• Journal of Korean Medical classics
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• v.23 no.2
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• pp.205-223
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• 2010

In Korean Traditional Medicine(abbreviated to K.T.M.), hyperhidrosis and anhidrosis are the targets of the medical treatment. Furthermore sweating appearance is also one of the important symptoms which explain a particular situation of the patient in K.T.M. And at "Sanghanron(傷寒論)" which is a traditional chief clinical bible written by Jang Gi(張機) later Han dynasty(漢代) in China made full use of the various kinds of diaphoresis[汗法] as a main medical treatment with purgation therapy[下法] and emetic therapy[吐法]. So the sweat in itself not only is the disease, but also is one of the symptoms explain a disease pattern. This thesis inquires into "Hwangjenaegyeong(黃帝內經)" referring to sweat which is the origin of recognition to the sweat in K.T.M. Some theses similar to this research had been made progresses and already reported, but most of them have classified the contents into biology, pathology, diagnosis, treatment after the model of western medical theory. In the aspect of comparative studying with other literature and clinic practical using, we found characteristics of referring to sweat in "Hwangjenaegyeong(黃帝內經)". And we classify the characteristics into some categories as follows. 1. There are some terms which make a title including sweat and symbolize the characteristics, for example sweat of soul[魄汗], sweat of death[絶汗], sweat of streaming[灌汗], sweat of weakness[白汗], sweat of sleep[寢汗], sweat of bright and heat[炅汗], sweat of kidney[腎汗], sweat of escaping[漉汗], cold sweat[寒汗], sweat on the head[頭汗], hyperhidrosis[多汗], heavy sweat[大汗]. But there aren't spontaneous sweat[自汗] or sweat like a thief[盜汗] which are the normal terms referring to sweat in history of K.T.M. And there are several descriptions about sweat appearance such as sweating in half of body[汗出偏沮], sweating in the rear end and thigh and knee[汗出尻陰股膝], hyperhidrosis in the neck and aversion to wind[頸多汗惡風], hyperhidrosis in the head and face and aversion to wind[頭面多汗惡風], cannot stopping the sweating under head[頭以下汗出不可止], make a person sweat to one's feet[令汗出至足], sweating like escaping[漯漯然汗出], sweating like soaking[汗出如浴], sweating become moist[汗出溱溱], hardly escaping sweat[汗大泄], escaping sweating[漉漉之汗], sweat moisten the pores [汗濡玄府], ceaseless sweating like pouring[汗注不休] sweating like pouring and vexation[汗注煩心], damp with sweat[汗汗然], sweating spontaneously[汗且自出], removal of fever with sweat drying[熱去汗稀]. That can be divided into sweat region and sweat form. 2. There are detailed explanations of the principle of perspirations caused by hot weather, hot food, hard working and meeting damp pathogen. 3. There are some explanations of the principle of removing fever due to the excessive heat from internal and external body through sweating by replenishing the body fluid. And many descriptions about overcoming the febrile disease by dropping temperature through sweating and many diaphoresis for curing. 4. There are some descriptions about five Jang organs perspirations and attachment of five mucous body fluid to five Jang organs. 5. There are pathogenic progresses after sweating affected by the Six Atmospheric Influences and water. And detailed explanations of disease mechanism a sweat leading to another disease. 6. There are descriptions about various sweat absent situations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.68-81
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• 2023

This study aims to establish a scenario based on the spacing and arrangement of the roadside trees to reduce heat waves and fine dust in cities that occurred during the urbanization process and to quantitatively analyze the degree of reduction. The ENVI-met 5.0.2v model, a micro-climate simulation program, was used to analyze the degree of improvement in the thermal environment and fine dust according to the roadside tree scenario. As a result of temperature analysis according to street tree spacing, the narrower the distance between roadside trees, the lower the temperature during the day as the number of planted trees increased, and a similar pattern was shown regardless of the distance between roadside trees in the morning and evening. In the case of fine dust emitted from the road, the concentration of fine dust increased slightly due to the increase in roadside trees, but the concentration of sidewalks where people walk increased slightly or there was no difference because of blocking fine dust on trees. The temperature according to the arrangement of street trees tended to decrease as the number of planted trees increased as the arrangement increased. However, not only the amount of trees but also the crown projected area was judged to have a significant impact on the temperature reduction because the temperature reduction was greater in the scenario of planting the same amount of trees and widening the interval of arrangement. In terms of the arrangement, the fine dust concentration showed a difference from the results according to the interval, suggesting that the fine dust concentration may change depending on the relationship between the main wind direction and the tree planting direction. By quantitatively analyzing the degree of thermal environment and fine dust improvement caused by roadside trees, this study is expected to promote policies and projects to improve the roadside environment efficiently, such as a basic plan for roadside trees and a project for wind corridor forests.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.77-100
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• 2024

