• Advances in Energy Research
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• v.8 no.3
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• pp.185-202
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• 2022

This research work focuses to design and simulate a 200W solar power system with electrical power conservation scheme as well as thermal power conservation modeling to improve power extraction from solar power plant. Many researchers have been already designed and developed different methods to extract maximum power while there were very researches are available on improving solar power thermally and mechanically. Thermal parameters are also important while discussing about maximizing power extraction of any power plant. A specific type of coolant which have very high boiling point is proposed to be use at the bottom surface of solar panel to reduce the temperature of panel in summer. A comparison between different maximum power point tracking (MPPT) technique and proposed MPPT technique is performed. Using this proposed Thermo-electrical MPPT (TE-MPPT) with Deep Learning Algorithm model 40% power is conserved as compared to traditional solar power system models.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.92-98
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• 2005

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

• Journal of dental hygiene science
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• v.17 no.5
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• pp.383-397
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• 2017

We attempted to develop a problem-based learning (PBL) module for integrated education in dental hygiene with the aim of helping students gain clinical competencies necessary for dental hygienist work. To develop the PBL Module for Clinical Dentistry in Dental Hygiene course, the researchers identified literature related to not only educational technology, but also medical science, nursing, dentistry, and dental hygiene. During the design phase of the PBL module, problem scenarios and a plan for the teaching and learning process were developed. Developing problem scenarios involved describing a problematic situation and three questions related with that situation. To cultivate competencies required in dental clinics, each question was related to the diagnosis of a dental disease, dental treatment, and dental hygiene procedures for care. Teaching-learning process plan included the designs of operating environment, operational strategies, learning resources, facilitation of problem-solving process, and evaluation. It is necessary to evaluate the PBL module for integrated education in dental hygiene to confirm its effectiveness.

• Hwankyungkyoyuk
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• v.24 no.3
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• pp.34-43
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• 2011

Project-based learning is an innovative approach to learning that teaches a multitude of strategies critical for success in the twenty-first century. Students drive their own learning through inquiry activity, as well as work collaboratively to research and create projects that reflect their knowledge. The purpose of this study was to investigate the effects of eco-friendly school project activity which is applied from one of project-based learning approach on learning outcomes of students in ninth-grade environment course in middle school. The participants were given a questionnaire before and after the environmental project activities. In solving the school environment issues themselves, students have practiced invaluable problem solving skills. This study indicates that school students' awareness about the environment has positively changed by experiencing the eco-friendly school project. In addition, this project affects students' variety of environmental awareness. This project could be applied to school environmental education programs and to environment lessons, developmental activities or club activities for a positive impact on students' environmental awareness.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.329-345
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• 2021

Recommender systems use data from customers to suggest personalized products. The recommender systems can be categorized into three cases; collaborative filtering, contents-based filtering, and hybrid recommender system that combines the first two filtering methods. In this work, we introduce and compare deep learning-based recommender system using autoencoder. Autoencoder is an unsupervised deep learning that can effective solve the problem of sparsity in the data matrix. Five versions of autoencoder-based deep learning models are compared via three real data sets. The first three methods are collaborative filtering and the others are hybrid methods. The data sets are composed of customers' ratings having integer values from one to five. The three data sets are sparse data matrix with many zeroes due to non-responses.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.97-106
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• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1673-1681
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• 2007

The u-Learning just begins to emerge as the next-generation knowledge-based business. Since it has a great potential to become a high value-added industry, there is much attention paid in this field. In this work, we first summarized the concept of the u-Learning where the architecture of various u-learning areas has been identified. Then we investigated the current status and problems of the u-Learning industry. Through the SWOT analysis, we have extracted the political strategies that will be essential for the rapid industrialization of u-Learning which will, in turn, contribute much to enhance the competitiveness of national economy.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• Journal of Haehwa Medicine
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• v.23 no.2
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• pp.1-4
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• 2015

Learning can be defined as its objects, main question for the objects, and its unique way to organize all the knowledge acquired as the results of the question. From the point of view like this, Traditional Korean Medicine(TKM) can be defined as learning for human body and its functions, health and diseases based on the theory of the Yin and Yang and of the five elements. Nowaday Many papers based on laboratory work publish for the name of scientization of TKM, but from the viewpoint of definition of learning, they have a problem that there is no basic theory. If TKM could be communicated with western natural science, it has to be solved. And oriental physiology has a same object and same questions with western physiology, so oriental physiology can be useful to make a bridge between TKM and western natural science.