• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.201-214
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• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.

• Journal of Environmental Health Sciences
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• v.39 no.1
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• pp.90-98
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• 2013

Objectives: Many studies have shown that air pollution and temperature have adverse effects on mortality and morbidity. But the interactive effect between air pollution and temperature on mortality has been rarely investigated. This study aims to explore whether temperature modifies the associations between ambient particulate matter less than $10{\mu}m$ in diameter ($PM_{10}$) and mortality in Seoul, Korea. Methods: The time-series analysis examined the effect of the interaction between $PM_{10}$ and temperature on mortality from 1999 to 2010 in Seoul. In order to examine the interactive effect between $PM_{10}$ and temperature on mortality, we fitted a response surface model controlling the time-trends and meteorological variables. The effects of $PM_{10}$ were stratified by temperature stratum to quantitatively estimate the $PM_{10}$-health outcome associations. Results: When temperature was low (below the threshold temperature), the percentage increases per $10{\mu}g/m^3$ increase of $PM_{10}$ increased 0.38% (95% Confidence Interval[CI]: 0.09~0.68%) and 0.31% (95% CI: - 0.07~0.68%) of mortality in the all age group and ${\geq}65$ year age group, respectively. When temperature was high (above the threshold temperature), the percentage increases per $10{\mu}g/m^3$ increase of $PM_{10}$ increased 1.09% (95% CI: 0.47~1.72%) and 1.35% (95% CI: 0.65~2.06%) for mortality in the all age group and ${\geq}65$ year age group, respectively. Conclusion: The results of this study showed strong modification by temperature in the association between $PM_{10}$ and mortality. We recommend that public health strategies to minimize adverse health impact of heat and $PM_{10}$ should be considered in control and prevention measures for air pollution and weather-related health impacts.

• Analytical Science and Technology
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• v.24 no.1
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• pp.24-37
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• 2011

The ionic compositions of atmospheric aerosols were investigated and characterized in coastal and mountainous sites of Jeju Island, which were Gosan and Mt. Halla-1100 sites respectively. It was found that the concentrations of nss-$SO_4^{2-}$ (non-sea salt sulfate) and $NH_4^+$ at two measurement sites were almost similar and they showed high concentrations in June at the same time. The concentration of soil-originated nss-$Ca^{2+}$ (non-sea salt calcium) was generally associated with that of anthropogenic $NO_3^-$ and its concentration was increased during spring season. From the comparison of time-series variation and regression analysis of ionic species between two measurement sites, TSP (total suspended particulate) in Gosan site was directly influenced by sea salt particles and the concentration levels of nss-$SO_4^{2-}$, $NH_4^+$, nss-$Ca^{2+}$, $K^+$, and $NO_3^-$ were mainly related with long-range transported air pollutants rather than local pollution sources.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.160-166
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• 2018

The worldwide semi-conductor market has been growing for a long time. Manufacturing lines of semi-conductors need to handle several types of toxic gases. In particular, they need to be controlled accurately in real time. This type of toxic gas control system consists of many different kinds of parts, e.g., fittings, valves, tubes, filters, and regulators. These parts obviously need to be manufactured precisely and be corrosion resistant because they have to control high pressure gases for long periods without any leakage. For this, surface machining and hardening technologies of the metal block and metal gasket need to be studied. This type of study depends on various factors, such as geometric shapes, part materials, surface hardening method, and gas pressures. This paper presents strong concerns on a series of simulation processes regarding the differences between the inlet and outlet pressures considering several different fluid velocity, tube diameters, and V-angles. Indeed, this study will very helpful to determine the important design factors as well as precisely manufacture these parts. The EP (Electrolytic Polishing) process was used to obtain cleaner surfaces, and hardness tests were carried out after the EP process.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.1960-1971
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• 1998

