• English Language & Literature Teaching
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• v.13 no.2
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• pp.25-45
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• 2007

In this article, I discuss an International Collaborative Writing Course on the Internet (ICWCI) that focused on the learning effectiveness Korean EFL students (KEFLSs) perceived to be necessary to exchange with international EFL students (IEFLSs). The course development was based on an internet-based instructional module, applying widely accepted EFL theories for modern foreign language instruction: collaborative learning, process writing, project-based learning, and integrated approaches. Data from online discussion forum, mid-of-semester and end-of-semester surveys, and final oral interviews are conducted and discussed. KEFLSs and IEFLSs were questioned about (a) changes in attitude towards computers assisted language learning (CALL); (b) effect of computer background on motivation; (c) perception of their acquired writing skills; and (d) attitude towards collaborative learning. The result of this study demonstrated that the majority of ICWCI participants said they enjoyed the course, gained fruitful confidence in English communication and computer skills, and felt that they made significant progress in writing skills. In spite of positive benefits created by the ICWCI, it was found that there were some issues that are crucial to run appropriate networked collaborative courses. This study demonstrates that participants' computer skills, basic language proficiency, and local time differences are important factors to be considered when incorporating the ICWCI as these may affect the quality of online instructional courses and students' motivation toward network based collaboration interaction.

• Smart Structures and Systems
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• v.29 no.6
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.61-69
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• 2021

Recently, autonomous navigation technology is actively being developed due to the increasing demand of an unmanned surface vehicle(USV). Local planning is essential for the USV to safely reach its destination along paths. the dynamic window approach(DWA) algorithm is a well-known navigation scheme as a local path planning. However, the existing DWA algorithm does not consider path line tracking, and the fixed weight coefficient of the evaluation function, which is a core part, cannot provide flexible path planning for all situations. Therefore, in this paper, we propose a new DWA algorithm that can follow path lines in all situations. Fixed weight coefficients were trained using reinforcement learning(RL) which has been actively studied recently. We implemented the simulation and compared the existing DWA algorithm with the DWA algorithm proposed in this paper. As a result, we confirmed the effectiveness of the proposed algorithm.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3277-3290
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• 2023

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.33-43
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• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• The Journal of Korean Academic Society of Nursing Education
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• v.3
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• pp.26-33
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• 1997

Problem-Based Learning(PBL) is at the forefront of educational reform. The acceptance of PBL as an educational approach with wide application represents a major change in thinking about educational processes and their relationships to the wider community. In 1969, PBL as a method was introduced at the Medical School of McMaster University in Hamilton, Canada. The most important advantages in PBL are acquiring knowledge that can be retrieved and applied, learning to learn(self-directed learning) and learning to analyze and solve Problems. PBL is widely used within the sector where it had its origin, namely health profession education. A generally accepted starting point in the development of a problem-based curriculum is the set of professional competencies of future graduates, which describe the typical problems professionals have to deal with. Formulating learning objectives highly depends on the format and content of the presented problems. Contrary to that, in a classic course in higher education, it is customary that teachers express objectives in a compulsory subject matter. Curricula which advocate problem-based learning generally use case studies in the form of paper cases, simulations and real patients with the intention of stimulating classroom discussion of clinical and basic science concepts within a problem-solving framework. One goal of using paper cases is to stimulate the learning of basic science within a clinical situation. Through self-directed study the students solve problems and explore the psycho-social dimensions within the cases. The general outcome based on the program evaluation research of PBL is that PBL students respond positively about the learning experience. In summary, PBL is a curriculum design and a teaching/learning strategy which simultaneously develops higher order thinking and disciplinary knowledge bases and skills by placing students in the active role of practitioners(or problem solvers) confronted with a situation(ill-structured problem) which reflects the real world.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.221-228
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• 2013

A number of research studies focus on the efficacy of using such as scenario based learning. However, desirable methods have not been introduced to assess the scenario based learning. This article is to suggest a fuzzy logic based framework for scenario base learning in which more reasonable learning effects are measured. It can be solved uncertain problems of linguistic variables. Also, we suggest three measures of accuracy, comprehensibility and completeness in order to evaluate accurate effects of scenario based learning. This assessment provides the scenario to the learner in which the scenario is presented in an authentic context, and enable the learner to reach an outcome through an adequate sequence and choices. This approach enables the system to present new scenarios and outcomes based on what a user selects. In particular, the application of fuzzy logic in scenario based learning can be easily pursued certain successful path or wrong path all the way through to reach major outcome in real situation.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.67-74
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• 2023

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.144-155
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• 2018

This study was conducted to explore the direction of culinary education based brain-based education with analysis of comprehensive research. Questionnaire was completed by frequency analysis, factor analysis, reliability analysis and regression analysis by using SPSS 21. The purpose of this study was to investigate the educational system for creative development through cooking sources and to develop brain-based learning theory, and thus to generate the characteristics and effects of the practice in culinary educational context. The basic principles of brain- based learning are brain plasticity, emotional brain, and ecological brain. Students need to be able to enrich their understanding of social interaction so that social brain's function will be activated through consistent and high-quality feedback. Likewise, students should be capable of collecting everything what they have learned. Defining main ideas and goal of the lesson, four factors were derived from development of competency, personality, application, and diversity. Regarding to the result of this study, the implications for the development of a brain-base program were suggested.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.