• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.195-200
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• 2016

Water vapor leading various scale of atmospheric circulation and accounting for about 60% of the naturally occurring warming effect is important climate variables. Using the Total Precipitable Water (TPW) from Moderate Resolution Imaging Spectroradiometer (MODIS) operating on both Terra and Aqua, we study long-term Variation of TPW and define relationship among TPW and climatic parameters such as temperature and precipitation to quantitatively demonstrate the impact on climate change over East Asia focusing on the Korea peninsula. In this study, we used linear regression analysis to detect the correlation of TPW and temperature/precipitation and harmonic analysis to analyze changeable aspects of periodic characteristics. A result of analysis using linear regression analysis between TPW and climate elements, TPW shows a high determination coefficient ($R^2$) with temperature and precipitation (determination coefficient between TPW and temperature: 0.94, determination coefficient between TPW anomaly and temperature anomaly: 0.8, determination coefficient between TPW and precipitation: 0.73, determination coefficient between TPW anomaly and precipitation anomaly: 0.69). A result of harmonic analysis of TPW and precipitation of two-year to five-year cycle, amplitude contribution ratio of 3.5-year cycle are much higher and two phases are similar in 3.5-year cycle.

• Journal of the Korean Society for Marine Environment & Energy
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• v.9 no.1
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• pp.55-62
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• 2006

Correlations between air-temperature variation and SST variation around Jeju Island have been studied with data JRMO($1924{\sim}2004$) and NFRDI($l971{\sim}2000$). Air-temperature has increased about $0.02^{circ}C/year$ for the period of $1924{\sim}2004$ but relatively high 0.035/year for the last 30 years. SST has increased about $0.024^{circ}C/year$ for the period of $1971{\sim}2000$ and relatively high $0.047^{circ}C/year$ in December. According to the analysis of time series of the two kind of variation, the SST and air-temperature are positively correlated. They are generally in phase, and SST anomaly is similar to air-temperature anomaly as well. Consequently, SST variation has high correlation with air-temperature variation around Jeju Island.

• Journal of the Korean Ceramic Society
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• v.43 no.2 s.285
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• pp.131-135
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• 2006

In this study, the dielectric, magnetic and transport properties of $Gd_{0.33}Sr_{0.67}FeO_3$ have been analyzed. The dielectric loss anomaly was found to be around 170 K. The activation energy corresponding to relaxation process of this dielectric anomaly was 0.17 eV. From the temperature dependence of the characteristic frequency, we concluded that the elementary process of the dielectric relaxation peak observed is correlated with polaron hopping between $Fe^{3+}\;and\;Fe^{4+}$ ions. The electrical resistivity displayed thermally activated temperature dependence above 200 K with an activation energy of 0.16 eV. In addition, the temperature dependence of thermoelectric power and resistivity suggests that the charge carrier responsible for conduction is strongly localized.

• ETRI Journal
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• v.44 no.2
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• pp.183-193
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• 2022

Smart cities are expected to provide residents with convenience via various agents such as CCTV, delivery robots, security robots, and unmanned shuttles. Environmental data collected by various agents can be used for various purposes, including advertising and security monitoring. This study suggests a surveillance map data framework for efficient and integrated multimodal data representation from multi-agents. The suggested surveillance map is a multilayered global information grid, which is integrated from the multimodal data of each agent. To confirm this, we collected surveillance map data for 4 months, and the behavior patterns of humans and vehicles, distribution changes of elevation, and temperature were analyzed. Moreover, we represent an anomaly detection algorithm based on a surveillance map for security service. A two-stage anomaly detection algorithm for unusual situations was developed. With this, abnormal situations such as unusual crowds and pedestrians, vehicle movement, unusual objects, and temperature change were detected. Because the surveillance map enables efficient and integrated processing of large multimodal data from a multi-agent, the suggested data framework can be used for various applications in the smart city.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.1
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• pp.85-94
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• 2022

To detect anomaly signs of thermal runaway in advance, this study analyzed the signals from various sensors installed in lithium-ion batteries. The thermal runaway mechanism was analyzed, and measurement variables for anomalies of a battery cell were surface temperature, strain, and gas concentration. The changes and characteristics of three variables during the thermal runaway process were analyzed under the abuse environment: the overheat and the overcharge. In experiment, the thermal runaway of the battery proceeded in the initial developing stage, the outgassing stage, and the ignition stage. Analysis from the measured data indicated that the suitable variable to detect all stages of thermal runaway is the surface temperature of the battery, and surface strain is alternative.

