• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.207-211
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• 2015

In this paper, 2 kinds of models about bike frame are simulated with static structural analysis. A bike frame with diamond type is compared with another model that Down tube is eliminated from original diamond frame. About both types of models, Property of a material and conditions of restriction & load are the same. This study shows reinforcement effects of a partial frame by adding down tube and impacts generated by applying a load at the frame such as weak points & high stress parts as well as expected deformation. The structural result of this study indicates that the equivalent stress or total deformation decreases by 57.1% or 36.4%, respectively. Also stress concentration sites are leg connecting parts, front/rear wheels fixed region and Max deformation is generated from Seat tube. In conclusion, A Down tube is highly efficient as reinforcement than frame without non down tube. Furthermore, The safety rises in case of reducing top tube thickness and increasing a reinforcement at leg connecting parts or concentration regions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.3
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• pp.188-193
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• 1998

An experimental study was performed on the pull-out behavior of 90-deg standard hooks from exterior beam-column connections. The effects of lateral confinement and fiber reinforcement of joint area were investigated. It was concluded ; (1) Substitution of the transverse column (confining) reinforcement with steel fibers at the joint region effectively reduces the extent of cracking in exterior joints caused by pull-out of hooked bars; and (2) The strength and ductility of hooked bars under pull-out forces are positively influenced by substituting the conventional confining reinforcement of exterior joints with steel fibers. Application of steel fibers to exterior joints seems to be an effective technique for improving the anchorage conditions of hooked bars, and also for reducing the congestion of reinforcement in exterior beam-column connections.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.285-294
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• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• Advances in nano research
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• v.14 no.5
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• pp.421-433
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• 2023

In the present paper, the influences of the variation of exponent of volume fraction of carbon nanotubes (CNTs) on the natural frequencies (NFs) of the carbon nanotube-reinforced composite (CNTRC) beams under four different boundary conditions (BCs) are investigated. The single-walled carbon nanotubes (SWCNTs) are assumed to be aligned and dispersed in a polymeric matrix with various reinforcing patterns, according to the variation of exponent of volume fraction of CNTs for functionally graded (FG) reinforcements. Besides, uniform distribution (UD) of reinforcement is also considered to analyze the influence of the non-linear (NL) variation of the reinforcement of CNTs. Using Hamilton's principle and third-order shear deformation theory (TSDT), the equations of motion of the CNTRC beam are derived. Under four different BCs, the resulting equations are solved analytically. To verify the present formulation, comparison investigations are conducted. To examine the impacts of several factors on the NFs of the CNTRC beams, numerical examples and some benchmark results are presented.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3515-3524
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• 2023

A reinforcement learning framework is proposed for the control problem of outlet steam pressure of the once-through steam generator(OTSG) in this paper. The double-layer controller using Proximal Policy Optimization(PPO) algorithm is applied in the control structure of the OTSG. The PPO algorithm can train the neural networks continuously according to the process of interaction with the environment and then the trained controller can realize better control for the OTSG. Meanwhile, reinforcement learning has the characteristic of difficult application in real-world objects, this paper proposes an innovative pretraining method to solve this problem. The difficulty in the application of reinforcement learning lies in training. The optimal strategy of each step is summed up through trial and error, and the training cost is very high. In this paper, the LSTM model is adopted as the training environment for pretraining, which saves training time and improves efficiency. The experimental results show that this method can realize the self-adjustment of control parameters under various working conditions, and the control effect has the advantages of small overshoot, fast stabilization speed, and strong adaptive ability.

• Geomechanics and Engineering
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• v.20 no.1
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• pp.67-73
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• 2020

With the increasing number of underground projects, the problem of rock-water coupling catastrophe has increasingly become the focus of safety. Grouting reinforcement is gradually applied in subway, tunnel, bridge reinforcement, coal mine floor and other construction projects. At present, cement-based grouting materials are easy to shrink and have low strength after solidification. In order to overcome the special problems of high water pressure and high in-situ stress in deep part and improve the reinforcement effect. In view of the mining conditions of deep surrounding rock, a new type of cement-based reinforcement material was developed. We analyses the principle and main indexes of floor strengthening, and tests and optimizes the indexes and proportions of the two materials through laboratory tests. Then, observes and compares the microstructures of the optimized floor strengthening materials with those of the traditional strengthening materials through scanning electron microscopy. The test results show that 42.5 Portland cement-based grouting reinforcement material has the advantages of slight expansion, anti-dry-shrinkage, high compressive strength and high density when the water-cement ratio is 0.4, the content of bentonite is 4%, and the content of Nano Silica is 2.5%. The reinforcement effect is better than other traditional grouting reinforcement materials.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1118-1125
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• 2009

This study, as basic research which was intended to develope the surface reinforcement method using reinforcement material which is applicable to very soft ground in Korea, was aimed at proposing Bearing Capacity Evaluation method for the surface ground improvement method. To that end, a wide width tensile test using geotextile, geogrid and steel bar (substitute for bamboo) and 21 kinds of the laboratory model tests with the end restraint conditions of the reinforcement that comprises the constrained and partially constrained (3 types) conditions were conducted. According to result of tests, Terzaghi's bearing capacity method is adequate to calculate bearing capacity in non-stiff material(geotextile, geogrid). But, It can't adequate to stiff material(bamboo net). So, New bearing capacity method suggest surface reinforcement method of very soft ground which Terzaghi's bearing capacity method modify for effect of stiffness.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Textile Coloration and Finishing
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• v.34 no.2
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• pp.109-116
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• 2022

Tire codes are made of materials such as hemp, cotton, rayon, nylon, steel, polyester, glass, and aramid are fiber reinforcement materials that go inside rubber to increase durability, driveability, and stability of vehicle tires. The reinforcement of the tire cord may construct a composite material using tires such as automobiles, trucks, aircraft, bicycles, and fibrous materials such as electric belts and hoses as reinforcement materials. Therefore, it is essential to ensure that the adhesive force between the rubber and the reinforced fiber exhibits the desired physical properties in the rubber composite material made of a rubber matrix with reinforced fibers. This study is a study on the heat treatment conditions for improving the adhesion strength of the tire cord and the reinforced fiber for tires. The core technology of the drying process is a uniform drying technology, which has a great influence on the quality of the reinforcement. Therefore, the uniform airflow distribution is determined by the geometry and operating conditions of the dryer. Therefore, this study carried out a numerical analysis of the shape of a drying nozzle for improving the performance of hot air drying in a dryer used for drying the coated reinforced fibers. In addition, the flow characteristics were examined through numerical analysis of the study on the change in the shape of the chamber affecting drying.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.423-434
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• 2014

This study examined the relative effects of positive and negative reinforcement on customer service behaviors. In addition, we examined whether the intervention would lead to response generalization on non-target behaviors. Five employees at three different convenient stores participated and ABC/ACB within-subject design was adopted. For the three participants, followed by the baseline(A), the positive reinforcement was first introduced(B) and the negative reinforcement(C) was introduced for the next phase. For the remaining two participants, the negative reinforcement(C) was first introduced after baseline(A) and the positive reinforcement (B) was introduced. Results showed a greater improvement in target behaviors under the positive reinforcement condition than that of the negative reinforcement condition. In addition, both reinforcement condition cause response generalization on non-targeted service behaviors, however, the comparable effects was found between two reinforcement conditions. Post-interview indicated that participants experienced positive emotions under positive reinforcement condition and negative emotions under negative reinforcement condition. These results suggest that the he techniques using positive reinforcement can be more effective and efficient to improve the work performance.