The researches and developments to provide multimedia communication services such as Video on Demand(VoDJ), real time video phonc and multipoint vidco conferencing on broadband ISDN environmcnts have been proceeded with activity. Specifications for Vol) services which is worked by Digital Audio-Visual Council(DAVIC) to support detail technologies including total service system that is consist of VoD server. delive[\! networl, and Set-Top Box(STB) had been already finished and ITU-T SG16 also recommended the standards of H.300 series terminal aspects for conversational multimedia services, But the architectures of multimedia tenninals recommended and specified by these organizations do not have an efficient st11lcture to provide all of retrieval, distrihution and conversational service due to a different point of view about multimedia terminals and services. In this paper, we analyzed the recornmendatio!E and the specifications of intemational public and private organizations like lTU-T, DAVIC and ATM forum. As a result of these analysis. we propose an efficient terminal architecture, and then we have designed, lmplemented the multimedia communication terminal for offering VoI) and real- time conversation ,,, functional module test according to the individual commumication service session and confirined the validiry or terminal implemented to be used on broadband ISDK environments.

• Journal of Korean Society of Forest Science
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• v.73 no.1
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• pp.70-75
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• 1986

The secular trend and seasonal variation of chestnut prices have been analyzed, and the production and price for the next two decades (1985-2004) have been forecasted by the derived equation model. The results of the study can be summarized as follows; 1) The chestnut prices went up at the rate of 10.95% per annum during 1965-1972, but, due to excessive supply of chestnuts, went down at the rate of 7.25% during 1973-1984. 2) In a year, the prices were lowest at the harvesting season, especially on October, and highest on July. Such a seasonal fluctuations of chestnut prices tend to be even with the passage of time, but the range of fluctuation is still wide. 3) It was forecasted under certain premises that the annual chestnut production will be increased by 99,000 tons in 1992, but the amount will fall rapidly to about 23,000 tons in 2004. The prices will be similar to the present level or have slightly upward Tendency until 1992, but this will be rapidly raised thereafter.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• 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.34 no.6_1
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• pp.1143-1153
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• 2018

For TOPS InSAR processing, high-precision image co-registration is required. We propose an image co-registration method suitable for the TOPS mode by comparing the performance of cross correlation method, the geometric co-registration and the enhanced spectral diversity (ESD) matching algorithm based on the spectral diversity (SD) on the Sentinel-1 TOPS mode image. Using 23 pairs of interferometric pairs generated from 25 Sentinel-1 TOPS images, we applied the cross correlation (CC), geometric correction with only orbit information (GC1), geometric correction combined with iterative cross-correlation (GC2, GC3, GC4), and ESD iteration (ESD_GC, ESD_1, ESD_2). The mean of co-registration errors in azimuth direction by cross correlation and geometric matching are 0.0041 pixels and 0.0016 pixels, respectively. Although the ESD method shows the most accurate result with the error of less than 0.0005 pixels, the error of geometric co-registration is reduced to 0.001 pixels by repetition through additional cross correlation matching between the reference and resampled slave image. The ESD method is not applicable when the coherence of the burst overlap areas is low. Therefore, the geometric co-registration method through iterative processing is a suitable alternative for time series analysis using multiple SAR data or generating interferogram with long time intervals.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.203-209
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• 2019

In this work, a new land-specific resistance measuring device (GM) and a measuring probe (Grounding Rod) are connected to the WENNER quadrant as power-line communication (PLC). In groups of two (P1,P2) probes, five to ten probes are installed in series on the ground at intervals of 1m, 2m, 4m, 8m, and 16m, respectively. If the PLC signal from the GMD is detected by the receiver of the Probe 1 (P1) for measurement, the minute voltage and current for measurement flow from the PSD (power supply) attached to the probe to the ground, and then, through the soil between P1 and P2, enters the Probe 1 (P2). The resistance value is then measured by the principle of voltage drop due to ground resistance. Measure the earth resistance every T seconds up to 1 trillion and store the measured data on the Arduino Server mounted on the main equipment. Stored measurement data can be derived from formulas by Ohm's Law and from inherent resistance (here,). Data obtained in real time will be linked to CDGES programs installed on Main PC, enabling data analysis and real-time monitoring of the ground environment on land. In addition, a three-dimensional display is possible with 3D graph support by identifying seasonal characteristics such as temperature and humidity of land (soils). The limitations of the study will require specific application measures of Test Bed for commercial access to a model that has been developed and operated experimentally.