• Journal of The Korean Association For Science Education
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• v.25 no.3
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• pp.371-381
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• 2005

In this study recent trends of research about beliefs in physics education were discussed and cultural approaches were suggested. Cultural aspects in the contexts of science education were discussed and diverse aspects of beliefs in physics education-beliefs about nature, physics, learning physics, value and expectation, and learning physics-were analyzed considerating cultural aspects. Finally, directions for future studies about beliefs and cultural approaches in physics education were suggested.

• Research in Mathematical Education
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• v.16 no.3
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• pp.177-194
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• 2012

What strategies are used to help students understand quadratic functions in mathematics classroom? In specific, how does Chinese teacher highlight a connection between algebraic representation and graphic representation? From October to November 2009, an experienced teacher classroom was observed. It was found that when students started learning a new type of quadratic function in lessons, the teacher used two different teaching strategies for their learning: (1) Eliciting students to plot the graphs of quadratic functions with pointwise approaches, and then construct the function image in their minds with global approaches; and (2) Presenting a specific mathematical problem, or introducing conception to elicit students to conjecture, and then encouraging them to verify it with appoint approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.21-27
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• 2020

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• 한국디지털정책학회:학술대회논문집
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• 2007.06a
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• pp.377-384
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• 2007

This paper is about how to establish the strategic teaming organization in digital age. Through the case study of action teaming, this research can give some implications to small-sized organizations who want to establish teaming culture and positive activities in their own companies. The case site was S-steel, which belongs to the steel industry. To improve and drive teaming activities, I made use of skills: action learning, fishbone analysis, creative thinking, brainstorming, creative discussion skill, and organization diagnostic method.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.648-650
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• 1998

This paper proposes a constructive algorithm for generating the Takagi-Sugeno type fuzzy model through the sequential learning from training data set. The proposed algorithm has a two-stage learning scheme that performs both structure and parameter learning simultaneously. The structure learning constructs fuzzy model using two growth criteria to assign new fuzzy rules for given observation data. The parameter learning adjusts the parameters of existing fuzzy rules using the LMS rule. To evaluate the performance of the proposed fuzzy modeling approach, well-known benchmark is used in simulation and compares it with other modeling approaches.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.08a
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• pp.103-127
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• 2001

Gene expression studies require statistical experimental designs and validation before laboratory confirmation. Various clustering approaches, such as hierarchical, Kmeans, SOM are commonly used for unsupervised learning in gene expression data. Several classification methods, such as gene voting, SVM, or discriminant analysis are used for supervised lerning, where well-defined response classification is possible. Estimating gene-condition interaction effects require advanced, computationally-intensive statistical approaches.

• Asia pacific journal of information systems
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• v.9 no.3
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• pp.177-194
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• 1999

Identifying knowledge as the single most important asset ultimately defining organizational competitiveness, enterprises are trying to move towards knowledge-oriented practices. Such practices have given rise to learning and agile organization, This paper presents applied information technologies to realize the learning and agile organization, focusing on systems thinking. Firstly, in order to establish a framework for the systems thinking, an information systems development method based on process chain is proposed. Then, an agent-based microworld simulation approach is presented. The approaches provide visible and analytical information to knowledge workers so that they can have systems thinking capabilities eventually. Various microworlds on the top of the information system can be constructed with agents and simulated for possible business events. All decision makings are dynamic in nature. To let knowledge workers look ahead the possible outcomes of the whole relevant processes is the core capability of the approaches. Through watching, the knowledge workers would be able to acquire new insights or problem solving knowledge for the problem in hand.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.