• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.7
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• pp.424-430
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• 2014

In this paper, we propose a compressed sensing-based signal recovery technique for an uplink multi-user spatial modulation (MU-SM) system. In the MU-SM system, only one antenna among $N_t$ antennas of each user becomes active by nature. Thus, this characteristics is exploited for signal recovery at a base station. We modify the conventional orthogonal matching pursuit (OMP) algorithm which has been widely used for sparse signal recovery in literature for the MU-SM system, which is called MU-OMP. We also propose a parallel OMP algorithm for the MU-SM system, which is called MU-POMP. Specifically, in the proposed algorithms, antenna indices of a specific user who was selected in the previous iteration are excluded in the next iteration of the OMP algorithm. Simulation results show that the proposed algorithms outperform the conventional OMP algorithm in the MU-SM system.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.412-419
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• 2010

Concurrent Multi Path-Differential Optical Absorption Spectroscopy (CMP-DOAS) is a novel active optical system to measure simultaneously ambient trace gases (such as $NO_2$, $SO_2$, $O_3$, and HCHO) present on several light paths. The CMP-DOAS system consists of a 2D CCD camera, spectrometer, receiving telescopes, and artificial light sources. The system receives spectra, which have been transported through several paths. It also covers wavelength ranges of which trace gases of interest share at the same time. This study presents the instrumental setup of a CMP-DOAS in detail. A field campaign for a comparative measurement was carried out at an urban site in Gwangju for a month on January 2009. $NO_2$ mixing ratios measured by the CMP-DOAS system and in-situ $NO_2$ analyzers were in good agreement by 83%. It demonstrates the high capacities of the CMP-DOAS technique to cover atmospheric trace gases dispersed across wide light paths.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.80-80
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• 2016

GPS로 대표되는 위성항법시스템(GNSS : Global Navigation Satellite System)은 지구 주위를 돌면서 연속적으로 항법신호를 보내고 있다. 그 중 지구표면으로부터 반사되는 항법신호를 수신하고 해석함으로써 지구표면에 관한 정보를 취득할 수가 있다. GPS로 대표되는 항법신호는 L밴드를 사용하기 때문에 토양수분의 변화 등에 대한 반사강도의 감도가 비교적 높다고 알려져 있으며, 토양수분 측정 등에 사용할 수 있다. 뿐만 아니라 경량화, 소형화하기 쉬운 점, 능동적 마이크로웨이브 리모트센싱시스템(Active Microwave Remote Sensing System)과 달리 스스로 신호를 발사하지 않기 때문에 관측의 스텔스성(Stealth)dl 뛰어난 점 등의 장점을 가지고 있다. 또한 향후 10년 이내에 준천정위성(QZSS), Galileo, COMPAS, IRNSS 등 많은 위성항법시스템이 본격 운용되어 GPS와 함께 120기 정도의 항법위성이 항법신호를 송신할 예정이므로 이용 가능성은 크게 늘어날 것으로 기대된다.한편, 항법위성을 이용한 바이스테이틱 리모트센싱은 반사파의 강도가 상당히 미약하기 때문에 정량적 계측모델의 구축은 미미한 상태이다. 즉, 지상 타워에서의 관측, 항공기에서의 관측, 소형 위성에서의 관측 등이 수행되고 있으나, 타워관측과 같이 지상의 거의 동일한 장소를 계속적으로 관측하는 경우를 제외한 기존의 연구에서는 토지의 피복상황이나 토양수분 등의 상관관계를 제시하는 수준으로써 정량적인 계측방법은 아직 확립되어 있지 않다. 이러한 관점에서 본 연구에서는 GPS위성으로부터의 항법신호를 이용하여 지구표면에 관한 정보를 얻는 바이스테이틱 리모트센싱(Bi-static Remote Sensing) 기술을 바탕으로 육지면과 해면의 판별에 신호특성이 어떻게 유효한가를 실험적으로 밝혔다. 이러한 기술은 토양수분 측정 등 수자 원인자를 추출하는데 유용할 뿐만 아니라 수면의 고도 측정, 해상풍 산출 등에도 응용 가능하다.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.641-659
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• 2010

Structural Health Monitoring (SHM) gradually becomes a technique for ensuring the health and safety of civil infrastructures and is also an important approach for the research of the damage accumulation and disaster evolving characteristics of civil infrastructures. It is attracting prodigious research interests and the active development interests of scientists and engineers because a great number of civil infrastructures are planned and built every year in mainland China. In a SHM system the sheer number of accompanying wires, fiber optic cables, and other physical transmission medium is usually prohibitive, particularly for such structures as offshore platforms and long-span structures. Fortunately, with recent advances in technologies in sensing, wireless communication, and micro electro mechanical systems (MEMS), wireless sensor technique has been developing rapidly and is being used gradually in the SHM of civil engineering structures. In this paper, some recent advances in the research, development, and implementation of wireless sensors for the SHM of civil infrastructures in mainland China, especially in Dalian University of Technology (DUT) and Harbin Institute of Technology (HIT), are introduced. Firstly, a kind of wireless digital acceleration sensors for structural global monitoring is designed and validated in an offshore structure model. Secondly, wireless inclination sensor systems based on Frequency-hopping techniques are developed and applied successfully to swing monitoring of large-scale hook structures. Thirdly, wireless acquisition systems integrating with different sensing materials, such as Polyvinylidene Fluoride(PVDF), strain gauge, piezoresistive stress/strain sensors fabricated by using the nickel powder-filled cement-based composite, are proposed for structural local monitoring, and validating the characteristics of the above materials. Finally, solutions to the key problem of finite energy for wireless sensors networks are discussed, with future works also being introduced, for example, the wireless sensor networks powered by corrosion signal for corrosion monitoring and rapid diagnosis for large structures.

