• Journal of Korea Society of Industrial Information Systems
• /
• v.19 no.1
• /
• pp.13-18
• /
• 2014

Sensor nodes are used for monitoring and collecting the environmental data via wireless sensor network. The wireless sensor network with various sensor nodes draws attention as a key technology in ubiquitous computing. Sensor nodes has very small memory capacity and limited power resource. Thus, it is essential to have energy efficient strategy for the sensor nodes. Since the sensor nodes are operating on the same frequency bands with ISM frequency bands, the interference by the devices operating on the ISM band degrades the quality of communication integrity. In this paper, the convolutional code is proposed instead of ARQ for the error control for the sensor network. The proposed convolutional code was implemented and the BER performance is measured. For the fixed transmitting powers of -19.2 dBm and -25dBm, the BER with various communication distances are measured. The packet loss rate and the retransmission rate are calculated from the measured BER. It is shown that the porposed method obtained about 9~12% and 12-19% reduction in retransmission rate for -19.2 dBm and -25 dBm respectively.

• Journal of Internet Computing and Services
• /
• v.15 no.6
• /
• pp.125-132
• /
• 2014

Since frequent pattern mining was proposed in order to search for hidden, useful pattern information from large-scale databases, various types of mining approaches and applications have been researched. Especially, frequent graph pattern mining was suggested to effectively deal with recent data that have been complicated continually, and a variety of efficient graph mining algorithms have been studied. Graph patterns obtained from graph databases have their own importance and characteristics different from one another according to the elements composing them and their lengths. However, traditional frequent graph pattern mining approaches have the limitations that do not consider such problems. That is, the existing methods consider only one minimum support threshold regardless of the lengths of graph patterns extracted from their mining operations and do not use any of the patterns' weight factors; therefore, a large number of actually useless graph patterns may be generated. Small graph patterns with a few vertices and edges tend to be interesting when their weighted supports are relatively high, while large ones with many elements can be useful even if their weighted supports are relatively low. For this reason, we propose a weight-based frequent graph pattern mining algorithm considering length-decreasing support constraints. Comprehensive experimental results provided in this paper show that the proposed method guarantees more outstanding performance compared to a state-of-the-art graph mining algorithm in terms of pattern generation, runtime, and memory usage.

• KIISE Transactions on Computing Practices
• /
• v.21 no.2
• /
• pp.126-131
• /
• 2015

As flash-based storage systems have been actively employed in large-scale servers and data centers, reliability has become an indispensable element. One promising technique for enhancing reliability is static wear-leveling, which distributes erase operations evenly among blocks so that the lifespan of storage systems can be prolonged. However, increasing the capacity makes the processing overhead of this technique non-trivial, mainly due to searching for blocks whose erase count would be minimum (or maximum) among all blocks. To reduce this overhead, we introduce a new randomized block selection method in static wear-leveling. Specifically, without exhaustive search, it chooses n blocks randomly and selects the maximal/minimal erased blocks among the chosen set. Our experimental results revealed that, when n is 2, the wear-leveling effects can be obtained, while for n beyond 4, the effect is close to that obtained from traditional static wear-leveling. For quantitative evaluation of the processing overhead, the scheme was actually implemented on an FPGA board, and overhead reduction of more than 3 times was observed. This implies that the proposed scheme performs as effectively as the traditional static wear-leveling while reducing overhead.

• Journal of KIISE:Computing Practices and Letters
• /
• v.9 no.1
• /
• pp.13-24
• /
• 2003

MPEG-4 is a standard for multimedia applications and provides a set of technologies to satisfy the needs of authors, service providers and end users alike. In this paper, we suggest a Real-time Multiple Circular Buffer (M4RM Buffer) model, which is suitable for streaming these MPEG-4 contents efficiently. M4RM buffer generates each structure of the buffer, which matches well with each object composing an MPEG-4 content, according to the transferred information, and manipulates multiple read/write operations only by its reference. It divides the decoder buffer and the composition buffer, which are described in the standard, by the unit of frame allocated to minimize the range of access. This buffer unit of a frame is allocated according to the object description. Also, it processes the objects synchronization within the buffer and provides APIs for an efficient buffer management to process the real-time user events. Based on the performance evaluation, we show that M4RM buffer model decreases the waiting time in a buffer frame, and so allows the real-time streaming of an MPEG-4 content using the smaller size of the memory block than IM1-2D and Window Media Player.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.4
• /
• pp.153-163
• /
• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.3
• /
• pp.240-247
• /
• 2001

Java is a promising runtime environment for embedded systems because it has many advantages such as platform independence, high security and support for multi-threading. One of the most famous Java run-time environments, Sun's ($PersonalJave^{TM}$) is based on Truffle architecture, which enables programmers to design various GUIs easily. For this reason, it has been ported to various embedded systems such as set-top boxes and personal digital assistants(PDA's). Basically, Truffle uses heavy-weight window managers such as Microsoft vVin32 API and X-Window. However, those window managers are not adequate for embedded systems because they require a large amount of memory and disk space. To come up with the requirements of embedded systems, we adopt Microwindows as the platform graphic system for Personal] ava A WT onto Embedded Linux. Although Microwindows is a light-weight window manager, it provides as powerful API as traditional window managers. Because Microwindows does not require any support from other graphics systems, it can be easily ported to various platforms. In addition, it is an open source code software. Therefore, we can easily modify and extend it as needed. In this paper, we implement Personal]ava A WT using Microwindows on embedded Linux and prove the efficiency of our approach.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.40 no.9
• /
• pp.597-604
• /
• 2016

A parallel algorithm of bi-conjugate gradient method was developed based on CUDA for parallel computation of the incompressible Navier-Stokes equations. The governing equations were discretized using splitting P2P1 finite element method. Asymmetric stenotic flow problem was solved to validate the proposed algorithm, and then the parallel performance of the GPU was examined by measuring the elapsed times. Further, the GPU performance for sparse matrix-vector multiplication was also investigated with a matrix of fluid-structure interaction problem. A kernel was generated to simultaneously compute the inner product of each row of sparse matrix and a vector. In addition, the kernel was optimized to improve the performance by using both parallel reduction and memory coalescing. In the kernel construction, the effect of warp on the parallel performance of the present CUDA was also examined. The present GPU computation was more than 7 times faster than the single CPU by double precision.

• KIISE Transactions on Computing Practices
• /
• v.23 no.3
• /
• pp.183-187
• /
• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

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

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.65-78
• /
• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.