• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.1
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• pp.68-82
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• 2017

Strengthening surveillance, one of the most important factors in the crime prevention environment of cultural heritages, has difficulty in evaluating and diagnosing the site. For this reasons, surveillance enhancement has been assessed by modelling the shape of cultural heritage, topography, and trees digitally. The purpose of this study is to develop the evaluation method of crime prevention environment for cultural heritage by using the 3D visual exposure index (3DVE) which can quantitatively evaluate the surveillance enhancement in three dimensions. For the study, the evaluation factors were divided into natural, organizational, mechanical, and integrated surveillance. To conduct the analysis, the buildings, terrain, walls, and trees of the study site were modeled in three dimensions and the analysis program was developed by using the Unity 3D. Considering the working area of the person, it is possible to analyze the surveillance point by dividing it into the head and the waist position. In order to verify the feasibility of the 3DVE as the analysis program, we assessed the crime prevention environment by digitally modeling the Donam Seowon(Historic Site No. 383) located in Nonsan. As a result of the study, it was possible to figure out the problems of patrol circulation, the blind spot, and the weak point in natural, mechanical, and organizational surveillance of Donam Seowon. The results of the 3DVE were displayed in 3D drawings, so that the position and object could be identified clearly. Surveillance during the daytime is higher in the order of natural, mechanical, and organizational surveillance, while surveillance during the night is higher in the order of organizational, mechanical, and natural surveillance. The more the position of the work area becomes low, the more it is easy to be shielded, so it is necessary to evaluate the waist position. It is possible to find out and display the blind spot by calculating the surveillance range according to the specification, installation location and height of CCTV. Organizational surveillance, which has been found to be complementary to mechanical surveillance, needs to be analyzed at the vulnerable time when crime might happen. Furthermore, it is note that the analysis of integrated surveillance can be effective in examining security light, CCTV, patrol circulation, and other factors. This study was able to diagnose the crime prevention environment by simulating the actual situation. Based on this study, consecutive researches should be conducted to evaluate and compare alternatives to design the crime prevention environment.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.