• Nano
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• v.13 no.11
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• pp.1850134.1-1850134.10
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• 2018

Increasing active sites and enhancing electric conductivity are critical factors to improve sensing performance toward phenol. Herein, Ni nanoparticle was successfully anchored on acidified multiwalled carbon nanotube (a-MWCNT) surface by electroless plating technique to avoid Ni nanoparticle agglomeration and guarantee high conductivity. The crystal structure, phase composition and surface morphology were characterized by XRD, SEM and TEM measurement. The as-prepared Ni/a-MWCNT nanohybrid was immobilized onto glassy carbon electrode (GCE) surface for constructing phenol sensor. The phenol sensing performance indicated that Ni/a-MWCNT/GCE exhibited an amazing detection performance with rapid response time of 4 s, a relatively wide detection range from 0.01 mM to 0.48 mM, a detection limit of $7.07{\mu}M$ and high sensitivity of $566.2{\mu}A\;mM^{-1}\;cm^{-2}$. The superior selectivity, reproducibility, stability and applicability in real sample of Ni/a-MWCNT/GCE endowed it with potential application in discharged wastewater.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.77-91
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• 2017

Many debris landforms in the mountains of Korea have formed in the periglacial environment during the last glacial stage when the generation of sediments was active. Because these landforms are generally located on steep slopes and mostly covered by vegetation, however, it is difficult to observe and access them through field investigation. A scientific method is required to reduce the survey range before performing field investigation and to save time and cost. For this purpose, the use of remote sensing and GIS technologies is essential. This study has extracted the potential area of debris landform formation using a fuzzy set model as a mathematical data integration method. The first step was to obtain information about the location of debris landforms and their related factors. This information was verified through field observation and then used to build a database. In the second step, we conducted the fuzzy set modeling to generate a map, which classified the study area based on the possibility of debris formation. We then applied a cross-validation technique in order to evaluate the map. For a quantitative analysis, the calculated potential rate of debris formation was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). The prediction accuracy of the model was found to be 83.1%. We posit that the model is accurate and reliable enough to contribute to efficient field investigation and debris landform management.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.21-32
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• 2022

The importance of the classical theme of how the Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance of high-resolution satellite images match the actual atmospheric reflectance and surface reflectance has been emphasized. Based on the Radiometric Calibration Network (RadCalNet) BTCN and BSCN data, this study compared the accuracy of TOA and TOC reflectance products of the currently available optical satellites, including KOMPSAT-3, WorldView-2, and Pléiades-1A image sets calculated using the absolute atmospheric correction function of the Orfeo Toolbox (OTB) tool. The comparison experiment used data in 2018 and 2019, and the Landsat-8 image sets from the same period were applied together. The experiment results showed that the product of TOA and TOC reflectance obtained from the three sets of images were highly consistent with RadCalNet data. It implies that any imagery may be applied when high-resolution reflectance products are required for a certain application. Meanwhile, the processed results of the OTB tool and those by the Apparent Reflection method of another tool for WorldView-2 images were nearly identical. However, in some cases, the reflectance products of Landsat-8 images provided by USGS sometimes showed relatively low consistency than those computed by the OTB tool, with the reference of RadCalNet BTCN and BSCN data. Continuous experiments on active vegetation areas in addition to the RadCalNet sites are necessary to obtain generalized results.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.683-690
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• 2006

Reducing power consumption to extend network lifetime is one of the most important challenges in designing wireless sensor networks. One promising approach to conserving system energy is to keep only a minimal number of sensors active and put others into low-powered sleep mode, while the active sensors can maintain a connected covet set for the target area. The problem of computing such minimum working sensor set is NP-hard. In this paper, a centralized Voronoi tessellation (CVT) based approximate algorithm is proposed to construct the near optimal cover set. When sensor's communication radius is at least twice of its sensing radius, the covet set is connected at the same time; In case of sensor's communication radius is smaller than twice of its sensing radius, a connection scheme is proposed to calculate the assistant nodes needed for constructing the connectivity of the cover set. Finally, the performance of the proposed algorithm is evaluated through theoretical analysis and extensive numerical experiments. Experimental results show that the proposed algorithm outperforms the greedy algorithm in terms of the runtime and the size of the constructed connected cover set.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1307-1315
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• 2021

The Changjiang Diluted Water (CDW) spreads over the East China Sea every summer and significantly affects the sea surface salinity changes in the seas around Jeju Island and the southern coast of Korea peninsula. Sometimes its effect extends to the eastern coast of Korea peninsula through the Korea Strait. Specifically, the CDW has a significant impact on marine physics and ecology and causes damage to fisheries and aquaculture. However, due to the limited field surveys, continuous observation of the CDW in the East China Sea is practically difficult. Many studies have been conducted using satellite measurements to monitor CDW distribution in near-real time. In this study, an algorithm for estimating Sea Surface Salinity (SSS) in the East China Sea was developed using the Geostationary Ocean Color Imager (GOCI). The Multilayer Perceptron Neural Network (MPNN) method was employed for developing an algorithm, and Soil Moisture Active Passive (SMAP) SSS data was selected for the output. In the previous study, an algorithm for estimating SSS using GOCI was trained by 2016 observation data. By comparison, the train data period was extended from 2015 to 2020 to improve the algorithm performance. The validation results with the National Institute of Fisheries Science (NIFS) serial oceanographic observation data from 2011 to 2019 show 0.61 of coefficient of determination (R²) and 1.08 psu of Root Mean Square Errors (RMSE). This study was carried out to develop an algorithm for monitoring the surface salinity of the East China Sea using GOCI and is expected to contribute to the development of the algorithm for estimating SSS by using GOCI-II.