• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.

• Educational Technology International
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• v.25 no.1
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• pp.27-65
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• 2024

The purpose of this study was to identify differences in learning patterns according to content complexity in video-based learning environments and to derive variables that have an important effect on learning achievement within particular learning contexts. To achieve our aims, we observed and collected data on learners' cognitive processes through perceived affordances, using behavioral logs and eye movements as specific indicators. These two types of reaction data were collected from 67 male and female university students who watched two learning videos classified according to their task complexity through the video learning player. The results showed that when the content complexity level was low, learners tended to navigate using other learners' digital logs, but when it was high, students tended to control the learning process and directly generate their own logs. In addition, using derived prediction models according to the degree of content complexity level, we identified the important variables influencing learning achievement in the low content complexity group as those related to video playback and annotation. In comparison, in the high content complexity group, the important variables were related to active navigation of the learning video. This study tried not only to apply the novel variables in the field of educational technology, but also attempt to provide qualitative observations on the learning process based on a quantitative approach.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.456-461
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• 2023

The Korea Maritime Environment Corporation is conducting a comprehensive survey of the national marine ecosystem under the commission of the Ministry of Oceans and Fisheries (MOF) to ensure continuous use of the ocean, preserve and manage the marine ecosystem. The survey has set major peaks to investigate changes in the marine ecosystem around the Korean Peninsula. However as the peak has been set around the coast, it is necessary to expand the scope of investigation to encompass offshore areas. Meanwhile, the Aids to Navigation Division of the MOF supports a comprehensive national marine ecosystem survey providing photographs of fouling organisms during the Aids to Navigation lifting inspection, however, the photographs are provided only in consultation with the Korea Maritime Environment Corporation. Therefore, a study was conducted to generate information on fouling organisms using deep learning-based image processing algorithms by the lifting Aids to Navigation and dorsal buoys so that Aids to Navigation could be used as the major component of a comprehensive national marine ecosystem. If the Aids to Navigation are used as the peak of the survey, they could serve as fundamental data to enhance their own value as well as analyze abnormal marine conditions and ecosystem changes in Korea.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.118-120
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• Archives of Craniofacial Surgery
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• v.25 no.5
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• pp.209-216
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• 2024

Craniofacial surgery requires comprehensive anatomical knowledge of the head and neck to ensure patient safety and surgical precision. Over recent decades, there have been significant advancements in imaging techniques and the development of real-time surgical navigation systems. Intraoperative navigation technology aligns surgical instruments with imaging-derived information on patient anatomy, enabling surgeons to closely follow preoperative plans. This system functions as a radiologic map, improving the accuracy of instrument placement and minimizing surgical complications. The introduction of first-generation navigation systems in the early 1990s revolutionized surgical procedures by enabling real-time tracking of instruments using preoperative imaging. Initially utilized in neurosurgery, intraoperative navigation has since become standard practice in otolaryngology, cranio-maxillofacial surgery, and orthopedics. Since the 2000s, second-generation navigation systems have been developed to meet the growing demand for precision across various surgical specialties. The adoption of these systems in craniofacial surgery has been slower, but their use is increasing, particularly in procedures such as foreign body removal, facial bone fracture reconstruction, tumor resection, and craniofacial reconstruction and implantation. In Korea, insurance coverage for navigation in craniofacial surgery began in 2021, and new medical technologies for orbital wall fracture treatment were approved in August 2022. These technologies have only recently become clinically available, but are expected to play an increasingly important role in craniofacial surgery. Intraoperative navigation enhances operative insight, improves target localization, and increases surgical safety. Although these systems have a steep learning curve and initially prolong surgery, efficiency improves with experience. Calibration issues, registration errors, and soft tissue deformation can introduce inaccuracies. Nonetheless, navigation technology is evolving, and the integration of intraoperative computed tomography data holds promise for further enhancements of surgical accuracy. This paper discusses the various types and applications of navigation employed in craniofacial surgery, highlighting their benefits and limitations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.233-236
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• 2015

