• International Journal of Advanced Culture Technology
• /
• v.10 no.4
• /
• pp.86-93
• /
• 2022

To combine the science and technology creativity necessary in the era of the 4th Industrial Revolution, it is necessary to cultivate talents who can discover new knowledge and create new values by combining various knowledge with self-directed mathematical competencies. This research attempted to lay the foundation for the curriculum for fostering future creative convergence talent by preparing, executing, and reflecting on the learning plan after learners themselves understand their level and status through self-directed learning. Firstly, We would like to present a teaching-learning plan based on the essential capabilities of the future society, where the development of a curriculum based on mathematics curriculum and intelligent informatization are accelerated. Secondly, an educational design model system diagram was presented to strengthen the self-directed learning ability of mathematics subjects in the electronic engineering curriculum. Consequently, through a survey, we would like to propose the establishment of an educational system necessary for the 4th industry by analyzing learning ability through self-directed learning teaching methods of subjects related to mathematics, probability, and statistics.

• Soft Computing and Machine Intelligence
• /
• v.1 no.1
• /
• pp.24-30
• /
• 2021

The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

• Electronics and Telecommunications Trends
• /
• v.33 no.4
• /
• pp.1-13
• /
• 2018

Deep learning (DL) models have been widely applied to AI applications such image recognition and language translation with big data. Recently, DL models have becomes larger and more complicated, and have merged together. For the accelerated training of a large-scale deep learning model, model parallelism that partitions the model parameters for non-shared parallel access and updates across multiple machines was provided by a few distributed deep learning frameworks. Model parallelism as a training acceleration method, however, is not as commonly used as data parallelism owing to the difficulty of efficient model parallelism. This paper provides a comprehensive survey of the state of the art in model parallelism by comparing the implementation technologies in several deep learning frameworks that support model parallelism, and suggests a future research directions for improving model parallelism technology.

• Korean Medical Education Review
• /
• v.23 no.3
• /
• pp.147-153
• /
• 2021

The coronavirus disease 2019 (COVID-19) pandemic made it necessary for medical schools to restructure their curriculum by switching from face-to-face instruction to various forms of flexible learning. Flexible learning is a student-centered approach to learning that has received interest in many educational sectors. It is a critical strategy for expanding access to higher education during the pandemic. As flexible learning includes online, blended, hybrid, and hyflex learning options, learners have the opportunity to select an instruction modality based on their needs and interests. The shift to flexible learning in medical education took place rapidly in response to the COVID-19 pandemic, and learners, instructors, and schools were not prepared for this instructional change. Through the lens of the technology acceptance model, human agency, and a social constructivist perspective, I examine students, instructors, and educational institutions' roles in successfully navigating the digital transformation era. The pandemic has also accelerated the use of advanced information and communication technologies, such as artificial intelligence and virtual reality, in learning. Through a review of the literature, this paper aimed to reflect on current flexible learning practices from the instructional design and educational technology perspective and explore emerging technologies that may be implemented in future medical education.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06a
• /
• pp.87-88
• /
• 2007

• Corrosion Science and Technology
• /
• v.18 no.2
• /
• pp.61-71
• /
• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.4
• /
• pp.270-275
• /
• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2019.05a
• /
• pp.30-31
• /
• 2019

Carbonation of the root concrete reduces the durability of the reinforced concrete, and it is important to check the carbonation resistance of the concrete to ensure the durability of the reinforced concrete structure. In this study, a basic study on the prediction of carbonation progress was conducted by considering the mixing conditions of concrete using deep learning algorithm during the theory of artificial neural network theory. The data used in the experiment used values that converted the carbonation velocity coefficient obtained from the mixing conditions of concrete and the accelerated carbonation experiment into the actual environment. The analysis shows that the error rate of the deep learning model according to the Hidden Layer is the best for the model using five layers, and based on the five Hidden layers, we want to verify the predicted performance of the carbonation speed coefficient of the carbonation test specimen in which the exposure experiment took place in the real environment.

• Journal of Korean Society for Quality Management
• /
• v.38 no.2
• /
• pp.161-170
• /
• 2010

Diffusion of e-learning has been accelerated according as the convenience and effectiveness have been increased rapidly due to the advancement of information technology. However, there has been few studies on systematic evaluation of its performance. SERVQUAL model was applied to evaluate the service quality of a 100% on-line lecture opened in a major Korean university. Two classes, one for 71 undergraduate students, the other for 79 graduate students, were opened for the lecture. The gaps between the expected service and the perceived service scores were compared with respect to sex, age, and e-learning experience. Although the gap score of male and female students were not different significantly, the gap scores among the other comparative groups were different. The perceived score of the older group with more than thirty ages was lower than that of the younger group. It seems that the older group evaluated the score based on the practical use of the subject since they are part-time students with jobs. Also, the perceived score of the group with previous e-learning experience was higher than that of the group with no e-learning experience. It seems that the experienced group evaluated it compared with the previous e-learning satisfaction. As it might be expected, the groups with higher perceived scores had stronger intention to recommend the e-learning lecture to other students.

• Journal of KIISE:Software and Applications
• /
• v.34 no.12
• /
• pp.1045-1055
• /
• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.