Circulation, tides, currents, harmful algal blooms, water quality, and hypoxic conditions in Jinhae-Masan Bay have been extensively studied. However, these previous studies primarily focused on short-term variations, and there was limited detailed investigation into the physical mechanisms responsible for ocean circulation in the bays. Oceanic processes in the bays, such as pollutant dispersal, changes on a seasonal time scale. Therefore, this study aimed to understand how the circulation in Jinhae-Masan Bay varies seasonally and to examine the effects of tides, winds, and river discharges on regional ocean circulation. To achieve this, a three-dimensional ocean circulation model was used to simulate circulation patterns from 2016 to 2018, and sensitivity experiments were conducted. This study reveals that convective estuarine circulation develops in Jinhae and Masan Bays, characterized by the inflow of deep oceanic water from the Korea Strait through Gadeoksudo, while surface water flows outward. This deep water intrusion divides into northward and westward branches. In this study, the volume transport was calculated along the direction of bottom channels in each region. The meridional water exchange in the eastern region of Jinhae Bay is 2.3 times greater in winter and 1.4 times greater in summer compared to that of zonal exchange in the western region. In the western region of Jinhae Bay, the circulation pattern varies significantly by season due to changes in the balance of forces. During winter, surface currents flow southward and bottom currents flow northward, strengthening the north-south convective circulation due to the combined effects of northwesterly winds and the slope of the sea surface. In contrast, during summer, southwesterly winds cause surface seawater to flow eastward, and the elevated sea surface in the southeastern part enhances northward barotropic pressure gradient intensifying the eastward surface flow. The density gradient and southward baroclinic pressure gradient increase in the lower layer, causing a strong westward inflow of seawater from Gadeoksudo, enhancing the zonal convective circulation by 26% compared to winter. The convective circulation in the western Jinhae Bay is significantly influenced by both tidal current and wind during both winter and summer. In the eastern Jinhae Bay and Masan Bay, surface water flows outward to the open sea in all seasons, while bottom water flows inward, demonstrating a typical convective estuarine circulation. In winter, the contributions of wind and freshwater influx are significant, while in summer, the influence of mixing by tidal currents plays a major role in the north-south convective circulation. In the eastern Jinhae Bay, tidally driven residual circulation patterns, influenced by the local topography, are distinct. The study results are expected to enhance our understanding of pollutant dispersion, summer hypoxic events, and the abundance of red tide organisms in these bays.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.492-501
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• 2007

Coastal disasters have become one of the most important issues in every coastal country. In Korea, coastal disasters such as storm surge, sea level rise and extreme weather have placed many coastal regions in danger of being exposed or damaged during subsequent storms and gradual shoreline retreat. A storm surge is an onshore gush of water associated with a tow pressure weather system, typically in typhoon season. However, it is very difficult to predict storm surge height and inundation due to the irregularity of the course and intensity of a typhoon. To provide a new scheme of typhoon damage prediction model, the scenario which changes the central pressure, the maximum wind radius, the track and the proceeding speed by corresponding previous typhoon database, was composed. The virtual typhoon scenario database was constructed with individual scenario simulation and evaluation, in which it extracted the result from the scenario database of information of the hereafter typhoon and information due to climate change. This virtual typhoon scenario database will apply damage prediction information about a typhoon. This study performed construction and analysis of the simulation system with the storm surge/coastal inundation model at Masan coastal areas, and applied method for predicting using the scenario of the storm surge.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.5
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• pp.274-279
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• 2006

The exact estimation of crop evapotranspiration containing reference or potential evapotranspiration is necessary for decision of crop water requirements. This study was carried out for the evaluation and application of various meteorological elements used for the calculation of reference evapotranspiration (RET) by FAO Penman-Monteith (PM) model. Meteorological elements including temperature, net radiation, soil heat flux, albedo, relative humidity, wind speed measured by meteorological instruments are required for RET calculation by FAO PM model. The average of albedo measured for crop growing period was 0.20, ranging from 0.12 to 0.23, and was slightly lower than 0.23. Determinant coefficients by measured albedo and green grass albedo were 0.97, 0.95 and standard errors were 0.74, 0.80 respectively. Usefulness of deductive regression models was admitted. To assess an influence of soil heat flux (G) on FAO PM, RET with G=0 was compared with RETs using G at 5cm soil depth ($G_{5cm}$) and G at surface ($G_{0cm}$). As the results, RET estimated by G=0 was well agreed with RET calculated by measured G. Therefore, estimated net radiation, G=0 and albedo of green grass could be used for RET calculation by FAO PM.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.161-176
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• 2021

In this study, one of the distributed hydrologic models, VELAS, was used to analyze the variation of hydrologic elements based on water balance analysis to evaluate the groundwater recharge in more detail than the annual time scale for the past and future. The study area is located in Yanggok-ri, Seobu-myeon, Hongseong-gun, Chungnam-do, which is very vulnerable to drought. To implement the VELAS model, spatial characteristic data such as digital elevation model (DEM), vegetation, and slope were established, and GIS data were constructed through spatial interpolation on the daily air temperature, precipitation, average wind speed, and relative humidity of the Korea Meteorological Stations. The results of the analysis showed that annual precipitation was 799.1-1750.8 mm, average 1210.7 mm, groundwater recharge of 28.8-492.9 mm, and average 196.9 mm over the past 18 years from 2001 to 2018 in the study area. Annual groundwater recharge rate compared to annual precipitation was from 3.6 to 28.2% with a very large variation and average 14.9%. By the climate change RCP 8.5 scenario, the annual precipitation from 2019 to 2100 was 572.8-1996.5 mm (average 1078.4 mm) and groundwater recharge of 26.7-432.5 mm (average precipitation 16.2%). The annual groundwater recharge rates in the future were projected from 2.8% to 45.1%, 18.2% on average. The components that make up the water balance were well correlated with precipitation, especially in the annual data rather than the daily data. However, the amount of evapotranspiration seems to be more affected by other climatic factors such as temperature. Groundwater recharge in more detailed time scale rather than annual scale is expected to provide basic data that can be used for groundwater development and management if precipitation are severely varied by time, such as droughts or floods.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.