• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.192-206
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• 2008

Multihop is the main communication style for wireless sensor networks composed of tiny sensor nodes. Until now, most applications have treated the periodic small sized sensing data. Recently, the burst traffic with the transient and continuous nature is increasingly introduced due to the advent of wireless multimedia sensor networks. Therefore, the efficient communication protocol to support this trend is required. In this paper, we propose a novel PIGAB(Packet Interval Gap based on Adaptive Backoff) protocol to efficiently transmit the burst data in multihop wireless sensor networks. The contention-based PIGAB protocol consists of the PIG(Packet Interval Gap) control algorithm in the source node and the MF(MAC-level Forwarding) algorithm in the relay node. The PIGAB is on basis of the newly proposed AB(Adaptive Backoff), CAB(Collision Avoidance Backoff), and UB(Uniform Backoff). These innovative algorithms and schemes can achieve the performance of network by adjusting the gap of every packet interval, recognizing the packet transmission of the hidden node. Through the simulations and experiments, we identify that the proposed PIGAB protocol considerably has the stable throughput and low latency in transmitting the burst data in multihop wireless sensor networks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.4
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• pp.77-85
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• 1990

The application of remote sensing in small scale mapping has recently been widened to various fields such as information analysis of landuse, environmental conservation and natural resources. SPOT imagery, in particular, offers data which can be processed for 3-dimensional point determination. This is made possible by its high resolution, appropriate swatch width/altitude ratio and stereo imaging capabilities. This study aims to develop a suitable polymonial and an algorithm in the determination of exterior orientation which is essential in the 3-dimensional point determination of SPOT imgery. An algorithm is presented in this study to determine the exterior orientation of a preprocessed level lB film of the satellite image. It was found that a polynominal of 15 parameters is the best fit polynominal for exterior orientation determination, where 1st order line function is used for positon ($X_o$, $Y_o$, $Z_o$) and 2nd order line function is used for orientation (${\kappa}_o$, ${\phi}_o$, ${\omega}_o$).

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.113-122
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• 2007

The rapid growth of aerial survey and remote sensing technology has enabled the rapid acquisition of very large amounts of geographic data, which should be analyzed using real-time visualization technology. The level of detail(LOD) algorithm is one of the most important elements for realizing real-time visualization. We chose the triangulated irregular network (TIN) method to generate normalized digital elevation model(DEM) data. First, we generated TIN data using contour lines obtained from a two-dimensional(2D) digital map and created a 2D grid array fitting the size of the area. Then, we generated normalized DEM data by calculating the intersection points between the TIN data and the points on the 2D grid array. We used constrained Delaunay triangulation(CDT) and ray-triangle intersection algorithms to calculate the intersection points between the TIN data and the points on the 2D grid array in each step. In addition, we simulated a three-dimensional(3D) terrain model based on normalized DEM data with real-time visualization using a Microsoft Visual C++ 6.0 program in the DirectX API library and a quad-tree LOD algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.81-87
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• 2010

The coverage ability is one of essential techniques for the Robotic Vacuum Cleaner (RVC). Most of the RVCs rely on random or regular pattern movement to cover a target space due to the technical difficulties to implement localization and map and constraints of hardwares such as controller and sensors. In this paper, we consider two main issues which are low computational load and using sensors with very limited sensing capabilities. First, in our approach, computing procedures to build map and detect the RVC's position are minimized by simplifying data obtained from sensors. To reduce computational load, it needs simply presenting an environment with objects of various shapes. Another isuue mentioned above is regarded as one of the most important problems in our approach, because we consider that many RVCs use low-cost sensor systems such as an infrared sensor or ultrasonic sensor with limited capabilities in limited range, detection uncertainty, measurement noise, etc. Methods presented in this paper are able to apply to general RVCs equipped with these sensors. By both simulation and real experiment, we evaluate our method and verify that the proposed method guarantees a complete coverage.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.286-291
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• 1998

SAR has provided weather independent images on land and sea surface, which can be used for extracting various useful informations. Recently attempts to estimate wind field parameters from SAR images over the oceans have been made by various groups over the world. Although scatterometer loaded in ERS-1 and ERS-2 observes the global wind vector field at spatial resolution of 50 Km with accuracies of $\pm$2m/s in speed, the spatial resolution may not be good enough for applications in coastal regions. It is weil known the sea surface roughness is closely correlated to the wind field, but the wind retrieval algorithms from SAR images are yet in developing stage. Since the radar backscattering properties of the SAR images are principally the same as that of scatterometer, some previous studies conducted by other groups report the success in mesoscale coastal wind field retrievals using ERS SAR images. We have tested SWA (SAR Wind Algorithm) and CMOD4 model for estimation of wind speed using an ERS-1 SAR image acquired near Cheju Island, Korea, in October 11, 1994. The precise estimation of sigma nought and the direction of wind are required for applying the CMOD4 model to estimate wind speed. The wind speed in the test sub-image is estimated to be about 10.5m/s, which relatively well agrees to the observed wind speed about 9.0m/s at Seoguipo station. The wind speed estimation through the SWA is slightly higher than that of CMOD4 model. The sea surface condition may be favorable to SWA on the specific date. Since the CMOD4 model requires either wind direction or wind speed to retrieve the wind field, we should estimate the wind speed first using other algorithm including SWA. So far, it is not conclusive if the SWA can be used to provide input wind speed data for CMOD4 model or not. Since it is only initial stage of implementing the wind field retrieval algorithms and no in-situ observed data is currently avaliable, we are not able to evaluate the accuracy of the results at the moment. Therefore verification studies should be followed in the future to extract reliable wind field information in the coastal region using ERS SAR images.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.6
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• pp.10-21
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• 2010

In the cluster-tree network which covers wide area network and has many nodes for monitoring purpose traffic is concentrated around the sink. There are long transmit delay and high data loss due to the intensive traffic when IEEE 802.15.4 is adapted to the cluster-tree network. In this paper we propose Dynamic Channel Allocation algorithm which dynamically allocates channels to increase the channel usage and the transmission success rate. To evaluate the performance of DCA, we assumed the monitoring network that consists of a cluster-tree in which sensing data is transmitted to the sink. Analysis uses the traffic data which is generated around the sink. As a result, DCA is superior when much traffic is generated. During the experiment assuming the least amount of traffic, IEEE 802.15.4, has the minimum length of active period and 90% data transmission success rate. However DCA maintains 11.8ms of active period length and results in 98.9% data transmission success rate.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.253-265
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• 2020

As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.285-293
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• 2010

Soil moisture is a key factor to control the exchange of water and energy between the surface and the atmosphere. In recent, many researches for spatial and temporal variability analyses of soil moisture have been conducted. In this study, we analyzed the spatio-temporal variability of soil moisture in Walnut Gulch Experimental Watershed, Arizona, U.S. during the Soil Moisture Experiment 2004 (SMEX04). The spatio-temporal variability analyses were performed to understand sensitivity of five observation sites with precipitation and relationship between mean soil moisture, and its standard deviation and coefficient of variation at the sites, respectively. It was identified that log-normal distribution was superior to replicate soil moisture spatial patterns. In addition, precipitation was identified as a key physical factor to understand spatio-temporal variability of soil moisure based on the temporal stability analysis. Based on current results, higher spatial variability was also observed which was agreed with the results of previous studies. The results from this study should be essential for improvement of the remotely sensed soil moisture retrieval algorithm.