전 세계 해기교육기관에서 해사영어(Maritime English, ME)를 가르치고 있는 교원들이 모여 매년 개최하는 IMEC (International Maritime English Conference)을 소개하고, 2015년에 개최된 제27차 컨퍼런스의 프로그램을 우리 회원들에게 안내하고자 하며, IMEC 27에서 소개된 ChatBot를 가지고 참석한 회원들과 함께 진행하는 워크숍을 개최하고자 한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.374-376
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• 2022

항로표지 배치의 적합성 평가 및 검증에 활용하기 위해 기계학습 (Machine Learning)을 통해 대표항적선을 결정한다. 이 연구에서는 대표항적선과 항로표지와의 최근접 거리를 계산하고 시인가능 거리 및 거리율 등을 통해 항로표지의 배치 적합성을 평가하고 검증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.361-365
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• 2002

오랜 세월동안 위대한 이동수단을 만들어내고자 하는 인간의 꿈은 오늘날 눈부신 각종 운송기구를 만들어 내는 결실을 얻고 있다. 자동차 네비게이션 시스템도 그러한 결실중의 한 예라고 할 수 있을 것이다. 지능적으로 판단하고 정보를 처리할 수 있는 자동차 네비게이션 시스템을 부착함으로써 한 단계 발전한 운송수단으로 진화할 수 있을 것이다. 이러한 자동차 네비게이션 시스템의 단점이라면 한정된 리소스만으로 여러 가지 작업을 수행해야만 하는 어려움이다. 그래서 네비게이션 시스템의 주요 작업중의 하나인 경로를 추출하는 경로추출(Route Planning) 작업은 한정된 리소스에서도 최적의 경로를 찾을 수 있는 지능적인 방법이어야만 한다. 이러한 경로를 추출하는 작업을 하는데 기존에 일반적으로 쓰였던 두 가지 방법에는 Dijkstra s algorithm과 A*algorithm이 있다. 이 두 방법은 최적의 경로를 찾아낸다는 점은 있지만 경로를 찾기 위해서 알고리즘의 특성상 각각, 넓은 영역에 대하여 탐색작업을 해야 하고 또한 수행시간이 많이 걸린다는 단점과 또한 경로를 계산하기 위해서 Heuristic function을 추가적인 정보로 계산을 해야 한다는 단점이 있다. 본 논문에서는 적은 탐색 영역을 가지면서 또한 최적의 경로를 추출하는데 드는 수행시간은 작으며 나아가 동적인 교통환경에서도 최적의 경로를 추출할 수 있는 최적 경로 추출방법을 강화학습의 일종인 Q- Learning을 이용하여 구현해 보고자 한다.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.148-154
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• 2020

Wrinkles are one of the main features of skin aging. Conventional image processing-based wrinkle detection is difficult to effectively cope with various skin images. In particular, Wrinkle extraction performance is significantly decreased when the wrinkles are not strong and similar to the surrounding skin. In this paper, deep learning is applied to extract wrinkles from microscopic skin images. In general, the microscope image is equipped with a wide-angle lens, so the brightness at the boundary area of the image is dark. In this paper, to solve this problem, the brightness of the skin image is estimated and corrected. In addition, We apply the structure of semantic segmentation network suitable for wrinkle extraction. The proposed method obtained an accuracy of 99.6% in test experiments on skin images collected in our laboratory.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.854-860
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• 2009

Ubiquitous is a new computing environment with IT technology and information communication, and appling various equipments likes PDA and application parts. Recently, user's using environment is changing to smart phone and is expanded learning tools to learner without educational environment. Thus, in this paper, we designed SCORM based meta-data to use smart phone. For this purpose, we made U-learning server and smart phone process server that is to handling with existence LMS and SCORM. To apply smart phones characteristics that have different ones each other, meta-data was able to have some resource information as like CPU, screen size and memory. The meta-data adapter could be process the characteristics.