• Journal of the Korean Society of Industry Convergence
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• v.12 no.2
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• pp.107-112
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• 2009

The effective reinforcement methods of structure is required to improve the durability of existing structures. Recently, the continuous fiber sheets to the concrete structures are widely used in the earthquake-proof reinforcement method. This study examines suitability and effect to concrete structure of fiber by FEM analysis. The result of analysis is as follows; All specimens occurred bending tensile failure at the middle span. Ultimate strength of specimen in the RC and reinforced RC specimen were 53.9 kN, 56.3 kN respectively and it was some low by degree 0.89, 0.82 to compare with calculated result. The deflection of specimen at the middle span occurred in approximately 0.2 mm, and did linear behavior in load 20 kN by seat reinforcement. Stiffness did not decrease by occurrence in the finer crack and reinforcement beam's flexure stiffness was increased until reach in failure. To compare calculated value and analysis value, it almost equal behavior in the elastic reign and can confirm effectiveness of analysis. Crack was distributed uniformly by reinforcement of fiber seat at failure and it do not occurred stiffness decreases.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.57-66
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• 2017

PURPOSES : This study develops eco-friendly asphalt reinforcement materials applicable to bridge deck pavement. The main purpose is to ensure highly reliable quality applicable to structures and the possibility of practical application. The main target of the study is to develop materials that are environmentally friendly and capable of improving performance. METHODS : The application of double-reinforcement fiber improves the performance of the road pavement. 1. We use recycled film for application of sheet-typed reinforcement. 2. We use preprocessing fibers to reinforce the properties of composite pavement materials. RESULTS : The developed products may produce materials that fit the purpose of achieving stability and environmental friendliness. Sheet-typed reinforcements use more than 50% recycled resin. The most important type of damage to the asphalt layer is deflection (plastic deformation). These products have a very high deflection resistance of not less than 6,000 cycles/mm. In addition, all performance is excellent. Thus, it will be easier to access the field in the future. CONCLUSIONS : Fiber-reinforced asphalt pavement showed excellent performance. Sheet-typed reinforcements containing 50% recycling resin produced good performance in terms of functionality as well as environmental friendliness. Thus, enhancing the field applicability will enhance the usability of the reinforcements.

• Journal of Korean Biological Nursing Science
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• v.20 no.2
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• pp.132-140
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• 2018

Purpose: This study has been carried out in order to develop and verify the effects of self-leadership reinforcement program for hospital nurses. Methods: The research design was a non-equivalent control group pre-posttest design. Participants were 64 individuals (32 in each group), all of whom were nurses working at a university hospital, with less than five years of job experience. Experimental group was provided with two hours of self-leadership reinforcement program, once per week, for four weeks. The questionnaire for pre and post test included general characteristics, transfer motivation for learning, self-leadership, communication ability, clinical nursing competency, organizational commitment, and turnover intentions. Results: There was a significant difference in self-leadership scores between experimental group and control group (F= 15.10, p<.001). There was also a significant difference between the experimental group and the control group in terms of transfer motivation for learning (t = -5.44 p<.001), communication ability (F = 15.29, p<.001), clinical nursing competency (F = 15.23, p<.001), and organizational commitment scores (F = 7.21, p=.009). Conclusion: The self-leadership reinforcement program developed in this study was effective in improving self-leadership, communication ability, clinical nursing competency, and organizational commitment. Thus, by implementing the program at clinical levels, it will be a basis for nursing personnel resource administration.

• Bulletin of the Society of Naval Architects of Korea
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• v.22 no.1
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• pp.9-20
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• 1985

Generally the stiffened plate in the ship structure is subjected to not only axial load but shear load. With respect to those combined loads buckling analysis in necessary. In this paper, buckling strength is analyzed by using Finite Element Method when the stiffened plate with hole is under loading conditions mentioned above. To obtain the higher buckling strength, we need some reinforcement. The methods of reinforcement are attaching doubler around hole and stiffeners in the arbitrary directions For the sake of convenience those arbitrary directions were selected paralleled($0^{\circ}C$), vertical($90^{\circ}C$)and oblique($45^{\circ}C$) to the edge. Two kinds of method mentioned above are investigated, it is clarified that which of the two is more effective reinforcement. From the viewpoint of buckling strength, following conclusions were obtained. When external load direction is unknown, doubler reinforcement is more effective than those of parallel and vertical stiffener. And oblique stiffener reinforcement is more effective than that of doubler when external load direction is know.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.81-86
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• 2019

Robots are used in various industrial sites, but traditional methods of operating a robot are limited at some kind of tasks. In order for a robot to accomplish a task, it is needed to find and solve accurate formula between a robot and environment and that is complicated work. Accordingly, reinforcement learning of robots is actively studied to overcome this difficulties. This study describes the process and results of learning and solving which applied reinforcement learning. The mission that the robot is going to learn is bottle flipping. Bottle flipping is an activity that involves throwing a plastic bottle in an attempt to land it upright on its bottom. Complexity of movement of liquid in the bottle when it thrown in the air, makes this task difficult to solve in traditional ways. Reinforcement learning process makes it easier. After 3-DOF robotic arm being instructed how to throwing the bottle, the robot find the better motion that make successful with the task. Two reward functions are designed and compared the result of learning. Finite difference method is used to obtain policy gradient. This paper focuses on the process of designing an efficient reward function to improve bottle flipping motion.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.131-139
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• 2024

