• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.357-359
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• 2018

최근 메모리 반도체 시장은 SD(Secure Digital) 메모리 카드, SSD(Solid State Drive)등의 보급률 증가로 메모리 반도체의 시장이 대규모로 증가하고 있다. 메모리 반도체는 개인용 컴퓨터 뿐만 아니라 스마프폰, 테플릿 PC, 교육용 임베디드 보드 등 다양한 산업에서 이용 되고 있다. 또한 메모리 반도체 생산 업체가 대규모로 메모리 반도체 산업에 투자하면서 메모리 반도체 시장은 대규모로 성장되었다. 플래시 메모리는 크게 NAND-Type과 NOR-Type으로 나뉘며 플로팅 게이트 셀의 전압의 따라 SLC(Single Level Cell)과 MLC(Multi Level Cell) 그리고 TLC(Triple Level Cell)로 구분 된다. SLC 및 MLC NAND-Type 플래시 메모리는 많은 연구가 진행되고 이용되고 있지만, TLC NAND-Tpye 플래시 메모리는 많은 연구가 진행되고 있지 않다. 본 논문에서는 기존에 제안된 SLC 및 MLC NAND-Type 플래시 메모리에서 제안된 큐브 패턴을 TLC NAND-Type 플래시 메모리에서 적용 가능한 큐브 패턴 및 알고리즘을 제안한다.

• 한국멀티미디어학회논문지
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• 제15권12호
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• pp.1417-1429
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• 2012

In neuroscientific research, image segmentation is one of the most important processes. The morphology of axons plays an important role for researchers seeking to understand axonal functions and connectivity. In this study, we evaluated the level set segmentation method for neuronal axons in a Brainbow confocal microscopy image. We first obtained a reconstructed image on an x-z plane. Then, for preprocessing, we also applied two methods: anisotropic diffusion filtering and bilateral filtering. Finally, we performed image segmentation using the level set method with three different approaches. The accuracy of segmentation for each case was evaluated in diverse ways. In our experiment, the combination of bilateral filtering with the level set method provided the best result. Consequently, we confirmed reasonable results with our approach; we believe that our method has great potential if successfully combined with other research findings.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.453-454
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• 2008

Modern VLSI designs get increasingly complex and time-to-market constraints get tighter. Using high level languages is one of the most promising solutions for improving design productivity by raising the level of abstraction. In high level synthesis process, most important step is scheduling. In this paper, we propose fast and efficient scheduling method under timing constraint based on list scheduling. Experimental results on well known data path intensive designs show fast execution times (less than 0.5 sec) and similar results when compared to optimal solutions [1].

• International Journal of Contents
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• 제3권2호
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• pp.1-5
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• 2007

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.31-41
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• 2018

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.

• 유통과학연구
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• 제14권3호
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• pp.55-62
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• 2016

