• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.33-36
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• 2011

본 논문에서는 SMART(Self-Monitoring Analysis and Reporting Technology)를 기반으로 SSD(Solid State Drives) 저장 장치의 신뢰성을 분석할 수 있는 방법론과 도구를 제안한다. 방법론은 SSD를 구성하는 플래시 메모리의 결함 종류, 결함들을 효과적으로 모니터링 할 수 있는 SMART 속성과 임계값, 그리고 이를 기반으로 SSD의 신뢰성을 예측할 수 있는 모델로 구성된다. 이 방법론은 신뢰성 분석 도구로 구현 되었으며, 이 도구는 Workload generator, SMART monitor, Dependability analyzer, 그리고 GUI viewer로 구성된다. 실제 두 회사에서 생산한 6개의 SSD를 이용하여 실험한 결과, SMART를 기반으로 SSD의 고장 예측이 가능하며, 여러 속성들을 동시에 고려하였을 때 예측의 정확도가 높아짐을 발견하였다.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.379-385
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• 2017

In enterprise environments, hybrid storage typically utilizes SSDs over disk-based RAID. Typically, SSDs over RAID are used as the data cache. Recently, the LeavO caching scheme was introduced to reduce the parity update overhead of the underlying RAID. In this paper, we combine the data caching and LeavO caching schemes and derive cost models of the combined cache to determine the optimal data and LeavO cache sizes. We also propose the Adaptive Combined Cache that dynamically adjusts the data cache and LeavO cache sizes for evolving workloads. Experimental results show that the performance of the Adaptive Combined Cache is significantly superior to that of the conventional data caching scheme and is comparable with that of the off-line optimal scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1118-1120
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• 2012

컴퓨터 메모리의 용량이 커지고 기술이 발전하며 메모리와 저장장치의 데이터 처리속도 차이는 나날이 커지고 있다. 이를 보완하고자 데이터 처리를 가급적 메모리에서 해결하여 처리속도를 높이고자 하는 연구가 많이 있다. 그 중 MapReduce에 대한 연구는 현재 주목이 되고 있는 분야이다. MapReduce는 빅데이터를 클러스터 환경에서 처리하기에 대중적인 프로그래밍 모델이다. 본 논문은 MapReduce 기반의 Hadoop을 SSD를 적용하여 실행속도를 증진시키려 한다. 전통적인 MapReduce 모델은 데이터를 정렬하는데에 I/O가 크게 발생하는데, MapRedce가 사용하는 병합정렬의 I/O 병목현상을 개선하고자 SSD를 사용하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.348-350
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• 2021

The core technology of augmented reality is object recognition technology. Recently, due to the development of various artificial intelligence algorithms such as CNN, it has become possible to effectively distinguish specific objects from images. It is possible to realize more realistic and immersive augmented reality contents only when technology for recognizing objects quickly and accurately is secured. In this study, an object recognition model using SSD (single shot multibox detector) and an object recognition model using YOLO were compared and evaluated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2449-2456
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• 2013

The column-oriented database storage is a very advanced model for large-volume data analysis systems because of its superior I/O performance. Traditional data storages exploit row-oriented storage where the attributes of a record are placed contiguously in hard disk for fast write operations. However, for search-mostly datawarehouse systems, column-oriented storage has become a more proper model because of its superior read performance. Recently, solid state drive using MLC flash memory is largely recognized as the preferred storage media for high-speed data analysis systems. In this paper, we introduce fast column-oriented data storage model and then propose a new storage management scheme using a cross compressed replication for the high-speed column-oriented datawarehouse system. Our storage management scheme which is based on two MLC SSD achieves superior performance and reliability by the cross replication of the uncompressed segment and the compressed segment under high workloads of CPU and I/O. Based on the results of the performance evaluation, we conclude that our storage management scheme outperforms the traditional scheme in the respect of update throughput and response time of the column segments.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.178-179
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• 2012

최근, 빅데이터 등 대용량의 데이터가 등장함에 따라 SSD 안에 추가적인 프로세서를 장착하여 데이터 처리 능력을 부여한 Intelligent SSD (ISSD)의 필요성이 대두되고 있다. 본 논문에서는 먼저, ISSD의 특징을 분석하고, ISSD가 대용량 처리에 적합함을 보인다. 더 나아가, ISSD를 위한 k-means 알고리즘을 제안하고, 비용 모델을 수립을 통해 제안한 알고리즘의 우수성을 검증한다.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.323-328
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• 2020

Recently, new approaches that significantly improve performance in object detection and recognition using deep learning technology have been proposed quickly. Of the various techniques for object detection, especially facial object detection (Faster R-CNN, R-CNN, YOLO, SSD, etc), SSD is superior in accuracy and speed to other techniques. At the same time, multiple object detection networks are also readily available. In this paper, among object detection networks, Mobilenet v2 network is used, models combined with SSDs are trained, and methods for detecting objects at a rate of four times or more than conventional performance are proposed using TensorRT engine, and the performance is verified through experiments. Facial object detector was created as an application to verify the performance of the proposed method, and its behavior and performance were tested in various situations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.838-846
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• 2004

In the case where both a camera and a target are moving at the same time, the image background is successively changed, and the overlap with other moving objects is apt to be generated. The snake algorithms have been variously used in tracking the object, but it is difficult to be applied in the excessive overlap with other objects and the large bias between the snake and the target. To solve this problem, this paper presents an extended snake model. It includes an additional energy function which considers the temporal variation rate of the snake's area and a SSD algorithm which generates the template adaptive to the snake detected in the previous frame. The new energy function prevents the snake from over-shrinking or stretching and the SSD algorithm with adaptively changing template allows the prediction of the target's position in the next frame. The experimental results have shown that the proposed algorithm successfully tracks the target even when the target is temporarily occluded by other objects.

• Smart Media Journal
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• v.9 no.2
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• pp.92-98
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• 2020

This paper proposes a vehicle license plate recognition system using light weight deep learning models without high-end server. The proposed license plate recognition system consists of 3 steps: [license plate detection]-[character area segmentation]-[character recognition]. SSD-Mobilenet was used for license plate detection, ResNet with localization was used for character area segmentation, ResNet was used for character recognition. Experiemnts using Samsung Galaxy S7 and LG Q9, accuracy showed 85.3% accuracy and around 1.1 second running time.