• Journal of Advanced Navigation Technology
• /
• v.28 no.4
• /
• pp.472-479
• /
• 2024

Half of the world's total population lives in cities, continuous urbanization is progressing, and the urban population is expected to exceed two-thirds of the total population by 2050. To resolve this phenomenon, the Korean government is focusing on building a new urban air mobility (UAM) industrial ecosystem. Airlines are also part of the UAM industry ecosystem and are preparing to improve efficiency in safe operations, passenger safety, aircraft operation efficiency, and punctuality. This study performs demand forecasting using time series data on the number of daily passengers on Korean Air's Gimpo to Jeju route from 2019 to 2023. For this purpose, statistical and machine learning models such as SARIMA, Prophet, CatBoost, and Random Forest are applied. Methods for effectively capturing passenger demand patterns were evaluated through various models, and the machine learning-based Random Forest model showed the best prediction results. The research results will present an optimal model for accurate demand forecasting in the aviation industry and provide basic information needed for operational planning and resource allocation.

• The Journal of the Korea Contents Association
• /
• v.16 no.10
• /
• pp.719-729
• /
• 2016

This study is analyzed by comparing the evaluation of usability and study achievement for simulation learning based on Web 3D and VR and it is aimed to verify the characteristics of the virtual reality through a difference in studying effect between each learning method. Therefore, this study is analyzed by comparing the evaluation of usability and study achievement for the CSI Forensics Lab simulation content that has been developed in two learning methods for scientific experiments of DNA analysis with the 75 university students of Life Science as a population(Web 3D=37, VR=38). The results of the study, in usability of user task action, exploratory and navigation, Web 3D simulation learning was positive in a significant difference, but in usability of satisfaction, VR simulation learning was positive in a significant difference. In study achievement, Web 3D simulation learning was slightly higher but did not confirm the significant differences between both of learning.

• The KIPS Transactions:PartA
• /
• v.18A no.4
• /
• pp.129-134
• /
• 2011

E-learning in Korea supports different educational needs of diverse learners. E-learning became one of the major source of educational services for schools, higher education, lifelong learning, and for special education. Many e-learning contents offered by cyber universities use HTML, CSS, and Flash, and these are known to have limitations on accessibilities. People with disabilities or aged have problems accessing such contents. The purpose of this study is to evaluate accessibility of Flash e-learning contents offered by 9 cyber universities. AccChecker is used to assess accessibility of the contents. The result shows many errors and warning with Text Equivalents, Keyboard Navigation, Properties, Depth of Tree, and Structures that restrict access. In order to improve the quality and expansion of quality e-learning contents, we need aggressive measures to obtain accessibility of contents, and these should be designed at the planning phase rather than adjusted during the development stage. Furthermore, it is vital to train instructional designers, developers and the CEOs to realize the importance of accessibility and learn appropriate skills to increase accessibilities of e-learning contents.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1259-1263
• /
• 2004

This paper describes position estimation algorithm using neural network for the navigation of the vision-based wheeled mobile robot (WMR) in a corridor with taking ceiling lamps as landmark. From images of a corridor the lamp's line on the ceiling in corridor has a specific slope to the lateral position of the WMR. The vanishing point produced by the lamp's line also has a specific position to the orientation of WMR. The ceiling lamps have a limited size and shape like a circle in image. Simple image processing algorithms are used to extract lamps from the corridor image. Then the lamp's line and vanishing point's position are defined and calculated at known position of WMR in a corridor. To estimate the lateral position and orientation of WMR from an image, the relationship between the position of WMR and the features of ceiling lamps have to be defined. But it is hard because of nonlinearity. Therefore, data set between position of WMR and features of lamps are configured. Neural network are composed and learned with data set. Back propagation algorithm(BPN) is used for learning. And it is applied in navigation of WMR in a corridor.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.221-227
• /
• 2024

In autonomous navigation systems, the need for fast and accurate image processing using deep learning and advanced sensor technologies is paramount. These systems rely heavily on the ability to process and interpret visual data swiftly and precisely to ensure safe and efficient navigation. Despite the critical importance of such capabilities, there has been a noticeable lack of research specifically focused on ship image classification for maritime applications. This gap highlights the necessity for more in-depth studies in this domain. In this paper, we aim to address this gap by presenting a comprehensive comparative study of ship image classification using two distinct neural network models: the Feedforward Neural Network (FNN) and the Convolutional Neural Network (CNN). Our study involves the application of both models to the task of classifying ship images, utilizing a dataset specifically prepared for this purpose. Through our analysis, we found that the Convolutional Neural Network demonstrates significantly more effective performance in accurately classifying ship images compared to the Feedforward Neural Network. The findings from this research are significant as they can contribute to the advancement of core source technologies for maritime autonomous navigation systems. By leveraging the superior image classification capabilities of convolutional neural networks, we can enhance the accuracy and reliability of these systems. This improvement is crucial for the development of more efficient and safer autonomous maritime operations, ultimately contributing to the broader field of autonomous transportation technology.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.1075-1087
• /
• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• Journal of Korea Planning Association
• /
• v.54 no.3
• /
• pp.106-118
• /
• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• The Journal of Korea Robotics Society
• /
• v.16 no.3
• /
• pp.260-269
• /
• 2021

To verify performance or conduct experiments using actual robots, a lot of costs are needed such as robot hardware, experimental space, and time. Therefore, a simulation environment is an essential tool in robotics research. In this paper, we develop the HUMIC simulator using ROS and Gazebo. HUMIC is a humanoid robot, which is developed by HCIR Lab., for human-robot interaction and an upper body of HUMIC is similar to humans with a head, body, waist, arms, and hands. The Gazebo is an open-source three-dimensional robot simulator that provides the ability to simulate robots accurately and efficiently along with simulated indoor and outdoor environments. We develop a GUI for users to easily simulate and manipulate the HUMIC simulator. Moreover, we open the developed HUMIC simulator and GUI for other robotics researchers to use. We test the developed HUMIC simulator for object detection and reinforcement learning-based navigation tasks successfully. As a further study, we plan to develop robot behavior intelligence based on reinforcement learning algorithms using the developed simulator, and then apply it to the real robot.

• Journal of Navigation and Port Research
• /
• v.34 no.7
• /
• pp.595-601
• /
• 2010

This paper aims to diagnose the strategy execution ability and to provide corresponding measures for Korean oceanic shipping companies. The analysis method is the t-test between importance awareness and Corresponding ability for strategy execution diagnosis index(XPP). According to the diagnosed results, the strategy development is unsatisfactory because the strategy implemented does not adhere to concurrent environmental change. Moreover, the execution of the strategy is also unsatisfactory. Therefore, an evaluation shows that there is a need for a SWOT analysis using BSC, an organization structure to strengthen the strategy execution ability and the support from the market condition analysis prediction center.

• Journal of Advanced Navigation Technology
• /
• v.15 no.6
• /
• pp.1047-1052
• /
• 2011

Various services are created using mobile networks after mobile devices such as smart phones and tablet PCs are propagated rapidly. However, the contents of smart devices are not enough diversity because they depend on games and messaging services primarily. In this paper, we described the study have progressed based on mobile devices and networks using augmented reality technologies about virtual education system.