• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Journal of Astronomy and Space Sciences
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• v.21 no.4
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• pp.371-382
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• 2004

To improve the precision of relative positioning for GPS single frequency(L1) receiver, we accomplished the GPS data processing using the weighted smoothing techniques. The weighted phase smoothing technique is used to minimize the measurement error of pseudorange and position domain smoothing technique is adopted to make the complement of cycle-slip affection. we also considered some component errors like as ionospheric error, which are related with baseline length, and processed for several baselines (5, 10, 30, 40, and 150 km) to check the coverage area of this algorithm. This paper shows that weighted phase smoothing technique give more stable results after using this technique and the position domain smoothing technique can reduce the errors which are sensitive to the observational environment. Based on the results, we could find out that this algorithm is available for post-time and real-time applications and these techniques can be substitution methods which is able to get the high accuracy and precision without resolving the Integer ambiguity.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.255-270
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• 2011

The purpose of this study was to examine the effects of nonspecific and specific cue on decision making during situation awareness. Participants were 36 male college students who were randomly assigned to one of three groups: (1) nonspecific situation awareness, (2) specific situation awareness, and (3) a control group. Every participant was in the level 3-4.5 according to American National Tennis Level Program. Participants were asked to watch tennis single defence, single offence, double defence rally and when the screen stops, they were required to push the button(left, middle, or right) appropriate for the ball's direction to return as soon as possible. The experiment was designed to be analyzed for group(3)${\times}$condition(3)${\times}$area(7) using three-way ANOVA. The dependent variables were reaction time, accuracy rate, and amplitude and latency of P300. The result showed that the latency of the nonspecific situation awareness group and the specific situation awareness group was shorter and their amplitudes were higher than the control group. Fz, Cz, Pz were prominent among areas, and the single defence condition was more prominent than the single offence and the double defence condition. As a result of the study, it can be suggested that the information about situation awareness provided beforehand directly affects the brain's information processing. In addition, it shows that ERP can be a useful index for studying situation awareness.

• Journal of Astronomy and Space Sciences
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• v.15 no.1
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• pp.119-138
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• 1998

Recently measurements of atmospheric trace gases from satellite are vigorous. So the development of its data processing algorithm is important. In this study, retrievalof vertical ozone profile from the atmospheric transmittance measured by satellite solar occultation method and its sensitivity to temperature and pressure are investigated. The measured transmittance from satellite is assumed to be given by the limb path transmittance simulated using annual averaged Umkehr data for Seoul. The limb path transmittance between wavelengths $9.89{\mu}m$ and $10.2{\mu}m$ is simulated with respect to tangent heights using the ozone data of HALOE SIDS(Hallogen Occultation Experiment Simulated Instrument Data Set) as an initial profile. Other input data such as pressure and temperature are also from HALOE SIDS. Vertical ozone profile is correctly retrieved from the measured transmittance by onion-peeling method from 50km to 11km tangent heights with the vertical resolution of 3km. The bias error of $\pm0.001$ in measured transmittance, the forced error of $\pm3K$ in each layer temperature, and the forced $\pm3%$ error in each layer pressure are assumed for sensitivity tests. These errors are based on the ADEOS/ILAS error limitation. The error in ozone amount ranges from -6.5% to +6.9% due to transmittance error, from -9.5% to +10.5% due to temperature error, and from -5.1% to +5.4% due to pressure error, respectively. The present study suggests that accurate vertical ozone profile can be retrieved from satellite solar occultation method. Accuracy of vertical temperature profile is especially important in the retrieval of vertical ozone profile.

• Journal of Satellite, Information and Communications
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• v.3 no.1
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• pp.41-47
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• 2008

Line-of-sight communications cannot easily support korean armed forces because of mountainous terrain. ADD(Agency for Defense Development) introduced ANASIS(Army Navy Air-force Satellite Information System) to meet the Korean warfighter's operational needs. Currently, army's military satcom terminal is designed for either fixed site or on-the-pause operation. The US army is under development of multi-band integrated on-the-move satellite terminals to let the army's communication capability to keep pace with globally deployable Joint Task Force for network-centric application. In this paper we analyzed X-band and Ka-band link and subsystem requirement. Our focus here is to describe key technical issues. Especially, On the basis of 3dB beam width of 0.9m antenna, Tracking accuracy and disturbances compensation signal processing on-the-move of Antenna Tracking system is analyzed. Also, protocol is analyzed that minimize blockage on the move due to an obstacle. when the received signal blocked, it stop to transmit burst signal and retransmit when blockage removed through received synchronization signal monitoring. Analyzed specification will be used to make prototype terminal to analyze risk for mass production

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7287-7291
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• 2014

A CDTA (current differencing transconductance amplifier) is an active building block for current mode analog signal processing with the advantages of high linearity and a wide frequency bandwidth. In addition, it can generate a stable voltage because all the differencing input current flows to the grounded devices. In this paper, a new small signal equivalent circuit is proposed to analyze a CDTA. The proposed small signal equivalent circuit provides greater precision in analyzing the magnitude and frequency response than its previous counterparts because it considers the parasitic components of the input, internal and output terminal. In addition, observations of the changes made in various devices, such as the resistor (Rz) confirmed that those devices heavily influence the characteristics of CDTA. The designed parameters of the proposed small signal equivalent circuit of the CDTA provides convenience and accuracy in the further design of analog integrated circuits. For verification purposes, a 2.5 MHz low pass filter was designed on the HSPICE simulation program using the proposed small signal equivalent circuit of CDTA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.21-31
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• 2017

In this paper, the Zynq system-on-chip (SoC) platform is used to design an adaptive noise reduction and edge-detection system using partial reconfiguration. Filters are implemented in a partially reconfigurable (PR) region to provide high computational complexity in real-time, 1080p video processing. In addition, partial reconfiguration enables better utilization of hardware resources in the embedded system from autonomous replacement of filters in the same PR region. The proposed edge-detection system performs adaptive noise reduction if the noise density level in the incoming video sequences exceeds a given threshold value. Results of implementation show that the proposed system improves the accuracy of edge-detection results (14~20 times in Pratt's Figure of Merit) through self-reconfiguration of filter bitstreams triggered by noise density level in the video sequences. In addition, the ZyCAP controller implemented in this paper enables about 2.1 times faster reconfiguration when compared to a PCAP controller.