• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.237-242
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• 2003

In this paper, we propose a new adaptive intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based IDS but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.270-281
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• 2021

With the development of information technology, the size of insurance fraud is increasing rapidly every year, and the method is being organized and advanced in conspiracy. Although various forms of prediction models are being studied to predict and detect this, insurance-related information is highly sensitive, which poses a high risk of sharing and access and has many legal or technical constraints. In this paper, we propose a machine learning insurance fraud prediction model based on blockchain, one of the most popular technologies with the recent advent of the Fourth Industrial Revolution. We utilize blockchain technology to realize a safe and trusted insurance information sharing system, apply the theory of social relationship analysis for more efficient and accurate fraud prediction, and propose machine learning fraud prediction patterns in four stages. Claims with high probability of fraud have the effect of being detected at a higher prediction rate at an earlier stage, and claims with low probability are applied differentially for post-reference management. The core mechanism of the proposed model has been verified by constructing an Ethereum local network, requiring more sophisticated performance evaluations in the future.

• The Journal of Korean Philosophical History
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• no.58
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• pp.307-336
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• 2018

This paper traces the historical genealogy of the subjectivity and the silsim (實心, true mind) that appear in Jeong In-Bo's "gukhak" (國學, the national learning) thought and illuminates its characteristics. In the modern East Asian history of thought, the beginning of the emergence of subjectivity and the silsim as the main philosophical topic comes from the Neo-Confucianism of Song Dynasty in China. Cheng Yi is the first thinker to emphasize subjectivity and consciousness. Zhu Xi and Wang Yang-ming inherit the Neo-Confucian thought based on Cheng Yi's principle of subjectivity, but only show difference in methodology. In the Chosun Dynasty, Jeong Je-Doo and his School were one example of the Neo-Confucian spirit of subjectivity and the silsim. Although Jeong In-Bo (鄭寅普) belongs to Jeong Je-Doo's school of Ganghwa in the school curriculum, he has only used it methodologically since he believed that Yangming's learning is more effective in the awareness and practice of the silsim. Especially noteworthy is that the principle of subjectivity led Jeong In-Bo to follow the frame of Zhu Xi's moral theory. Jeong's claim that selfish desire (jasasim 自私心) should be controlled by a conscious mind (silsim) being aware of the right and 'ought to do' corresponds to Zhu Xi's view that the moral mind (dosim 道心) should be selected in the conflict situation between sensual desire (insim 人心) and moral consciousness so that the insim should be supervised by the dosim. Such ethics is a position to emphasize the inner motive and the sense of duty of conduct, and there is no fundamental difference in Zhu Xi and Wang Yang-ming. At least on this point, it is necessary to look at modern and contemporary Korean studies from the perspective of continuity, not discontinuity from Confucian tradition.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.