• The Mathematical Education
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• v.61 no.2
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• pp.257-271
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• 2022

As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.6 no.3
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• pp.177-191
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• 2011

A community of practice (CoP) is a group of people who share an interest, a craft, and/or a profession. The group can evolve naturally because of the members' common interest in a particular domain or area, or it can be created specifically with the goal of gaining knowledge related to their field. It is through the process of sharing information and experiences with the group that the members learn from each other, and have an opportunity to develop themselves personally and professionally. This study focus on the 151 researchers of four government research institutes(GRIs) and research empirically an operational characteristics and performanceof CoP. Results show that characteristics like a new member increase, external linkage oriented- challenging climate, diversity oriented- autonomous climate affect a CoP performance. But external factors such as the number of participants, meeting frequency are not related with the CoP performance.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.78-86
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• 1999

In this paper, a distributed arithmetic digital neural network with learning and testing phase implemented in a body has been studied. The proposed technique is based on the two facts; one is that the weighting coefficients adjusted will be stored in registers without shift, because input values or input patterns are not changed while learning and the other is that the input patterns stored in registers are not changed while testing. The proposed digital neural network is simulated by hardware description language such as VHDL and verified the performance that the neural network was applied to the recognition of seven-segment. To verify proposed neural networks, we compared the learning process of modified perceptron learning algorithm simulated by software with VHDL for 7-segment number recognizer. The results are as follows: There was a little difference in learning time and iteration numbers according to the input pattern, but generally the iteration numbers are 1000 to 10000 and the learning time is 4 to 200$\mu\textrm{s}$. So we knew that the operation of the neural network is learned in the same way with the learning of software simulation, and the proposed neural networks are properly operated. And also the implemented neural network can be built with less amounts of components compared with board system neural network.

• Journal of Dental Rehabilitation and Applied Science
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• v.33 no.2
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• pp.88-96
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• 2017

Purpose: The purpose of this study is to assess the relationship between the time spent designing custom abutments and repeated learning using dental implant computer aided design (CAD) software. Materials and Methods: The design of customized abutments was performed four stages using the 3DS CAD software and the EXO CAD software, and measured repeatedly three times by each stage. Learning effect by repetition was presented with the learning curve, and the significance of the reduction in the total time and the time at each stage spent on designing was evaluated using the Friedman test and the Wilcoxon signed rank test. The difference in the design time between groups was analyzed using the repeated measure two-way ANOVA. Statistical analysis was performed using the SPSS statistics software (P < 0.05). Results: Repeated learning of the customized abutment design displayed a significant difference according to the number of repetition and the stage (P < 0.001). The difference in the time spent designing was found to be significant (P < 0.001), and that between the CAD software programs was also significant (P = 0.006). Conclusion: Repeated learning of CAD software shortened the time spent designing. While less design time on average was spent with the 3DS CAD than with the EXO CAD, the EXO CAD showed better results in terms of learning rate according to learning effect.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.121-124
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• 2001

