• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1739-1745
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• 2014

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.21-25
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• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.161-172
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• 2024

Approximately 89% of all capsizing accidents involve small vessels, and despite their relatively high accident rates, small vessels are not subject to ship stability regulations. Small vessels, where the provision of essential basic design documents for stability calculations is omitted, face challenges in directly calculating their stability. In this study, considering that the majority of domestic coastal small vessels are of the Chine-type design, the goal is to establish the major hull form characteristic data of vessels, which can be identified from design documents such as the general arrangement drawing, as input data. Through the application of a deep learning approach, specifically a multilayer neural network structure, we aim to infer hydrostatic curves, operational draft ranges, and more. The ultimate goal is to confirm the possibility of directly calculating the initial stability of small vessels.

• 한국디지털정책학회:학술대회논문집
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• 2005.06a
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• pp.571-589
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• 2005

Nowadays, demand for human resource for the e-learning industry is rapidly increasing along with the expansion of e-learning market capacity. However, there arc numerous difficulties in expansion and industrialization of e-learning due to insufficient supply of human resources to meet the demand. Therefore, the goal of this study is to present various policy measures that can supplement the supply of e-learn ing manpower. Overall contents of this study focus on presenting the long-term directions for fostering of human resources for e-learning industry. Among these, role of government policies for fostering of human resources for e-learning industry is being particularly emphasized because e-learning industry is still at its infant stage.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.173.5-173
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• 2001

Q-Learning is a most widely used reinforcement learning, which addresses the question of how an autonomous agent can learn to choose optimal actions to achieve its goal about any one problem. Q-Learning can acquire optimal control strategies from delayed rewards, even when the agent has no prior knowledge of the effects of its action in the environment. If agent has an ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. We present a novel reinforcement learning method using domain knowledge, which is represented by problem-independent features and their classifiers. Here neural network are implied as knowledge classifiers. To show that an agent using domain knowledge can have better performance than the agent with standard Q-Learner. Computer simulations are ...

• Journal of Educational Research in Mathematics
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• v.12 no.1
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• pp.1-16
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• 2002

The 7th Curriculum emphasizes that in mathematics classes, mathematical concepts be understood and mathematical problems be solved through student's own exploratory activities including the use of data, manipulatives, andtechnological devices. Following the main idea of the Seventh Mathematics Curriculum, this paper dealt with instructional methods applied suitably and effectively in mathematics classes, and focused on the 'exploratory learning in groups' method in mathematics education. For this purpose, this paper reviewed and summarized theories related to general pedagogy and of mathematics education. Based on the results, it investigated appropriate instructional methods in mathematics education. In particular, this paper focused on studying the exploratory learning method while investigating its properties and understand- ing the relationship between the 'exploratory learning in groups' method and the discussion-centered method. Finally, in order to show the usefulness of the exploratory learning method, this paper developed an example of a teaching module using the exploratory learning method in addition to discussion and lecture-centered methods by the use of manipulatives. The main goal of the module was to make students understand the principle of multiplication of integers.

• Educational Technology International
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• v.18 no.2
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• pp.131-157
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• 2017

The purposes of the study were to investigate the participation of artists in paper circuit computing learning and to conduct an in-depth study on the formation and development of practical knowledge. To do this, we selected as research participants six artists who participated in the learning program of an art museum, and used various methods such as pre-open questionnaires, participation observation, and individual interviews to collect data. The collected data were analyzed based on community of practice theory. Results showed that the artists participated in the learning based on a desire to use new technology or find a new work production method for interacting with their audiences. In addition, the artists actively formed practical knowledge in the curriculum and tried to apply paper circuit computing to their works. To continuously develop the research, participants formed a study group or set up a practical goal through planned exhibitions. The results of this study can provide implications for practical approaches to, and utilization of, paper circuit computing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.9-11
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• 2022

Applying Reinforcement Learning in everyday applications and varied environments has proved the potential of the of the field and revealed pitfalls along the way. In robotics, a learning agent takes over gradually the control of a robot by abstracting the navigation model of the robot with its inputs and outputs, thus reducing the human intervention. The challenge for the agent is how to implement a feedback function that facilitates the learning process of an MDP problem in an environment while reducing the time of convergence for the method. In this paper we will implement a reward shaping system avoiding sparse rewards which gives fewer data for the learning agent in a ROS environment. Reward shaping prioritizes behaviours that brings the robot closer to the goal by giving intermediate rewards and helps the algorithm converge quickly. We will use a pseudocode implementation as an illustration of the method.

• The Korean Journal of Community Living Science
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• v.19 no.2
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• pp.245-265
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• 2008

The Lifelong Learning City Project has made quantitative expansion as well as qualitative growth since 2001 but the project has been criticized by academic scholars and field practitioners. The Lifelong Learning City Project is a national policy project which has been promoted by the Ministry of Education and Human Resources Development and should be required to make production profits proportional to the amount of public finance. The Lifelong Learning City Project is a community development project intended to promote growth and progress by supporting the community in lifelong learning endeavors. Therefore, the community development theory could offer guidelines to the Lifelong Learning City Project. Based on this assumption, this study intends to investigate the Lifelong Learning City Project at the national, city, and county levels using the community development theory. The improvement methods of the Lifelong Learning City Project are role allotment between national and wide level projects supporting organizations, and the establishment of a system and a long term project policy. In addition, the project is to have a more systematic performance. It is to enhance opportunities for community members' participation, and practice in planning, performance of learning, and the proper performance in regard to the community conditions and specificity. The most important goal of the Lifelong Learning City Project is to support the empowerment of community members by making opportunity planning, practicing and sharing lifelong learning more accessible.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.