• Journal of The Korean Association For Science Education
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• v.25 no.4
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• pp.441-449
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• 2005

The purpose of this study was to examine the degree of cognitive conflict by learner personality and the method of presenting anomalous data to induce cognitive conflict. The participants of this study were 461 high school students. To arose cognitive conflict, an actual demonstration was done for half of the participants and a logical article for the rest. MBTI (Myers-Briggs Type Indicator) was used to find the learner personality types, and CCLT (Cognitive Conflict Level Test) was used to measure the degree of cognitive conflict aroused when anomalous data was confronted. The results of this study indicated that learner personality types influence the degree of cognitive conflict. First, participants were divided into two personality types via preferences on each of the four preference indices; extraversion (E) or introversion (I), sensing (S) or intuition (N), thinking (T) or feeling (F), judgment (J) or perception (P). The cognitive conflict scores of the thinking types were significantly higher than those of the feeling types. Participants were also divided four personality types according to personality functional types: ST, SF, NT and NF. SF type showed a significantly lower cognitive conflict score than any of the other types. According to the type of learner personality, cognitive conflict was influenced differently by the method of presenting anomalous data. For example, the judgment types had a higher cognitive conflict score by logical argument, and the perception types showed a higher score by demonstration. In conclusion, learner cognitive conflicts were influenced by personality types and the methods of presenting anomalous data.

• Membrane Journal
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• v.10 no.3
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• pp.139-147
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• 2000

Non-Fickian (or anomalous) diffusion was observed in transient sorption of aromatic solvents(such as benzene, toluene, and chlorobenzene) in fluoropolymers (such as ETFE, ECTFE and PVDF). In this study, five other transient sorption models (Crank, Long ＆ Richman, Berens ＆ Hopfenberg, Neogi, Li) based on Fick's law were employed to fit the anomalous sorption data for aromatic solvents. The adjustable parameters were determined by least square analysis of the measured and predicted fractional uptake. For ETFE sorption data slightly deviating from Fickian behavior, all the models exhibited satisfactory results in fitting the anomalous sorption data. In particular, Neogj model predicted intrinsic diffusivity (0.4～0.8$\times$10$^{-5}$ $\textrm{cm}^2$/day) and equilibrium diffusivity (0.13～0.31$\times$10$^{-4}$ $\textrm{cm}^2$/day) as well as relaxation kinetics related to non-Fickain diffusion. For a typical sigmoidal sorption behavior in PVDF, only Crank's model could give the reasonable evaluation on transport properties. The ratio of intial diffusivity (D$_{i}$) to final equilibrium diffusivity (D$_{\infty}$) was ranged from 80 to 200. For the final stage of uptake In ECTFE with drastic acceleration, all the models exhibited significant deviations from the sorption data. New diffusion models based on thermodynamics and continuum mechanics should be employed to get valuable information on transport properties as well as relaxation kinetics coupled with non-Fickian diffusion.

• Journal of The Korean Association For Science Education
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• v.21 no.4
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• pp.658-667
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• 2001

In this study, the relationships among students' cognitive/affective variables, cognitive conflict induced by anomalous data, and conceptual change were investigated. Tests regarding background knowledge, field dependence-independence, learning strategy, logical thinking ability, goal orientation, self-efficacy on prior concept and ability, and control belief were administered. Tests of prior conceptions, responses to anomalous data, conception, and retention of conception were also administered. There were no significant correlations of cognitive conflict induced by anomalous data with students' cognitive and affective variables. However, prior knowledge on molecular motion, field dependence-independence, and learning strategy were significantly correlated with students' conception and retention of conception. Logical thinking ability was also correlated with their conception. Multiple regression analysis indicated that learning strategy significantly predicted students' conception and retention of conception. For the affective variables, self-efficacy on ability was significantly correlated with students' conception and retention of conception, and goal orientation was correlated with their conception. Self-efficacy on ability was a significant predictor on students' conception and retention of conception, and goal orientation on their conception.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.384-394
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• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• Journal of the Korean Chemical Society
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• v.28 no.3
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• pp.185-193
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• 1984

At the spermine concentration to cover the number of the binding site of spermine 0.016 per nucleotide, the Hill coefficient of the ethidium binding to the calf thymus DNA was 1.7, while the value was 0.38 in the absence of the spermine. On the basis of the data, together with other present data on the viscometric titration of the DNA with spermine and anomalous absorbance-temperature profile at 260nm and viscosity-temperature profile, it can be speculated that allosteric propagation of the conformational transition induced by the binding of the spermine may be involved in the monomolecular collapse of the DNA to a condensed structure.

• Journal of KIISE:Information Networking
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• v.33 no.3
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• pp.201-217
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• 2006

In this paper, we collected the physical traces from high speed Internet backbone traffic and analyze the various characteristics of the underlying packet traces. Particularly, our work is focused on analyzing the characteristics of an anomalous traffic. It is found that in our data, the anomalous traffic is caused by UDP session traffic and we determined that it was one of the Denial of Service attacks. In this work, we adopted the unsupervised machine learning algorithm to classify the network flows. We apply the k-means clustering algorithm to train the learner. Via the Cramer-Yon-Misses test, we confirmed that the proposed classification method which is able to detect anomalous traffic within 1 second can accurately predict the class of a flow and can be effectively used in determining the anomalous flows.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.35-40
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• 2017

Out-of-limit (OOL) alarm method that is threshold checking of telemetry value is widely used for the satellites fault diagnosis and health monitoring. However, it requires engineering knowledge and effort to define delicate threshold value and has limitations that anomalous behaviors within the defined limits can't be detected. In this paper, we propose a satellite anomalous behavior detection system through fuzzy model that is composed by important statistical feature selected by rough-set theory. Not pre-defined anomaly is detected because only normal state data is used for fuzzy model. Also, anomalous behavior within the threshold limit is detected by using statistic feature that can be collected without engineering knowledge. The proposed system successfully detected non-ordinary state for battery temperature telemetry.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.