• Investigative Magnetic Resonance Imaging
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• v.12 no.2
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• pp.131-141
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• 2008

Purpose : To investigate the feasibility and accuracy of Proton Resonance Frequency (PRF) shift based magnetic resonance (MR) temperature mapping utilizing the self-developed center array-sequencing phase unwrapping (PU) method for non-invasive temperature monitoring. Materials and Methods : The computer simulation was done on the PU algorithm for performance evaluation before further application to MR thermometry. The MR experiments were conducted in two approaches namely PU experiment, and temperature mapping experiment based on the PU technique with all the image postprocessing implemented in MATLAB. A 1.5T MR scanner employing a knee coil with $T2^*$ GRE (Gradient Recalled Echo) pulse sequence were used throughout the experiments. Various subjects such as water phantom, orange, and agarose gel phantom were used for the assessment of the self-developed PU algorithm. The MR temperature mapping experiment was initially attempted on the agarose gel phantom only with the application of a custom-made thermoregulating water pump as the heating source. Heat was generated to the phantom via hot water circulation whilst temperature variation was observed with T-type thermocouple. The PU program was implemented on the reconstructed wrapped phase images prior to map the temperature distribution of subjects. As the temperature change is directly proportional to the phase difference map, the absolute temperature could be estimated from the summation of the computed temperature difference with the measured ambient temperature of subjects. Results : The PU technique successfully recovered and removed the phase wrapping artifacts on MR phase images with various subjects by producing a smooth and continuous phase map thus producing a more reliable temperature map. Conclusion : This work presented a rapid, and robust self-developed center array-sequencing PU algorithm feasible for the application of MR temperature mapping according to the PRF phase shift property.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.3
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• pp.33-38
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• 2021

Recently, rollable and foldable displays are attracting great attention in the flexible display market due to their excellent form factor. To predict and prevent the mechanical failure of the display panels, it is essential to accurately understand the mechanical properties of brittle SiN_x thin films, which have been used as an insulating film in flexible displays. In this study, tensile properties of the ~130 nm- and ~320 nm-thick SiN_x thin films were successfully measured by coating a ~190 nm-thick organic nano-support-layer (PMMA, PS, P3HT) on the fragile SiN_x thin films and stretching the films as a bilayer state. Young's modulus values of the ~130 nm and ~320 nm SiN_x thin films fabricated through the controlled chamber pressure and deposition power (A: 1250 mTorr, 450 W/B: 1000 mTorr, 600 W/C: 750 mTorr, 700 W) were calculated as A: 76.6±3.5, B: 85.8±4.6, C: 117.4±6.5 GPa and A: 100.1±12.9, B: 117.9±9.7, C: 159.6 GPa, respectively. As a result, Young's modulus of ~320 nm SiN_x thin films fabricated through the same deposition condition increased compared to the ~130 nm SiN_x thin films. The tensile testing method using the organic nano-support-layer was effective in the precise measurement of the mechanical properties of the brittle thin films. The method developed in this study can contribute to the robust design of the rollable and foldable displays by enabling quantitative measurement of mechanical properties of fragile thin films for flexible displays.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2473-2481
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• 2013

The two characteristics of two-lane-and-two-way traffic flow are platoon and overtaking triggered by low-speed vehicle. It is crucial to develop a robust model which simultaneously generates the behaviors of platoon by low-speed vehicle and overtaking using opposite lane. Hence, a microscopic two-lane and two-way vehicle model was introduced (B. Yoon, 2011), which is based on CA (Cellular Automata) which is one of discrete time-space models, in Korea. While the model very reasonably explains the behaviour of overtaking low-speed vehicle in stable traffic flow below critical density, it has shortcomings to the overtaking process in unstable traffic flow above the critical density. Therefore, the objective of this study is to develope a vehicle model to more realistically explain overtaking process in unstable traffic flow state based on the model developed in the previous study. The experimental results revealed that the car-following model robustly generates the various macroscopic relationships of traffic flow generating stop-and-go traffic flow and the overtaking model reasonably explains the behaviors of overtaking under the conditions of both opposite traffic flow and stochastic parameter to consider overtaking in unstable traffic flow state. The vehicle model presented in this study can be expected to be utilized for the analysis of two-lane-and-two-way traffic flows more realistically than before.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.370-375
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• 2013

