• Korean Medical Education Review
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• v.22 no.2
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• pp.131-142
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• 2020

A systematic educational program evaluation system for continuous quality improvement in undergraduate medical education is essential. Monitoring and evaluation (M&E) are two distinct but complementary processes referred to in an evaluation system that emphasizes formative purpose. Monitoring involves regular data collection for tracking process and results, while evaluation requires periodic judgment for improvement. We have recently completed implementing an educational evaluation using the M&E concept in a medical school. The evaluation system consists of two loops, one at the lesson/course level and the other at the phase/graduation level. We conducted evaluation activities in four stages: planning, monitoring, evaluation, and improvement. In the planning phase, we clarified the purpose of evaluation, formulated a plan to engage stakeholders, determined evaluation criteria and indicators, and developed an evaluation plan. Next, during the monitoring phase, we developed evaluation instruments and methods and then collected data. In the evaluation phase, we analyzed results and evaluated the criteria of the two loops. Finally, we reviewed the evaluation results with stakeholders to make improvements. We have recognized several problems including excessive burden, lack of expertise, insufficient consideration of stakeholders' evaluation questions, and inefficient data collection. We need to share the value of evaluation and build a system gradually.

• Smart Structures and Systems
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• v.24 no.5
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• pp.649-657
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• 2019

Recently, an advanced video deflectometer based on the principle of off-axis digital image correlation was presented and advocated for remote and real-time deflection monitoring of large engineering structures. In engineering practice, measurement accuracy is one of the most important technical indicators of the video deflectometer. However, it has been observed in many outdoor experiments that data drift often presents in the measured deflection-time curves, which is caused by the instability of imaging system and the unavoidable influences of ambient interferences (e.g., ambient light changes, ambient temperature variations as well as ambient vibrations) in non-laboratory conditions. The non-ideal unstable imaging conditions seriously deteriorate the measurement accuracy of the video deflectometer. In this work, to perform high-accuracy deflection monitoring, potential sources for the drift error are analyzed, and a drift error model is established by considering these error sources. Based on this model, a simple, easy-to-implement yet effective reference point compensation method is proposed for real-time removal of the drift error in measured deflections. The practicality and effectiveness of the proposed method are demonstrated by in-situ deflection monitoring of railway and highway bridges.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.80.1-80.1
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• 2021

Intensive Monitoring Survey of Nearby Galaxies (IMSNG) is a program monitoring nearby galaxies with a high cadence within a day. The main goal of the project is to constrain the SNe explosion mechanism and properties of their progenitors by catching the early lights from the shock-heated cooling emission. The observation campaign began in 2014 with two 1-m class telescopes in the northern hemisphere. Now more than ten telescopes are monitoring galaxies with 60 IMSNG targets, which have a high probability of supernova explosion every night all around the world. Since the project started, the following observations have been carried out on 14 SNe Ia(including -pec), 27 core-collapse supernovae (CCSNe), and around 40 transients in other types. In this poster, we present the current status of IMSNG SNe data first and then focus more on the CCSNe. CCSNe are the explosion of massive stars, more massive than eight times of the Sun. They have been studied for more than a half decades but still have key questions to be solved, such as distinct types, the characteristics driving their diversity, and so on. Here, we show our ongoing studies of CCSNe in IMSNG, focusing on their usefulness as distance indicators and properties of early light curves.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.303-317
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• 2010

In this paper, we propose a novel approach to fault prediction for real-time business process monitoring method using extended KNN imputation based LOF prediction. Existing rule-based approaches to process monitoring has some limitations like late alarm for fault occurrence or no indicators about real-time progress, since there exist unobserved attributes according to the monitoring phase during process executions. To improve these limitations, we propose an algorithm for LOF prediction by adopting the imputation method to assume unobserved attributes. LOF of ongoing instance is calculated by assuming next probable progresses after the monitoring phase, which is conducted during entire monitoring phases so that we can predict the abnormal termination of the ongoing instance. By visualizing the real-time progress in terms of the probability on abnormal termination, we can provide more proactive operations to opportunities or risks during the real-time monitoring.

