• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.271-274
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• 2014

This paper proposes high-performance SAO(Sample Adaptive Offset) in HEVC(High Efficiency Video Coding) encoder for Ultra HD video processing in real time. SAO is a newly adopted technique belonging to the in-loop filter in HEVC. The proposed SAO encoder hardware architecture uses three-layered buffers to minimize memory access time and to simplify pixel processing and also uses only adder, subtractor, shift register and feed-back comparator to reduce area. Furthermore, the proposed architecture consists of pipelined pixel classification and applying SAO parameters, and also classifies four consecutive pixels into EO and BO concurrently. These result in the reduction of processing time and computation. The proposed SAO encoder architecture is designed by Verilog HDL, and implemented by 180k logic gates in TSMC $0.18{\mu}m$ process. At 110MHz, the proposed SAO encoder can support 4K Ultra HD video encoding at 30fps in real time.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.141-151
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• 2021

This study analyzed the meaning and functions of '-(으)ㄹ 것 같다' and '-나 보다' among the various semantic functions depending on the situation, and discussed ways to train speculative expressions more efficiently by expanding them from traditional teaching methods through visualizations applied visual thinking at real Korean language education. The speculative representation, which is the subject of this study, represents the speaker's speculation about something or situation, with slight differences in meaning depending on the basis of the speculation and the subject of the speculation. We propose a training method that can enhance the diversification and efficiency of teaching-learning through visualization of information or knowledge, speculative representations that exhibit fine semantic differences in various situations. Utilizing visual thinking in language education can simplify and provide language information through visualization of language knowledge, and learners can be efficient at organizing and organizing language knowledge. It also has the advantage of long-term memory of language information through visualization of language knowledge. Attempts of various educational methods that can be applied at the Korean language education site can contribute to establishing a more systematic and efficient education method, which is meaningful in that the visual thinking proposed in this study can give interest and efficiency to international students.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

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

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.41-49
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• 2012

Wireless network support and extended mobile network environment with exponential growth of smart phone users allow us to utilize the network anytime or anywhere. Malicious attacks such as distributed DOS, internet worm, e-mail virus and so on through high-speed networks increase and the number of patterns is dramatically increasing accordingly by increasing network traffic due to this internet technology development. To detect the patterns in intrusion detection systems, an existing research proposed an efficient algorithm called the jumping window algorithm and analyzed approximately 2,000 patterns in Snort 2.1.0, the most famous intrusion detection system. using the algorithm. However, it is inappropriate from the number of TCAM lookups and TCAM memory efficiency to use the result proposed in the research in current environment (Snort 2.9.0) that has longer patterns and a lot of patterns because the jumping window algorithm is affected by the number of patterns and pattern length. In this paper, we simulate the number of TCAM lookups and the required TCAM size in the jumping window with approximately 8,100 patterns from Snort-2.9.0 rules, and then analyse the simulation result. While Snort 2.1.0 requires 16-byte window and 9Mb TCAM size to show the most effective performance as proposed in the previous research, in this paper we suggest 16-byte window and 4 18Mb-TCAMs which are cascaded in Snort 2.9.0 environment.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.24 no.4
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• pp.220-227
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• 2013

Objectives : This study aims to investigate the clinical characteristics and neuropsychological profiles of children with attention-deficit hyperactivity disorder (ADHD) and their siblings. Methods : Eighteen children (age $8.2{\pm}1.7$ years, 12 boys) with ADHD and their 18 siblings (age $7.8{\pm}1.6$ years, 8 boys) completed Continuous Performance (CPT), Stroop, Children's Trail Making, Rey-Kim Memory, and Kim's Frontal Executive Function tasks. The parents of these subjects underwent the Attention-Deficit/Hyperactivity Disorder Rating Scale (ARS), 10-item Parent General Behavior Inventory (P-GBI), and the Social Responsiveness Scale (SRS). Paired t-tests were used. Results : The inattention (p=.020), and hyperactivity-impulsivity (p=.001), scores of the ARS and the P-GBI score (p=.004) were significantly higher in children with ADHD than in their siblings. Deficits in social communication and motivation on SRS were higher in children with ADHD than in their siblings (p=.017 and p=.011, respectively). Z-scores of omission and commission errors as well as response time variability on visual CPT and omission errors on auditory CPT were in clinically significant range, and z-score of omission errors on auditory CPT was in borderline range in siblings. Omission (p=.018) and commission errors on Visual CPT (p=.007) were significantly higher in children with ADHD compared to their siblings. Recognition efficiency on Kim's Frontal Executive Function Task was lower in children with ADHD compared to their siblings, but in normal range in both groups. Stroop interference and figure fluency on Kims Frontal Executive Function Task were in borderline range in ADHD group, and figure fluency was in borderline range in siblings. Conclusion : Our results support a preliminary evidence for mild degree of attention deficit in ADHD siblings. Further studies are needed to examine the cognitive functions of siblings with ADHD in larger samples.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.3
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• pp.171-178
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• 2020

