• Journal of Industrial Technology
• /
• v.33 no.A
• /
• pp.31-39
• /
• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Journal of Korean Clinical Health Science
• /
• v.6 no.1
• /
• pp.1048-1055
• /
• 2018

Purpose: The purpose of this study was to investigate the nursing students' metacognition and learning flow. Methods: The participants in this study were 272 nursing students. Between November and December 2017, data were collected through questionnaires. Data analysis was performed using PASW (SPSS) 21.0 program, and descriptive statistics, t-test, one-way ANOVA and Pearson correlation coefficients. Results: The mean metacognition of this study was 3.53/5, and mean of learning flow was 3.34/5. The significant learning flow according to metacognition level (F=46.75, p<.001). The significant correlates of metacognition were learning flow (r=.54, p<.001). Conclusions: The finding of study showed that metacognition was very important for enhancing learning flow influenced these relationship. This study suggested that it is important to develop and implement teaching and learning strategies with improved metacognition in nursing education field.

• Journal of The Korean Digital Architecture Interior Association
• /
• v.12 no.3
• /
• pp.67-79
• /
• 2012

The purpose of this study is to suggest the interior architecture design studio through the pedagogical method of educational technology for college students who lack self-directed learning. The pedagogical method has been organized to make a student-centered class based on the operation of existing architectural design studios. This teaching and learning method emphasizes the role of teachers as facilitators to help students lacking in self-directed learning in the design process, the BIM visualization to give students an expression of design project and the critics to give students an experience of working circumstances. The results of this study can be summarized as follows. First, This pedagogical model can improve the self-directed learning of students, accomplish the design process well through teamwork, and provide problem based learning (PBL) to settle obstacles that come up during the project. Second, through this model, students can improve their field design capacity by instructor, design feedback and criticism. Finally, This model can suggest new pedagogical methods for interior architectural design studios and management of student-centered studios.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 1996.10a
• /
• pp.217-227
• /
• 1996

The new filed of learning control develops controllers that learn to improve their performance at executing a given task , based on experience performing this specific task. In a previous work[6], authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controller indecentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an asssembly line. This paper starts with decentralized discrete time systems. and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The resultof the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample tie in the digital learning controller is sufficiently short.

• Journal of Practical Engineering Education
• /
• v.6 no.2
• /
• pp.77-84
• /
• 2014

As the interests in the flipped learning has recently been increased in the educational field, the number of engineering professors has tended to apply the flipped learning method to their classes. However there has been few research which guide to design engineering courses applying the flipped learning method. This study aims to suggest an instructional design model for the flipped learning according to the case of applying the flipped learning method to an engineering course, "Mechanical Vibrations". This study provides practical guidelines for engineering professors to design their theory oriented courses applying the flipped learning method.

• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Investigative Magnetic Resonance Imaging
• /
• v.23 no.2
• /
• pp.81-99
• /
• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.21-39
• /
• 2021

The acceptance of smartphone applications in the learning field is one of the most significant challenges for higher education institutions in Saudi Arabia. These institutions serve large and varied sectors of society and have a tremendous impact on the knowledge gained by student segments at various ages. M-learning is of great importance because it provides access to learning through a wide range of mobile networks and allows students to learn at any time and in any place. There is a lack of quality requirements for M-learning applications in Saudi societies partly because of mandates for high levels of privacy and gender segregation in education (Garg, 2013; Sarrab et al., 2014). According to the Saudi Arabian education ministry policy, gender segregation in education reflects the country's religious and traditional values (Ministry of Education, 2013, No. 155). The opportunity of many applications would help the Saudi target audience more easily accept M-learning applications and expand their knowledge while maintaining government policy related to religious values and gender segregation in the educational environment. In addition, students can share information through the online framework without breaking religious restrictions. This study uses a quantitative perspective to focus on defining the technical aspects and learning requirements for distributing knowledge among students within the digital environment. Additionally, the framework of the unified theory of acceptance and use of technology (UTAUT) is used to modify new constructs, called application quality requirements, that consist of quality requirements for systems, information, and interfaces.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.111-117
• /
• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.99-106
• /
• 2011

Recent demands from construction industry have emphasized the capability for graduates to have improved skills both technical and non-technical such as problem solving, interpersonal communication. To satisfy these demands, problem-based learning that is an instructional method characterized by the use of real world problem has been adopted and has proven its effectiveness various disciplines. However, in spite of the importance of field senses and dealing with real problem, construction engineering education has generally focused on traditional lecture-oriented course. In order to improve limitations of current construction education and to satisfy recent demands from construction industry, this paper proposes a new educational approach that is Failure-Based Learning for using combination of the procedural characteristics of the problem-based learning theory in construction technology education utilizing failure information that has the educational value in the construction area by reinterpreting characteristics of construction industry and construction failure information. The major results of this study are summarized as follows. 1) Educational effect of problem-based learning methodology and limitation of application in construction area 2) The educational value of the information on construction failure and limitation in application of the information in construction sector 3) Anticipated effect from application of the failure-based learning 4) Development and application of the failure-based learning model