• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.30-39
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• 2021

Research in the field of health lighting has continued to advance to reproduce the color temperature of natural light which periodically changes. However, most of this research could only reproduce a uniform circadian color temperature of natural light, therefore failing to realize the characteristics of the circadian cycle of color temperature difference by latitude and longitude. To reproduce the color temperature of natural light on which the characteristics of a region are reflected, the collection technology of real-time characteristics of natural light is needed. If the color temperatures which are not within a periodical pattern due to climate changes, etc., are measured, it will be difficult to judge the occurrence (presence) of the anomalies and to reproduce the circadian cycle of the color temperature of natural light. Therefore, this study proposes an algorithm for judging the anomalies in real time based on the sliding window to reproduce the color temperature of natural light. First, the natural light characteristics DB collected through the on-site measurement were analyzed, the differential values at a one-minute interval were calculated and examined, and then representative color temperature circadian patterns by solar terms were drawn. The anomalies were then detected by the application of the sliding window that calculated the deviation of the color temperature for the measured color temperature data set, which was collected through RGB sensors, while moving along the time sequence. In addition, the presence of anomalies was verified through the comparison study between the detection results and the representative circadian cycle of the color temperature by solar term. The judgment method for the anomalies from the measured color temperature of natural light was proposed for the first time, confirming that the proposed method was capable of detecting the anomalies with an average accuracy of 94.6%.

• Management & Information Systems Review
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• v.37 no.1
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• pp.41-53
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• 2018

This study validates the trading rules based market anomalies and technical analysis in the Korean stock market. For the analysis, we built decile portfolios on the basis of corporate characteristics factors that clearly demonstrate specific patterns of stock returns including the firm size, book-to-market equity, and accruals. This portfolio was used to develop a portfolio based on the moving average trading strategy which was used for popular technical analysis tools, and then that was evaluated using the Sharpe ratio. We also created a zero-cost portfolio to identify the profitability and success rate of the moving average trading strategy. We lastly sought to ensure a more robust evaluation by calculating the Sortino ratio of the portfolio based on the moving average trading strategy with various lags. Key findings from this validation are as follows. First, a smaller firm size, a higher book-to-market equity, and lower accruals led to larger average returns. Second, the risk-adjusted performance of the moving average trading strategy was the highest in terms of the firm size, followed by book-to-market equity and accruals. Third, the returns of the zero-cost portfolios all had a positive value, with its overall success rate hovering over 68.8%, demonstrating the successfulness of the moving average trading strategy. Fourth, various evaluations revealed the economic usefulness of our trading strategy that used market anomalies and technical analysis.

• Journal of Environmental Science International
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• v.7 no.2
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• pp.191-194
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• 1998

In this research, two stochastic models are considered to detect and estimate the effect of air temperature change due to Industrialization In Ulsan area. Using the monthly mean minimum air temperature anomalies, the data are divided Into pre-Industrialization part and Industrialization one for analysis. The ARM(autoregressive moving-average) model and intervention model have been applied to the data for the analysis. The results show that the variability of minimum temperature anomalies are very significant In 1989, and also significant In 1971 when the Industrialization have started. Therefore, It Is stochastically possible to estimate the time when the affection of Increase of the temperature concerning Industrialization to climate change In Usm area has happened.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.283-288
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• 2016

In recent years, there have been many research efforts on Big Data, and many companies developed a variety of relevant products. Accordingly, we are able to store and analyze a large volume of log data, which have been difficult to be handled in the traditional computing environment. To handle a large volume of log data, which rapidly occur in multiple servers, in this paper we design a new data storage architecture to efficiently analyze those big log data through Apache Hive. We then design and implement anomaly detection methods, which identify abnormal status of servers from log data, based on moving average and 3-sigma techniques. We also show effectiveness of the proposed detection methods by demonstrating that our methods identifies anomalies correctly. These results show that our anomaly detection is an excellent approach for properly detecting anomalies from Hadoop log data.

• Ocean and Polar Research
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• v.31 no.1
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• pp.11-29
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• 2009

