• Journal of The Korean Association of Information Education
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• v.20 no.2
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• pp.189-196
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• 2016

The PATROL instructional model only uses digital textbooks. PATROL is an acronym for Planning, Action, Tracking, Recommending, Ordering, and Leading. Teachers have a difficult time using current digital textbooks to determine how much time students spend using course materials. This is because current digital textbooks can only show the content of paper textbooks and display additional multimedia materials. In this study, digital textbook functions were designed based on the PATROL model in order to analyze students' learning situations, diagnose problems, and offer solutions. Digital textbook are based on learning analytics named SEE-PAD. SEE-PAD is composed of the following: Social network analysis; Evaluation and assEssment analysis; Predictive analysis; Adaptive learning analysis; and the analysis Dashboard. I drew and showed the use case and sequence diagrams of SEE-PAD to help design digital textbook functions.

• Korean Journal of Construction Engineering and Management
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• v.3 no.4 s.12
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• pp.104-113
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• 2002

Knowledge Management(KM), represented as a way to sustain or gain competitive edge in domestic construction companies since late 1990s economic fluctuation, whose priority is to transform individual tacit knowledge into explicit organizational one. Also, accompanied by academic researches, they come to turn their interests on KM leveling and its results. However, they went too far to KM results without commenting what their KM capabilities are and where they should lead. Thus, this research work suggests a leveling model for KM, especially construction company, whose role is to diagnose which parts they should be encouraged or how to strengthen their present capabilities.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.6
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• pp.519-524
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• 2011

ABLB (alternate binaural loudness balance) test is one of the medical assessments to diagnose detailed lesion of sensory-neural hearing loss based on a recruitment phenomenon. However, current ABLB audiometry takes an operational model, so called face-to-face model, in which model one audiometrist can assess only one subject at a time. As a result, this face-to-face model leads to expensive audiometrist's labor cost and lengthy wait when there exist many subjects. As a solution, this paper suggests an ABLB audiometry system supporting one-to-many model in which model an audiometrist enables to assess several subjects concurrently. By providing such capabilities as real-time transfer of assessment result, video monitoring of subject and video chat, this solution can provide same effect as face-to-face model but overcome weakness of the existing face-to-face model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.505-508
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• 2014

Transmission error (TE) is defined as "the angular difference between the ideal output shaft position and actual position". As TE is one of the major source of the noise and vibration of gears, it is originally studied with relation of the noise and vibration of the gears. However, recently, with the relation of mesh stiffness, TE has been studied for fault detection of spur gear sets. This paper presents a physics-based theory on fault diagnostics of a planetary gear using transmission error. After constructing the lumped parameter model using DAFUL, multi-body dynamics software, we developed a methodology to diagnose the faults of the planetary gear including phase calculation, signal processing. Using developed methodology, we could conclude that TE could be a good signal for fault diagnostics of a planetary gear.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.107-111
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• 2006

According to IT(information technology) industry progress. our life is gradually convenient. The proliferation of environmental pollution and the threat of diseases proportional to the progress comes to be high gradually. We must prevent dangerous diseases which threatens the life of the human. Or we are bumped against irrevocable serious situation. In spite of the situation. managing one's own health against modern busy lifestyle is very difficult. Therefore, we need to manage our health situation by using sensors based on ubiquitous IT environment. In this paper. we propose a diagnostic model which is able to diagnose and prevent a cerebrovascular disease based on ubiquitous technology. Also. as a step of implementing the u-health care diagnosis system, the diagnosis model of cerebrovascular disease plays an important role to decide a clinic result. In the future, by using this model. we may improve our welfare and health.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1491-1499
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• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.12
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• pp.1288-1296
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• 2001

A scheme for on-line fault detection and diagnosis of an air-handling unit is presented. The fault detection scheme uses residuals which are generated by comparing each measurement with analytical redundancies computed from the reference models. In this paper, artificial neural networks (ANNs) are used to estimate analytical redundancy and to classify faults. The Lebenburg-Marquardt algorithm is used to train feed forward ANNs that provide estimates of continuous states and diagnosis results. The simulation result demonstrated that the ANNs can effectively detect and diagnose faults in the highly non-linear and complex HVAC systems.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.16 no.1
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• pp.33-41
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• 2003

Infantile atopic dermatitis(AD) may be developed by food allergens due to immature intestinal epithelium and its mechanism, which may have no clear-cut evidence, is thought to be IgE mediated immediate and late phase hypersensitivity. It is not easy to diagnose AD caused by food allergens exactly so it is likely to be underestimated more than it be. But we must consider it as a major factor of not only infantile AD but childhood and adult AD. We can see similar theory at previous Oriental medicine. Allergens can be transmitted to a fetus through the placenta and infantile AD is inflammatory condition by food allergens and immature function of intestines. So we must consider those factors at infantile AD treatment. We expect a new model of infantile AD treatment combining the conventional therapy with the diet therapy based on the Oriental medical theory.