• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4C
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• pp.333-341
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• 2002

This article is written to make a foundation for effective radio network engineering by enabling the accurate traffic assumption between Base Station (BTS : Base Station Transceiver Subsystem) md Repeater through the scientific and systematic analysis of mobile traffic dealt within CDMA Base Station. Among these, Repeater, having direct physical connection with BTS, simply does the remote relay function but the volume of the traffic flowed into the repeater is not accountable. The total BTS traffic is the sum of traffic dealt by the BTS and multiple Repeaters. In this article, we tried traffic analysis of this kind by adopting RTD (Round Trip Delay) which is specially designed to measure distribution of the distance between the Base Station and the Mobile station by differentiating the traffic dealt by BTS from traffic dealt by Repeaters. The fact that the connection between mobile station and BTS via Repeater (which is located remotely) yields far more delay than the direct connection between mobile station and BTS is the clue of this article. Based on this fact, Time Delay equipment was put at the receiving side of the Repeater to add certain amount of delay to the traffic to BTS and the result showed that the 99.78% of the traffic can be identified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1708-1721
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• 2011

Recent outbreak of cattle diseases such as foot-and-mouth disease(FMD) requires constant monitoring of burial sites of mass cull of cattles. However, current monitoring system takes environmental samples from burial sites with period of between one and two weeks, which makes it impossible for non-stop management of hazardous bio-waste. Therefore, in this study, we suggest an improved real-time environmental monitoring system for such bio-hazardous sites based on wireless sensor networks, which makes constant surveillance of the FMD burial sites possible. The system consists mainly several wireless environmental monitoring sensors(i.e dust, Co2, VOC, NH3, H2S, temperature, humidity) nodes and GPS location tracking nodes. Through analysis of the relayed of the environmental monitoring data via gateway, the system makes it possible for constant monitoring and quick response for emergency situation of the burial sites. In order to test the effectiveness of the system, we have installed a set of sensor to gas outlets of the burial sites, then collected and analyzed measured bio-sensing data. We have conducted simulated emergency test runs and was able to detect and monitor the foul smell constantly. With our study, we confirm that the preventive measures and quick response of bio environmental accident are possible with the help of a real-time environmental monitoring system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.274-284
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• 2022

To use kenaf (Hibiscus cannabinus L.) as a fiber and livestock feed, a high-yielding variety needs to be identified. For this, accurate phenotyping of plant height is required for this breeding purpose due to the strong relationship between plant height and yield. Plant height can be estimated using RGB images from unmanned aerial vehicles (UAV-RGB) and photogrammetry based on Structure from Motion (SfM) algorithms. In kenaf, accurate measurement of height is limited because kenaf stems have high flexibility and its height is easily affected by wind, growing up to 3 ~ 4 m. Therefore, we aimed to identify a method suitable for the accurate estimation of plant height of kenaf and investigate the feasibility of using the UAV-RGB-derived plant height map. Height estimation derived from UAV-RGB was improved using multi-point calibration against the five different wooden structures with known heights (30, 60, 90, 120, and 150 cm). Using the proposed method, we analyzed the variation in temporal height of 23 kenaf cultivars. Our results demontrated that the actual and estimated heights were reliably comparable with the coefficient of determination (R²) of 0.80 and a slope of 0.94. This method enabled the effective identification of cultivars with significantly different heights at each growth stages.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.1 s.53
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• pp.88-96
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• 2009

This study proposes a wireless measurement system that is a new safety management system by using an FBG sensor and a PDA. The sensor part has many advantages of implementing a wireless measurement system, and the study emploies an FBG-LVDT sensor, FBG-STRAIN sensor, FBG-TEMP sensor, and FBG-ACC sensor, using FBG sensors. Also, the study show a configuration of a signal process system for operating a wireless transmission system of FBG sensors applied to the signal process system, and engrafted the cutting edge information technology industry in order to display from a remote distance using a PDA. In order to verify the applicability of the developed FBG sensors and wireless measurement monitoring system to the field, their accuracy, and usability, the study has conducted a static and dynamic test to a bridge in the field. The study made an assessment of service for the vibration of the bridge by applying dynamic data measured by an FBG-LVDT sensor and FBG-ACC sensor to Meister's curve and prepared methods for assessing the vibration of the bridge by proposing a standard of vibration limitation given the service of vibration of the bridge. As a follow up for this study, it would be necessary to set up an overall model for the standard of service assessment established in this study.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Journal of KIISE:Information Networking
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• v.29 no.3
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• pp.286-298
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• 2002

The Remote Procedure Call(RPC) has been traditionally used for Inter Process Communication(IPC) among precesses in distributed computing environment. As distributed applications have been complicated more and more, the Mobile Agent paradigm for IPC is emerged. Because there are some paradigms for IPC, researches to evaluate and compare the performance of each paradigm are issued recently. But the performance models used in the previous research did not reflect real distributed computing environment correctly, because they did not consider the evacuation elements for providing security services. Since real distributed environment is open, it is very vulnerable to a variety of attacks. In order to execute applications securely in distributed computing environment, security services which protect applications and information against the attacks must be considered. In this paper, we evaluate and compare the performance of the Remote Procedure Call with that of the Mobile Agent in IPC paradigms. We examine security services to execute applications securely, and propose new performance models considering those services. We design performance models, which describe information retrieval system through N database services, using Petri Net. We compare the performance of two paradigms by assigning numerical values to parameters and measuring the execution time of two paradigms. In this paper, the comparison of two performance models with security services for secure communication shows the results that the execution time of the Remote Procedure Call performance model is sharply increased because of many communications with the high cryptography mechanism between hosts, and that the execution time of the Mobile Agent model is gradually increased because the Mobile Agent paradigm can reduce the quantity of the communications between hosts.