• Journal of IKEEE
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• v.26 no.4
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• pp.742-745
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• 2022

In this paper, we propose a hybrid image processing and deep learning-based method for detecting the presence of power lines in infrared images. Deep learning-based methods can learn feature vectors from a large number of data without much effort, resulting in outstanding performances in various fields. However, it is difficult to apply human intuition to the deep learning-based methods while image processing techniques can be used to apply human intuition. Based on these, we propose a method that exploits both advantages to detect the existence of power lines in infrared images. To this end, five methods have been applied and compared to find the most effective image processing technique for detecting the presence of power lines. As a result, the proposed method achieves 99.48% of accuracy which is higher than those of methods based on either image processing or deep learning.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.147-152
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• 2023

This paper is a study of a linear algebra course taught in a high school on-demand course. Compared to the regular course, flipped learning was added to the course, and applications to signal processing related problems were covered in consideration of students' career aspirations. Overall, the class was a mixture of traditional lectures and flipped learning. Flipped learning was implemented twice. The flipped class consisted of pre-class, in-class and post-class. To verify the effectiveness of the course, a survey was conducted and most of the evaluation items were above 4. The topics of the flipped learning were Markov chains and least squares problem, which are very important in the field of signal processing.

• 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.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.843-850
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• 2004

This study investigated the effects of group processing in cooperative learning using task-oriented reward on students' science achievement, science learning motivation, and attitude toward science instruction. Eighth graders (N=58) selected from a middle school in Seoul, were randomly assigned to either the treatment or comparison group, and taught on the 'Separation of Mixture' over 8 class hours. The treatment group received cooperative learning using task-oriented reward containing group processing (GCL), while the comparison group received cooperative learning using task-oriented reward without group processing (CL). Significant interactions between the instruction and prior achievement level were found in the achievement and the attitude toward science instruction. High-level students in the GCL group performed better than those in the CL group, while low-level students in the CL group performed better than their counterparts.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.521-538
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• 2012

As the knowledge society matures, not only distant, but also off-line universities are trying to provide learners with on-line educational contents. Particularly, high effectiveness of mobile devices for e-Learning has been demonstrated by the university sector, which uses distant learning that is based on blended learning. In this paper, we analyzed previous m-Learning scenarios and future technology prospects. Based on the proposed m-Learning scenario, we designed cellular phone-based educational contents and service structure, implemented m-Learning system, and analyzed m-Learning service satisfaction. The design principles of the m-Learning service are 1) to provide learners with m-Learning environment with both cellular phones and desktop computers; 2) to serve announcements, discussion boards, Q&A boards, course materials, and exercises on cellular phones and desktop computers; and 3) to serve learning activities like the reviewing of full lectures, discussions, and writing term papers using desktop computers and cellular phones. The m-Learning service was developed on a cellular phone that supports H.264 codex in 3G communication technology. Some of the functions of the m-Learning design principles are implemented in a 3G cellular phone. The contents of lectures are provided in the forms of video, text, audio, and video with text. One-way educational contents are complemented by exercises (quizzes).

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.60-67
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• 2020

Recently, artificial intelligence related technologies including machine learning are being applied to various fields, and the demand is also increasing. In particular, with the development of AR, VR, and MR technologies related to image processing, the utilization of computer vision based on deep learning has increased. The algorithms for object recognition and detection based on deep learning required for image processing are diversified and advanced. Accordingly, problems that were difficult to solve with the existing methodology were solved more simply and easily by using deep learning. This paper introduces various deep learning-based object recognition and extraction algorithms used to detect and recognize various objects in an image and analyzes the technologies that attract attention.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.543-554
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• 2014

It is difficult for mobile learners to maintain a high level of concentration when learning content for more than an hour while they are on the move. Despite the attention span issue, many m-learning systems still provide their mobile learners with the same content once used in e-learning systems. This has called for an investigation to identify the suitable characteristics of the m-learning environment. With this in mind, we have conducted a survey in hopes of determining the requirements for developing more suitable m-learning content. Based on the results of the survey, we have developed a content model comprised of two types: a segment type and a supplement type. In addition, we have implemented a prototype system of the content model for Apple iPhones and Android smartphones in order to investigate a feasibility study of the model application.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1223-1230
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• 2020

Recently, extensive studies have been conducted to apply deep learning to reinforcement learning to solve the state-space problem. If the state-space problem was solved, reinforcement learning would become applicable in various fields. For example, users can utilize dance-tutorial systems to learn how to dance by watching and imitating a virtual instructor. The instructor can perform the optimal dance to the music, to which reinforcement learning is applied. In this study, we propose a method of reinforcement learning in which the action space is dynamically adjusted. Because actions that are not performed or are unlikely to be optimal are not learned, and the state space is not allocated, the learning time can be shortened, and the state space can be reduced. In an experiment, the proposed method shows results similar to those of traditional Q-learning even when the state space of the proposed method is reduced to approximately 0.33% of that of Q-learning. Consequently, the proposed method reduces the cost and time required for learning. Traditional Q-learning requires 6 million state spaces for learning 100,000 times. In contrast, the proposed method requires only 20,000 state spaces. A higher winning rate can be achieved in a shorter period of time by retrieving 20,000 state spaces instead of 6 million.

• English Language & Literature Teaching
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• v.12 no.3
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• pp.267-286
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• 2006

The purpose of the study was to investigate what kinds of learning strategies EFL learners use to learn English grammar and what is benefit from structured grammar input processing. Students of the study consisted of 48 college students who took Practical English Grammar at a university in Kyung-Gi area and were divided into two groups based on grammar scores. The students were asked to take two grammar tasks and grammar tests and complete a survey including questions on grammar strategy and input processing. The results of the study are as follows. First, learners' grammar level has an effect on use of grammar attack strategy including asking teachers, using grammar books and given contexts whereas there was no significant difference between groups in the planning strategies, Among memory strategies, using grammar exercise and linking with already known structure demonstrated a significant difference between groups. Second, with regard to input processing, high level students got higher score on how much they understood the structured grammar input compared with low level students. Third, explicit implicit instruction added to input processing seems more comprehensible and more available than structured input only, Finally, it showed that there is positive relationship between perception and score of input processing tasks and grammar tests. Especially, learners' perception of input processing correlated more with final tests and tasks. Therefore, it suggests that the more input processing task need to develop and utilize in order to facilitate learners' intake.