• The Journal of Korean Medicine
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• v.27 no.1 s.65
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• pp.36-46
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• 2006

Objectives : Ginseng Research Group in Korea Food Research Institute developed Saeng Mac San (KFRI-2)and Je Ho Tang (KFRI-3) with their sensory factors more acceptable. And we examined their effects on the short-term recovery capacity for cycling exercise (EX) maintained to all-out. Methods : Seven healthy young subjects (aged $24.0{\pm}2.1yr$) were volunteered at this double blind test. Each of KFRI-2, 3, a commercial sport beverage and control (CON) was offered randomly on a series of EX protocol including 65% VO2max-90min EX (D-ride). 1h-recovery and 85% VO2max EX to all-out (P-ride) under the control of their heart rate (HR) and rating perception of exertion (RPE). Blood samples were collected before D-ride, 30, 60 and 90min in D-ride, 30 and 60min in the recovery period and each 10min in P-ride. Plasma analysis items were glucose, insulin, cortisol (CORT), testosterone (TEST), free fatty acid (FFA), $Na^+$, Cl-and $K^+$. The collected data (Means${\pm}$SE) were analysed by two-way ANOVA and statistically significant differences between treatments (p<0.05) by LSD.; the significant level in FFA, $Na^+$, Cl-and $Na^+$ was p<0.01 Results : At 30min during recovery. plasma glucose level in KFRI-3 was significantly higher than CON, and also insulin in KFRI-3 was than CON and KFRI-2. FFA in KFRI-3 was significantly lower than CON during recovery. $Na^+$ in KFRI-3 significantly higher than CON at 90min in D-ride, and also KFRI-2 was at 60min during recovery. However CORT, TEST, Cl-and $Na^+$ in treated beverages were not significant. KFRI-2, 3 elevated the time for P-ride more than CON did. Conclusions : KFRI-2, 3 elevated the time for P-ride about 12% more than CON did. It is based on rapid recovery of plasma glucose level and inhibition of lipolysis during recovery.

• 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.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Clinics in Shoulder and Elbow
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• v.26 no.4
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• pp.343-350
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• 2023

Background: Our purpose was to evaluate a custom reverse total shoulder arthroplasty glenoid baseplate for severe glenoid deficiency, emphasizing the challenges with this approach, including short-term clinical and radiographic outcomes and complications. Methods: This was a single-institution, retrospective series of 29 patients between January 2017 and December 2022 for whom a custom glenoid component was created for extensive glenoid bone loss. Patients were evaluated preoperatively and at intervals for up to 5 years. All received preoperative physical examinations, plain radiographs, and computed tomography (CT). Intra- and postoperative complications are reported. Results: Of 29 patients, delays resulted in only undergoing surgery, and in three of those, the implant did not match the glenoid. For those three, the time from CT scan to implantation averaged 7.6 months (range, 6.1-10.7 months), compared with 5.5 months (range, 2-8.6 months) for those whose implants fit. In patients with at least 2-year follow-up (n=9), no failures occurred. Significant improvements were observed in all patient-reported outcome measures in those nine patients (American Shoulder and Elbow Score, P<0.01; Simple Shoulder Test, P=0.02; Single Assessment Numeric Evaluation, P<0.01; Western Ontario Osteoarthritis of the Shoulder Index, P<0.01). Range of motion improved for forward flexion and abduction (P=0.03 for both) and internal rotation up the back (P=0.02). Pain and satisfaction also improved (P<0.01 for both). Conclusions: Prolonged time (>6 months) from CT scan to device implantation resulted in bone loss that rendered the implants unusable. Satisfactory short-term radiographic and clinical follow-up can be achieved with a well-fitting device. Level of evidence: III.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.29-39
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• 2017

