• International journal of advanced smart convergence
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• v.7 no.4
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• pp.19-26
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• 2018

One of the distinct features of Wireless Sensor Networks (WSNs) is duty cycling mechanism, which is used to conserve energy and extend the network lifetime. Large duty cycle interval introduces lower energy consumption, meanwhile longer end-to-end (E2E) delay. In this paper, we introduce an energy consumption minimization problem for duty-cycled WSNs. We have applied Q-learning algorithm to obtain the maximum duty cycle interval which supports various delay requirements and given Delay Success ratio (DSR) i.e. the required probability of packets arriving at the sink before given delay bound. Our approach only requires sink to compute Q-leaning which makes it practical to implement. Nodes in the different group have the different duty cycle interval in our proposed method and nodes don't need to know the information of the neighboring node. Performance metrics show that our proposed scheme outperforms existing algorithms in terms of energy efficiency while assuring the required delay bound and DSR.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.71-82
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• 2024

This paper proposes a new communication system for e-learning applications to mitigate the negative impacts of COVID-19 where the online massive demands impact the current commu-nications systems infrastructures and capabilities. The proposed system utilizes high-altitude platforms (HAPs) for fast and efficient connectivity provision to bridge the communication in-frastructure gap in the current pandemic. The system model is investigated, and its performance is analyzed using adaptive antenna arrays to achieve high quality and high transmission data rates at the student premises. In addition, the single beam and multibeam HAP radio coverage scenarios are examined using tapered uniform concentric circular arrays to achieve feasible communication link requirements.

• Educational Technology International
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• v.23 no.2
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• pp.207-237
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• 2022

As technological changes continue to accelerate every day, meeting the needs of a shifting educational landscape requires leaving an exclusively "in-person" education behind. Gamified learning environments should be carefully designed in light of conflicting studies to suit students' needs. The purpose of this meta-analysis is to draw conclusive results regarding the application of the most commonly used game elements in education, i.e., badges and leaderboards, through a comprehensive analysis of their impact on academic performance in online learning. Review Manager (RevMan 5.4) was used to analyze eligible studies selected from Emerald, SAGE, ERIC, EBSCO, and ProQuest between January 2011 and January 2022. Analyzing 37 studies found that using leaderboards and badges in online education enhanced academic performance when compared to traditional learning without gamification (SMD = 0.39). The badge-only intervention showed a larger effect size (SMD = 0.33) than the leaderboard-only intervention (SMD = 0.27). Badges and leaderboards together exhibited a larger effect size (SMD = 0.48) than individual game elements (SMD = 0.40). The impact of the game elements on academic performance was greater in the humanities (SMD = 0.51) than in STEM fields (SMD = 0.32) and was greater for K-12 students (SMD = 0.63) than for college students (SMD = 0.31). This study contributes to a timely discussion of the use of badges and leaderboards in COVID-19 online learning trends and provides relevant data for designing integrations of online education and gamification models.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.395-405
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• 2018

Seakeeping safety module in Korean e-Navigation system is one of the ship remote monitoring services that is employed to ensure the safety of ships by monitoring the ship's real time performance and providing a warning in advance when the abnormal conditions are encountered in seakeeping performance. In general, seakeeping performance has been evaluated by simulating ship motion analysis under specific conditions for its design. However, due to restriction of computation time, it is not realistic to perform simulations to evaluate seakeeping performance under real-time operation conditions. This study aims to introduce a reasonable and faster method to predict a ship's roll motion which is one of the factors used to evaluate a ship's seakeeping performance by using a machine learning-based surrogate model. Through the application of various learning techniques and sampling conditions on training data, it was observed that the difference of roll motion between a given surrogate model and motion analysis was within 1%. Therefore, it can be concluded that this method can be useful to evaluate the seakeeping performance of a ship in real-time operation.

• Journal of Science Education
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• v.32 no.2
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• pp.17-32
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• 2008

The purpose of this study was to analyze the use of the e-learning courseware which is about the properties of earth environmental change. I made these materials using Lectora program, and to have more visual effect, I used movies and animations as much as possible. A lot of movies and pictures are added to help student understand the geographical age. Especially the forms of performance test questions are various, and student can check the answer right after taking test. Because of this, they can get a immediate feedback. In addition, this allows you to adapt yourself to the age of information by using the internet. Every plug-in is already linked together, so you only need to once. Pictures and moving reflections can be edited with ease. I hope this study will provide valuable aid for the education of earth science and a chance to develop better materials.

• 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.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.137-145
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• 2023

E-Commerce is a buzzword well known for electronic commerce activities including but not limited to the online shopping, digital payment transactions, and B2B online trading. In today's digital age, e-commerce has been playing a very important and vital role in areas such as retail shopping, sales automation, supply chain management, marketing and advertisement, and payment services. With a huge amount of data been collected from various e-commerce services available, there are multiple opportunities to use that data to analyze graphs and trends. Strategize profitable activities, and forecast future trade. This paper explains a contemporary approach for collecting key data metrics and implementing cost-effective automation that will support in improving conversion rates and sales performance of the e-commerce websites resulting in increased profitability.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.1-13
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• 2020

Machine learning refers to a model generation technique that can solve specific problems from the generalization process for given data. In order to generate a high performance model, high quality training data and learning algorithms for generalization process should be prepared. As one way of improving the performance of model to be learned, the Ensemble technique generates multiple models rather than a single model, which includes bagging, boosting, and stacking learning techniques. This paper proposes a new Ensemble technique with multiple stacking that outperforms the conventional stacking technique. The learning structure of multiple stacking ensemble technique is similar to the structure of deep learning, in which each layer is composed of a combination of stacking models, and the number of layers get increased so as to minimize the misclassification rate of each layer. Through experiments using four types of datasets, we have showed that the proposed method outperforms the exiting ones.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.67-74
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• 2012

This study elicits alternatives for improving blended education by comparing the face-to-face education and blended education of VET teacher license program. To produce a basis for the methods of blended education, this study compared the scores of satisfaction and academic performance of face-to-face and of blended education. Also it examined the three factors of learners' characteristics, teaching and learning design, and its environment as they affect the education effect. The results showed that academic performance of face-to-face education is higher than blended and that competition rate of blended education is higher than that of face-to-face. To improve the effect of blended learning, various teaching and learning activities, design of contents that promotes participation and interaction, and development of contents which satisfy the various levels of learning needs were discussed.