• Korean Journal of Construction Engineering and Management
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• v.7 no.3 s.31
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• pp.76-85
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• 2006

Quality is the core competence for customer satisfaction in current competitive business environments. The manufacturing companies regard the quality as the success factor in enhancing competitiveness and foremost concept of the management innovation. But in many cases, the basis for the quality management and the action programs are not prepared yet. In construction industry, normally schedule and cost have priority over quality and the level of overall quality is relatively lower than other industries. This is caused by the vague quality goal and result-oriented management. This study suggests the quality performance indicators for measuring performance objectively, and develops the continuous quality monitoring system based on those indicators. By using this system, the quality improvement can be expected and corporate quality competitiveness can be ensured.

• 재활복지
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• v.21 no.2
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• pp.1-27
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• 2017

The purpose of this study is to evaluate the level of implementation of the Incheon Strategy and to develop measures for effective implementation. This research method used literature review and monitoring method. The results of the study are summarized as follows: First, 9 key indicators and 7 supplementary indicators, which are classified into implementation and non-implementation, 3 key indicators have been implemented, one indicator has been partially implemented, 5 indicators have not yet been implemented, Also, In the case of supplementary indicators, five were implemented and two were not. Second, the ten target areas are lacking in implementation, but the objective of 7 is to ensure the comprehensive disaster risk reduction and management, the ratification and implementation of the Convention on the Rights of Persons with Disabilities and the harmonization of the Convention with the domestic law, Also, it is analyzed that the level of implementation is in the order of improving the reliability and comparability of the data of goal 8, ensuring gender equality of goal 6 and strengthening the capacity of women. Based on these results, we propose an improvement plan for implementation. First, it is necessary to formulate policy issues and implement measures for the implementation of Incheon Strategy. Second, it is necessary to establish a system to implement and monitor for Incheon strategy. Third, Korean standards for goals and targets, key indicators and supplementary indicators are needed. Fourth, it is necessary to prioritize the target implementation and to take preemptive action. Lastly, it is necessary to educate and publicize for the Incheon strategy.

• Science of Emotion and Sensibility
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• v.22 no.1
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• pp.89-100
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• 2019

Sleep apnea, a medical condition associated with a variety of complications, is generally monitored by standard sleep polysomnography, which is expensive and uncomfortable. To overcome these limitations, this study proposes an unconstrained wearable monitoring system with stretch-fiber sensors that integrate with the wearer's clothing. The system allows patients to undergo examinations in a familiar environment while minimizing the occurrence of skin allergies caused by adhesive tools. As smart clothing for adult males with sleep apnea, long-sleeved T-shirts embedding fibrous sensors were developed, enabling real-time monitoring of the patients' breathing rate, oxygen saturation, and airflow as sleep apnea diagnostic indicators. The gauge factor was measured as 20.3 in sample 4. The maximum breathing intake, measured during three large breaths, was 2048 ml. the oxygen saturation was measured before and during breath-holding. The oxygen saturation change was 69.45%, showing a minimum measurable oxygen saturation of 70%. After washing the garment, the gauge factor reduced only to 18.0, confirming the durability of the proposed system. The wearable sleep apnea monitoring smart clothes are readily available in the home and can measure three indicators of sleep apnea: respiration rate, breathing flow and oxygen saturation.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.59-67
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• 2020

This study aims to develop the fuzzy risk assessment model of the debris flow to verify the accuracy of risk assessment in order to help related organizations reduce losses caused by landslides. In this study, actual cases of landslides that occurred are utilized as the database. The established models help us assess the occurrence of debris flows using computed indicators, and to verify the model errors. In addition, comparisons are made between the models to determine the best one to use in practical applications. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.590-595
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• 2001

Vibration transient generated by developing localized fault in gear can be used as indicators of condition monitoring in a gear. In this paper, we propose the phase map for a fault signal using continuous wavelet transform to detect this vibration transient. Local fault induces the abrupt fluctuation of load exciting tooth and phase lag in the vibration signal measured on the gearbox. The relatively large fault like "tip breakage" easily can be detected by the clear fluctuation of exciting load. However, minor fault like "initial pitting" cannot be detected using the load fluctuation. To detect this kind of minor fault, the phase map for a fault signal is taken into account. The phase lag by minor fault is observed well in the phase map.