This paper attempts to estimate the city gas demand function in Korea over the period 1981-2012. As the city gas demand function provides us information on the pattern of consumer's city gas consumption, it can be usefully utilized in predicting the impact of policy variables such as city gas price and forecasting the demand for city gas. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the city gas demand function. The results show that short-run price and income elasticities of the city gas demand are estimated to be -0.522 and 0.874, respectively. They are statistically significant at the 1% level. The short-run price and income elasticities portray that demand for city gas is price- and income-inelastic. This implies that the city gas is indispensable goods to human-being's life, thus the city gas demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for city gas is price- and income-elastic in the long-run.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.531-548
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• 2012

To identify Cochlodinium polykrikoides red tide from non-red tide water (satellite high chlorophyll waters) in the South Sea of Korea (SSK), we improved a spectral classification method proposed by Son et al.(2011) for the world first Geostationary Ocean Color Imager (GOCI). C. polykrikoides blooms and non-red tide waters were classified based on four different criteria. The first step revealed that the radiance peaks of potential red tide water occurred at 555 and 680 nm (fluorescence peak). The second step separated optically different waters that were influenced by relatively low and high contributions of colored dissolved organic matter (CDOM) (including detritus) to chlorophyll. The third and fourth steps discriminated red tide water from non-red tide water based on the blue-to-green ratio, respectively. After applying the red tide classification, the spectral response of C. polykrikoides red tide water, which is influenced by pigment concentration as well as CDOM (detritus), showed different slopes for the blue and green bands (lower slope at blue bands and higher slope at green bands). The opposite result was found for non-red tide water. This modified spectral classification method for GOCI led to increase user accuracy for C. polykrikoides and non-red tide blooms and provided a more reliable and robust identification of red tides over a wide range of oceanic environments than was possible using chlorophyll a concentration, or proposed red tide detection algorithms. Maps of C. polykrikoides red tide in SSK outlined patches of red tide covering the area near Naro-do and Tongyeong during the end of July and early of August, 2012 and extending into from Wan-do and Geoje-do during the middle of August, 2012.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.63-70
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• 2014

Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.1-9
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• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.17-31
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• 2004

This paper proposes an algorithm for the robust transmission of video in error Prone environment using multiple description codingby optimal split of DCT coefficients and rate-distortionoptimization framework. In MDC, a source signal is split Into several coded streams, which is called descriptions, and each description is transmitted to the decoder through different channel. Between descriptions, structured correlations are introduced at the encoder, and the decoder exploits this correlation to reconstruct the original signal even if some descriptions are missing. It has been shown that the MDC is more resilient than the singe description coding(SDC) against severe packet loss ratecondition. But the excessive redundancy in MDC, i.e., the correlation between the descriptions, degrades the RD performance under low PLR condition. To overcome this Problem of MDC, we propose a hybrid MDC method that controls the SDC/MDC switching according to channel condition. For example, the SDC is used for coding efficiency at low PLR condition and the MDC is used for the error resilience at high PLR condition. To control the SDC/MDC switching in the optimal way, RD optimization framework are used. Lagrange optimization technique minimizes the RD-based cost function, D+M, where R is the actually coded bit rate and D is the estimated distortion. The recursive optimal pet-pixel estimatetechnique is adopted to estimate accurate the decoder distortion. Experimental results show that the proposed optimal split of DCT coefficients and SD/MD switching algorithm is more effective than the conventional MU algorithms in low PLR conditions as well as In high PLR condition.