• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Journal of the Korean earth science society
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• v.23 no.6
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• pp.461-473
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• 2002

Concept mapping is a device for representing the conceptual structure of a subject discipline in a two dimensional form which is analogous to a road map. In the teaching and learning of earth science, each concept depends on its relationships to many others for meaning. Using concept mapping in teaching helps teachers and students to be more aware of the key concepts and relationships among them. The purpose of this study is to investigate the effect of the use of concept mapping on science achievement and the scientific attitude in ocean units of earth science. The results of this study are as follows; first, the science achievement of a group of concept mapping teaching is significantly higher than that of the group of traditional teaching. Also, when the achievement levels are compared among different cognitive ability groups, the effect is more significant in mid or lower level student groups than in high level groups. The use of concept mapping is more effective when the concepts have a distinct concept hierarchy. Second, the scores of the test of ‘attitude toward scientific inquiry’ and ‘application of scientific attitude’ of the group of concept mapping teaching are significantly higher than those of the group of traditional teaching, whereas the scores of the test of ‘interest in science learning’ of concept mapping teaching is not different from those of group of traditional teaching. Third, the survey on the use of concept mapping shows a positive response across the tested groups. The use of concept mapping is more beneficial in fostering the comprehension of the topic. A concept map of student's own construction facilitates the assessment of learning, thus promising the usefulness of concept mapping as a means of evaluation. In regard to retention aspect, concept mapping is considered to be more effective in confirming and remembering the topic, while less effective in the aspects of activity and interest. In conclusion, the use of concept maps makes learning an active meaningful process and improves student's academic achievement and scientific attitude. If the concept mapping is more effectively as an active teaching strategy, more meaningful learning will be attained.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.67-76
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• 2011

In this paper, a touch interface of a smart device using, learner controlled by three-dimensional learning content for more realistic learning environment will be constructed. Fabrication of three-dimensional learning content is difficult. So teachers and learners to create content and share content, a framework was designed. The framework consists of an XML language and intuitive. Android-based devices are available from the playback and authoring. Server environment for content sharing was established. The proposed framework is verified through expert evaluation. In result, it was positively evaluated in terms of usability.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.615-616
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• 2016

최근 4차 산업으로 패러다임이 변화함에 따라 SW산업이 더욱 중요하게 되었다. 이에 따라 전 세계적으로 코딩 교육에 대한 수요도 증가하게 되었고 기업에서도 SW를 잘 만들기 위한 코드 관리 중요성도 증가하게 되었다. 많은 양의 프로그래밍 소스코드를 사람이 일일이 채점하고 관리하는 것은 사실상 불가능하기 때문에 이러한 문제를 해결할 수 있는 코드 평가 시스템이 요구되고 있다. 하지만 어떤 코드가 좋은 코드인지 코드를 어떻게 평가해야하는지에 대한 명확한 기준은 없으며 이에 대한 연구도 부족한 상황이다. 최근에 주목 받고 있는 Deep Learning 기술은 이미지 처리, 자연어 처리등 기존의 Machine Learning 알고리즘이 냈던 성과보다 훨씬 뛰어난 성과를 내고 있다. 하지만 Programming language 영역에서는 아직 깊이 연구된 바가 없다. 따라서 본 연구에서는 Deep Learning 기술로 알려진 Convolutional Neural Network의 변형된 형태엔 Tree-based Convolutional Neural Network를 사용하여 프로그래밍 소스코드를 분석, 분류하는 알고리즘 및 코드의 Representation Learning에 대한 연구를 진행함으로써 이러한 문제를 해결하고자 한다.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.3
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• pp.417-425
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• 2015

Purpose: The purpose of this study was to identify the relationship between self-leadership and the perception of learning in nursing students. Methods: A descriptive research design was used in this study. The participants were 378 nursing students in M city and C city who were surveyed between June 1 and June 30, 2014 using self-report questionnaires. The data was analyzed using SPSS WIN 21.0 Program, which determined frequency, percentage, mean, standard deviation; a One-way ANOVA; a $Scheff{\acute{e}}$ test; a Pearson correlation coefficient; and a Stepwise multiple regression analysis. Results: There were significant positive correlations between nursing students' self-leadership and their perception of learning. In the multiple regression analysis, self-leadership was the most significant predictor in explaining nursing students' perception of learning. Conclusion: Study findings suggest that nursing students' self-leadership is defined as having an important influence on nursing students' perception of learning. In order to strengthen nursing students' self-leadership, there is a need to develop education programs that increase nursing students' self-leadership.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• Educational Technology International
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• v.23 no.2
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• pp.183-205
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• 2022

Mental status examination is an important stage in the assessment process because it serves as the foundation for establishing nursing diagnosis and intervention. Until now many students still feel difficult to understand the terms in the assessment of mental status. Interactive Mobile Learning in one of the media that is currently being developed. The use of this media will provide more in-depth learning opportunities, and students can practice their skills in carrying out practicals because of the mobility principle possessed by smartphones. The purpose of this study was to develop a smartphone-based app and evaluate the app's effectiveness by measuring nursing students' knowledge of mental status examination. Design: A randomized trial with a pre-and post-test design was conducted at a university in Indonesia. A total of seventy junior nursing students participated in this study. The intervention group received a smartphone-based app, and the control group received one-time lecture-based learning. We offered the experimental group the app and information about how to use it, and we encouraged them to use it. The control group received classroom instruction. Results: The intervention group scored significantly higher than the control group on knowledge score (t = 19.40, p = 0.000) and satisfaction with the learning method (t = 0.640, p = 0.021) Conclusion: These findings suggest that smartphonebased education could be an effective method in nursing education for teaching mental status examinations.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.447-450
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• 2011

This paper shows the application of P-N Learning [4] method in the soccer ball detection and improvement for increasing the speed of processing. In the P-N learning, the learning process is guided by positive (P) and negative (N) constraints which restrict the labeling of the unlabeled data, identify examples that have been classified in contradiction with structural constraints and augment the training set with the corrected samples in an iterative process. But for the long-view in the soccer game, P-N learning will produce so many ferns that more time is spent than other methods. We propose that color histogram of each frame is constructed to delete the unnecessary details in order to decreasing the number of feature points. We use the mask to eliminate the gallery region and Line Hough Transform to remove the line and adjust the P-N learning's parameters to optimize accurate and speed.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.61-63
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• 2021

Machine learning model gained the popularity in recent years as multi-national companies have incorporated machine learning in their services. Such service is called machine learning as a service (MLaSS). Such services are provided to users based on charge-per-query which triggers the motivations for adversaries to steal the trained victim model to reduce the cost of using the service. Therefore, it is important for companies that provide MLaSS to protect their intellectual property (IP) against adversaries. It has been arms race between the attack and defence in a context of the privacy of machine learning models. In this paper, we provide a comprehensive study of recent development in protecting privacy of machine learning models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.