Variability of sea surface temperature (SST) in the Japan/East Sea (JES) was studied using complex empirical orthogonal function (CEOF) analysis. Two daily data sets were analyzed: (1) New Generation 0.05o-gridded SST from Tohoku University, Japan (July 2002-July 2006), and (2) 0.25o-gridded SST from the Japan Meteorological Agency (October 1993-November 2006). Linkages with wind stress curl were revealed using 6-h 1o-gridded surface zonal and meridional winds from ancillary data of the Sea- WiFS Project, a special National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) product (1998-2005). SST anomalies (SSTA) were obtained by removing the seasonal signal, estimated as the leading mode of the CEOF decomposition of the original SST. Leading CEOF modes of residual SSTA obtained from both data sets were consistent with each other and were characterized by annual, semiannual, and quasi-biennial time scales estimated with 95% statistical significance. The Semiannual Mode lagged 2 months behind the increased occurrence of the anticyclonic (AC) wind stress curl over the JES. Links to dynamic processes were investigated by numerical simulations using an oceanic model. The suggested dynamic forcings of SSTA are the inflow of subtropical water into the JES through the Korea Strait, divergence in the surface layer induced by Ekman suction, meridional shifts of the Subarctic Front in the western JES, AC eddy formation, and wind-driven strengthening/weakening of large-scale currents. Events of west-east SSTA movement were identified in July-September. The SSTA moved from the northeastern JES towards the continental coast along the path of the westward branch of the Tsushima Current at a speed consistent with the advective scale.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.365-384
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• 2023

We applied the partitioned waveform inversion to 2,026 event data recorded at 173 seismic stations from the Incorporated Research Institutions for Seismology Data Managing Center and the Ocean Hemisphere network Project to estimate S-wave velocity and radial anisotropy models beneath the Western Pacific. In the Philippine Sea plate, high-Vs anomalies reach deeper in the West Philippine basin than in the Parece-Vela basin. Low-Vs anomalies found at 80 km below the Parece-Vela basin extend deeper into the West Philippine Basin. This velocity contrast between the basins may be caused by differences in lithospheric age. Low-Vs anomalies are observed beneath the Caroline seamount chain and the Caroline plate. Overall positive radial anisotropy anomalies are observed in the Western Pacific, but negative radial anisotropy is found at > 220 km depth on the subducting plate along the Mariana trench and at ~50 km in the Parece-Vela basin. Positive radial anisotropy is found at > 200 km depth beneath the Caroline seamount chain, which may indicate the 'drag' between the plume and the moving Pacific plate. High-Vs anomalies are found at 40 ~ 180 km depth beneath the Ontong-Java plateau, which may indicate the presence of unusually thick lithosphere due to underplating of dehydrated plume material.

• Proceedings of the KSRS Conference
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• v.2
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• pp.926-929
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• 2006

Some features of vertical structure of the frontal interaction zone of the warm Kuroshio Current and cold Oyashio Current are known from 1930 from analysis of ship data. Ship data however do not allow carrying out the area detailed survey opposite to satellite infrared (IR) observations which possess by high spatial and temporal resolution. Analysis of NOAA AVHRR IR images demonstrated that process of formation and development of the Kuroshio warm core rings is highly complex. They are formed as a result of development of anticyclonic meanders of the warm Kuroshio waters and spin off them from the current. Joint analysis of thermal infrared images and altimetry data has also indicated that interaction of eddies to the frontal zone plays a crucial role in formation of large eddies moving to the Southern Kuril region.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.128-133
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• 2017

In recent years, the number of systems for the analysis of large volumes of data is increasing. Hadoop, a representative big data system, stores and processes the large data in the distributed environment of multiple servers, where system-resource management is very important. The authors attempted to detect anomalies from the rapid changing of the log data that are collected from the multiple servers using simple but efficient anomaly-detection techniques. Accordingly, an Apache Hive storage architecture was designed to store the log data that were collected from the multiple servers in the Hadoop ecosystem. Also, three anomaly-detection techniques were designed based on the moving-average and 3-sigma concepts. It was finally confirmed that all three of the techniques detected the abnormal intervals correctly, while the weighted anomaly-detection technique is more precise than the basic techniques. These results show an excellent approach for the detection of log-data anomalies with the use of simple techniques in the Hadoop ecosystem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.77-83
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• 2015

Leaf water content is one of important indicators that shows states of vegetation. It is important to monitor vegetation water content using remote sensing for forest management. In this study, we investigated the degree of water stress in Korean peninsula with Normalized Difference Water Index (NDWI) to study the water content of vegetation canopy. We calculated the NDWI using SPOT/VEGETATION S10 channel data over forest from 1999 to 2013. We calculated Simple Moving Average (SMA) to remove temporal noises of NDWI in time series, and used standardized anomaly to investigate temporal changes. We classified the NDWI anomalies into three scales (low, moderate, and high) in order to monitor intuitively. We also investigated suitability of the NDWI as an evaluation criterion about water stress of vegetation canopy by comparing and verifying forest fires damaged area over 150 ha. Consequently, huge forest fire occurred 24 times during the study period. Also, negative anomalies appeared in every forest fire location and their neighboring areas. In particular, we found huge forest fires where NDWI anomalies were in 'high' scale.