• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.1-9
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• 2010

As the process technology scales down, an interconnection has became a major performance constraint for multi-core processors. Recently, in order to mitigate the performance bottleneck of the interconnection for multi-core processors, a 3D integration technique has drawn quite attention. The 3D integrated multi-core processor has advantage for reducing global wire length, resulting in a performance improvement. However, it causes serious thermal problems due to increased power density. For this reason, to design efficient 3D multi-core processors, thermal-aware design techniques should be considered. In this paper, we analyze the temperature on the 3D multi-core processors in function unit level through various experiments. We also present temperature characteristics by varying application features, cooling characteristics, and frequency levels on 3D multi-core processors. According to our experimental results, following two rules should be obeyed for thermal-aware 3D processor design. First, to optimize the thermal profile of cores, the core with higher cooling efficiency should be clocked at a higher frequency. Second, to lower the temperature of cores, a workload with higher thermal impact should be assigned to the core with higher cooling efficiency.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.547-557
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• 2005

The purpose of selectivity estimation is to maintain the summary data in a very small memory space and to minimize the error of estimated value and query result. In case of estimating selectivity for large spatial data, the existing works need summary information which reflect spatial data distribution well to get the exact result for query. In order to get such summary information, they require a much memory space. Therefore In this paper, we propose a new technique cumulative density wavelet Histogram, called CDW Histogram, which gets a high accurate selectivity in small memory space. The proposed method is to utilize the sub-histograms created by CD histogram. The each sub-histograms are used to generate the wavelet summary information by applying the wavelet transform. This fact gives us good selectivity even if the memory sire is very small. The experimental results show that the proposed method simultaneously takes full advantage of their strong points - gets a good selectivity using the previous histogram in ($25\%\~50\%$) memory space and is superior to the existing selectivity estimation techniques. The proposed technique can be used to accurately quantify the selectivity of the spatial range query in databases which have very restrictive memory.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1273-1284
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• 2016

In this paper, we analyzed the patents on machine learning using keyword network analysis and clique analysis. To construct a keyword network, important keywords were extracted based on the TF-IDF weight and their association, and network structure analysis and clique analysis was performed. Density and clustering coefficient of the patent keyword network are low, which shows that patent keywords on machine learning are weakly connected with each other. It is because the important patents on machine learning are mainly registered in the application system of machine learning rather thant machine learning techniques. Also, our results of clique analysis showed that the keywords found by cliques in 2005 patents are the subjects such as newsmaker verification, product forecasting, virus detection, biomarkers, and workflow management, while those in 2015 patents contain the subjects such as digital imaging, payment card, calling system, mammogram system, price prediction, etc. The clique analysis can be used not only for identifying specialized subjects, but also for search keywords in patent search systems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.5
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• pp.21-30
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• 2008

The fuel cell generation convert fuel source, and gas directly to electricity in an electro-chemical process. Unlike traditional and conventional turbine engines, the process of fuel cell generation do not burn the fuel and run pistons or shafts, and it has not revolutionary machine, so have fewer efficiency losses, low emissions and no noisy moving parts. A high power density allows fuel cells to be relatively compact source of electric power, beneficial in application with space constraints. In this system, the fuel cell itself is nearly small-sized by other components of the system such as the fuel reformer and power inverter. So, the fuel cell energy's stationary fuel cells produce reliable electrical power for commercial and industrial companies as well as utilities. In this paper, a fuel cell system has been modeled using PSCAD/EMTDC to analyze its electric signals and characteristics. Also the power quality of the fuel cell system has been evaluated and the problems which can be occurred during its operation have been studied by modeling it more detailed. Particularly, we have placed great importance on its power quality and signal characteristics when it is connected with a power grid.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.141-147
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• 2019

