• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2279-2284
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• 2011

Recently as AR(Augmented Reality) is focus of attention, AR is applied to various fields and is expected its valuable use. In this paper, we suggested the method to combine existing e_Book with augmented reality technology based on mobile equipment. We ascertained that augmented reality contents implemented on PC work well in pITX embedded lines (CPU Intel ATOM Z530) and we implemented augmented reality using marker in e_ Book in pITX embedded lines through these experiments. As the result of it, we could show the contents at the same time which had difficulty to be expressed on e_Book before. Also the existing augmented reality contents could be used as it is. Finally we expected that the user could interact with virtual contents or services directly and intuitively in the real world.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.20-25
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• 2016

With the advent of the era of digital big data, the Hadoop platform has become widely used in various fields. However, the Hadoop MapReduce framework suffers from problems related to the increase of the name node's main memory and map tasks for the processing of large number of small files. In addition, a method for running C++-based tasks in the MapReduce framework is required in order to conjugate GPUs supporting hardware-based data parallelism in the MapReduce framework. Therefore, in this paper, we present a face detection system that generates a sequence file for images to process big data for images in the Hadoop platform. The system also deals with tasks for GPU-based face detection in the MapReduce framework using Hadoop Pipes. We demonstrate a performance increase of around 6.8-fold as compared to a single CPU process.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.205-212
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• 2018

With the development of high performance CPU / GPU, artificial intelligence algorithms such as deep neural networks, and a large amount of data, machine learning has been extended to various applications. In particular, a large amount of data collected from the Internet of Things, social network services, web pages, and public data is accelerating the use of machine learning. Learning data sets for machine learning exist in various formats according to application fields and data types, and thus it is difficult to effectively process data and apply them to machine learning. Therefore, this paper studied a method for building a learning data set for machine learning in accordance with standardized procedures. This paper first analyzes the requirement of learning data set according to problem types and data types. Based on the analysis, this paper presents the reference model to build learning data set for machine learning applications. This paper presents the target standardization organization and a standard development strategy for building learning data set.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.335-338
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• 2017

Currently Artificial Inteligence and Deep Learning are social issues, and These technologies are applied to various fields. A good method among the various algorithms in Artificial Inteligence is Convolutional Neural Network. Convolutional Neural Network is a form that adds convolution layers that extracts features by convolution operation on a general neural network method. If you use Convolutional Neural Network as small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning time is long as the size of the learning data is large and the structure of layers is complicated. So, GPU-based parallel processing is a lot. In this paper, we developed Convolutional Neural Network using CUDA and Learning speed is faster and more efficient than the method using the CPU.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.587-595
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• 2015

Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.13-21
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• 2013

The goal of this paper is to provide performance improvement and stability for satellite data correction of some distortions due to cloud or radiance through distributed processing on cluster. To do this, we proposed and implemented SGE(Sun Grid Engine) based distributed processing methods using local storages and a status table. In the verification, the experiment result revealed that the proposed system on seven nodes improved the processing speed by 138.81% as compare to the existing system and provided good stability as well. This result showed that the proposed distributed processing work is more appropriate to process CPU bound jobs than I/O bound jobs. We expect that the proposed system will give scientists improved analysis performance in various fields and near-real time analysis services.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.29-36
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• 2017

In these days, 3D dynamic simulation is closely related to many industries. In the past, physically-based 3D simulation was used mainly in the car crash or construction related fields, but it also plays an important role in movies or games today. Many mathematical computations are needed to represent the 3D object realistically, but it is difficult to process a large amount of calculations for simulation of application based on CPU in real-time. Recently, with the advanced graphic hardware and improved architecture, GPU can be utilized for the general purposes of computation function as well as graphic computation. Many approaches using GPU have been applied for various research fields. In this paper, we analyze the performance variation of two cloth simulation algorithms based on GPU according to the change of execution properties of GPU shaders in oder to optimize the performance of GPU-based cloth simulation. Cloth simulation is implemented by the spring centric algorithm and node centric algorithm with GPU parallel computing using compute shader of GLSL 4.3. We compare the performance of between these algorithms according to the change of the size and dimension of work group. The experiment is repeated to 10 times during 5,000 frames for each test and experimental results are provided by averaging of FPS. The experimental result shows that the node centric algorithm is executed in higher speed than the spring centric algorithm.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.113-118
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• 2010

Recently, 3D-reconstruction for geographic information and study of geospatial information is progressing in various fields through national policy such as R&D business and pilot project. LiDAR system has a advantage of acquisition the 3D information data easily and densely so that is used in many different fields. Considering to characterist of the point data formed with 3D, it need a high specification CPU because it requires a number of processing operation for 2D form expressed by monitor. In contrast, 2D grid structure, like DEM, has a advantage on costs because of simple structure and processing speed. Therefore, purpose of this study is to solve the problem of requirement of more storage space, when LiDAR data stored in forms of 3D is used for 3D-geographic and 3D-buliding representation. Additionally, This study reconstitutes 2D-gird data to supply the representation data of 3D-geographic and presents the storage method which is available for detailed representation applying tree-structure and reduces the storage space.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.11
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• pp.2999-3007
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• 1994

The unsteady numerical simulations have been presented for the laminar natural convection in a partially open compartment. Computations were performed within the domain of the compartment in order to show the thermal radiation and the partially opening effects on the flow fields and heat transfer characteristics. The results were shown for different Planck numbers(0.05~5) and opening ratios(0.25~0.75) being fixed with Ra=$10^5$ and Pr=0.71. Considering the flow which is buoyancy driven from the heated wall, and the buoyancy is not much affected by the further outside region from the opening, the numerical computations have been performed without an outer region by the particular boundary treatments on the flow velocity and temperature at the different partial openings. The confined numerical domain reduced the CPU time and the memory of computer. P-1 approximation of radiative transfer equation was employed with Marshak type boundary conditions along with the pseudo-black body approximation at the partial openings. The numerical results clearly show that the natural convective flow and heat transfer are much affected by increase of thermal radiation particularly from the initial state. When thermal radiation is not much affecting the flow ($PL{\le}1$), it was found that thermal radiation effects are almost negligible.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.1-9
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• 2009

Radio Frequency Identification (RFID) is an automatic identification method. Information such as identification, logistics history, and specification of products are written and stored into the memory of RFID tags (that is, transponders), and retrieved through RF communication between RFID reader device and RFID tags. RFID systems have been applied to many fields of transportation, industry, logistics, environment, etc in order to improve business efficiency and reduce maintenance cost as well. Recently, some research results are announced in which RFID devices are combined with other sensors for mobile robot localization. In this paper, design of multi-protocol baseband for RFID reader device is proposed, and the baseband modem is implemented into SoC (System On a Chip). The baseband modem SoC for multi-protocol RFID reader is composed of several IP (Intellectual Property) blocks such as multi-protocol blocks, CPU, UART(Universal Asynchronous Receiver and Transmitter), memory, etc. As a result, the SoC implemented with FPGA(Field Programmable Gate Array) is applied to real product. It is shown that the size of RFID Reader module designed with the FPGA becomes smaller, and the SoC chip price for the same function becomes cheap. In addition, operation performance could be the same or better than that of the product with no SoC applied.