• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.47-55
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• 2009

As the wireless internet technology such as mobile phone, WIBRO, HSDPA develops, customized location-based services for traffic, tourism, shopping, and emergency relief has lately attracted attention. For giving customized services. we should consider dynamic characteristics of moving object which continuously change their location. In this paper, we define the context trigger type of moving object and design triggering method for processing context generated by moving object. Also we propose location-based push service platform including context trigger of moving object for supporting location-based information to user. The proposed system gathers moving object stream from the terminal based on MS-assisted or Stand-alone positioning mode of embedded GPS in terminal extract user context by user device agent, and send context information to server.

• The Journal of the Korea Contents Association
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• v.4 no.2
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• pp.43-49
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• 2004

SIP Caller Preference is an useful function that allows a caller to express preferences about request handling in servers. It can also feat appropriate call processing according to the callee capabilities. However, only the category of the media is considered as a criteria for target selection in the caller preference. In this case, if the callee's media information such as codec is different from the caller, an additional re­negotiation occurs for SIP call setup. Therefore, in this paper, we have suggested an extended caller preference to solve this problem. In our SIP based VoIP system, a network sewer uses detailed media informations for media stream in the session to select the target for SIP call setup. The sewer gives higher priority to the candidate which do not require re-negotiation for call setup, so that an effective call setup can be achieved in our system.

• The KIPS Transactions:PartA
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• v.15A no.2
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• pp.75-86
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• 2008

Black/white comic rendering is one of the researches in the field of non-photorealistic rendering(NPR). Black/white comics have been produced manually as yet. But these previous systems require lots of time and manual work. So we propose the COmics Rendering system on VIdeo Stream (CORVIS) which transforms video streams into black/white comic cuts. Stylized font, one of comic representations, can be used to express onomatopoeic words and mimetic dialogue exaggeratively. But current comic generation systems do not provide enough effects of stylized font. This paper proposes a model for stylized fonts to express various effects. Effects of stylized fonts we proposed include geometric deformations. Thus we could represent stylized fonts on the still cut of movies and the background texture on a cuts of plain black/white comics. The final quality of our system produced is good enough to compare with manual black/white comics.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.345-356
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• 2004

In this paper, we present a design and implementation of a personalized broadcasting system using TV-Anytime metadata for providing personalized services. The TV-Anytime specifies metadata schema, metadata coding and delivery, and provides service models to provide personalized broadcasting content services at anytime when users want to consume using metadata, which includes ECG (Electronic Content Guide) and content descriptive information in a PDR (Personal Digital Recorder)-centric environment. The proposed personalized broadcasting system consists of a server that provides metadata binary-coding, encapsulation and multiplexing, and a client terminal that takes charge of de-multiplexing, metadata decoding, and metadata processing for personalized content accessing and consumption. This paper presents the details of the design of each functional module, and the evaluation results with a set of service scenarios in an end-to-end broadcasting test-bed.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.25-34
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• 2007

Existing approaches that select an order for the join of three or more data streams have always used the simple heuristics. For their disadvantage - only one factor is considered and that is join selectivity or arrival rate, these methods lead to poor performance and inefficiency In some applications. The graph-based sliding window multi -join algorithm with optimal join sequence is proposed in this paper. In this method, sliding window join graph is set up primarily, in which a vertex represents a join operator and an edge indicates the join relationship among sliding windows, also the vertex weight and the edge weight represent the cost of join and the reciprocity of join operators respectively. Then the optimal join order can be found in the graph by using improved MVP algorithm. The final result can be produced by executing the join plan with the nested loop join procedure, The advantages of our algorithm are proved by the performance comparison with existing join algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.842-849
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• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.55-62
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• 2009

In u-GIS environment, we collect spatial data needed through sensor network and provide them with information real-time processed or stored. When information through Internet is requested on Web based applications, it is transmitted in XML. Especially, when requested information includes spatial data, GML, S-XML, and other document that can process spatial data are used. In this processing, real-time stream data processed in DSMS is transformed to S-XML document type and spatial information service based on web receive S-XML document through Internet. Because most of spatial application service use existing spatial DBMS as a storage system, The data used in S-XML and SDBMS needs transformation between themselves. In this paper, we propose S-XML a transformation method using caching of spatial data. The proposed method caches the spatial data part of S-XML to transform S-XML and relational spatial database for providing spatial data efficiently and it transforms cached data without additional transformation cost when a transformation between data in the same region is required. Through proposed method, we show that it reduced the cost of transformation between S-XML documents and spatial information services based on web to provide spatial information in u-GIS environment and increased the performance of query processing through performance evaluation.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.1059-1066
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• 2003

In this paper, the magnetic sensing system is designed and implemented for the safe security in internet commerce system. When the payment is required inthe internet commerce system, the magnetic sensing system will get the information from a credit card without keyboard input and then encrypt and transmit the information to server. The credit card sensing system, which is proposed in this paper, is safe from keyboard hacking because it encrypts card information immediately in its internal chip and sends the information to host system. For the protection of information, the magnetic sensing system is basically based on a synchronous stream cipher cryptosystem which is related to a group of matrices. The size of matrices and the bits of keys for the best performances are determined for various cases. It is shown that for credit card payments. matrices of size 2 have good performance even at most 128bits keys with the consideration of inverse matrices. For authentication of general-purpose data, the magnetic sensing system needs more than 1.5KB data and in this case, the optimum size of matrices is 2 or 3 at more 256bits keys with consideration of inverse matrices.