• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.53-59
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• 2002

This paper presents a model for diagnosing students'learning misconceptions in the domain of heat and temperature using fuzzy cognitive maps(FCM) and fuzzy associative memory(FAM). In a model for diagnosing learning misconceptions. an FCM can represent with cause and effect between preconceptions and misconceptions that students have about scientific phenomenon. An FAM which represents a neurallike memory for memorizing causal relationships is used to diagnose causes of misconceptions in learning. This study will present a new method for more autonomous and intelligent system than a model to diagnose misconceptions that was being done with classical methods in learning and may contribute as an intelligent tutoring system for learning diagnosis within various educational contexts.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1104-1107
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• 2011

경동맥 내중막 두께는 심혈관계 질환의 위험인자와 상관성이 높으므로 경동맥 내중막 영상 분석을 통해 심혈관계 질환의 1차 검진 방법으로 사용이 가능하다. 이 논문에서는 내중막 영상에서 이상지질혈증의 진단지표가 될 수 있는 특징벡터 추출 방법을 제안하였으며, 패턴기반, 함수기반의 분류모델 생성과 평가를 통해 추출된 진단 지표가 이상지질혈증 분류에 적합함을 검증하였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.10
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• pp.931-940
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• 2017

This paper describes a predictive fault diagnosis algorithm for autonomous vehicles based on a kinematic model that uses a sliding mode observer. To ensure the safety of autonomous vehicles, reliable information about the environment and vehicle dynamic states is required. A predictive algorithm that can interactively diagnose longitudinal environment and vehicle acceleration information is proposed in this paper to evaluate the reliability of sensors. To design the diagnosis algorithm, a longitudinal kinematic model is used based on a sliding mode observer. The reliability of the fault diagnosis algorithm can be ensured because the sliding mode observer utilized can reconstruct the relative acceleration despite faulty signals in the longitudinal environment information. Actual data based performance evaluations are conducted with various fault conditions for a reasonable performance evaluation of the predictive fault diagnosis algorithm presented in this paper. The evaluation results show that the proposed diagnosis algorithm can reasonably diagnose the faults in the longitudinal environment and acceleration information for all fault conditions.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2079_2080
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• 2009

MEMS 기술을 이용한 다양한 센서의 개발과정에서 신뢰성 확보는 매우 중요한 문제이며, 여러 가지 신뢰성 항목 가운데 낙하시험은 가장 기본이 되는 항목이다. 단시간 내에 낙하에 대한 내충격성을 확보하는 MEMS 센서를 개발하기 위해 본 논문에서는 FEA와 high-level 모델을 결합한 낙하시험 모의진단법을 제안하였다. 제안된 모의진단법을 통해 MEMS 소자에서의 최대응력과 응력분포, 최대변위, 그리고 낙하시의 과도응답과 오신호 등의 결과를 확보할 수 있으며 이들을 토대로 MEMS 소자에서의 취약부위를 파악하고 이를 보완할 수 있으며 낙하시의 오동작을 제거하도록 신호처리 회로 등을 보완할 수도 있을 것이며 이를 통해 단시간 내에 최소의 비용으로 내충격성을 확보한 MEMS 센서를 개발하는 것이 가능해질 것이다.

• Journal of IKEEE
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• v.27 no.4
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• pp.518-523
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• 2023

The diagnosis of Inter Turn Short Circuits (ITSC) in induction motors is critical due to the escalating severity of faults resulting from even minor disruptions in the stator windings. However, diagnosing ITSC presents significant challenges due to similarities in noise and losses shared with 3-phase induction motors. Although artificial intelligence techniques have been explored for efficient diagnosis, practical applications heavily rely on model-based methods, necessitating further research to enhance diagnostic performance. This study proposed a diagnostic method applied the Clarke Transformation approach, focusing solely on current components while disregarding changes in rotating flux. Experimental results conducted over a 30-minute period, encompassing both normal and ITSC conditions, demonstrate the effectiveness of the proposed approach, with FAR(False Accept Rates) of 0.2% for normal-to-ITSC FRR(False Rejection Rates) and 0.26% for ITSC-to-normal FRR. These findings underscore the efficacy of the proposed approach.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.3
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• pp.235-242
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• 2004

Bridge Management System (BMS) requires a more objective condition assessment over the lifespan of a given bridge. Thus, a quantitative assessment model of resistance capacity was developed here to meet the requirement for deteriorated concrete bridges. The model focuses on damage mechanisms of concrete bridges deteriorated by traffic loads and environment factors such as chloride and carbonation attacks. Also, it was applied to a typical concrete slab bridge which was severely damaged due to both load and environmental conditions. It was shown that the proposed quantitative model simulates well the deterioration level considering the two damage criteria.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.147-154
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• 2012

The higher thinking skills, such as creativity and problem-solving about a given problem, are difficult to assess and diagnose. For an accurate diagnosis of these higher thinking abilities, we need to fully observe learner's problem-solving process or learner's individual reports. However, in an online learning or virtual class environments, evaluation of learner's problem-solving process becomes more difficult to diagnose. The best way to solve this problem is through reporting by tracking learner's actions when he tries to solve a problem. In this study, we developed a module which can evaluate and diagnose student's problem-solving ability by tracking actions in MS-Office suite, which is used by students to solve a given problem. This module performs based on the learner's job history through user tracking. To evaluate the effectiveness of this diagnostic module, we conducted satisfaction survey from students who were preparing the actual MOS exams. As a result, eighty-one (81) of the participants were positive on the effectiveness of the learning system with the use of this module.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.30-39
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• 2023

Large dams, which are critical infrastructures for disaster prevention, are exposed to various risks such as aging, floods, and earthquakes. Better dam safety inspection and diagnosis using digital transformation technologies are needed. Traditional visual inspection methods by human inspectors have several limitations, including many inaccessible areas, danger of working at heights, and know-how based subjective inspections. In this study, drone photogrammetry was performed on two large dams to evaluate the applicability of digital data-based dam safety inspection and propose a data management methodology for continuous use. High-quality 3D digital models with GSD (ground sampling distance) within 2.5 cm/pixel were generated by flat double grid missions and manual photography methods, despite reservoir water surface and electromagnetic interferences, and severe altitude differences ranging from 42 m to 99.9 m of dam heights. Geometry profiles of the as-built conditions were easily extracted from the generated 3D mesh models, orthomosaic images, and digital surface models. The effectiveness of monitoring dam deformation by photogrammetry was confirmed. Cracks and deterioration of dam concrete structures, such as spillways and intake towers, were detected and visualized efficiently using the digital 3D models. This can be used for safe inspection of inaccessible areas and avoiding risky tasks at heights. Furthermore, a methodology for mapping the inspection result onto the 3D digital model and structuring a relational database for managing deterioration information history was proposed. As a result of measuring the labor and time required for safety inspection at the SYG Dam spillway, the drone photogrammetry method was found to have a 48% productivity improvement effect compared to the conventional manpower visual inspection method. The drone photogrammetry-based dam safety inspection is considered very effective in improving work productivity and data reliability.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.