In this study, we develop MPI-OpenMP hybrid parallelization for multibody peridynamic simulations. Peridynamics is suitable for analyzing complicated dynamic fractures and various discontinuities. However, compared with a conventional finite element method, nonlocal interactions in peridynamics cost more time and memory. In multibody peridynamic analysis, the costs increase due to the additional interactions that occur when computing the nonlocal contact and ghost interlayer models between adjacent bodies. The costs become excessive when further refinement and smaller time steps are required in cases of high-velocity impact fracturing or similar instances. Thus, high computational efficiency and performance can be achieved by parallelization and optimization of multibody peridynamic simulations. The analytical code is developed using an Intel Fortran MPI compiler and OpenMP in NURION of the KISTI HPC center and parallelized through MPI-OpenMP hybrid parallelization. Further parallelization is conducted by hybridizing with OpenMP threads in each MPI process. We also try to minimize communication operations by model-based decomposition of MPI processes. The numerical results for the impact fracturing of multiple bodies show that the computing performance improves significantly with MPI-OpenMP hybrid parallelization.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.4
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• pp.56-67
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• 2004

This study analyzes human error of workers performing simple repetitive tasks, and in order to prepare preventative measures, 486 people were used as subjects. The results of the study are like the following. First, the biggest cause of human error showed to be the worker himself in $77.8\%$ of the cases, machinery showed to be the cause in $16.3\%$ of the cases and management showed to be the cause in $6.0\%$ of the cases. The results show that most of the human error occurred due to the worker performing simple repetitive tasks and the human errors showed to be caused more by bad ergonomics and long hours rather than by problems with machinery. In addition, the area with the highest rate of human error showed to be the Human Information Processing System with Task Input Error being the highest with $46.9\%$, followed by Judgement and Memory Error with $36.4\%$ and Recognition Verification Error with $16.7\%$. Although fully automated tasks may reduce the rate of human error we must focus on lowering the rate of problems arising from spontaneous errors caused by workers performing simple repetitive tasks by continuously renewing plans and budgets in order to standardize tasks by incorporating cyclic positioning according to experience and positional exchange and by inspecting the workplace to increase efficiency of the workers.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.1-11
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• 2010

Recently, there has been a growing interest in the processing of continuous queries over multiple data streams. When the arrival rates of tuples exceed the memory capacity of the system, a load shedding technique is used to avoid the system becoming overloaded by dropping some subset of input tuples. In this paper, we propose an effective load shedding algorithm for multi-way windowed joins over multiple data streams. Most previous load shedding algorithms estimate the productivity of each tuple, i.e., the number of join output tuples produced by the tuple, based on its "join attribute value" and drop tuples with the lowest productivity. However, the productivity of a tuple cannot be accurately estimated from its join attribute value when the join attribute values are unique and do not repeat, or the distribution of the join attribute values changes over time. For these cases, we estimate the productivity of a tuple based on its "arrival order" on data streams, rather than its join attribute value. The proposed method can effectively estimate the productivity of a tuple even when the productivity of a tuple cannot be accurately estimated from its join attribute value. Through extensive experiments and analysis, we show that our proposed method outperforms the previous methods in terms of effectiveness and efficiency.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.745-756
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• 2009

An autonomous virtual character can conduct itself like a human after recognizing and interpreting the virtual environment. Artificial vision is mainly used in the recognition of the environment for a virtual character. The present artificial vision that has been developed takes all the information at once from everything that comes into view. However, this can reduce the efficiency and reality of the system by saving too much information at once, and it also causes problems because the speed slows down in the dynamic environment of the game. Therefore, to construct a vision system similar to that of humans, a visual observation system which saves only the required information is needed. For that reason, this research focuses on the descriptive artificial intelligence engine which detects the most important information visually recognized by the character in the virtual world and saves it into the memory by degrees. In addition, a visual system is constructed in accordance with an image transaction theory to make it sense and recognize human feelings. This system finds the attention area of moving objects quickly and effectively through the experiment of the virtual environment with three dynamic dimensions. Also the experiment enhanced processing speed more than 1.6 times.