• 2015

Cutaneous evaporative heat loss in Bos indicus and Bos taurus has been well documented. Nonetheless, how crossbreds with different fractional genetic proportions respond to such circumstances is of interest. A study to examine the physiological responses to cutaneous evaporative heat loss, also lactation period and milk yield, were conducted in Sahiwal (Bos indicus, n = 10, $444{\pm}64.8kg$, $9{\pm}2.9years$), Holstein Friesian (Bos taurus, HF100% (n = 10, $488{\pm}97.9kg$, $6{\pm}2.8years$)) and the following crossbreds: HF50% (n = 10, $355{\pm}40.7kg$, $2{\pm}0years$) and HF87.5% (n = 10, $489{\pm}76.8kg$, $7{\pm}1.8years$). They were allocated so as to determine the physiological responses of sweating rate (SR), respiration rate (RR), rectal temperature (RT), and skin temperature (ST) with and without hair from 06:00 h am to 15:00 h pm. And milk yield during 180 days were collected at days from 30 to 180. The ambient temperature-humidity-index (THI) increased from less than 80 in the early morning to more than 90 in the late afternoon. The interaction of THI and breed were highly affected on SR, RR, RT, and ST (p<0.01). The SR was highest in Sahiwal ($595g/m^2/h$) compared to HF100% ($227g/m^2/h$), and their crossbreds both HF50% ($335g/m^2/h$) and HF87.5% ($299g/m^2/h$). On the other hand, RR was higher in HF87.5% (54 bpm) and both HF100% (48 bpm) and HF50% (42 bpm) than Sahiwal (25 bpm) (p<0.01). The RT showed no significant differences as a result of breed (p>0.05) but did change over time. The ST with and without hair were similar, and was higher in HF100% ($37.4^{\circ}C$; $38.0^{\circ}C$) and their crossbred HF50% ($35.5^{\circ}C$; $35.5^{\circ}C$) and HF87.5% ($37.1^{\circ}C$; $37.9^{\circ}C$) than Sahiwal ($34.8^{\circ}C$; $34.8^{\circ}C$) (p<0.01). Moreover, the early lactation were higher at HF100% (25 kg) and 87.5% (25 kg) than HF50% (23 kg) which were higher than Sahiwal (18 kg) while the peak period of lactation was higher at HF100% (35 kg) than crossbreds both HF87.5% and HF50% (32 kg) which was higher than Sahiwal (26 kg) (p<0.05). In conclusion, sweating and respiration were the main vehicle for dissipating excess body heat for Sahiwal, HF and crossbreds, respectively. The THI at 76 to 80 were the critical points where the physiological responses to elevated temperature displayed change.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.10
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• pp.1396-1404
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• 2017

Objective: This study was determined the influence of virginiamycin supplementation on growth-performance and characteristics of digestion of cattle with decreasing dietary net energy value of the diet for maintenance ($NE_m$) from 2.22 to 2.10 Mcal/kg. Methods: Eighty crossbred beef steers ($298.2{\pm}6.3kg$) were used in a 152-d performance evaluation consisting of a 28-d adaptation period followed by a 124-d growing-finishing period. During the 124-d period steers were fed either a lesser energy dense (LED, $2.10Mcal/kg\;NE_m$) or higher energy dense (HED, $2.22Mcal/kg\;NE_m$) diet. Diets were fed with or without 28 mg/kg (dry matter [DM] basis) virginiamycin in a $2{\times}2$ factorial arrangement. Four Holstein steers ($170.4{\pm}5.6kg$) with cannulas in the rumen (3.8 cm internal diameter) and proximal duodenum were used in $4{\times}4$ Latin square experiment to study treatment effects on characteristics of digestion. Results: Neither diet energy density nor virginiamycin affected average daily gain (p>0.10). As expected, dry matter intake and gain efficiency were greater (p<0.01) for LED- than for HED-fed steers. Virginiamycin did not affect estimated net energy value of the LED diet. Virginiamycin increased estimated NE of the HED diet. During daylight hours when the temperature humidity index averaged $81.3{\pm}2.7$, virginiamycin decreased (p<0.05) ruminal temperature. Virginiamycin did not influence (p>0.10) ruminal or total tract digestion. Ruminal (p = 0.02) and total tract digestion (p<0.01) of organic matter, and digestible energy (p<0.01) were greater for HED vs LED. Ruminal microbial efficiency was lower (p<0.01) for HED vs LED diets. Conclusion: The positive effect of virginiamycin on growth performance of cattle is due to increased efficiency of energy utilization, as effects of virginiamycin on characteristics of digestion were not appreciable. Under conditions of high ambient temperature virginiamycin may reduce body temperature.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.5
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• pp.105-112
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• 2017

In order to analyze the effect of street trees on human thermal sensation(thermal comfort) in summer, microclimatic data were measured and analyzed at sunny and shaded locations of two deciduous broadleaf and three broadleaf evergreen species of street trees. As a result, the mean differences by species in air temperature, relative humidity and wind speed were small: $0.2{\sim}1.5^{\circ}C$, 0.9~5.3% and $0.1{\sim}0.5 ms^{-1}$, respectively, but the mean difference in the mean radiant temperature was great, $27.1^{\circ}C$. In the results of physiological equivalent temperature(PET) and universal thermal climate index(UTCI), which are human thermal sensation(thermal comfort) indexes, the shaded locations by the trees showed mean reduction rates of 21.2~31.3% in the PET compared with the sunny location, which are equivalent to 1.5~2.5 levels of thermal perception. Also, 12.7~20.0% in the UTCI was reduced by the trees' shadows, which is equivalent to 1~1.5 levels of heat stress. In addition, although the broadleaf evergreen trees had 5% greater mean reduction in PET than that of the deciduous broadleaf trees, the Zelkova serrata that belonged to the deciduous broadleaf trees showed the equivalent thermal reduction effect as the broadleaf evergreen trees because of the high density of branches and leaves. Therefore, the mean radiant temperature and the density of the crown(branches and leaves) were the main influences in thermal modification by these street trees in summer.

