• Geophysics and Geophysical Exploration
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• v.12 no.2
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• pp.208-214
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• 2009

Using the GNSS data and tilt-meter data of Boeun (BOEN) and Goesan (GSAN) GNSS stations, we have calculated the differential distance vector variation with the calculation time span set to 1 hour and 3 hour and differential tilt vector variation along time and derived an indicator of similarity between the two variations along time. The similarity such calculated is rather lower than high. But as the existence of a circular type movement of the antenna's phase center's location due to the tilt's variation of the antenna tower because of the sunlight's diurnal change is certain, we recommend to take such diurnal variation of antenna's location into consideration when the correction error in DGNSS or the measured data at reference stations in VRS (Virtual Reference System) is broadcast.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.9-18
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• 2013

In this paper, we design and implement an intelligent system for finding objects with RFID(Radio Frequency IDentification) tag in which an mobile robot can do. The system we developed is a learning system of artificial neural network that uses RSSI(Received Signal Strength Indicator) value as input and absolute coordination value as target. Although a passive RFID is used for location estimation, we consider an active RFID for expansion of recognition distance. We design the proposed system and construct the environment for indoor location estimation. The designed system is implemented with software and the result related learning is shown at test bed. We show various experiment results with similar environment of real one from earning data generation to real time location estimation. The accuracy of location estimation is verified by simulating the proposed method with allowable error. We prepare local test bed for indoor experiments and build a mobile robot that can find the objects user want.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.677-693
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• 2013

IPCC showed that calculation of climate vulnerability index requires standardization process of various proxy variables for the estimation of climate exposure, sensitivity, and adaptive capacity. In this study, four different methodologies of standardization methods: Z-score, Rescaling, Ranking, and Distance to the reference country, are employed to evaluate climate vulnerability-VRI (Vulnerability-Resilience Indicator) over Korean peninsula, and the error ranges of VRI, arising from employing the different standardization are estimated. All of proxy variables are provided by CCGIS (Climate Change adaptation toolkit based on GIS) which hosts information on both past and current socio-economic data and climate and environmental IPCC SRES (A2, B1, A1B, A1T, A1FI, and A1 scenarios) climate data for the decades of 2000s, 2020s, 2050s, and 2100s. The results showed that Z-score and Rescaling methods showed statistically undistinguishable results with minor differences of spatial distribution, while Ranking and Distance to the reference country methods showed some possibility to lead the different ranking of VRI among South Korean provinces, depending on the local characteristics and reference province. The resultant VRIs calculated from different standardization methods showed Cronbach's alpha of more than 0.84, indicating that all of different methodologies were overall consistent. Similar horizontal distributions were shown with the same trends: VRI increases as province is close to the coastal region and/or it close toward lower latitude, and decreases as it is close to urbanization area. Other characteristics of the four different standardization are discussed in this study.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.1
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• pp.1-6
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• 2015

The purpose of this study is to suggest the effective operation method by developing prediction model for the gas production rate, an indicator of the effectiveness of anaerobic digestion tank, using data mining. At the result, gas production estimate model is developed by using ANN within 10% error. It is expected to help operation of anaerobic digestion by suggesting selected parameter. Meanwhile case based reasoning is applied to develop dewatering cake management technology. Case based reasoning uses the most similar examples of past when a new problem occurs, therefore in this study, management measures are developed that proposes dewatering cake minimization with the minimum change by applying the case based reasoning to sludge disposal process.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.11
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• pp.60-66
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• 2011

LTE (Long Term Evolution) supports QoS (Quality of Service) with several service classes. For each class of traffic, a big difference exists on BER (Bit Error Rate) requirement. This leads to a considerable difference in transmission power for various classes of traffic. In this paper, a novel scheduler is designed and proposed for LTE which supports CoS (Class of Service) with the consideration of priority as well as target BER. By the CQI (Channel Quality Indicator) and QCI (QoS Class Identifier), a minimum transmission power is assigned from the target BER for each class of traffic per each user. Hence, with the other information such as user's used rate in the past and the priority of traffic, the probability of occupying channels is determined. The simulation results of Service Class scheduling are compared with that of Maximum Rate and Proportional Fair. The results show that the service class-aided scheduling can improve the throughput of whole system significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.5
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• pp.429-434
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• 2007

This study was conducted to estimate site productivity of Quercus acutissima and Quercus mongolica by four forest climatic zones. We used site environmental variables (28 geographical and pedological factors) and site index as a site productivity indicator from nation-wide 23,315 stands. Based on multiple regression analysis between site index and major environmental variables, the best-fit multivaliate models were made by each species and forest climatic zone. Most of site index prediction models by species were regressed with seven to eight factors, including altitude, relief, soil depth, and soil moisture etc. For those models, three evaluation statistics such as mean difference, standard deviation of difference, and standard error of difference were applied to the test data set for the validation of the results. According to the evaluation statistics, it was found that the models by climatic zones and species fitted well to the test data set with relatively low bias and variation. Also having above middle of site index range, total area of productive sites for the two Quercus spp. estimated by those models would be about 6% of total forest area. Northern temperate forest zone and central temperate forest zone had more productive area than southern temperate forest zone and warm temperate forest zone. As a result, it was concluded that the regressive prediction with site environmental variables by climatic zones and species had enough estimation capability of forest site productivity.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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• pp.193-208
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• 2022

In this study, we proposed a mathematical model that can explain the source of the observed variability of repeated measurement data collected in Korean medicine clinical practice, and conducted a pilot analysis to infer the source of these variability based on our model. Mathematical model was constructed by dividing the observed variations into three components: common time-dependent variations, signal shift, and measurement error. To show the applicability of our model in real data, we analyzed 20 repeated measurement data of Korean clinical indicators in graduate students of Pusan National University Graduate School of Korean Medicine. We showed how to infer each source of variations based on our model and also showed the limitation of inference given the acquired the dataset. On the basis of objective recognition of these source of the variability, we hope that quantitative investigations on these sources for each Korean medicine clinical indicator are made in the future, so that they can be used in the clinical and educational areas of Korean medicine.

• Asian Journal of Business Environment
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• v.13 no.2
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• pp.9-20
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• 2023

Purpose: This study aims to map the spatial distribution of poverty using nighttime light satellite images as a proxy indicator of economic activities and infrastructure distribution in D.I Yogyakarta, Indonesia. Research design, data, and methodology: This study uses official poverty statistics (National Socio-economic Survey (SUSENAS) and Poverty Database 2015) to compare satellite imagery's ability to identify poor urban areas in D.I Yogyakarta. National Socioeconomic Survey (SUSENAS), as poverty statistics at the macro level, uses expenditure to determine the poor in a region. Poverty Database 2015 (BDT 2015), as poverty statistics at the micro-level, uses asset ownership to determine the poor population in an area. Pearson correlation is used to identify the correlation among variables and construct a Support Vector Regression (SVR) model to estimate the poverty level at a granular level of 1 km x 1 km. Results: It is found that macro poverty level and moderate annual nighttime light intensity have a Pearson correlation of 74 percent. It is more significant than micro poverty, with the Pearson correlation being 49 percent in 2015. The SVR prediction model can achieve the root mean squared error (RMSE) of up to 8.48 percent on SUSENAS 2020 poverty data.Conclusion: Nighttime light satellite imagery data has potential benefits as alternative data to support regional poverty mapping, especially in urban areas. Using satellite imagery data is better at predicting regional poverty based on expenditure than asset ownership at the micro-level. Light intensity at night can better describe the use of electricity consumption for economic activities at night, which is captured in spending on electricity financing compared to asset ownership.