• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.16-24
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• 2011

The sound speed of seawater can be calculated by the empirical formula as a function of temperature, salinity and pressure. It is little affected by salinity because the average salinity is 34 psu and varies within a few psu seasonally and spatially in the ocean. Recently, low-salinity water of 24 psu flows into the western sea area of Jeju Island due to the flood of the Yangtze River in China during summer, affecting sound speed profile. In this paper, it was analyzed how environmental changes affected to the underwater communication - the sound speed of low-salinity water was calculated, and the communication channel was estimated by the simulated acoustic rays while the transmitting and receiving depth and the range were varied with and without the low-salinity layer. And The BER (Bit error rate) was calculated by BPSK(Binary phase shift key) modulation and the effects of the low-salinity water on the BER was investigated. The sound speed profile was changed to have positive slope by the low-salinity layer at the sub-surface up to 20 m of depth, forming acoustic wave propagation channel at the sub-surface resulting in the decrease of most of the BER Consequently, this paper suggests that it is important to consider changes of the ocean environment for correctly analyzing the underwater communication and the detection capability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1479-1487
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• 2013

The river levee collapse and flood damages are dramatically increased due to the floods which caused by abnormal weather nowadays. The counterplan like TDR(Time Domain Reflectometry) river levee leaking exploration technique is needed to that levee failure causes of levee failure such as levee failure by penetration, piping, inadequate levee materials selection, poor compaction are almost 52% of the failure. This research practiced various comparing experiments of existing TDR(probe and tube types) and developing CAP type TDR to evaluate acrylic small CAP mould and low-cost TDR levee leaking monitoring system which was used probe type TDR. As the result, evaluated TDR system had 20cm critical exploration performance which was a leaking exploration performance, The functional ratio of TDR exploration sensitivity of dry density was sensitive more than 3 times than dry density, and weathered granite soil foundation water contents(w)-dielectric constant(${\epsilon}$) corelation formula was suggested to measure functional ratio on developing cap type TDR system.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.96-110
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• 2020

Recently, due to global warming, it is easily exposed to various disasters such as fire, flood, and earthquake. In particular, large-scale disasters have continuously been occurring in crowded areas such as traditional markets, facilities for the elderly and children, and public facilities where various people stay. Purpose: This study aims to detect a fire occurred in crowded facilities early in the event to analyze and provide an optimal evacuation route using big data and advanced technology. Method: The researchers propose a new algorithm through context-aware 3D object model technology and A* algorithm optimization and propose a scenario-based optimal evacuation route selection technique. Result: Using the HPA* E algorithm, the evacuation simulation in the event of a fire was reproduced as a 3D model and the optimal evacuation route and evacuation time were calculated for each scenario. Conclusion: It is expected to reduce fatalities and injuries through the evacuation induction technique that enables evacuation of the building in the shortest path by analyzing in real-time via fire detection sensors that detects the temperature, flame, and smoke.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.889-900
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• 2017

In this study, we analyzed the paper about NIR (Near-Infrared) remote sensing data and systematically summarized the research and application fields of NIR. To do this, we conducted a case study on the use of NIR in domestic journals, and SCI journals in the field of technology development for the last 5 years. After selection, a total of 281 journals were analyzed. For the statistical analysis, the classification was divided into subclasses and the dominant research trends were examined. As a result, the researchers who wrote the papers made the highest score of about 60% or more at university. In the field of application, 50% of land, 30% of environment, and 11% of disaster were distributed on SCI journals. In Korea, on the other hand, 55% of land, 24% of environment and 10% of disasters were distributed. In addition, 17% of the national land management and 8% of the geological / natural resources. Disaster observation using NIR was used for landslide, drought, weather disaster and flood. In particular, meteorological disasters are a result of study on Asian dust. However, there were no results of forest fire detection in Korea. Considering the domestic situation, it seems necessary to carry out additional and active research on this. It is expected that this statistical analysis data will be used as basic data to help expand the NIR technology development and utilization field in Korea in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Korean Journal of Environmental Agriculture
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• v.28 no.1
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• pp.1-8
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• 2009

This study was aimed at assess environmental conservation functions by analyzing the change of land use areas in agricultural fields between 1999 and 2006, and comparing land surface temperature distribution between 1994 and 2006 in Yongin city. Land use maps of Yongin city were obtained from soil maps for 1999, Quickbird satellite images(less than 1 m) and parcel map for 2006. The land use area for Yongin city was in the order of forest > paddy field > upland > residence & building in 1999, and forest > residence & building > paddy field > upland in 2006. Decrease of paddy and upland fields reduced 34% and 41% of the capability of agricultural multifunctionality as to environment including flood control, groundwater recharge, and air cooling. Land surface temperature(LST) was derived from Landsat TM thermal infrared band acquired in September of 1994 and 2006 and classified into three grades. The results impplied that green vegetation in agricultural field and forest play an important role to reduce land surface temperature in warm season.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.