• The Mathematical Education
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• v.43 no.2
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• pp.199-210
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• 2004

This study investigates 55 preservice secondary mathematics teachers' situational understanding of functional relationship. Functional thinking is fundamental and useful because it develops students' quantitative thinking about the world and analytical thinking about complex situations through examination of the relations between interdependent factors. Functional thinking is indispensable for understanding natural phenomena, for investigation by science, and for the technological inventions in engineering and navigation. Therefore, it goes without saying that teachers should be able to represent and communicate about various functional situations in the course of teaching and learning functional relationships to develop students' functional thinking. The result of this study illustrates that many preservice teachers were not able to appropriately represent and communicate about various functional situations. Additionally, it shows that most preservice teachers have limited understanding of the value of teaching function.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.703-705
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• 2005

연속적이고 동적인 실세계에서의 경로 탐색 문제는 이동 로봇 분야에서 주된 문제 중 하나였다. 최근 컴퓨터 성능이 크게 발전하면서 컴퓨터 게임들이 실제에 가까운 연속적인 3차원 환경 모델을 사용하기 시작하였고, 그에 따라 보다 복잡하고 동적인 환경 모델 하에서 경로 탐색을 할 수 있는 능력이 요구되고 있다. 강화 학습 기반의 경로 탐색 알고리즘인 평가치 반복(Value iteration) 알고리즘은 실시간 멀티에이전트 환경에 적합한 여러 장점들을 가지고 있으나, 문제가 커질수록 속도가 크게 느려진다는 단점을 가지고 있다. 본 논문에서는 연속적인 3차원 상황에서 빠르게 동적 변화에 적응할 수 있도록 하기 위하여 작은 세상 네트웍 모델을 사용한 환경 모델 및 경로 탐색 알고리즘을 제안한다. 3차원 게임 환경에서의 실험을 통해 제안된 알고리즘이 연속적이고 복잡한 실시간 환경 하에서 우수한 경로를 찾아낼 수 있으며, 환경의 변화가 관측될 경우 이에 빠르게 적응할 수 있음을 확인할 수 있었다.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.8-13
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• 2019

Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.271-280
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• 2023

FMCW (Frequency Modulated Continuous Wave) radar system is widely used in autonomous driving and navigation applications due to its high detection capabilities independent of weather conditions and environments. However, radar signals can be easily contaminated by various noises such as speckle noise, receiver saturation, and multipath reflection, which can worsen sensing performance. To handle this problem, we propose a learning-free noise removal technique for radar to enhance detection performance. The proposed method leverages adaptive thresholding to remove speckle noise and receiver saturation, and wavelet transform to detect multipath reflection. After noise removal, the radar image is reconstructed with the geometric structure of the surrounding environments. We verify that our method effectively eliminated noise and can be applied to autonomous driving by improving the accuracy of odometry and place recognition.

• Annual Conference of KIPS
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• 2023.11a
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• pp.624-627
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• 2023

Unmanned aerial vehicles are gaining in popularity with the development of science and technology, and are being used for a wide range of purposes, including surveillance, rescue, delivery of goods, and data collection. In particular, the ability to avoid obstacles during navigation without human oversight is one of the essential capabilities that a drone must possess. Many works currently have solved this problem by implementing deep reinforcement learning (DRL) model. The essential core of a DRL model is reward function. Therefore, this paper proposes a new reward function with appropriate action space and employs dueling double deep Q-Networks to train a drone to navigate in indoor environment without collision.

• Annual Conference of KIPS
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• 2024.10a
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• pp.1020-1021
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• 2024

해운산업에 인공지능 기술이 결합됨에 따라 자율 항해 기술이 급격히 발전하고 있다. 선박 운항의 안정성과 효율성을 높이기 위해 강화학습을 이용한 충돌 방지 및 경로 생성 연구가 활발히 이루어지고 있으나, 인공지능은 적대적 공격에 취약하다는 한계점이 있다. 이에 본 논문에서는 선박의 안전 운항을 위협하는 적대적 공격기법을 비교 분석하고, 강화학습 기술을 평가하여 가장 적합한 기법을 제안함으로써, 향후 선박운항을 위한 연구 방향을 제시하고자 한다.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.483-496
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• 2012

The purpose of this study is to examine difference of information processing style on lesson comprehension scores and usability ratings in e-Learning containing visual information structure. To address this goal, 68 university students were participated in this research. They were asked information processing style test, lesson comprehension test, and usability ratings after completed e-Learning lesson. According to the result, there was not significant difference between visual and verbal information process style on lesson comprehension as learn outcomes. However, students who are visual information processing style were significantly higher ratings than students who are verbal information processing style on 4 of 8 usability scales; awareness of lesson structure, awareness of lesson length, ease of navigation, and ease of lesson learning. These result indicate that there will be needed the design of aptitude treatment interaction for e-Book according to information processing style.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1065-1079
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• 2012

This research is done with the objectives to examine and to identify how the main factors of airline services, which become fundamentals for airline customers to select their favorite one, affect their customer satisfaction and their customer preference, with a focus on adjustable influence of converting cost. The survey has been conducted with the population of the airline customers who use international airlines through Incheon International Airport and processed with the objectives to verify the influence of the main factors of airline services to customer satisfaction and customer preference and also to collect the verified data for adjustable influence of converting cost between customer satisfaction and customer preference, and its results are as follows: Firstly, airline services have significant impact on their customer satisfaction. Secondly, they also have significant impact on their customer preference, Thirdly, the converting cost between both of customer satisfaction and customer preference to airline services doesn't involve the adjustable function in consecutive cost nor in learning cost but it does involve the one in sunk cost.