• The KIPS Transactions:PartC
• /
• v.13C no.3 s.106
• /
• pp.353-358
• /
• 2006

Unlike TCP Reno, TCP Vegas recognizes network congestion through the measuring of RTT (Round Trip Time) and decides the main congestion control parameters, such as Windows size. But, congestion avoidance scheme of Vegas poorly reflects asymmetric characteristics of packet path because TCP Vegas uses the measuring of RTT that reflects forward/backward packet transmission delay as a forward delay. The RTT can't infer the forward/backward transmission delay variation because it only measures the packet's turn around time. In this paper, We have designed and implemented a new Vegas congestion control algorithm that can distinguish forward/backward network congestion. We have modified the source codes of TCP Vegas in Linux 2.6 kernel and verified their performance.

• Journal of Digital Contents Society
• /
• v.18 no.3
• /
• pp.525-534
• /
• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• Spatial Information Research
• /
• v.19 no.2
• /
• pp.29-36
• /
• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• Smart Structures and Systems
• /
• v.20 no.6
• /
• pp.769-780
• /
• 2017

Cable force monitoring is an essential and critical part of the safety evaluation of cable-supported bridges. A reasonable cable force monitoring scheme, particularly, sensor placement related to accurate safety assessment and budget cost-saving becomes a major concern of bridge administrative authorities. This paper presents optimal sensor placement for cable force monitoring by selecting representative sensor positions, which consider the spatial correlativeness existing in the cable group. The limited sensors would be utilized for maximizing useful information from the monitored bridges. The maximum information coefficient (MIC), mutual information (MI) based kernel density estimation, as well as Pearson coefficients, were all employed to detect potential spatial correlation in the cable group. Compared with the Pearson coefficient and MIC, the mutual information is more suitable for identifying the association existing in cable group and thus, is selected to describe the spatial relevance in this study. Then, the bond energy algorithm, which collects clusters based on the relationship of surrounding elements, is used for the optimal placement of cable sensors. Several optimal placement strategies are discussed with different correlation thresholds for the cable group of Nanjing No.3 Yangtze River Bridge, verifying the effectiveness of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.8
• /
• pp.1508-1516
• /
• 2017

Multipath TCP (MPTCP) is a transport layer protocol that supports multipath transmission. If a bufferbloat occurs in one of the subflows of MPTCP, HoL blocking occurs at the receiver due to the difference in packet arrival time among paths. In MPTCP, HoL blocking degrades not only the performance of the path where bufferbloat occurs, but also the performance of other paths. In this paper, we propose a bandwidth measurement based scheduler to solve this problem. Bandwidth measurement based scheduler is designed to measure the bandwidth of each subflow and to perform packet scheduling based on it. In order to verify the proposed scheduler, we implemented the proposed scheduler in the Linux kernel and constructed a testbed in which bufferbloat occurs. Experimental results show that the proposed scheduler has better performance than the legacy MPTCP in bufferbloat environment.

• Journal of Korea Water Resources Association
• /
• v.38 no.8 s.157
• /
• pp.595-604
• /
• 2005

Precipitation simulation for making the data size larger is an important task for hydrologic analysis. The simulation can be divided into two major categories which are the parametric and nonparametric methods. Also, precipitation simulation depends on time intervals such as daily or hourly rainfall simulations. So far, Markov model is the most favored method for daily precipitation simulation. However, most models are consist of state transition probability by using the homogeneous Markov chain model. In order to make a state vector, the small size of data brings difficulties, and also the assumption of homogeneousness among the state vector in a month causes problems. In other words, the process of daily precipitation mechanism is nonstationary. In order to overcome these problems, this paper focused on the nonparametric method by using uni-variate and multi-variate when simulating a precipitation instead of currently used parametric method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.10
• /
• pp.5129-5152
• /
• 2016

Multi-view super-resolution (MVSR) aims to estimate a high-resolution (HR) image from a set of low-resolution (LR) images that are captured from different viewpoints (typically by different cameras). MVSR is usually applied in camera array imaging. Given that MVSR is an ill-posed problem and is typically computationally costly, we super-resolve multi-view LR images of the original scene via image fusion (IF) and blind deblurring (BD). First, we reformulate the MVSR problem into two easier problems: an IF problem and a BD problem. We further solve the IF problem on the premise of calculating the depth map of the desired image ahead, and then solve the BD problem, in which the optimization problems with respect to the desired image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Our approach bridges the gap between MVSR and BD, taking advantages of existing BD methods to address MVSR. Thus, this approach is appropriate for camera array imaging because the blur kernel is typically unknown in practice. Corresponding experimental results using real and synthetic images demonstrate the effectiveness of the proposed method.

• Journal of the Economic Geographical Society of Korea
• /
• v.23 no.4
• /
• pp.408-424
• /
• 2020

This study attempts to analyze the spatial clustering of survived and closed coffee shops based on the land price and land use for each coffee shop location. The locational characteristics of survived and closed coffee shops for each cluster type are identified through various locational properties such as transport factors (physical accessibility), shop properties (franchise information, newly open/closed business experience), and spatial density (kernel density estimation). To this end, we categorize the clusters of survived and closed coffee shops into three types (general locational distribution type, commercialization type of residential area and location type of commercial center), and then analyze their locational characteristics. As the result, we found that the locations of newly open and closed coffee shops show different distribution characteristics, even though they are classified into the same type due to the double sidedness of new open and closed locations. The results of this study can be provided as basic data for planning the location of coffee shop as well as regional commercial district.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.174-174
• /
• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.

• Journal of Information Technology Services
• /
• v.22 no.2
• /
• pp.1-21
• /
• 2023

Due to the convergence and complexity of the 4th Industrial Revolution, the boundaries between industries have become unclear and ambiguous. Consequently, there is a lack of research on how firms engaged in this industry are changing their location behavior. Recently, some attempts to classify the industrial groups of the 4th Industrial Revolution and their detail occupations have been made, and this study adopts the classification of Lee and Jung (2020) of the Korea Institute for Industrial Economics & Trade. In this study, the 18 detailed industries commonly included in multiple industrial groups are defined as 'core industries' and are classified into manufacturing and service industries to explore the spatial patterns of firms' location. Specifically, this study aims to examine how the location behavior of firms in core industries of the 4th Industrial Revolution has changed from 2010 to 2019 in the Seoul metropolitan area, using the 「National Business Survey」 data. We employed two methods based on spatial auto-correlation: (i) spatial kernel density estimation analysis and (ii) local Moran's I_i analysis. The results indicate that the core industry firms form more distinct and larger clusters in 2019 based on the clusters formed in 2010. Specifically, manufacturing industry firms tended to concentrate in the southern region of Gyeonggi and parts of Seoul, while serivce industry firms were more concentrated in Seoul. These core industries play a critical role in industries and are closely related to the ICT industries, which generate high-added value and increase productivity in the front and rear industries. This study reveals that the agglomeration of these industries in specific regions is intensifying and may exacerbate regional inequality.