• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.

• Journal of Distribution Science
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• v.12 no.4
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• pp.85-92
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• 2014

Purpose - This study conducted empirical research on non-financial corporations listed on the stock exchange from 2001 to 2010, focusing on the effects of corporate governance on real earnings management of corporations. In particular, this study examined primarily the impact of the largest shareholder who could use earnings management to pursue his own self-interest, and foreign investors who played a checking role against the largest shareholders. The study also reviewed the relationship between corporate governance and earnings management while also considering corporate growth. Research design, data, and methodology - As for the measurements of real earnings management, abnormal operating cash flow and abnormal production cost were utilized. As for the independent variables, share ratio of the largest shareholder and affiliate person (M) and share ratio of foreign investors (FT) were leveraged. This study excluded those organizations that had changed their fiscal years, those that had not submitted an audit report, corporations under supervision, delisted corporations, corporations that had changed their business type, and so on, from the non-financial corporations out of the publicly traded corporations whose fiscal year ended in December from 2001 to 2010 in addition, KIS values were utilized for the corporate financial data in the study. To verify whether management structure and growth had an impact on real earnings management of a corporation through empirical analysis, a multiple regression analysis model was applied. Result - First, as a result of the analysis, the share ratio (M) of the largest shareholder and affiliate person was found to have a significant positive correlation with abnormal cash flow from operations(ACF) and abnormal production cost (APD). When controlling the growth, the share ratio (M) of the largest shareholder and affiliate person was found to have an insignificant correlation with abnormal cash flow from operations(ACF) but a significant correlation with abnormal production cost (APD). Second, foreign ownership (FT) was found to have a significant positive correlation with abnormal cash flow from operations(ACF) and abnormal production cost (APD) at the confidence level of 1 percent when not including the growth dummy. When controlling the growth, foreign ownership (FT) was found to have a significant negative correlation with abnormal cash flow from operations (ACF) and with abnormal production cost (APD). Conclusion - The results imply that the largest shareholder is closely related to earnings management through real activities regardless of corporate growth. It is also possible to determine from these results that foreign investors are related to earnings management through real activities when not considering corporate growth, but that they would reduce earnings management in the case of considering the growth. Thus, this study verified along with the existing studies that foreign investors were conducting the control function on controlling shareholders.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.466-476
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• 2008

The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Journal of Korean Public Health Nursing
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• v.14 no.2
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• pp.260-270
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• 2000

This study aimed at examining participation rate in taking health examinations. abnormal findings. and recognition and responses for re-examination recommendation among junior students at a university. Data were collected by interviews and self-reports between March and April. 2000. five months after receiving findings of health examinations. Question items in the questionnaire were made by the researchers. Participation rate for the health examination was $22.5\%$ of all registered junior students: $25.4\%$ for men and $16.6\%$ for women. College of medicine ranked the first in the participation rate. Of the examinees. $22.8\%$ showed abnormal findings. Of those students with abnormal findings. 149 students who were registered at the time of data collection became the subjects of the present study. The average age of the subjects was 23.7 years. The proportion of those with very good or. good self-evaluated physical health was $24.1\%$. while the proportion for mental health was $55.1\%$. The most prevalent problem for men was liver problem and for women anemia. More than $92\%$ of the subjects were aware of their abnormal findings. Those who sought advice were $71.8\%$ and their parents were most frequently asked for advice. As for the contents. $33.7\%$ were advised to visit a hospital. Of the 65 students recommended for re-examination. $60.9\%$ with liver problems took re-examination. while $37.2\%$ with urine problems. The multiple responses of the reasons for not following the recommendation for re-examination were 'not a serious problem $(63.9\%)$,' 'having no time $(22.2\%)$,' Students' recognition of the importance of health examination should be raised to increase their participation rate. When abnormal findings were detected. parents need to be informed for achieving adequate follow-up. All the students with abnormal findings need to be consulted by university health personnel to facilitate proper actions.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.317-330
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• 2013

The yield of rice production is affected severely by abnormal weather events, such as flood, drought, high temperature etc. The objective of this paper is to assess impacts of abnormal weather events on rice production, using a panel model which analyzes both cross-section data and ti- me series data. Abnormal weather is defined as the weather event which goes beyond the range of ${\pm}2{\sigma}$ from the average of a weather factor. The result of an analysis on impacts of high temperature on rice production showed that the yield of rice was decreased 5.8% to 16.3% under the conditions of extremely high temperature, and it was decreased 8.8 to 20.8% under the conditions of both extremely high and heavy rain. Adaptation strategies, development of new varieties enduring high temperature and heavy rain, adaptation of crop insurance, modernization of irrigation facilities are needed to minimize the impacts of abnormal weather on rice production, and to stabilize farmers' income.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.11-21
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• 2023

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMY_normal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.2-4
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• 2023

Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.63-74
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• 2020

This study investigates the behavior of foreign investors in the Stock Exchange of Thailand (SET) in the time of coronavirus disease 2019 (COVID-19) as to whether trading is abnormal, what strategy is followed, whether herd behavior is present, and whether the actions destabilize the market. Foreign investors' trading behavior is measured by net buying volume divided by market capitalization, whereas the stock market behavior is measured by logged return on the SET index portfolio. The data are daily from Tuesday, August 28, 2018, to Monday, May 18, 2020. The study extends the conditional-regression model in an event-study framework and extracts the unobserved abnormal trading behavior using the Kalman filtering technique. It then applies vector autoregressions and impulse responses to test for the investors' chosen strategy, herd behavior, and market destabilization. The results show that foreign investors' abnormal trading volume is negative and significant. An analysis of the abnormal trading volume with stock returns reveals that foreign investors are not positive-feedback investors, but rather, they self-herd. Although foreign investors' abnormal trading does not destabilize the market, it induces stock-return volatility of a similar size to normal trade. The methodology is new; the findings are useful for researchers, local authorities, and investors.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.39-45
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• 2012

Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by the state of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiac arrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on the detection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processing techniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized data and variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECG and abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MITBIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for Rpeak detection is 99.63 % and abnormal detection is 99.60 %.