In this study, the GPM (Global Precipitation Mission) IMERG (Integrated Multi-satellitE retrievals for GPM) rainfall data was verified and evaluated using ground AWS (Automated Weather Station) and radar in order to investigate the availability of GPM IMERG rainfall data. The SPI (Standardized Precipitation Index) was calculated based on the GPM IMERG data and also compared with the results obtained from the ground observation data for the Hoengseong Dam and Yongdam Dam areas. For the radar data, 1.5 km CAPPI rainfall data with a resolution of 10 km and 30 minutes was generated by applying the Z-R relationship ($Z=200R^{1.6}$) and used for accuracy verification. In order to calculate the SPI, PERSIANN_CDR and TRMM 3B42 were used for the period prior to the GPM IMERG data availability range. As a result of latency verification, it was confirmed that the performance is relatively higher than that of the early run mode in the late run mode. The GPM IMERG rainfall data has a high accuracy for 20 mm/h or more rainfall as a result of the comparison with the ground rainfall data. The analysis of the time scale of the SPI based on GPM IMERG and changes in normal annual precipitation adequately showed the effect of short term rainfall cases on local drought relief. In addition, the correlation coefficient and the determination coefficient were 0.83, 0.914, 0.689 and 0.835, respectively, between the SPI based GPM IMERG and the ground observation data. Therefore, it can be used as a predictive factor through the time series prediction model. We confirmed the hydrological utilization and the possibility of real time drought monitoring using SPI based on GPM IMERG rainfall, even though results presented in this study were limited to some rainfall cases.

• Computers and Concrete
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• v.31 no.5
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• pp.405-417
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• 2023

The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT.

• The Korean Journal of Quaternary Research
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• v.11 no.1
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• pp.57-68
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• 1997

Climatic change of the late-Quaternary period has been record-ed in the loess deposits of the central Great plains and the record of such change is extractable using a number of approaches and parameters. The stratigraphy of loess deposits which have been investigated on Fort Riley exhibits the same sequence of loess units and intercalated buried soils as is found elsewhere in the re-gion but adds detail unique to the reservation Upland late-Qua-ternary composite stratigraphy preserved on the reservation con-sists of the basal Sangamon soil of the Last interglacial(c. 120-110ka), Gilman Canyon Formation(c. >40 -20ka), Peoria loess(c. 20 -10ka) Brady soil(c. 11 -10ka) Bignell loess(c. 9-\ulcornerka). and mod-ern surface soil. Application of magnetic analyses has provided proxy data sets that represent a time series of climatically regulated pedogenesis/weathering and botanical composition. magetic data have yielded an impression of the variation in climate from Sangamon time to the late Holocene through a reconstruction of the history of pedogenesis/weathering. Sangamon soil formation dominated the reservation durin the Last interglacial as indicated by magnetic parameters. During Gil-man Canyon time loess influx was usually sufficiently slow as to permit pedogenesis which appears to have been at a maximum twice during that time. Warm season grasses were important dur-ing soil formation but diminished in importance during the peri-ods of more rapid loess fall which were cooler and perhaps wet-ter. Peoria loess fall a function of the deterioration of climate during the last Glacial Maximum thinly blanketed the reservation with thickest accumulations occurring to the north-west(Bala Cemetery site)proximal to the source region. Long-term surface stability did not apparently occur within Peoria time but short-term stability may be indicaed by the presence of thin weathering zones(incipient soils) in the Peoria loess. Re-gional landscape stability prevailed during the environmental shift at the Pleistocene/Holocene transition resulting in forma-tion of the well expressed Brady soil. One or more weak soils developed in the Bignell loess as it ac-cumulated. A notable feature of the Bignell loess is the appear-ance of the Altithermal dry period: the loess experienced little weathering and was dominated by warm season grasses until the latter of the Holocene.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.20-35
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• 2015

Underwater acoustic channel impulse responses (CIR) are influenced by sound speed profile (SSP), and the variation of CIR has significant effects on the performance of underwater acoustic communication systems. A significant change of SSP can occur within a short period, which must be considered during the design of underwater acoustic modems. This paper statistically analyzes the effect of the variation of SSP on the long-range acoustic signal propagation in shallow-water with thermocline using numerical modeling based on the data acquired from JACE13 experiment near Jeju island. The analysis result shows that CIR changes variously according to the SSP and the depth of the transmitter and receiver. We also found that when the transmitter and receiver are deeper, the variation of sound wave propagation pattern is smaller and signal level becomes higher. All CIR obtained in this study show that a series of bottom reflections due to downward refraction and small bottom loss in the shallow water with thermocline can be very important factor for long-range signal transmission and the performance of underwater acoustic communication system in time varying ocean environment can be very sensitive to the variation of SSP even for a short period of time.