Recently, the biggest issue in the electricity industry is the increase in renewable energy, and various technologies are being developed to ensure the capacity of the power system. In addition, super-grids linking power systems are being pushed to utilize eco-friendly energy between countries and regions worldwide. The HVDC transmission technology is required to link the power network between regions with different characteristics of the power system such as frequency and voltage. Until now, Korea has applied HVDC transmission technology that connects mainland and Jeju Island with submarine cables. But, the HVDC transmission technology is still developing for long-distance high-capacity power transmission from power parks on the east coast to load-tight areas near the metropolitan area. Considering the high population density and mountainous domestic environment, it is pushing for commercialization of the design technology of the ${\pm}500kV$ Double Bipole with metallic return wire transmission line to transmit large-scale power of 8 GW using minimal right of ways. In this paper, the insulation characteristics were studied for the design of double-bipole transmission tower with metallic return wire, which is the first time in the world. And the air insulation characteristics resistant to the various overvoltage phenomena occurring on transmission lines were verified through a full-scale impulse voltage test.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.849-858
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• 2019

Recent developments in computer graphics technology and image processing technology have increased interest in point cloud technology for inputting real space and object information as three-dimensional data. In particular, point cloud technology can accurately provide spatial information, and has attracted a great deal of interest in the field of autonomous vehicles and AR (Augmented Reality)/VR (Virtual Reality). However, in order to provide users with 3D point cloud contents that require more data than conventional 2D images, various technology developments are required. In order to solve these problems, an international standardization organization, MPEG(Moving Picture Experts Group), is in the process of discussing efficient compression and transmission schemes. In this paper, we provide a region division transfer method of 3D point cloud content through extension of existing MPEG-DASH (Dynamic Adaptive Streaming over HTTP)-SRD (Spatial Relationship Description) technology, quality parameters are further defined in the signaling message so that the quality parameters can be selectively determined according to the user's request. We also design a verification platform for ROUTE (Real Time Object Delivery Over Unidirectional Transport)/DASH based heterogeneous network environment and use the results to validate the proposed technology.

• Journal of KIISE
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• v.44 no.9
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• pp.932-945
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• 2017

In this paper, a new hardware and software architecture for a stereo vision processing system including rectification, disparity estimation, and visualization was developed. The developed method, named LArge scale stereo matching method using Support Point Interpolation (LASPI), shows excellence in real-time processing for obtaining dense disparity maps from high quality image regions that contain high density support points. In the real-time processing of high definition (HD) images, LASPI does not degrade the quality level of disparity maps compared to existing stereo-matching methods such as Efficient LArge-scale Stereo matching (ELAS). LASPI has been designed to meet a high frame-rate, accurate distance resolution performance, and a low resource usage even in a limited resource environment. These characteristics enable LASPI to be deployed to safety-critical applications such as an obstacle recognition system and distance detection system for autonomous vehicles. A Field Programmable Gate Array (FPGA) for the LASPI algorithm has been implemented in order to support parallel processing and 4-stage pipelining. From various experiments, it was verified that the developed FPGA system (Xilinx Virtex-7 FPGA, 148.5MHz Clock) is capable of processing 30 HD ($1280{\times}720pixels$) frames per second in real-time while it generates disparity maps that are applicable to real vehicles.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.699-707
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• 2016

This paper proposes a low-complexity central processing unit (CPU) that is suitable for deeply embedded systems, including Internet of things (IoT) applications. The core features a 16-bit instruction set architecture (ISA) that leads to high code density, as well as a multicycle architecture with a counter-based control unit and adder sharing that lead to a small hardware area. A co-processor, instruction cache, AMBA bus, internal SRAM, external memory, on-chip debugger (OCD), and peripheral I/Os are placed around the core to make a system-on-a-chip (SoC) platform. This platform is based on a modified Harvard architecture to facilitate memory access by reducing the number of access clock cycles. The SoC platform and CPU were simulated and verified at the C and the assembly levels, and FPGA prototyping with integrated logic analysis was carried out. The CPU was synthesized at the ASIC front-end gate netlist level using a $0.18{\mu}m$ digital CMOS technology with 1.8V supply, resulting in a gate count of merely 7700 at a 50MHz clock speed. The SoC platform was embedded in an FPGA on a miniature board and applied to deeply embedded IoT applications.