• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1313-1315
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• 2003

This paper is dedicated to studying the quantitative analysis method with remote-sensing data in shallow geological structure reconstruction by the example of TM data in western China. A new method of computing attitude of geological contacts from remote-sensing data is developed and assessed. We generate several geological profiles with remotely derived measurements to constrain the shallow geological structure reconstruction in three dimensions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1618-1637
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• 2018

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.638-643
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• 2002

MODIS (MODerate-resolution Imaging Spectroradiometer) onboard the first Earth Observing System (EOS) satellite, Terra, was launched successfully at the end of 1999. The direct broadcast MODIS data has been received and utilized in Korea Meteorological Administration (KMA) since february 2001. This study introduces utilizations of this data, especially for the derivation of sea surface temperature (SST). To produce the MODIS SST operationally, we used a simple cloud mask algorithm and MCSST algorithm. By using a simple cloud mask algorithm and by assumption of NOAA daily SST as a true SST, a new set of MCSST coefficients was derived. And we tried to analyze the current NASA's PFSST and new MCSST algorithms by using the collocated buoy observation data. Although the number of collocated data was limited, both algorithms are highly correlated with the buoy SST, but somewhat bigger bias and RMS difference than we expected. And PFSST uniformly underestimated the SST. Through more analyzing the archived and future-received data, we plan to derive better MCSST coefficients and apply to MODIS data of Aqua that is the second EOS satellite. To use the MODIS standard cloud mask algorithm to get better SST coefficients is going to be prepared.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.74-80
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• 2015

In cognitive radios, spectrum sensing plays an important role in accurately detecting the presence or absence of a licensed user. However, the intervention of malicious users (MUs) degrades the performance of spectrum sensing. Such users manipulate the local results and send falsified data to the data fusion center; this process is called spectrum sensing data falsification (SSDF). Thus, MUs degrade the spectrum sensing performance and increase uncertainty issues. In this paper, we propose a method based on the Hausdorff distance and a similarity measure matrix to measure the difference between the normal user evidence and the malicious user evidence. In addition, we use the Dempster-Shafer theory to combine the sets of evidence from each normal user evidence. We compare the proposed method with the k-means and Jaccard distance methods for malicious user detection. Simulation results show that the proposed method is effective against an SSDF attack.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1006-1008
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• 2003

In this paper, we describe the development of a Southeast Asia environmental information web portal based on near real time MODIS Level 2 and higher level products generated from the direct broadcast data received at the Centre for Remote Imaging, Sensing and Processing (CRISP). This web portal aims to deliver timely environmental information to interested users in the region. Interpreted data will be provided instead of raw satellite data to reduce operational requirements on our system, and to enable users with limited bandwidths to have access to the system.

• Journal of the Earthquake Engineering Society of Korea
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• v.21 no.6
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• pp.287-293
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• 2017

Sometimes, it is impossible to install a sensor on a certain location of a structure due to the size of a structure or poor surrounding environments. Even if possible, sensors can be frequently malfunctioned or improperly operated due to lack of adequate maintenance. These kind of problems are solved by the virtual sensing methods in various engineering fields. Virtual sensing technology is a technology that can measure data even though there is no physical sensor. It is expected that this technology can be also applied to the construction field effectively. In this study, a virtual sensing technology based on ARX model is proposed. An ARX model is defined by using the simulated data through a structural analysis rather than by actually measured data. The ARX-based virtual sensing model can be applied to estimate unmeasured response using a transfer function that defines the relationship between two point data. In this study, a simulation and experimental study were carried out to examine the proposed virtual sensing method with a laboratory test on a cable-stayed model bridge. Acceleration measured at a girder is transformed to estimate a cable tension through the ARX model-based virtual sensing.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.969-969
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• 2003

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.97-107
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• 1991

This paper was suggested the detailed methods on the integration of Remote Sensing and GIS for various application of two functions at the one system with making the most use of respective merits rather than make use of independent systems. It developed of algorithm about simultaneous overlay of raster and vector data for remote sensing and GIS for these objects. For test application on integration of remote sensing and GIS, it used of remote sensing data of satellite and used to topographic map of the same area for vector data acquisition of GIS application. For the practical application, it proved of effective value of integration of raster and vector data by present of useful technique with multilateral approach method through data conversion about thematic application for major application fields of remote sensing and GIS and it suggested that new application technique for integrated application of remote sensing GIS through synthetic situation analysis.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.79-84
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• 2017

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.