• Ocean and Polar Research
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• v.34 no.4
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• pp.395-401
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• 2012

Ten-year AVHRR sea surface temperature data obtained in the Yellow Sea are put into EOF analyses. Temperature variation is predominated by the first mode which is associated with the seasonal fluctuation of temperature with annual range decreasing with the bottom depth. Since such a strong annual signal may mask the upwind or downwind flows occurring intermittently during the winter, only the data obtained during this season are put into EOF analyses. Every winter shows similar results. The first mode, explaining more than 90% of total variance, appears to be a part of the seasonal variation of temperature mentioned above. In the second mode, the time coefficient is well correlated with northerly winds to which the responses of the trough and shallow coastal areas are opposite to each other. A simple theoretical consideration suggests the following physical explanation: The northerly wind stress anomaly creates an upwind (downwind) flow over the trough (coastal) areas, which then induces a temperature increase (decrease) by advection of heat, and vice versa for the southerly wind stress anomaly. Hence, this paper provides further evidence of the intermittent upwind or downwind flows occurring in the Yellow Sea every winter.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.317-322
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• 2002

Dramatic changes in the patterns of satellite-derived pigment concentrations, sea-level height anomaly, sea surface temperature anomaly, and zonal wind anomaly are observed during the 1997-1998 El Nino. By some measures, the 1997-1998 El Nino was the strongest of the 20$^{th}$ century. A very strong El Nino developed during 1997 and matured late in the year. A dramatic recovery occurred in mid-1998 and led to a La Nina conditions. The largest spatial extent of the phytoplankton bloom was followed recovery from El Nino over the equatorial Pacific. The evolution towards a warm episode (El Nino) continued in the equatorial Pacific from March 2002 and further development toward mature El Nino conditions may be possible in late 2002. The OSMI (Ocean Scanning Multispectral Imager) data can be used for detection of dramatic changes in the patterns of pigment concentration during next El Nino.

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.569-581
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• 2023

This paper introduces a system to detect and diagnose anomalies in pumped storage hydropower plants. We collect data from various types of sensors, including those monitoring temperature, vibration, and power. The data are classified according to the operation modes (pump and turbine operation modes) and normalized to remove the influence of the external environment. To detect anomalies and diagnose their types, we adopt a multivariate normal distribution analysis by learning the distribution of the normal data. The feasibility of the proposed system is evaluated using actual monitoring data of a pumped storage hydropower plant. The proposed system can be used to implement condition monitoring systems for other plants through modifications.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.361-371
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• 2015

To understand the impact of 2015 spring drought on crop production of DPRK (Democratic People's Republic of Korea), we analyzed satellite and weather data to produce 2015 spring outlook of rice paddy field and rice growth in relation to weather anomaly. We defined anomaly of 2015 for weather and NDVI in comparison to past 5 year-average data. Weather anomaly layers for rainfall and mean temperature were calculated based on 27 weather station data. Rainfall in late April, early May, and late May in 2015 was much lower than those in average years. NDVI values as an indicator of rice growth in early June of 2015 was much lower than in 2014 and the average years. RapidEye and Radarsat-2 images were used to monitor status of rice paddy irrigation and transplanting. Due to rainfall shortage from late April to May, rice paddy irrigation was not favorable and rice planting was not progressed in large portion of paddy fields until early June near Pyongyang. Satellite images taken in late June showed rice paddy fields which were not irrigated until early June were flooded, assuming that rice was transplanted after rainfall in June. Weather and NDVI anomaly data in regular basis and timely acquired satellite data can be useful for grasping the crop and land status of DPRK, which is in high demand.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.