Purpose - Average using time of smart-phone for Korean people is 3 hours 39 minutes and most people who are using a computer at home and their workplace can be affected over force to neck and shoulder due to unstable body posture. musculoskeletal disorders which caused by unstable body posture can affect strongly to decrease work efficiency. So this research is designed to measure the effect of using computer & smart-phone on decreased work efficiency due to musculoskeletal disorders and mediating effect between decreased work efficiency and musculoskeletal disorders. Research Design, Data, and Methodology - The author has developed a questionnaire with 6 hypothesis on the basis of previous research result with 5 constructs. The questionnaires were also made by interview and E-mail. 300 copies of questionnaires were distributed and 282 questionnaire were used for the analysis as valid data responses. SPSS ver.21.0 were used and made Cronbach's α and reliability test, correlation, Baron & Kenny 3 step mediated regression analysis. Result - Cronbach's α shows 0.770~0.954 and C.R. is 0.963~0.997 which is higher than 0.7. and AVE was 0.867~0.933. So the data are all acceptable condition. Using for a long time of a computer & smart-phone has a positive effect on musculoskeletal disorders. This means, it can cause musculoskeletal disorders if people use a computer & smart-phone for a long time due to unstable body posture. And musculoskeletal disorders can effect strongly decrease work efficiency. This study also found out that a long time of using computer can cause musculoskeletal disorders rather than using smart-phone a long time. To check mediate effect of musculoskeletal disorders between using a computer & smart-phone and Decreased Work Efficiency, author used 3-step mediated regression analysis of Baron & Kenny (1986). Using a computer for a long time mediate partially and using a smart-phone for a long time mediate completely. This means that using a smart-phone a long time is not the actual reason to decrease work efficiency. But using level of smart-phone is increasing rapidly day by day. So we need to make additional research about this matter seriously. Conclusion - Nowadays, people can not live on without a computer & smart-phone even a moment. But, using a computer for a long time will affect to cause musculoskeletal disorders and it will effect strongly to decrease work efficiency. Before, we thought over that musculoskeletal disorders were diseases of elder people. But, we found out from this study that musculoskeletal disorders can be happen to any people, even children, or workers in heavy industry or engaged in brain work. So we need to be careful when we use a computer for a long time. People also need to be careful to keep correct body posture when using both a computer and smart-phone since a smart-phone became more popular and using time level became longer. Due to increased income and living standard of people, physical growth of young people is so rapid. But the physical environment of society is not suitable for them since it can not follow up the speed of growth. Suitable work table is very important to prevent musculoskeletal disorder which can affect decrease work efficiency. For a person, a society or country, increased productivity is very important since it can directly connected to the job satisfaction. Education and reeducation for the people is also important, but to teach them how to keep good condition of health will be more important since it can increase the quality of work efficiency and quality of life. Computer and Smart-phone is one the best invention of modern society, but it can cause mental and physical disease which can affect decrease work efficiency and productivity. So it is necessary to observe attentively for the situation continually.

• 반도체디스플레이기술학회지
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• 제12권1호
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• 반도체공학회 논문지
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• 제1권1호
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• pp.1-8
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• 2023

본 논문에서는 High-Level Synthesis(HLS)을 이용하여, 차세대 양자내성암호인 Crystals-Kyber를 하드웨어 가속기로 설계하여 FPGA에 구현하였으며, 성능 분석결과 우수성을 제시한다. Crystals-Kyber 알고리즘을 Vitis HLS 에서 제공하는 여러 Directive 를 활용해서 최적화 설계를 진행하고, AXI Interface 를 구성하여 FPGA-기반 양자내성암호 하드웨어 가속기를 설계하였다. Vivado 툴을 이용해서 IP Block Design 를수행하고 ZYNQ ZCU106 FPGA 에 구현하였다. 최종적으로 PYNQ 프레임워크에서 Python 코드로 동영상 촬영 및 H.264 압축을 진행한 후, FPGA 에 구현한 Crystals-Kyber 하드웨어 가속기를 사용해서 동영상 암호화 및 복호화 처리를 가속화하였다.

• 대한전자공학회논문지SP
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• 제42권6호
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• pp.73-82
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• 2005

본 논문은 weighted FCM(Fuzzy C-Means) 알고리즘을 적용한 컬러 영상 multi-level thresholding을 제안한다. FCM 알고리즘은 기존의 thresholding 방법들과 달리 최적의 임계치를 결정할 수 있으며 multi-level thresholding으로의 확장이 가능하다. 그러나 공간정보를 포함하고 있지 않기 때문에 잡음 등에 민감하다는 단점을 가진다. 본 논문은 이러한 단점을 해결하기 위해서 이웃 화소들로부터 얻은 entropy 기반의 가중치(weight)를 FCM 알고리즘에 적용함으로써 잡음의 제거가 가능하다. 그리고 각 색상별 성분의 군집 화소들을 기반으로 생성한 코드 영상에 대해서 군집 내부의 거리값을 이용하여 최적의 군집수를 결정한다. 실험에서 제안한 방법이 기존의 방법들보다 잡음에 대해서 강건하며 우수한 분할 성능을 보였다.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.129-135
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• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.