• Journal of Korea Game Society
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• v.9 no.2
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• pp.13-22
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• 2009

Recently, mobile games are developed as the various genre and form with the hardware performance improvement and communication network speed increase of the mobile phone. But it shows the limit of the game material to be developed, because the mobile game has to play by using only the keypad handing of the mobile phone. In this research, 'Flip On / Off interface' and 'Microphone interface' were developed in order to utilize the physical device of the mobile phone as the game interface. 'Flip On / Off interface' and 'Microphone interface' have the advantage that there is no need to purchase a device as an additional interface with utilizing the hardware of the mobile phone itself and the progressing of a game is smooth in comparison with the keypad method of the pre-existence mobile game because the interface waiting time for a call response is short. Moreover, by applying to a commercializing game, the developed interface tests, we confirmed an availability at the commercialization inspection standards on mobile phone without the heap memory deficiency phenomenon to be smooth of an operation. Furthermore, in the result of usability evaluation accessability, reliability, and aesthetics were rated as 'high'. The results of this research reveal that the interface environment of the mobile game limited to a keypad will be able to be made with diversification.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.12
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• pp.1175-1183
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• 2013

Even if some CFD software developers and its users think that a state-of-the-art CFD software can be used to reasonably solve at least single-phase nuclear reactor safety problems, there remain limitations and uncertainties in the calculation result. From a regulatory perspective, the Korea Institute of Nuclear Safety (KINS) is presently conducting the performance assessment of commercial CFD software for nuclear reactor safety problems. In this study, to examine the prediction performance of commercial CFD software with the porous model in the analysis of the scale-down APR (Advanced Power Reactor Plus) internal flow, a simulation was conducted with the on-board numerical models in ANSYS CFX R.14 and FLUENT R.14. It was concluded that depending on the CFD software, the internal flow distribution of the scale-down APR was locally somewhat different. Although there was a limitation in estimating the prediction performance of the commercial CFD software owing to the limited amount of measured data, CFX R.14 showed more reasonable prediction results in comparison with FLUENT R.14. Meanwhile, owing to the difference in discretization methodology, FLUENT R.14 required more computational memory than CFX R.14 for the same grid system. Therefore, the CFD software suitable to the available computational resource should be selected for massively parallel computations.

• The KIPS Transactions:PartC
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• v.14C no.4
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• pp.371-382
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• 2007

In this paper, we propose a real-time sensor node platform that efficiently manages periodic and aperiodic tasks. Since existing sensor node platforms available in literature focus on minimizing the usage of memory and power consumptions, they are not capable of supporting the management of tasks that need their real-time execution and fast average response time. We first analyze how to structure periodic or aperiodic task decomposition in the TinyOS-based sensor node platform as regard to guaranteeing the deadlines of ail the periodic tasks and aiming to providing aperiodic tasks with average good response time. Then we present the application and efficiency of the proposed real-time sensor node platform in the sensor node equipped with a low-power 8-bit microcontroller, an IEEE802.15.4 compliant 2.4GHz RF transceiver, and several sensors. Extensive experiments show that our sensor node platform yields efficient performance in terms of three significant, objective goals: deadline miss ratio of periodic tasks, average response time of aperiodic tasks, and processor utilization of periodic and aperiodic tasks.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.177-187
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• 2000

In the past several years, many systems which adopted ring topology with high-speed unidirectional point-to-point links have emerged to overcome the limit of bus for interconnection network of clustered multiprocessor system. However, rapid increase of processor speed and performance improvement of local bus and memory system limit scalability of system with point-to-point link of standard bandwidth. Therefore, necessity to extend bandwidth is emphasized. In this paper, we adopt PANDA system as base model, which is clustering-based multiprocessor system. By simulating a model adopting commercial processor and local bus specification, we show that point-to-point link is bottleneck of system performance, and bandwidth expansion by more than 200% is needed. To expand bandwidth of interconnection network, it needs excessive design cost and time to develop new point-to-point link with doubled bandwidth. As an alternative to double bandwidth, we propose several ways to implement dual ring -simple dual ring, transaction-separated dual ring, direction-separated dual ring- by using off-the-shelf point-to-point links with IEEE standard bandwidth. We analyze pros. and cons. of each model compared with doubled-bandwidth single ring by simulation.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.10-21
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• 2012

