• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1656-1663
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• 2004

As the structures become larger, higher and more complicated, the demand for safety level has increased. In recent years, TLD(Tuned Liquid Damper) proved to be a successful control tool for reducing structural vibrations. For this reason, the influence of some key parameters of the U-type TLD on the dynamic response is studied. And simple and effectively developed learning control logic is used to control vibration of U type Tuned Liquid Damper system. The purpose of this paper is design optimal control system to deal with unknown errors from non linearity and variation that cost modeling difficulty in complex structure and is followed with the desired behavior. Finally this hybrid control method applied to U type Tuned Liquid Damper structure gives the benefit from better performance of precision and stability of the structure by reducing vibration effect. This research leads to safety design in various structure to robust unspecified foreign disturbances such as windy-load and earthquake.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.101-110
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• 2004

In dynamic systems, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to Changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results are presented to demonstrate that co-evolutionary learning with coalitions and confidence allows better performing strategies that generalize well.

• Korean Medical Education Review
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• v.23 no.1
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• pp.11-22
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• 2021

This review of the literature explored the experiences and effects of peer-assisted learning in basic medical education. Peer-assisted learning is most commonly utilized to teach clinical skills (including technical skills) and medical knowledge (76.4%). It has also been used, albeit less frequently, to facilitate small-group discussions including problem-based learning, to promote students' personal and professional development, to provide mentoring for career development and adaptation to school, to give tutoring to at-risk students, and to implement work-based learning in clinical settings. Near-peer learning is a common type. The use of active learning techniques and digital technology has been increasingly reported. Students' leadership had frequently been described. Student tutor training, programs for teaching skills, institutional support, and assessments have been conducted for effective peer-assisted learning. There is considerable positive evidence that peer-assisted learning is effective in teaching simple clinical skills and medical knowledge for tutees. However, its effects on complex skills and knowledge, small-group discussions, personal and professional development, peer mentoring, and work-based learning have rarely been studied. Additionally, little evidence exists regarding whether peer-assisted learning is effective for student tutors. Further research is needed to develop peer-assisted learning programs and to investigate their learning effects on student tutors, small-group discussion facilitation, personal and professional development, peer mentoring, and peer-led work-based learning in the clinical setting in South Korea. Formal programs and system advancement for a student-led learning culture is needed for effective peer-assisted learning.

• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.13-23
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• 2018

Deep learning is a promising solution to a number of complex problems based on its inherent capability to approximate almost all types of functions without the demand for handcrafted feature extraction. New wireless transmission and access schemes based on deep learning are being increasingly proposed as substitutes for existing approaches, providing a lower complexity and better performance gain. Among such schemes, a communications system is viewed as an end-to-end autoencoder. The learning process applied in autoencoders can automatically deal with some nonlinear or unknown properties in communications systems. Deep learning can also be used to optimize each processing block for required tasks such as channel decoding, signal detection, and multiple access. On top of recent related research trends, we suggest appropriate research approaches for communications systems to adopt deep learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.3
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• pp.93-114
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• 2002

This paper suggests a dynamic balanced scorecard (DBSC) model employing the concept of system dynamics (SD), which could overcome the limitations inherent in the conventional balanced scorecard (BSC) and facilitate strategic learning process in organizations. The BSC has been a successful framework for measuring an organization＇s performance in various Perspectives through translating an organization＇s vision and strategy into an interrelated set of key performance indicators and specific actions. The BSC, while having significant strengths over traditional performance measurement methods, however, has its own limitations, due to its static nature, such as overlooking two-way causation between performance Indicators and neglecting the impact of delayed feedback flowing from the adoption of new strategies or policy changes. To overcome these limitations, we employs SD, a methodology for understanding complex systems where dynamic feedback among the interrelated system components significantly impact on the system outcomes. The SD simulation model in the form of DBSC we suggest in this paper would serve as a useful strategic learning tool for facilitating an organization＇s communication process through various scenario analyses as well as predicting the dynamic behavior pattern of their key performance measures over a future time frame. For the demonstration purpose, we apply the DBSC model to Korea Coal Corporation (KoCoal ) BSC case.

• Journal of Korean Elementary Science Education
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• v.32 no.2
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• pp.139-158
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• 2013

This study investigated elementary students' (grade 4~6) learning progressions for water cycling drawn from iterative assessments using ordered multiple-choice (OMC) items. An assessment system, which consisted of construct map, item design, outcome space, and measurement model, was employed in this study to examine children's learning progressions. At the first stage of the assessment system, a construct map was designed on which children's conceptual understandings from naive to most sophisticated were represented. At the item design stage, 8 OMC items were drawn from the construct map. Each item option of the OMC items was scored from 0 to 3 according to its level of understanding at the stage of outcome space. As a measurement model, Rasch model, a branch of item response theory, was applied to interpreting the outcomes of the OMC items. This cycle of assessment system was furtherly implemented iteratively in order to elaborate on the first version of water cycling learning progression. In conclusion, children's understanding of water cycling could be described in two aspects: water distribution and water movement. We identified children's conjectural developmental pathways about water cycling existed from superficial and naive accounts to more complex and abstract accounts.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• Smart Structures and Systems
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• v.26 no.6
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• pp.797-807
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• 2020

Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. This paper proposes a novel artificial intelligence based EBA (Evolved Bat Algorithm) controller with machine learning matched membership functions in the complex nonlinear system. The proposed affine transformed membership functions are adopted and stabilization and performance criterion of the closed-loop fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by machine learning affine matched membership functions. The trajectory of the closed-loop dithered system and that of the closed-loop fuzzy relaxed system can be made as close as desired. This enables us to get a rigorous prediction of stability of the closed-loop dithered system by establishing that of the closed-loop fuzzy relaxed system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.173-178
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• 2022

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.330-336
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• 2000

This paper discusses the potential of system dynamics modelling to support balanced scorecard. The balanced scorecard is a conceptual framework for translating an organisation's strategy into a set of performance indicators. These performance indicators are distributed across the 'classic'model's four perspective: Customers, Internal Business Processes, Financial, and Learning and Growth. This balanced scorecard, whilst having significant strength, suffers from the limitation of all performance indicator systems, namely that the interrelationships between indicators are overlooked and there is no way of taking into account the impact of delayed feedback which flows from introduction of new policy and legislative changes. System Dynamics is a methodology for understanding complex problems where there is dynamic behaviour and where feedback impacts significantly on system outcomes. System dynamics provides a rigorous basis for qualitative testing of the effects of performance indicators in complex environments such as health or social security. This can be supplemented with quantitative system dynamics simulation tools that further test the validity of indicators and the business rules implicit in them. System dynamics modelling has an important role to play in extending feedback cycle in performance measurements to a full systems approach.