• Journal of Environmental Science International
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• v.4 no.2
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• pp.121-139
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• 1995

Based on the Results of Marine Meteorological and Oceanographical Observations (1966 -1987), the phenomenon of chimney is found as a candidate for the formation of the Japan Sea Proper Water (JSPW). The chimney phenomenon occurs twelve times Inuring 1966∼ 1987. The water types in the chimney denoting the deep convection are similar to those of the JSPW 0∼ 1℃ in potential temperature, 34.0∼34.1 ‰ in salinity and 68∼80 cl/t in potential thermosteric anomaly from the sea surface to the deep layer. The static stabilities in the chimney stations are unstable or neutral. This indicates that the winter time convection occurs. The JSPW sunken from the surface layer of chimney in winter spreads out under the Tsushima Warm Current area, following the isosteric surface of about 76 cl/t in Potential thermosteric anomaly. The formation of the deep water of the JSPW is mainly affected by the cooling of the sea surface than the evaporation of winds because the temperature and the salinity on the isoteric surface of about 76 cl/t in potential thermosteric anomaly ate cold and low The phenomenon of chimney occurred in here and there of the area in the north of 40" 30'N, west of 138" E. This suggests that the deep water of the JSPW is formed not in a limited area but probably in the overall region of the northern open ocean.

• Journal of Korean Ophthalmic Optics Society
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• v.3 no.1
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• pp.269-277
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• 1998

The TSDC(Thermally Stimulated Depolarization Current) measurement were carried out in the temperature range $30{\sim}500^{\circ}C$. We observed the anomalous two peaks that have a thousand times longer relaxation time than that of the space charge. It seems that the origin of the two peak are due to the electron trapping effect and to the adsorption of the vacancies at silver electrode.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.20 no.6
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• pp.249-253
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• 2010

After the specimen of A-site defect perovskite $La_{1/3}NbO_3$ single crystal was manufactured, the dielectric properties were studied between the temperature range of 10 and 800 K. The dielectric anomaly appeared at 50 K and 650 K, and, at about 650 K, the thermal hysteresis of dielectric constant was shown. The ac-conductivity of bulk showed the lowest activation energy of 0.43 eV at 560~690 K. Based on the results, it is assumed that the dielectric anomaly at 50K and 650 K was due to the antiparallel shift of $Nb^{5+}$-ion and the rearrangement of $Nb^{3+}$-ion, respectively.

• Journal of Environmental Science International
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• v.4 no.5
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• pp.403-411
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• 1995

The atmospheric responses to a Sea Surface Temperature Anomaly(SSTA) over the equatorial eastern Pacific Ocean have been investigated using the horizontally fine resolution model based on OSU 2-layer Atmospheric General Circulation Model(AGCM). The SSTAS daring the peak phase of 1982-83 El Nino have been applied to the model as the boundary conditions of the experiment. The model simulates the eastward movement of the rising branch of the Walker circulation. That is, the major features associated with the El Nino such as the increase of the precipitation rate over the center of the Pacific and decrease over the Indonesia, and the 500hPa geopotential height anomaly in the middle latitude are properly describes in the fine resolution model experiment. The model results indicate that this horizontally fine resolution UM can successfully simulate the ENSO anomalies and be more effectivelly used for the study of the climate and the climate changes.

• Journal of the Korean association of regional geographers
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• v.5 no.2
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• pp.143-153
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• 1999

This study is analyzed the correlation between El-Nino and La-Nina and Korea's temperature and precipitation in summer and winter, and the results of this analysis are as follows: (1) The extraction of the occurrences of El-Nino reveals are 5, but La-Nina reveals 6 years. (2) The tendency of change of sea surface temperature around NINO.3 and that of or country are about the same, but the anomaly of Janggi and Pusan was much greater than that of Inchon. (3) The anomaly of sea surface temperature around NINO.3 and that of the temperature showed the similar changing tendency, the temperature of Korea has something to do with that of NINO.3sea surface temperature as the correlation of ground temperature with the temperature of sea surface showed 0.06. Anomaly warm winter has something to do with El-Nino because the temperature of our country was high when El-Nino phenomena appeared. But the precipitation over our country is not significant for La-Nina. (4) Temperature in El-Nino year is lower than normal in summer and higher than normal in winter. But precipitation is more in summer and winter of El-Nino year, but it is not significant of La-Nina year.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.678-686
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• 2019

In this study, marine heat waves on coastal waters of Republic of Korea were detected using satellite-based Sea Surface Temperature Anomaly (SSTA). The detected results were compared with the warm water issues reported by the National Institute of Fisheries Science (NIFS). Marine heat waves detection algorithm using SSTA based on a threshold has proposed. The threshold value was defined as 2℃ for caution and 3℃ for warning issues, respectively. Daily averaged SST data from July to September of 2017-2018 were used to generate SSTA. The satellite-based detection results were classified into nine areas according to the place names used in the NIFS warm water issues. In the comparison of frequency of marine heat waves occurrence to each area with the warm water issue, most areas in the southern coast showed a similar pattern, that is probably NIFS uses spatially well distributed buoys. On the other hand, other sea areas had about two times more satellite detection results. This result seems to be because NIFS only considers the water temperature data measured at limited points. The results of this study are expected to contribute to the development of a satellite-based warm/cold water monitoring system in coastal waters.

• Proceedings of the Korean Ceranic Society Conference
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• 2002.10a
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• pp.86.1-86
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• 2002

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1211-1218
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• 2005

Temperature variation according to space and time on the inner parts of engineering constructions(e.g.: dam, slope) can be a basic information for diagnosing their safety problem. In general, as constructions become superannuated, structural deformation(e.g.: cracks, defects) could be occurred by various factors. Seepage or leakage of water through these cracks or defects in old dams will directly cause temperature anomaly. Groundwater level also can be easily observed by abrupt change of temperature on the level. This study shows that the position of seepage or leakage in dam body can be detected by multi-channel temperature monitoring using thermal line sensor. For this, diverse temperature monitoring experiments for a leakage physical model were performed in the laboratory. In field application of an old dam, temperature variations for water depth and for inner parts of boreholes located at downstream slope were measured. Temperature monitoring results for a long time at the bottom of downstream slope of the dam showed the possibility that temperature monitoring can provide the synthetic information about flowing path and quantity of seepage of leakage in dam body.

• Atmosphere
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• v.26 no.1
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• pp.193-201
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• 2016

An anomalous cold-weather period occurred during January 2011 in East Asia, and this study investigates the event by focusing on the blocking phenomena formed at Northeastern Asia. The area of cold weather is determined to represent the characteristic features of abnormal cold temperature. The 2010/11 winter is divided into three periods P1, P2 (cold period), and P3. For the cold area ($30-50^{\circ}N$, $115-135^{\circ}E$) the corresponding cold period P2 is determined to be 39 days from 23 December 2010 through 30 January 2011. During P1 and P3 temperature anomalies from the climatological mean are small with large standard deviation compared to those of P2, which has large negative anomaly and small standard deviation. The period P2 is dominated by blocking, which was determined by distributions of 500-hPa geopotential height and potential temperature on the 2 PVU surface. Correlation-coefficient analyses show that during P2 the temperature in the cold area is related with pressure of Northeastern Asia, while the temperature during P1 and P3 is related with pressure of Northwest of Korea. Also, during P1 and P3 the temperature pattern shows eastward propagation, but during P2, a stationary pattern. All the observations imply that, during the cold period P2, the temperature in the cold area is related with blocking in Northeastern Asia. During P1 and P3 temperature pattern is related with 500-hPa geopotential height in Siberia, and this relationship is also observed in the climatological mean state.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.