• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.107-116
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• 2012

In this paper we propose a method that can generate the risk probability map in the form of raster shape by using Markov Chain methodology applied to the object interpretation keys and quantified risk indexes. These object interpretation keys, which are primarily characteristics that can be identified by the naked eye, are set based on the objects that comprise the spatial information of a certain urban area. Each key is divided into a cell, and then is weighted by its own risk index. These keys in turn are used to generate the unified risk probability map using various levels of crime prediction probability maps. The risk probability map may vary over time and means of applying different sets of object interpretation keys. Therefore, this method can be used to prevent crimes by providing the ways of setting up the best possible police patrol beat as well as the optimal arrangement of surveillance equipments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2B
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• pp.44-52
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• 2005

This paper proposes a neural network prediction model to improve the valid packet transmission rate for the QoS(Quality of Service) of multimedia transmission. The Round Trip Time(RTT) and Packet Loss Rate(PLR) are predicted using a neural network and then the transmission rate is decided based on the predicted RTT and the PLR. The suggested method will improve the transmission rate since it uses the rate control factors corresponding to time of data is being transmitted, while the conventional one uses the transmission rate determined based on the past informations. An experimental set-up has been established using a Linux PC system, and the multimedia data are transmitted using UDP protocol in real time. The valid transmitted packets are about 5% higher than the one in the conventional TCP-Friendly congestion control method when the suggested algorithm was applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• Korean Journal of Environment and Ecology
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• v.10 no.2
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• pp.251-269
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• 1997

To investigate the plant community structure of Chohangyoung valley in Soraksan National Park, thirty nine plots(each size was 100m$^{2}$) were set up and surveyed and to analyze the plant community characteristics of valley area and slope area, twenty five plots in five sites were set up and surveyed. According to DCA ordination techniques, the communities were six divided into community types, which were Pinus densiflora community, P. densiflora-Populus maximowiczii community, Po. maximowiczii-P. densiflora community, Po. maszimowiczii community, Fraxinus rhyuchophylla community, Quercus serrata community. Q. serrata community was only distrivuted at slope area and the others were distributied at valley area. The successional trend of six communities was not clearly inferred. Shannon's diversity was 0.9458~1.1769(unit area:500m$^{2}$), and soil acidity was pH 4.65~6.09 in surveyed areas. According to the belt-transect analysis, the dominant species of valley area were P. densiflora, Po. maximowiczii, but the dominant species of slope area was Q. serrata.

• Spatial Information Research
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• v.10 no.1
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• pp.29-43
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• 2002

The purpose of this study is to set up the GIS Audit methodology through the definition of standard procedure ailed GIS Audit criteria and the result of preliminary study about the GIS Audit. the frame of GIS Audit criteria and the result of preliminary study about the GIS Project and check the GIS Audit methodology is to define a standard process of GIS Project and check the GIS Project according to the standard process. We named the method as CASP(GIS Audit Standard Procedure). GASP method means doing Pilot Project for setting the sample standard procedure(named Prototype) of total GIS Project process, and checking the activity to fulfill the prototype. GASP method is advanced method for GIS Audit to analyze the full range of GIS Project and characteristics of GIS contents.

• KIEAE Journal
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• v.9 no.4
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• pp.29-36
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• 2009

Artificial ground greening has been developed gradually in accordance with increasing demands of out-door space in Apartment complexes. Nowadays other social demand, environmental load abatement, needs qualitative growth of artificial ground greening as well as quantitative growth. So the objects of this study would be seizing and analyzing changeable items in artificial ground greening in the future, and show drafting materials for the development of spheres in connected with artificial ground greening. For this study, Delphi method was applied. First, three groups of panel, 48 people, were selected. Second, set up items of changes possible in the future from the first questionnaire and additional inquiry. Third, made up the second questionnaire of change possible in the future with Likert summated scale, and finally one way - ANOVA executed; independent variables were items of changes, and dependent variables were three groups of panel. To conclude, although limits of this study, we could glance over general flows and changes in artificial ground greening, and discover items which are hardly changeable and necessary to change in present condition of artificial ground greening.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2844-2851
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• 2011

The purpose of this study is to set up an acoustic prediction technique and to perform the IL test of scale down noise reducing device for the development of the noise reducing device as the development of 400km/h class high speed train. First of all, the IL prediction of noise reducing device was performed with the 2D BEM method. And the noise test of scale down noise reducing device in anechoic chamber was performed for the verification of acoustic prediction technique and IL performance evaluation. As the results, the acoustic prediction technique for the development of noise reducing device was verified because the averaged IL difference between prediction and test is in 2dB(A). And the measured IL value of noise reducing device is less than 2dB(A), and additional IL with polyester absorption material is increased about 0.5dB(A).

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• v.y2005m4
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• pp.233-236
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• 2005

In this study, the fundamental design procedure for the Smart UAV fuel supply system was set up, and the preliminary design was performed to meet the vehicle system requirements. The fuel system layout was determined through consideration of vehicle system requirements, and then fuel tank layout, design of components such as booster pump, jet pump, pipe, vent system, weight estimation, etc. were carried out.

• Journal of Environmental Science International
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• v.15 no.2
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• pp.141-155
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• 2006

This study is aimed at the development of a runoff forecasting model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting, The study area is the downstream of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model. The model performance was improved as the measuring time interval$(T_m)$ was smaller than the sampling time interval$(T_s)$. The Neuro-Fuzzy(NF) and TANK models can give more accurate runoff forecasts up to 4 hours ahead than the Feed Forward Multilayer Neural Network(FFNN) model in standard above the Determination coefficient$(R^2)$ 0.7.