• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.541-543
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• 2015

최근 스마트폰 카메라를 이용하여 심방세동을 진단하기 위해 PPG를 이용하여 Pulse Peak 사이의 간격을 구하고 RMSSD와 섀넌 엔트로피를 이용하여 불규칙 정도를 계산하는 연구가 활발히 진행되고 있다. 본 논문에서는 심방세동 환자와 정상인을 포함한 한국인을 대상으로 스마트폰을 이용한 심방세동 검출을 실험하고 정확률을 계산하였다. 심방세동 환자를 포함한 한국인 29명을 대상으로 PPG 신호를 수집하여 실험하였다. 실험결과에서 정확률 85.18%를 보였다.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.813-822
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• 2012

Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.