The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, therefore the location determination technology has been placed in an important position. This study suggests a method that can provide the estimate of users’ location by using PDR method and smartphone geomagnetic sensor data. This method assists the measure of enhancing the accuracy of indoor localization. Moreover, it is to study ways to provide the exact indoor layout for evacuating the workers in emergency such as fires and natural disasters.

한반도에서 측정되고 있는 시계열 지자기 자료에 대해 결측 자료에 대한 복원과 측정 자료에 기반한 예측, 그리고 기관별 관측 자료에 대한 잡음도를 분석하였다. 결측 자료의 복원을 위해 주성분 분석을 통한 최적화 기법과 지구 통계학적 접근에 의한 방법을 적용하고 그 효과를 비교하였다. 주성분 기법은 자료의 주기성을 효율적으로 반영하는 특성을 보였으며, 지구통계학적 방법은 안정적인 복원 능력을 보였다. 관측소 별 잡음도를 파악하기 위해 이천 및 청양에서 동일 기간에 관측한 지자기 자료에 대해 공간적 분산성을 스캐터그램을 이용해 파악하였다. 그 결과 청양 관측소의 자료가 이천 관측소의 자료보다 연속적이며 안정적인 측정이 이루어진 것을 알 수 있었으며, 복원을 위한 크리깅 추정에서도 실제 자료의 추정이 매우 정확하게 이루어졌다. 결측자료의 복원의 경우 20분 이내의 결측 자료에 대해서는 크리깅 기법과 주성분 기법 모두 유사한 결과를 보였으나, 그 이상의 결측에 대한 복원과 지자기 자료의 예측이 필요한 경우에는 주성분 기법을 적용해야 주파수 영역에서의 특성이 실제와 더욱 유사하게 나타났다. 또한 지자기 자료의 예측을 위해서는 주성분 분석이 효율적으로 이용될 수 있음을 파악하였으며, 하루 정도의 지자기장을 예측할 수 있는 것으로 보인다.

2009년 5월 2일 규모 4.0의 안동 지진이 발생한 시기에 기상청 청양 지자기 관측소에 기록된 자료를 이용하여 지자기 변동성을 분석하였다. 먼저 지자기 관측자료의 주성분을 이용하여 지자기 예측을 수행하고, 지진이 발생한 전후로 예측한 지자기장과 실제 관측된 지자기장 사이에 유의미한 변화량이 있는지 분석하였다. 두 번째로, 지진 발생일과 다른 날의 지자기장을 웨이블릿 셈블런스 기법을 통해 상호 비교하였다. 이 결과에서는 자기장의 수직성분에서 차이가 있음을 발견하였다. 마지막으로 3성분 자료에 대한 고유값 분석을 통해 지진 발생 시기 부근에 고유값의 변화가 발생하였는지 분석하였다. 청양 관측소의 위치가 지진 발생지점과 매우 많이 떨어져 있고 규모가 작아서 명확한 전조 현상을 발견할 수는 없었으나, 지진과 상관성이 높은 지자기 변동성을 발견하였다. 본 연구에서 개발된 다양한 지자기 신호처리 기술은 향후 전조현상 탐지를 위해 유용하게 활용될 수 있을 것으로 기대 한다.

Through the coupling between the near-earth space environment and the polar ionosphere via geomagnetic field lines, the variations occurred in the magnetosphere are transferred to the polar region. According to recent studies, however, the polar ionosphere reacts not only passively to such variations, but also plays active roles in modifying the near-earth space environment. So the study of the polar ionosphere in terms of geomagnetic disturbance becomes one of the major elements in space weather research. Although it is an indirect method, ground magnetic disturbance data can be used in estimating the ionospheric current distribution. By employing a realistic ionospheric conductivity model, it is further possible to obtain the distributions of electric potential, field-aligned current, Joule heating rate and energy injection rate associated with precipitating auroral particles and their energy spectra in a global scale with a high time resolution. Considering that the ground magnetic disturbances are recorded simultaneously over the entire polar region wherever magnetic station is located, we are able to separate temporal disturbances from spatial ones. On the other hand, satellite measurements are indispensible in the space weather research, since they provide us with in situ measurements. Unfortunately it is not easy to separate temporal variations from spatial ones specifically measured by a single satellite. To demonstrate the usefulness of ground magnetic disturbance data in space weather research, various ionospheric quantities are calculated through the KRM method, one of the magneto gram inversion methods. In particular, we attempt to show how these quantities depend on the ionospheric conductivity model employed.

Because of the small number of spacecraft available in the Earth’s magnetosphere at any given time, it is not possible to obtain direct measurements of the fundamental quantities, such as the magnetic field and plasma density, with a spatial coverage necessary for studying, global magnetospheric phenomena. In such cases, empirical as well as physics-based models are proven to be extremely valuable. This requires not only having high fidelity and high accuracy models, but also knowing the weakness and strength of such models. In this study, we assess the accuracy of the widely used Tsyganenko magnetic field models, T96, T01, and T04, by comparing the calculated magnetic field with the ones measured in-situ by the GOES satellites during geomagnetically disturbed times. We first set the baseline accuracy of the models from a data-model comparison during the intervals of geomagnetically quiet times. During quiet times, we find that all three models exhibit a systematic error of about 10% in the magnetic field magnitude, while the error in the field vector direction is on average less than 1%. We then assess the model accuracy by a data-model comparison during twelve geomagnetic storm events. We find that the errors in both the magnitude and the direction are well maintained at the quiet-time level throughout the storm phase, except during the main phase of the storms in which the largest error can reach 15% on average, and exceed well over 70% in the worst case. Interestingly, the largest error occurs not at the Dst minimum but 2–3 hours before the minimum. Finally, the T96 model has consistently underperformed compared to the other models, likely due to the lack of computation for the effects of ring current. However, the T96 and T01 models are accurate enough for most of the time except for highly disturbed periods.

Coronal Mass Ejections (CME), which originate from active regions of the Sun’s surface, e.g., sunspots, result in geomagnetic storms on Earth. The variation of the Earth’s geomagnetic field during such storms induces surface currents that could cause breakdowns in electricity power grids. Hence, it is essential to both monitor Geomagnetically Induced Currents (GICs) in real time and analyze previous GIC data. In 2012, in order to monitor the variation of GICs, the Korean Space Weather Center (KSWC) installed an induced current measurement system at SINGAPYEONG Substation, which is equipped with 765 kV extra-high-voltage transformers. Furthermore, in 2014, two induced current measurement systems were installed on the 345 kV high-voltage transformers at the MIGEUM and SINPOCHEON substations. This paper reports the installation process of the induced current measurement systems at these three substations. Furthermore, it presents the results of both an analysis performed using GIC data measured at the SINGAPYEONG Substation during periods of geomagnetic storms from July 2013 through April 2015 and the comparison between the obtained GIC data and magnetic field variation (dH/dt) data measured at the Icheon geomagnetic observatory.