• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.205-208
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• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.13-38
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• 2024

As economic uncertainties have increased recently due to COVID-19, there is a growing need to quickly grasp private consumption trends that directly reflect the economic situation of private economic entities. This study proposes a method of estimating private consumption in real-time by comprehensively utilizing big data as well as existing macroeconomic indicators. In particular, it is intended to improve the accuracy of private consumption estimation by comparing and analyzing various machine learning methods that are capable of fitting ultra-high-dimensional big data. As a result of the empirical analysis, it has been demonstrated that when the number of covariates including big data is large, variables can be selected in advance and used for model fit to improve private consumption prediction performance. In addition, as the inclusion of big data greatly improves the predictive performance of private consumption after COVID-19, the benefit of big data that reflects new information in a timely manner has been shown to increase when economic uncertainty is high.

• Korean Journal of Environment and Ecology
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• v.34 no.1
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• pp.9-17
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• 2020

Domestic cats (Felis catus) introduced to insular environments can be invasive predators that often threaten endemic species and cause biodiversity loss or local extinction on the island. This study was conducted from March to July 2018 to understand the population size, home range, and spatial use of cats introduced to Mara Islet (N 33° 07', E 126° 16') in Jeju Special Governing Province, the Republic of Korea. Observation records based on their natural marks revealed that there were 20 adult cats on Mara Islet. A capture-recapture method also estimated 20 adult individuals (95% confidence interval: 20-24 individuals). According to our telemetry study on ten adults deployed with GPS-based telemetry units, the home range size was 12.05±6.99 ha (95% KDE: kernel density estimation), and the core habitat size was 1.60±0.77 ha (50% KDE). There were no significant differences in the home range and core habitat sizes by sex. The home range of domestic cats overlapped with the human residential area, where they might secure easy foods. Five of ten tracked cats were active at potential breeding colonies for the Crested Murrlet (Synthliboramphus wumizusume), and six approached potential breeding areas of the Styan's Grasshopper Warbler (Locustella pleskei), suggesting the predation risk of the two endangered species by cats. This study provides novel information on the population size and home range of introduced cats on Mara Islet which is an important stopover site of migratory birds as well as a breeding habitat of the two endangered avian species. Reducing the potential negative impacts of the introduced cats on migratory birds and the endangered species on Mara Islet requires monitoring of the predation rate of birds by cats, the population trends of cats and endangered breeding birds as well as the effective cat population control and management.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.271-281
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• 2007

Real-time support of embedded OS is not optional, but essential in contemporary embedded systems. In order to achieve these system#s real-time property, it is crucial that schedulability analysis for tasks having its property have been accomplished before system execution. Acquiring Worst-Case Execution Time(WCET) of task is a core part of schedulability analysis. Because traditional WCET tools analyze only its estimation of application task(i.e. program), it is not considered that application tasks are affected by scheduling primitives(e.g. scheduler, interrupt service routine, etc.) of OS when it schedules them. In this paper, we design and implement WCET analysis tool which deliberates on scheduling primitives of system using embedded Linux widely used in embedded OSes. This tool can estimate either WCET of normal application programs or corresponding primitives which have an influence on schduling property in embedded Linux kernel. Therefore, precision of estimation about schedulability analysis is improved. We develop this tool as Eclipse#s plug-in to work properly in any platform and support convenient interface or functionality for user.

• The KIPS Transactions:PartA
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• v.12A no.4 s.94
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• pp.305-312
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• 2005

In this paper, we present algorithm-level techniques for energy-efficient design at the algorithm level using FPGAs. We then use these techniques to create energy-efficient designs for two signal processing kernel applications: fast Fourier transform(FFT) and matrix multiplication. We evaluate the performance, in terms of both latency and energy efficiency, of FPGAs in performing these tasks. Using a Xilinx Virtex-II as the target FPGA, we compare the performance of our designs to those from the Xilinx library as well as to conventional algorithms run on the PowerPC core embedded in the Virtex-II Pro and the Texas Instruments TMS320C6415. Our evaluations are done both through estimation based on energy and latency equations on high-level and through low-level simulation. For FFT, our designs dissipated an average of $50\%$ less energy than the design from the Xilinx library and $56\%$ less than the DSP. Our designs showed an EAT factor of 10 times improvement over the embedded processor. These results provide a concrete evidence to substantiate the idea that FPGAs can outperform DSPs and embedded processors in signal processing. Further, they show that PFGAs can achieve this performance while still dissipating less energy than the other two types of devices.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.36-50
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• 2015

The purpose of this study is to assign emission source profiles of volatile organic compounds (VOCs) and particulate matters (PMs) for chemical speciation, and to correct the temporal allocation factor and the chemical speciation of source profiles according to the source classification code within the sparse matrix operator kernel emission system (SMOKE) in the Seoul metropolitan area. The chemical speciation from the source profiles of VOCs such as gasoline, diesel vapor, coating, dry cleaning and LPG include 12 and 34 species for the carbon bond IV (CBIV) chemical mechanism and the statewide air pollution research center 99 (SAPRC99) chemical mechanism, respectively. Also, the chemical speciation of PM2.5 such as soil, road dust, gasoline and diesel vehicles, industrial source, municipal incinerator, coal fired, power plant, biomass burning and marine was allocated to 5 species of fine PM, organic carbon, elementary carbon, $NO_3{^-}$, and $SO_4{^2-}$. In addition, temporal profiles for point and line sources were obtained by using the stack telemetry system (TMS) and hourly traffic flows in the Seoul metropolitan area for 2007. In particular, the temporal allocation factor for the ozone modeling at point sources was estimated based on $NO_X$ emission inventories of the stack TMS data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.92-98
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• 2017

Generally, image contains geometrical and radiometric errors. Census transform can solve the stereo mismatching problem caused by the radiometric distortion. Since the general census transform compares center of window pixel value with neighbor pixel value, it is hard to obtain an accurate matching result when the difference of pixel value is not large. To solve that problem, we propose a census transform method that applies different 4-step weight for each pixel value difference by applying an assistance window inside the window kernel. If the current pixel value is larger than the average of assistance window pixel value, a high weight value is given. Otherwise, a low weight value is assigned to perform a differential census transform. After generating an initial disparity map using a weighted census transform and input images, the gradient information is additionally used to model a cost function for generating a final disparity map. In order to find an optimal cost value, we use guided filtering. Since the filtering is performed using the input image and the disparity image, the object boundary region can be preserved. From the experimental results, we confirm that the performance of the proposed stereo matching method is improved compare to the conventional method.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.457-467
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• 2021

This paper aims to analyze changes in urban space use due to social distancing measures for COVID-19 using de facto population data in Seoul during daytime, which is estimated by Seoul Metropolitan Government and telecommunication company of KT using public big data and LTE signal data. The result of kernel density estimation and spatial autocorrelation analysis shows that the distribution patterns of de facto population in 2019 and 2020 were generally similar. This is a result of showing that the government's social distancing measures enabled a certain level of normal activities while suppressing the spread of COVID-19. However, analyzing de facto population subtracting 2019 from 2020 showed different results at the micro level. De facto population decreased in commercial areas but increased in residential areas. This means that COVID-19 social distancing measures had spatially uneven effect. The results of analyzing the effect of regional, land use, economic, educational, and accessibility characteristics on the changes of de facto population using spatial regression analysis are as follows. The higher the density of commercial facilities, the more businesses subject to regulations and schools and universities that require non-face-to-face classes, the more de facto population decreased. Conversely, it was found that de facto population increased in areas with many houses and parks due to telecommuting.