• Computers and Concrete
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• v.12 no.4
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• pp.443-498
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• 2013

A detailed finite element modeling is presented for the simulation of the nonlinear behavior of reinforced concrete structures which manages to predict the nonlinear behavior of four different experimental setups with computational efficiency, robustness and accuracy. The proposed modeling method uses 8-node hexahedral isoparametric elements for the discretization of concrete. Steel rebars may have any orientation inside the solid concrete elements allowing the simulation of longitudinal as well as transverse reinforcement. Concrete cracking is treated with the smeared crack approach, while steel reinforcement is modeled with the natural beam-column flexibility-based element that takes into consideration shear and bending stiffness. The performance of the proposed modeling is demonstrated by comparing the numerical predictions with existing experimental and numerical results in the literature as well as with those of a commercial code. The results show that the proposed refined simulation predicts accurately the nonlinear inelastic behavior of reinforced concrete structures achieving numerical robustness and computational efficiency.

• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Journal of the Korean Society for Railway
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• v.3 no.2
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• pp.68-76
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• 2000

Such traditional construction methods as the interchange or improvement of railway subbase maintenance and reinforcement have many problems in the matters of construction period, constructive and economical efficiency aspects. This paper will describe the alternative railway subbase reinforcement method based on geosynthetics system. It is presently very popular in Germany and Japan. In summary it is to say, that geosynthetics used at fine grained subsoils to a strong improvement of the whole railroad system.

• Geomechanics and Engineering
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• v.11 no.6
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• pp.787-803
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• 2016

DDARF (Discontinuous Deformation Analysis for Rock Failure) is a numerical algorithm for simulating jointed rock masses' discontinuous deformation. While its reinforcement simulation is only limited to end-anchorage bolt, which is assumed to be a linear spring simply. Here, several new reinforcement modes in DDARF are proposed, including lining reinforcement, full-length anchorage bolt and equivalent reinforcement. In the numerical simulation, lining part is assigned higher mechanical strength than surrounding rock masses, it may include multiple virtual joints or not, depending on projects. There must be no embedding or stretching between lining blocks and surrounding blocks. To realize simulation of the full-length anchorage bolt, at every discontinuity passed through the bolt, a set of normal and tangential spring needs to be added along the bolt's axial and tangential direction. Thus, bolt's axial force, shearing force and full-length anchorage effect are all realized synchronously. And, failure criterions of anchorage effect are established for different failure modes. In the meantime, from the perspective of improving surrounding rock masses' overall strength, a new equivalent and tentative simulation method is proposed, it can save calculation storage and improve efficiency. Along the text, simulation algorithms and applications of these new reinforcement modes in DDARF are given.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.33-36
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• 2021

In this paper, we propose a new method for improving compression efficiency of immersive video using reinforcement learning. Immersive video means a video that a user can directly experience, such as 3DOF+ videos and Point Cloud videos. It has a vast amount of information due to their characteristics. Therefore, lots of compression methods for immersive video are being studied, and generally, a method, which projects an 3D image into 2D image, is used. However, in this process, a region where information does not exist is created, and it can decrease the compression efficiency. To solve this problem, we propose the reinforcement learning-based filling method with considering the characteristics of images. Experimental results show that the performance is better than the conventional padding method.

• Journal of Industrial Technology
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• v.41 no.1
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• pp.7-13
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• 2021

Fiber-reinforced polymer reinforcement or polyurea reinforcement techniques are applied to strengthen unreinforced masonry walls (UMWs). The out-of-plane reinforcing effect of sprayed glass fiber-reinforced polyurea (GFRPU), which is a composite elastomer made of polyurea and milled glass fibers on UMW, is experimentally verified. The out-of-plane strengths and ductile behaviors based on various coating shapes are compared in this study. An empirical formula to describe the degree of reinforcement on the out-of-plane strength of the UMW is derived based on the experimental results. It is reported that the peak load-carrying capacity, ductility, and energy absorption capacity gradually improve with an increase in the strengthening degree or area. Compared with the existing masonry wall reinforcement method, the GFRPU technique is a construction method that can help improve the safety performance along with ease of construction and economic efficiency.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.4
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• pp.323-336
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• 2003

Recently, the development of tunnel reinforcement method has been required relating to the shallow tunnelling in soft ground. In this study, the improvement method on tunnel stability is proposed by evaluating the efficiency of face reinforcement which enables to control extrusion of advance core, however, it is often neglected in urban tunnelling under the poor ground conditions. Systematic pre-confinement ahead of the face improves the tunnel stability, subsequently, displacement of the crown and surface settlement can be restrained by proper method. 3-dimensional numerical analysis including horizontal reinforcement modelling on a face is applied to estimate the behaviour of a tunnel in relation to the ground and reinforcement conditions. Consequently, extrusion at the face decreases significantly after using the horizontal reinforcement and the effect of reinforcement is much increased in case of applying the supplemental reinforcement ahead of the face together. Especially, confinement effect around the tunnel and the core is proved by means of the core reinforcement in poor ground conditions.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.835-840
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• 2002

The purpose of this study is to analyze the reinforcement effect of the RC compressive member confined with carbon fiber sheets and to suggest better transverse confinement coefficient(k$_1$) than one's in the existing analysis equations. Showing amounts of CPS in terms of ratio of transverse reinforcement to cross-section, it comes to be possible to calculate the objective and quantitative reinforcement amounts and to estimate the overlapping length of CFS that can influence on all its confinement effect. The previous parameters were compared using the existing experimental test data, then analyzed for the merits and demerits of existing parameters through the coefficient of correlation(R). The proposed parameters were derived in such a way that established parameters and their combination were obtained from the analytical study and then determined by regression analysis using the previous test data.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.121-128
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• 2001

This is a case study of stability analysis and reinforcement design for the tunnel where the collapse of the entrance slops occured along the fault zone developed in the bed rock. According to the site investigation, the main factor of sliding is the influence of fault gouge and heavy rainfall. Considering the in-situ condition, the versatile reinforcement methods is needed, and so the close investigation on the site area was accompanied with the stability analysis of tunnel and slops. The FRP(Fiberglass reinforced plastic) grouting method improved the defect of Steel Umbrella Arch Method, such as oxidation, low work efficiency, the material's heavy weight, is adapted as the reinforcement methods.