Crew Transfer Vessels (CTVs) are primarily used for the maintenance of offshore wind farms. Despite being manually operated by professional captains and crew, collisions with other ships and marine structures still occur. To prevent this, the introduction of autonomous navigation systems to CTVs is necessary. In this study, research on the obstacle avoidance system of the autonomous navigation system for CTVs was conducted. In particular, research on obstacle avoidance simulation for CTVs using deep reinforcement learning was carried out, taking into account the currents and wind loads in offshore wind farms. For this purpose, 3 degrees of freedom ship maneuvering modeling for CTVs considering the currents and wind loads in offshore wind farms was performed, and a simulation environment for offshore wind farms was implemented to train and test the deep reinforcement learning agent. Specifically, this study conducted research on obstacle avoidance maneuvers using MATD3 within deep reinforcement learning, and as a result, it was confirmed that the model, which underwent training over 10,000 episodes, could successfully avoid both static and moving obstacles. This confirms the conclusion that the application of the methods proposed in this study can successfully facilitate obstacle avoidance for autonomous navigation CTVs within offshore wind farms.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.573-588
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• 2023

Purpose: This study presents a research approach that utilizes deep reinforcement learning to construct optimal portfolios based on the business cycle for stocks and other assets. The objective is to develop effective investment strategies that adapt to the varying returns of assets in accordance with the business cycle. Methods: In this study, a diverse set of time series data, including stocks, is collected and utilized to train a deep reinforcement learning model. The proposed approach optimizes asset allocation based on the business cycle, particularly by gathering data for different states such as prosperity, recession, depression, and recovery and constructing portfolios optimized for each phase. Results: Experimental results confirm the effectiveness of the proposed deep reinforcement learning-based approach in constructing optimal portfolios tailored to the business cycle. The utility of optimizing portfolio investment strategies for each phase of the business cycle is demonstrated. Conclusion: This paper contributes to the construction of optimal portfolios based on the business cycle using a deep reinforcement learning approach, providing investors with effective investment strategies that simultaneously seek stability and profitability. As a result, investors can adopt stable and profitable investment strategies that adapt to business cycle volatility.

• Geotechnical Engineering
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• v.12 no.3
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• pp.49-62
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• 1996

The pull-out test is a common test for detemining the strength and deformation parameters between reinforcement and soil inl the design of reinforced earth structures. It is often assumed in the interpretation of the results from the test that the mobilization of shear strength along the reinforcement is uniform. The progressive shearing at the soil-reinforcement interface during the pull-out test often leads to incorrect calculation of the shear displacement response between the reinforcement and the soil. To investigate the effect of progressive shearing during the calculation of the shear stiffness of the soil-reinforcement interface, the finite element method is used to simulate the pullout test. The reinforcement, soil and interface behaviors are modeled by rosing linear and non-linear constitutive models. Shear stiffnesses are calculated by uaiHg conventional methods. It is found that there are considerable discrepancies 13etween the calculated shear stiffnesses and the correct stiffnesses which are used in the finite element analysis. The amount of error depends on the relative stiffness between reinforcement and soil and the size of the specimen being analyzed. The finite element results are also compared with the observed response from laboratory experiments. A revised interpretation of the pull-out test results is discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.1-5
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• 2005

The purpose of reinforcement learning is to maximize rewards from environment, and reinforcement learning agents learn by interacting with external environment through trial and error. Q-Learning, a representative reinforcement learning algorithm, is a type of TD-learning that exploits difference in suitability according to the change of time in learning. The method obtains the optimal policy through repeated experience of evaluation of all state-action pairs in the state space. This study chose n-Queen problem as an example, to which we apply reinforcement learning, and used Q-Learning as a problem solving algorithm. This study compared the proposed method using reinforcement learning with existing methods for solving n-Queen problem and found that the proposed method improves the convergence rate to the optimal solution by reducing the number of state transitions to reach the goal.

• Journal of Technologic Dentistry
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• v.42 no.2
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• pp.91-98
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• 2020

Purpose: This study is to compare the flexural strength and modulus by inserting a mesh and stick type fiberglass reinforcement into resin specimens. Methods: Wax specimens (length 64 mm, width 39 mm, thickness 5 mm) are prepared according to ISO 20795-1:2013. Mesh type and stick type glass fiber reinforcements were prepared. The prepared wax specimens were used plaster and flask for investment. The flask was separated and the wax was removed. The heat curing resin was injected into the flask, and then a mesh type and stick type fiberglass reinforcement were inserted. The prepared resin specimen was cut into three equal parts (length 64 mm, width 10 mm, thickness 3.3 mm). The mesh type glass fiber reinforcement (MT group) and the stick type glass fiber reinforcement (ST group) were classified into two groups. The prepared specimen was measured using a universal testing machine (UTM). The data were analyzed by Mann-Whitney U test, and the significance level was set to 0.05. Results: In the flexural strength, the ST group was higher than the MT group, and there was a significant difference between the two groups (p<0.05). In the flexural modulus, the ST group was higher than the MT group, and there was a significant difference between the two groups (p<0.05). Conclusion: The stick-type glass fiber inreased the flexural strength than the mesh-type glass fiber reinforcement.