The role of GPU has evolved from graphics-specific processing to general-purpose processing with the development of unified shader core architecture. Especially, execution methods for general-purpose parallel applications using GPU have been researched intensively, since the parallel hardware architecture can be utilized efficiently when the parallel applications are executed. However, current GPU architecture has limitations in executing general-purpose parallel applications, since the GPU is not specialized for general-purpose computing yet. To improve the GPU performance when general-purpose parallel applications are executed, the GPU architecture should be evolved. In this work, we analyze the GPU performance according to the architecture varying the number of cores and clock frequency. Our simulation results show that the GPU performance improves by up to 125.8% and 16.2% as the number of cores increases and the clock frequency increases, respectively. However, note that the improvement of the GPU performance is saturated even though the number of cores increases and the clock frequency increases continuously, since the data cannot be provided to the GPU due to the limit of memory bandwidth. Consequently, to accomplish high performance effectiveness on GPU, computational resources must be more suitably considered.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Journal of KIISE
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• v.44 no.7
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• pp.658-667
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• 2017

The demand for large-scale storage systems has continued to grow due to the emergence of multimedia, social-network, and big-data services. In order to improve the response time and reduce the load of such large-scale storage systems, DRAM-based in-memory cache systems are becoming popular. However, the high cost of DRAM severely restricts their capacity. While the method of compressing cache entries has been proposed to deal with the capacity limitation issue, compression and decompression, which are technically difficult to parallelize, induce significant processing overhead and in turn retard the response time. A selective compression scheme is proposed in this paper for in-memory file system caches that rapidly estimates the compression ratio of incoming cache entries with their Shannon entropies and compresses cache entries with low compression ratio. In addition, a description is provided of the design and implementation of an in-kernel in-memory file system cache with the proposed selective compression scheme. The evaluation showed that the proposed scheme reduced the execution time of benchmarks by approximately 18% in comparison to the conventional non-compressing in-memory cache scheme. It also provided a cache hit ratio similar to the all-compressing counterpart and reduced 7.5% of the execution time by reducing the compression overhead. In addition, it was shown that the selective compression scheme can reduce the CPU time used for compression by 28% compared to the case of the all-compressing scheme.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.7 s.349
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• pp.93-103
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• 2006

Network solutions for protecting against worm attacks that complement partial end system patch deployment is a pressing problem. In the content-based worm filtering, the challenges focus on the detection accuracy and its performance enhancement problem. We present a worm filter architecture using the bloom filter for deployment at high-speed transit points on the Internet, including firewalls and gateways. Content-based packet filtering at multi-gigabit line rates, in general, is a challenging problem due to the signature explosion problem that curtails performance. We show that for worm malware, in particular, buffer overflow worms which comprise a large segment of recent outbreaks, scalable -- accurate, cut-through, and extensible -- filtering performance is feasible. We demonstrate the efficacy of the design by implementing it on an Intel IXP network processor platform with gigabit interfaces. We benchmark the worm filter network appliance on a suite of current/past worms, showing multi-gigabit line speed filtering prowess with minimal footprint on end-to-end network performance.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.813-818
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• 2020

Recently, with the rapid increase in technology and users of smart devices, the smart watch market has grown, and its utility and usability are continuously expanding. The strengths of smartwatches are wearable portability, application immediacy, data diversity and real-time capability. Despite these strengths, smartwatches have limitations such as battery limitations, display and user interface size limitations, and memory limitations. In addition, there is a need to supplement developers and standard devices, operating system standard models, and killer application modules. In particular, monitoring and application of user's biometric information is becoming a major service for smart watches. The biometric information of such a smart watch generates a large amount of data in real time. In order to advance the biometric information service, stable peer-to-peer transmission of sensing data to a remote smartphone or local server storage must be performed. We propose a synchronization method to ensure wireless remote peer-to-peer transmission stability in a smart watch system. We design a wireless peer-to-peer transmission process based on this synchronization method, analyze asynchronous transmission process and proposed synchronous transmission process, and propose a transmission efficiency method according to an increase in transmission amount.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.