• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5A
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• pp.511-518
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• 2009

This study was intended to compare and evaluate the adhesion of High flowing Self-compacting Concrete (HSCC), Conventional Concrete (CC) and deformed bar based on concrete strength 3 (30, 50 and 70 MPa), among the factors affecting the bond strength between concrete and rebar, after fabricating the specimen by modifying the rebar position at Horizontal reinforcement at bottom position (HB), horizontal reinforcement at top position (HT) and vertical reinforcement type (V). As a result of measuring bond strength of HB/HT rebar to evaluate the factor of the rebar at top position, the bond strength of HB/HT rebar at 50 and 70 MPa was 1.3 or less and at 30 MPa, HSCC and CC appeared to be 1.2 and 2,1, respectively. Thus, when designing the anchorage length according to the concrete structure design standard (2007) at HSCC 30, 50 and 70 MPa, it would be desirable to reduce the correction factor of anchorage length of the horizontal reinforcement at top position, which is suggested for the reinforcement at top position, to less than 1.3 of CC.

• Culinary science and hospitality research
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• v.10 no.3
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• pp.83-96
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• 2004

According to construction of global administration environment, all over the world was ended up in infinite competition period. Because eating out company also competes with another company, it must enjoy competitive power high position to secure high position continuously, classified by formation constituent's individuation and two aspects of organic special quality by the factor. Individuation classified by formation constituent's business ability, personal relations, age factors of Information Technology, and organic special quality classifies by factor of formation culture and result that analyze eating out company's business condition competitive power reinforcement effect factor is same as following. First, was construed by company culture, personal relations, business ability period of ten days with family Restaurant, and Information Technology showed that do not influence. Competitive power reinforcement effect factor with special restaurant was construed by company culture, personal relations, business ability period of ten days, and fast food company culture and Information Technology factor with hotel restaurant company culture by competitive power reinforcement effect factor construe.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.75-83
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• 2019

In this paper, we propose a method that can detect and trace the end point of real - time reinforcement steel to various environmental conditions of industrial field by using Median flow and Depth information. We proposed a method to derive two steel end points by using Median filter, Binarization, Morphology, and Blob algorithm on image depth information. The coordinates of the final position were determined by comparing the coordinates of the reinforcement steel endpoints detected in the Depth image and the position tracking coordinates of the reinforcement steel using Median Flow. As a result, when the existing Median Flow method was used, the success rate of the final position determination of reinforcement steel of 75% was increased to 95% when the Depth of reinforcement steel was used.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.5
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• pp.145-151
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• 2016

We consider the position and thickness of reinforcement with respect to fatigue fracture of welded bogie frames and propose an appropriate reinforcement method for many cases. The bogie frame is usually designed in accordance with JIS and KS, and operates under harsh load conditions: dynamic loads generated while driving, various loads during operation, and large load differences between loading and unloading. Consequently, fatigue failure often occurs throughout the bogie frame. We modelled the reinforcing method using ANSYS software and reviewed stress in the vicinity of common fatigue failure sites through computer simulation, optimizing the position and thickness of reinforcement.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.1
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.665-671
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• 2024

This research presents a method for Augmented Reality (AR) object superimposition leveraging reinforcement learning techniques to significantly reduce manual input during the exploration of digital information on construction sites. A distinctive feature of this approach is the application of a reinforcement learning neural network, trained with pairs of real and virtual view images, for AR superimposition. This approach enables the precise adjustment of the virtual camera's position and orientation within a virtual scene, aiming to seamlessly integrate AR objects into real-world views. This research initially focuses on simpler scenarios involving 2 and 3 degrees of freedom for orientation and position adjustments. The purpose is to explore the feasibility of the application through those experiments, with the expectation that the results would be interpreted positively. These initial findings highlight the promise of the suggested method in improving AR applications, especially within the construction sector, by enabling more natural and precise merging of virtual and physical objects, without requiring user intervention.

• Computers and Concrete
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• v.18 no.3
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• pp.337-354
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• 2016

To study the influence on fracture properties of reinforced concrete wedge splitting test specimens by the addition of reinforcement, and the restriction of steel bars on crack propagation, 7 groups reinforced concrete specimens of different reinforcement position and 1 group plain concrete specimens with the same size factors were designed and constructed for the tests. Based on the double-K fracture criterion and tests, fracture toughness calculation model which was suitable for reinforced concrete wedge splitting tensile specimens has been obtained. The results show that: the value of initial craking load Pini and unstable fracture load Pun decreases gradually with the distance of reinforcement away from specimens's top. Compared with plain concrete specimens, addition of steel bar can reduce the value of initial fracture toughness KIini, but significantly increase the value of the critical effective crack length ac and unstable fracture toughness KIun. For tensional concrete member, the effect of anti-cracking by reinforcement was mainly acted after cracking, the best function of preventing fracture initiation was when the steel bar was placed in the middle of the crack, and when the reinforcement was across the crack and located away from crack tip, it plays the best role in inhibiting the extension of crack.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of the Korean Geosynthetics Society
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• v.10 no.2
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• pp.45-53
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• 2011

Recently reinforced retaining walls secure their position as a stabilized method of construction replacing concrete retaining walls gradually. However, in the event of using extensible reinforcement, a bulging phenomenon can be happened in the front of reinforced retaining walls. Bulging of reinforced retaining walls means a phenomenon that, in the height of an arbitrary block, the upper part and the lower part of the block don't secure a relative position in design. Therefore, it is judged that it has the necessity to be examined in design since reinforcement needs metamorphosis to some degree to display tensile force. Therefore, the study examined about how extensibility of reinforcement had an effect on movement of reinforced retaining walls through a small-scale model test with aluminum rods. The study used Changhoji(traditional korean paper made from mulberry bark) as inextensible reinforcement and membrane as extensible reinforcement. As the result of the test, rigidity of reinforcement had a lot of effects on displacement of reinforced retaining walls and generally occurrence point of the maximum horizontal displacement had a tendency transferring to the upper part of walls according to rigidity of reinforcement was increased.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.863-866
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• 2008

One of the most important requirements for reinforced concrete constructions is the bond behavior between concrete and reinforcement. In this study, the positions (i.e., vertical, horizontal) and the locations (i.e., 225mm and 75mm) of reinforcement were considered as a main test parameter. The ready mixed recycled aggregate concrete concrete with specified strength of 21MPa was prepared with different replacement ratio(i.e 0%, 100%) of recycled coarse aggregate. From the test results, it was bond that under the same mix proportion (i.e., the mix proportions are the same, except for deformed bars position), the bond strength between the recycled coarse aggregate concrete and the reinforcement has obvious relation with reinforcement position. Also, the specimens of top position showed a lower bond stress than that provided in CEB-FIP Code.