• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.288-296
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• 2000

In this study, the types and the characteristics of students' responses to anomalous data were investigated. The criteria for classifying students' responses were 'acceptance of validity of anomalous data', 'acceptance of inconsistency between anomalous data and initial theory', and 'change of belief in initial theory'. Seven types of responses were identified as follows: Rejection, reinterpretation, exclusion, uncertainty, peripheral theory change, partial belief change, and theory change. Absolute belief in the intial theory and doubts about methodological accuracy were found to be the major reasons for rejecting anomalous data. The students did not accept the inconsistency between anomalous data and initial theory because they ignored the experimental procedures and focused on the similarity of the experimental results.

• Journal of The Korean Association For Science Education
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• v.22 no.3
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• pp.595-603
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• 2002

The influences of the characteristics of anomalous data on cognitive conflict and conceptual change in learning density were investigated. The subjects were 416 seventh graders. First, the Group Assessment of Logical Thinking and a preconception test were administered. A questionnaire on the responses to anomalous data was then administered. In the questionnaire, four types of anomalous data varying presentation type (movie/text) and authority level (high/low) were randomly presented. After a computer-assisted instruction on density, a conception test was administered. The results indicated that anomalous data presented in movie type significantly induced more cognitive conflict than that in text type. Students presented with anomalous data of high authority scored higher in the conception test than those of low authority. There were no significant interactions between the characteristics of anomalous data and students' logical thinking ability in the scores of both the cognitive conflict and the conception test.

• Journal of the Korean Chemical Society
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• v.45 no.6
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• pp.589-594
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• 2001

In this study, the effects of the number and the presentational type of anomalous data on students'cognitive conflict and conceptual change in studying 'conservation of mass before and after combustion'were investigated. The subjects were 128 eighth graders in a co-ed middle school. A preconception test, a test of response to anomalous data, and a conception test were administered. Four types of anomalous data varying the number (one/two) and the presentational type (text/text+figure) were presented. The results indicated that students with two anomalous data showed more cognitive conflicts than those with one. However, no significant differences in the degree of cognitive conflict were found by the presentational types of anomalous data. The ANOVA results indicated that there were no significant differences by the characteristics of anomalous data in the conception test scores.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1063-1068
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.647-660
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• 2022

Recent works demonstrate that the semi-supervised anomaly detection method functions quite well in the environment with normal data and some anomalous data. However, abnormal data shortages can occur in an environment where it is difficult to reserve anomalous data, such as an unknown attack in the cyber security fields. In this paper, we propose ADA-PH(Abnormal Data Augmentation Method using Perturbation based on Hypersphere), a novel anomalous data augmentation method that is applicable in an environment where abnormal data is insufficient to secure the performance of the semi-supervised anomaly detection method. ADA-PH generates abnormal data by perturbing samples located relatively far from the center of the hypersphere. With the network intrusion detection datasets where abnormal data is rare, ADA-PH shows 23.63% higher AUC performance than anomaly detection without data augmentation and even performs better than the other augmentation methods. Also, we further conduct quantitative and qualitative analysis on whether generated abnormal data is anomalous.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.89-90
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the echo.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.127-140
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• 2021

Recently, sequence data containing time information, such as sensor measurement data and purchase history, has been generated in various applications. So far, many methods for finding sequences that are significantly different from other sequences among given sequences have been proposed. However, most of them have a limitation that they consider only the order of elements in the sequences. Therefore, in this paper, we propose a new anomalous sequence detection method that considers both the order of elements and the time interval between elements. The proposed method uses an extended LSTM autoencoder model, which has an additional layer that converts a sequence into a form that can help effectively learn both the order of elements and the time interval between elements. The proposed method learns the features of the given sequences with the extended LSTM autoencoder model, and then detects sequences that the model does not reconstruct well as anomalous sequences. Using experiments on synthetic data that contains both normal and anomalous sequences, we show that the proposed method achieves an accuracy close to 100% compared to the method that uses only the traditional LSTM autoencoder.

• Journal of the Korean earth science society
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• v.28 no.5
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• pp.556-562
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• 2007

The interdecadal variation of wintertime blocking frequency over the Siberia ($60^{\circ}E-140^{\circ}E$) is examined using the ECMWF/NCEP-NCAR re-analysis data for the period 1958-2006. The wintertime blocking frequency over the Siberia significantly decreased for the period 1986-2006, compared to the period 1958-1985, which is mainly due to the anomalous circulation of 500-hPa geopotential height field. During the period 1986-2006, there was enhancement in both the anomalous cyclonic flow over the western Siberia and the anomalous anticyclonic flow over the east Asia. These anomalous circulation patterns, which might be associated with changes in surface temperatures over the Asian continent, are suspected to playa possibly important role as an obstacle to the formation of blocking flow over the Siberia.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.513-515
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• 2022

Manufacturing data collected using IoT devices in a smart factory environment is generally reliable except for noises caused by external factors. However, unlike manufacturing data that is collected mechanically, process data manually recorded by field-workers can cause problems such as the misspelled entries or the missing entries. Therefore, process data must be validated before being used as training data for artificial intelligence models. In this paper, based on the fact that which is a linear relationship between the power consumption of the MCT machine and the production of the product recorded by the field-workers, we detect anomalous records of the workers using robust linear regression.

• Geosystem Engineering
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• v.21 no.5
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• pp.262-272
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• 2018

The U-statistic method is one of the most important structural methods to separate the anomaly from background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, 3D U-statistic method has been applied for the first time through the three-dimensional (3D) modeling of an ore deposit. In order to achieve this purpose, 3D U-statistic is applied on the data (Fe grade) resulted from the drilling network in Baghak mine, central part of the Sangan iron mines (in Khorassan Razavi Province, Iran). Afterward, results from applying 3D U-statistic method are used for 3D modeling of the iron mineralization. Results show that the anomalous values are well separated from background so that the determined samples as anomalous are not dispersed and according to their positioning, denser areas of anomalous samples could be considered as anomaly areas. And also, final results (3D model of iron mineralization) show that output model using this method is compatible with designed model for mining operation. Moreover, seen that U-statistic method in addition for separating anomaly from background, could be very efficient for the 3D modeling of different ore type.