• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.111-118
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• 1997

Adding a noise tolerance factor to the Relaxation learning algorithm in Hop-field network improves noise tolerance without effecting storage capacity. The new algorithm is called the Pseudo-Relaxation algorithm, and the convergence of the algorithm has been proved. It is also shown that the noise tolerance factor does not effect learning speed.

• Journal of Korean Home Economics Education Association
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• v.9 no.1
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• pp.71-82
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• 1997

The aims of this research study is to clear the differences of the learning effects and the cognition of Home Economics by sex in the middle school and to collect the future-oriented educational dates, after implementing the 6th education curriculum. The results of the study are as follows ； 1. Most of students had the cognition that Home Economics is a subject matter of a needful knowledge and skill for the family life, half students a cultural subject matter for the modern life. The former was supported by female students, the latter by male students. 2. Most of students were interested in actual training fields of cooking and hand-sewing, had a strong interest about a field of physical and social-psychological growth. The differences by sex interest about significant in these fields. 3. Most of students had positive responses in the learning effects of Home Economics. The most useful field was cooking and the worst was the structure and method of sewing machine. 4. The shortage and the dissatisfaction in the learning of Home Economics were lacking of the actual training hours, the old-fashioned teaching methods and the shortage of teaching materials etc.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.179-202
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• 2006

The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.

• Communications of Mathematical Education
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• v.32 no.1
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• pp.23-36
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• 2018

Recently, we are working to utilize it in various fields with the expectation of the potential of artificial intelligence. There is also interest in applying to the field of education. In the field of education, machine learning and deep learning, which are used in artificial intelligence technology, are deeply interested in how to learn on their own. We are interested in how artificial intelligence and artificial intelligence technologies can be used in education and we have an interest in how artificial intelligence can be applied to mathematics education. The purpose of this study is to investigate the direction of mathematics education as the change of education paradigm and the development of artificial intelligence according to the development of information and communication technology. Furthermore, we examined how artificial intelligence can be applied to mathematics education.

• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.29-37
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• 2019

Since construction sites are exposed to outdoor environments, working conditions are significantly dangerous. Thus, wearing of the personal protective equipments such as safety helmet is very important for worker safety. However, construction workers are often wearing-off the helmet as inconvenient and uncomportable. As a result, a small mistake may lead to serious accident. For this, checking of wearing safety helmet is important task to safety managers in field. However, due to the limited time and manpower, the checking can not be executed for every individual worker spread over a large construction site. Therefore, if an automatic checking system is provided, field safety management should be performed more effectively and efficiently. In this study, applicability of deep learning based computer vision technology is investigated for automatic checking of wearing safety helmet in construction sites. Faster R-CNN deep learning algorithm for object detection and classification is employed to develop the automatic checking model. Digital camera images captured in real construction site are used to validate the proposed model. Based on the results, it is concluded that the proposed model may effectively be used for automatic checking of wearing safety helmet in construction site.

• Korean Medical Education Review
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• v.23 no.2
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• pp.104-107
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• 2021

Building professional identity is the most basic purpose of medical education. Students who enter medical schools do not have an identity rooted in the medical profession, and universities should therefore take steps to help students form their identity as doctors, attitudes, beliefs, and values through the curriculum. However, while medical knowledge and clinical skills are fully reflected in basic medical education, issues persist regarding education on values, attitudes, and beliefs that are important for professional identity. Regarding the process of professional identity formation, it is important to keep in mind that rapid changes in modern society lead to corresponding changes in socio-cultural expectations and demands related to professional identity, resulting in discrepancies between the reality of medical education and the actual field of medicine. Medical schools need to prepare students for these discrepancies, and in-depth discussions should address what is important and what should be solved first at medical education sites. However, it is difficult to generalize the tasks of professional identity formation in the field of medical education because each medical school may have unique circumstances. This article discusses the tasks that medical education should solve for professional identity formation education in terms of five aspects: establishing learning outcomes, training educational experts, introducing transformative learning, utilizing self-directed learning, and developing evaluation methods.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.73-79
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• 2021

Recently, in the field of computer animation, a method for generating motion using deep learning has been studied away from conventional finite-state machines or graph-based methods. The expressiveness of the network required for learning motions is more influenced by the diversity of motion contained in it than by the simple length of motion to be learned. This study aims to find an efficient network structure when the types of motions to be learned are diverse. In this paper, we train and compare three types of networks: basic fully-connected structure, mixture of experts structure that uses multiple fully-connected layers in parallel, recurrent neural network which is widely used to deal with seq2seq, and transformer structure used for sequence-type data processing in the natural language processing field.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3985-3995
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• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.