• Journal of Environmental Health Sciences
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• v.33 no.5
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• pp.365-371
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• 2007

To evaluate the effect of air pollution on respiratory health in children, We conducted a longitudinal study in which children were asked to record their daily levels of Peak Expiratory Flow Rate(PEFR) using potable peak flow meter(mini-Wright) for 4 weeks. The relationship between daily PEFR and ambient air particle levels was analyzed using a mixed linear regression models including gender, age in year, weight, the presence of respiratory symptoms, and relative humidity as an extraneous variable. The daily mean concentrations of $PM_{10}$ and $PM_{2.5}$ over the study period were $64.9{\mu}g/m^3$ and $46.1{\mu}g/m^3$, respectively. The range of daily measured PEFR in this study was $182{\sim}481\;l/min$. Daily mean PEFR was regressed with the 24-hour average $PM_{10}(or\;PM_{2.5})$ levels, weather information such as air temperature and relative humidity, and individual characteristics including sex, weight, and respiratory symptoms. The analysis showed that the increase of air particle concentrations was negatively associated with the variability in PEFR. We estimated that the IQR increment of $PM_{10}$ or $PM_{2.5}$ were associated with 1.5 l/min (95% Confidence intervals -3.1, 0.1) and 0.8 l/min(95% CI -1.8, 0.1) decline in PEFR. Even though this study showed negative findings on the relationship between respiratory function and air particles, it was worth noting that the findings must be interpreted cautiously because exposure measurement based on monitoring of ambient air likely resulted in misclassification of true exposure levels and this was the first Korean study that $PM_{2.5}$ measurement was applied as an index of air quality.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.3
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• pp.4195-4205
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• 1976

The purpose of this study is to evaluate the existing methods for calculating or estimating the consumptive use (Evaportranspiration) of any agricutural development project area. In determing the consumptive use water in the project area, there will require the best way for estimating irrigation requirement. Many methods for computing the evaportranspiration have been used, each of them with its merits and demerits at home and abroad. Some of these methods are listed as follows: 1.The Penman's formula 2.The B1aney-Criddle method 3.The Munson P.E. Index method 4.The Atmometer method 5.The Texas Water Rights Commission (TWRC) method 6.The Jensen-Haise method 7.The Christiasen method Therefore, the authors will introduce the more widely used method for calculating Consumptive Use by G.H. Hargreaves. The formula is expressed in the form Ep= K·d·T (1.0-0.01·Hn) Hn=1.0+0.4H+0.005H2. This method was adopted for the first time to determine the Irrigation requirements of Ogseo Comprehensive Agricultual Development project (Benefited area:100,500ha) in Korea. This method is presented in somewhat greater detail than the others. Formula is given for the computation of evaportranspiration (with various levels of data availability) Sampel computation of irrigation requirements for Ogseo irrigation project is included. The results and applied materials are summarized as follows. 1. In calculating the Hargreaves formula, the mean temperature relative, humidity, length of day, and percentage of sunshine from three stations of Iri, Jeonju, and Gunsan were used. 2. Monthly evaporation values were calculated by using the formula. 3. Meteological data from the three stations records for the ten years (1963∼1972) were used. 4. The annual irrigation requirements is 1,186mm per hectare, but the case to consider effective rainfall amount takes the annual irrigation demand being 700mm per hectare.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.5
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• pp.660-664
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• 2003

An attempt was made to evaluate the effects of total mixed rations (TMR) containing 17.5% forage neutral detergent fiber (NDF) from paragrass, paragrass+cassava chips and corn silages on the performance of dairy cows in the tropics. Experimental dietary treatments contained a similar content of total NDF, total non-fiber carbohydrates, crude protein and energy. Maximum and minimum temperature humidity index during the experimental period were 79.1-80.6 and 66.8-68.6, respectively. Among silage sources, there were no differences (p>0.05) in concentrations of acetic and propionic acids and butyric acid was undetectable. Concentration of lactic acid was higher (p<0.01) in corn silage but its pH was lower (p<0.01) than in paragrass and paragrass+cassava silages. Dairy cows on TMR containing corn silage not only gained more weight (161 and 46 vs. -189 g/d) but also consumed more feed (18.47, 15.84 and 14.49 kg/d), and produced more milk (23.89, 22.03 and 20.83 kg/d), 4% fat corrected milk (25.47, 24.05 and 22.02 kg/d), solids-not-fat (1.99, 18.3 and 1.73 kg/d) and total solid (3.10, 2.85 and 2.64 kg/d) compared with those on TMR containing paragrass+cassava and paragrass silages, respectively (p<0.01). Dairy cows on TMR containing paragrass+cassava silage were better in these respects (p<0.01). These results suggest that in formulating diets on an equal NDF basis for different forage qualities, diets higher in forage quality can stimulate higher DMI for dairy cows in the tropics and thus improve productivity.