• 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.

• Computers and Concrete
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• v.22 no.5
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• pp.493-500
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• 2018

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

• Journal of Ocean Engineering and Technology
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• v.33 no.2
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• pp.146-152
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• 2019

A design system was developed for the doubler plate of a ship structure simultaneously subjected to in-plane loads and lateral pressure based on general dimensions and those of a representative ship structure. An equivalent design equation that considers various structural design parameters was derived by introducing the equivalent plate thickness theory, and the design of the doubler plate reinforcement of the ship structure was developed. A hybrid structural design system was established for a doubler plate simultaneously subjected to in-plane loads and lateral pressure consisting of two modules: an optimized design module and a double plate strength & design review module. The practical application of this design system was illustrated to show its usability. It was found that the design safety of the doubler plate was ensured, and this system could be used as an initial design guide to review the double plate reinforcement for a dent or corrosion of the ship plate members. Using the developed design system would make it possible to obtain a more reasonable doubler plate structure that considers the rational reinforcement of plate members of ship structures. In addition, a more reliable structural analysis using a strength evaluation process can be performed to verify the efficiency of the optimum structural design for the doubler plate structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• Geomechanics and Engineering
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• v.32 no.6
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• pp.653-671
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• 2023

In practice, the interference influence caused by adjacent footings of structures on geo-reinforced loose soil has a considerable impact on their behavior. Thus, the goal of this study is to evaluate the behavior of two strip footings in close proximity on both geocell and geogrid reinforced soil with different reinforcement layers. Geocell was made from geogrid material used to compare the performance of cellular and planar reinforcement on the bearing pressure of twin footings. Extensive experimental tests have been performed to attain the optimum embedment depth and vertical distance between reinforcement layers. Particle image velocimetry (PIV) analysis has been conducted to monitor the deformation, tilting and movement of soil particles beneath and between twin footings. Results of tests and PIV technique were verified using finite element modeling (FEM) and the results of both PIV and FEM were used to utilize failure mechanisms and influenced shear strain around the loading region. The results show that the performance of twin footings on geocell-reinforced sand at allowable and ultimate settlement ranges are almost 4% and 25% greater than the same twin footings on the same geogrid-reinforced sand, respectively. By increasing the distance between twin footings, soil particle displacements become smaller than the settlement of the foundations.

• Journal of the Korean Geotechnical Society
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• v.25 no.12
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• pp.131-142
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• 2009

Geosynthetic reinforced soil walls are well recognized alternatives to conventional retaining walls due to many advantages in terms of ease of construction, economy, and aesthetics, among others. In recent years, the use of back-to-back (BTB) geosynthetic reinforced soil walls has been increasing for roadway and railway construction. However, there are insufficient studies concerning the behavior of BTB type geosynthetic reinforced soil walls. In this study a series of finite element analysis were performed for BTB walls with various wall geometry and reinforcement distribution. The results were then analyzed to relate the wall geometry and reinforcement distribution and the performance of BTB walls. Optimum reinforcement pattern was also investigated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.569-578
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• 1998

The mode I and II stress intensity factors have been calculated theoretically for the cracked plate reinforced with a plate by symmetric spot welding under remote mixed mode loading. This is the extension of authors' previous work for the reinforced cracked plate under remote normal stress. Regardless of loading types, the reinforcement effect gets better as one joining spot is closer to the crack tip and the others are closer to the crack surface, and optimum number of the joining spots can be existed. For the present model, the remote loading parallel to crack surface produces the mode I stress intensity factor.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.106-112
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• 2004

In particle or short-fiber reinforced composites, cracking or debonding of the reinforcements cause a significant damage mode because the damaged reinforcements lose load carrying capacity. The average stress in the inhomogeneity represents its load carrying capacity, and the difference between the average stresses of the intact and broken inhomogeneities indicates the loss of load carrying capacity due to cracking damage. The composite in damage process contains intact and broken reinforcements in a matrix. An incremental constitutive relation of discontinuously-reinforced composites including the progressive cracking damage of the reinforcements have been developed based on the Eshelby's equivalent inclusion method and Mori-Tanaka's mean field concept. Influence of the cracking damage on the stress-strain response of the composites is demonstrated.

• Steel and Composite Structures
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• v.15 no.4
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• pp.451-466
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• 2013

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.189-196
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• 2004

In this study, the colloidal silica grouts (PSG) with novel chemical compositions for permanent reinforcement and water cut-off of the ground were prepared and their engineering charateristics were investigated. The optimum mixing recipes for both homogeneous solution grouts and heterogeneous suspension grouts were investigated and established through many repeated lab tests. The various physical properties(such as compressive strength, durability and syneresis) of the grout gels derived from the colloidal silica were investigated and compared with those of the well-known existing watergalss grouts. The all experimental results showed that the novel colloidal silica grouts(PSG) had greatly excellent performances as permanent grouts, especially in comparison with the existing watergalss grouts.