음성 합성기의 합성음의 자연감을 높이기 위해 자연음에 내재하는 정확한 운율 법칙을 구하여 음성합성 시스템에서 이를 구현해 주어야 한다 무제한 어휘 음성합성 시스템의 문-음성 합성기에서 필요한 운율 법칙은 언어학적 정보를 이용해 구하거나, 자연음에서 추출하고 있다 그러나 추출한 운율 법칙이 자연음에 내재하는 모든 운율 법칙을 반영하지 못했거나, 잘못 구현되는 경우에는 합성음의 자연성이 떨어지게 된다. 이런 점을 고려하여 본 논문에서는 한국어 자연음을 분석하여 추출한 운율 정보를 인공 신경망이 학습하도록 하고 훈련을 마친 인공 신경망에 문장을 입력하고, 출력으로 나오는 운율 정보와 자연음의 운율 정보를 비교한 결과 제안한 인공 신경망이 자연음에 내재하고 있는 운율을 학습할 수 있음을 알 수 있었다. 운율의 3대 요소는 피치 , 지속시간, 크기의 변화이다. 제안한 인공 신경망이 한국어 문장의 음소 열을 입력으로 받아들이고, 각 음소의 지속시간에 따른 피치변화와 크기 변화를 출력으로 내보내면 자연음을 분석해 구한 각 음소의 운율 정보인 목표 패턴과 출력 패턴 의 오차를 최소화하도록 인공 신경망의 가중치를 조절할 수 있도록 설계하였다. 지속시간에 따른 각 음소의 피치와 크기 변화를 학습시키기 위해 피치 및 크기 인공 신경망을 구성하였다. 이들 인공 신경망을 훈련시키기 위해 먼저 음소 균형 문장 군을 구축하여야 하고, 이들 언어 자료를 특정 화자가 일정 환경에서 읽고 이를 녹음하여 , 분석하여 구한운율 정보를 운율 데이터베이스로 구축하였다. 문장 내의 각 음소에 대해 지속 시간과 피치 변화 그리고 크기 변화를 구하고, 곡선 적응 방법을 이용하여 각 변화 곡선에 대한 다항식 계수와 초기 값을 구해 운율 데이터베이스를 구축한다. 이 운율 데이터베이스의 일부는 인공 신경망을 훈련시키는데 이용하고, 나머지로 인공 신경망의 성능을 평가하여 인공 신경망이 운율 법칙을 학습할 수 있었다. 언어 자료의 문장 수를 늘리고 발음 횟수를 늘려 운율 데이터베이스를 확장하면 인공 신경망의 성능을 높일 수 있고, 문장 내의 음소의 수를 감안하여 인공 신경망의 입력 단자의 수는 계산량과 초분절 요인을 감안하여 결정해야 할 것이다

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.631-636
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• 2019

Over the last decade, many researchers have investigated a number of vision-based construction object detection algorithms for the purpose of construction site monitoring. However, previous methods require the ground truth labeling, which is a process of manually marking types and locations of target objects from training image data, and thus a large amount of time and effort is being wasted. To address this drawback, this paper proposes a vision-based construction object detection framework that employs an active learning technique while reducing manual labeling efforts. For the validation, the research team performed experiments using an open construction benchmark dataset. The results showed that the method was able to successfully detect construction objects that have various visual characteristics, and also indicated that it is possible to develop the high performance of an object detection model using smaller amount of training data and less iterative training steps compared to the previous approaches. The findings of this study can be used to reduce the manual labeling processes and minimize the time and costs required to build a training database.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.89-96
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• 2021

The number of confirmed cases of coronavirus-19 is soaring around the world and has caused numerous deaths. Wearing a mask is very important to prevent infection. Incidents and accidents have occurred due to the recommendation to wear a mask in public places such as buses and subways, and it has emerged as a serious social problem. To solve this problem, this paper proposes an open source-based face mask wearing monitoring system. We used open source software, web-based artificial intelligence tool teachable machine and open source hardware Arduino. It judges whether the mask is worn, and performs commands such as guidance messages and alarms. The learning parameters of the teachable machine were learned with the optimal values of 50 learning times, 32 batch sizes, and 0.001 learning rate, resulting in an accuracy of 1 and a learning error of 0.003. We designed and implemented a mask wearing monitoring system that can perform commands such as guidance messages and alarms by determining whether to wear a mask using a web-based artificial intelligence tool teachable machine and Arduino to prove its validity.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.15-30
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• 2023

Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

• Journal of Christian Education in Korea
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• v.75
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• pp.59-81
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• 2023

Purpose of study: The purpose of this study is to analyze the experience of writing a learning reflection journal, identify the effects, and propose an effective learning reflection journal activity way. Research content and method: This study examined the theoretical background of the concept and educational effect of the learning reflection journal through literature review in terms of educational and Christian educational aspects, and analyzed the learning reflection journal experience through the interviews with six students. Through this, this study identifies the effectiveness of the learning reflection journal and suggests effective learning reflection journal activity way that can be applied to training that can grow as a Christian educator who practices what he or she know through major classes. Conclusions and Suggestions: For effective learning reflection journal activities, it was proposed to reduce the number of review and reflection questions so that they would not be burdensome, to include questions in a balanced way, to help learners to improve class attitudes. It was also proposed that the submission period and the number of writing journals should be agreed upon with the students at the beginning of the semester.