• Title/Summary/Keyword: 자기 이상

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Magnetic anomaly in the southern part of the Yellow Sea (서해남부해역의 지자기 이상대 해석)

  • Kim, Sung-Bae;Choi, Sung-Ho;Suh, Man-Cheol
    • 한국지구물리탐사학회:학술대회논문집
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    • 2008.10a
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    • pp.85-92
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    • 2008

  • National Oceanographic Research Institute is carrying out an oceanographic survey for the entire sea areas around Korean Peninsula annually starting with the East Sea from 1996 by establishing a national oceanographic basic map survey plan for the sea areas under the jurisdiction of Korea, so this paper used the oceanographic geomagnetism data measured at the southern area of the Yellow Sea using 'Hae Yang 2000' in 1999, aiming at clarifying the cause of geomagnetic abnormality zone during the course of treating and analyzing the geomagnetic data. For treatment of magnetic data, we obtained electromagnetic force values and geomagnetic abnormality values around the investigated sea area through a process of searching and removal of bad data, correction of sensor positions, correction of magnetic field effects around the hull, correction of diurnal variation, normal correction, correction of cross point errors, etc. The electromagnetic force distribution around the investigated sea area was $49000\;{\sim}\;51600\;nT$, which is judged to be within the normal electromagnetic force intensity distribution range around the Yellow Sea. The isodynamic lines are distributed in Northeast-Southwest direction, and electromagnetic force values are increasing toward the northwest. The result of comparing the magnetic abnormality around the sea area among $124^{\circ}$ 49' 48" E, $35^{\circ}$ 10' 48" N $\sim$ $125^{\circ}$ 7' 48" E, and $35^{\circ}$ 33' 00" N sections with the elastic wave cross section and the result of modeling coincide well with the underground geological structure clarified from the existing elastic wave survey cross section. Therefore, it is judged that the distribution of magnetic force abnormality generally shows the effect pursuant to the distribution of the sedimentary basins in the Tertiary period and the bedrocks in the Cretaceous period which are well developed in the bottom of the sea.

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Effects of Nurses' Teamwork on Job Satisfaction at Hospital: Mediating Effect of Self-efficacy (간호사의 팀워크가 직무만족에 미치는 영향: 자기효능감의 매개효과)

  • Kang, So-Young;Kwon, Hae-Kyoung;Cho, Mi-Ra
    • The Journal of the Korea Contents Association
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    • v.14 no.12
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    • pp.881-894
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    • 2014

  • This study aimed at (a) examining the effects of teamwork, demonstrating with other nurses at a care unit on nurses' job satisfaction and self-efficacy, and (b) identifying if there is the mediating effect of self-efficacy on the relationship between teamwork and job satisfaction at a hospital. A descriptive study was conducted with a sample of 304 nurses, who were caring for patients with more than one nurse at a university hospital located in Southern area of Korea. The degree of teamwork demonstraing by registered nurses was 3.78(${\pm}0.59$) in the range from 1 to 5. Teamwork at caring units affected significantly nurses' job satisfaction(F=58.26, p<.001), and explained 42.80% in the variance of job satisfaction. The degree of self-efficacy that nurses perceived played as a mediator on the relationship between teamwork and job satisfaction at significant level(Z=5.25, p<.001).

  • https://doi.org/10.5392/JKCA.2014.14.12.881 인용 PDF KSCI

gMLP-based Self-Supervised Learning Anomaly Detection using a Simple Synthetic Data Generation Method (단순한 합성데이터 생성 방식을 활용한 gMLP 기반 자기 지도 학습 이상탐지 기법)

  • Ju-Hyo, Hwang;Kyo-Hong, Jin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.27 no.1
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    • pp.8-14
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    • 2023

  • The existing self-supervised learning-based CutPaste generated synthetic data by cutting and attaching specific patches from normal images and then performed anomaly detection. However, this method has a problem in that there is a clear difference in the boundary of the patch. NSA for solving these problems have achieved higher anomaly detection performance by generating natural synthetic data through Poisson Blending. However, NSA has the disadvantage of having many hyperparameters that need to be adjusted for each class. In this paper, synthetic data similar to normal were generated by a simple method of making the size of the synthetic patch very small. At this time, since the patches are so locally synthesized, models that learn local features can easily overfit synthetic data. Therefore, we performed anomaly detection using gMLP, which learns global features, and even with simple synthesis methods, we were able to achieve higher performance than conventional self-supervised learning techniques.

  • https://doi.org/10.6109/jkiice.2023.27.1.8 인용 PDF