• Journal of Ocean Engineering and Technology
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• v.38 no.2
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• pp.53-63
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• 2024

Submerged breakwaters can be installed underneath floating structures to reduce the external wave loads acting on the structure. The objective of this study was to establish an optimization analysis framework to determine the corresponding shape and position of the submerged breakwater that can minimize or maximize the external forces acting on the floating structure. A two-dimensional frequency-domain boundary element method (FD-BEM) based on the linear potential theory was developed to perform the hydrodynamic analysis. A metaheuristic algorithm, the advanced particle swarm optimization, was newly coupled to the FD-BEM to perform the optimization analysis. The optimization analysis process was performed by calling FD-BEM for each generation, performing a numerical analysis of the design variables of each particle, and updating the design variables using the collected results. The results of the optimization analysis showed that the height of the submerged breakwater has a significant effect on the surface piercing body and that there is a specific area and position with an optimal value. In this study, the optimal values of the shape and position of a single submerged breakwater were determined and analyzed so that the external force acting on a surface piercing body was minimum or maximum.

• Earthquakes and Structures
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• v.26 no.5
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• pp.327-336
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• 2024

Evaluation of tunnel performance in seismic-prone areas demands efficient means of estimating performance at different hazard levels. The present study introduces an innovative push-over analysis approach which employs the standard earthquake spectrum to simulate the performance of a tunnel. The numerical simulation has taken into account the lining and surrounding rock to calculate the rock-tunnel interaction subjected to a static push-over displacement regime. Elastic perfectly plastic models for the lining and hardening strain rock medium were used to portray the development of plastic hinges, nonlinear deformation, and performance of the tunnel structure. Separately using a computational algorithm, the non-linear response spectrum was approximated from the average shear strain of the rock model. A NATM tunnel in Turkey was chosen for parametric study. A seismic performance curve and two performance thresholds are introduced that are based on the proposed nonlinear seismic static loading approach and the formation of plastic hinges. The tunnel model was also subjected to a harmonic excitation with a smooth response spectrum and different amplitudes in the fully-dynamic phase to assess the accuracy of the approach. The parametric study investigated the effects of the lining stiffness and capacity and soil stiffness on the seismic performance of the tunnel.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.83-90
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• 2024

CNC tools contribute to the production of high-precision and consistent results. However, employing damaged CNC tools or utilizing compromised numerical control can lead to significant issues, including equipment damage, overheating, and system-wide errors. Typically, the assessment of external damage to CNC tools involves capturing a single viewpoint through a camera to evaluate tool wear. This study aims to enhance existing methods by using only a single manually focused Microscope camera to enable comprehensive external analysis from multiple perspectives. Applying the NeRF (Neural Radiance Fields) algorithm to images captured with a single manual focus microscope camera, we construct a 3D rendering system. Through this system, it is possible to generate scenes of areas that cannot be captured even with a fixed camera setup, thereby assisting in the analysis of exterior features. However, the NeRF model requires considerable training time, ranging from several hours to over two days. To overcome these limitations of NeRF, various subsequent models have been developed. Therefore, this study aims to compare and apply the performance of Instant NGP, Mip-NeRF, and DS-NeRF, which have garnered attention following NeRF.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Nonlinear Functional Analysis and Applications
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• v.29 no.1
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• pp.131-164
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• 2024

In this research, we study a modified relaxed Tseng method with a single projection approach for solving common solution to a fixed point problem involving finite family of τ-demimetric operators and a quasi-monotone variational inequalities in real Hilbert spaces with alternating inertial extrapolation steps and adaptive non-monotonic step sizes. Under some appropriate conditions that are imposed on the parameters, the weak and linear convergence results of the proposed iterative scheme are established. Furthermore, we present some numerical examples and application of our proposed methods in comparison with other existing iterative methods. In order to show the practical applicability of our method to real word problems, we show that our algorithm has better restoration efficiency than many well known methods in image restoration problem. Our proposed iterative method generalizes and extends many existing methods in the literature.

• Computers and Concrete
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• v.33 no.6
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• pp.671-688
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• 2024

The modeling efficiency of concrete meso-models close to real concrete is one of the important issues that limit the accuracy of mechanical simulation. In order to improve the modeling efficiency and the closeness of the numerical aggregate shape to the real aggregate, this paper proposes a method for generating a two-dimensional concrete meso-model based on pixel matrix and skeleton theory. First, initial concrete model (a container for placing aggregate) is generated using pixel matrix. Then, the skeleton curve of the residual space that is the model after excluding the existing aggregate is obtained using a thinning algorithm. Finally, the final model is obtained by placing the aggregate according to the curve branching points. Compared with the traditional Monte Carlo placement method, the proposed method greatly reduces the number of overlaps between aggregates by up to 95%, and the placement efficiency does not significantly decrease with increasing aggregate content. The model developed is close to the actual concrete experiments in terms of aggregate gradation, aspect ratio, asymmetry, concavity and convexity, and old-new mortar ratio, cracking form, and stress-strain curve. In addition, the cracking loss process of concrete under uniaxial compression was explained at the mesoscale.

• Advances in aircraft and spacecraft science
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• v.11 no.2
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• pp.195-213
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• 2024

This article deals with the changes in flow air properties across an oblique shock wave for a real gas. The flow through is investigated to find a general form for oblique shock waves. The main objective of this work will result in the development of a new numerical algorithm to determine the effect of the stagnation pressure on supersonic flow for thermally and calorically imperfect gases with a molecular dissociation threshold, thus giving a better affinity to the physical behavior of the waves. So, the effects of molecular size and intermolecular attraction forces are used to correct a state equation, emphasizing the determination of the impact of upstream stagnation parameters on oblique shock waves. As results, the specific heat pressure does not remain constant and varies with the temperature and density. At Mach numbers greater than 2.0, the temperature rise considerably, and the density rise is well above, that predicted assuming ideal gas behavior. It is shown that caloric imperfections in air have an appreciable effect on the parameters developed in the processes is considered. Computation of errors between the present model based on real gas theory and a perfect gas model shows that the influence of the thermal and caloric imperfections associated with a real gas is important and can rise up to 16%.

• Journal of applied mathematics & informatics
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• v.42 no.5
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• pp.1195-1210
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• 2024

This research aims to determine an optimal (best) solution for transporting the logistics at a minimum cost from various sources to various destinations. We proposed a new algorithm for the initial basic feasible solution (IBFS). Developing a new IBFS is the first step towards finding the optimal solution. A new approach for the initial basic feasible solution that reduces iterations and produces the best answer in the initial process of the transportation issue. Different IBFS approaches have been generated in the literature review. Some statistical fundamentals, such as normal distribution and the root mean square technique, are employed to find new IBFS. A TP is transformed into a normal distribution, and penalties are determined using the root mean square method. Excel Solver is used to calculate normal distribution values. The second step involves using a stepping-stone approach to compute the optimum solution. The results of our study were calculated using numerical examples and contrasted with a few other methodologies, such as Vogel's approximation, the Continuous Allocation Method (CAM), the Supply Demand Repair Method (SDRM), and the Karagul-Sahin Approximation Method (KSAM). The conclusion of our proposed method gives more accurate results than the existing approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2546-2563
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• 2024

The Internet of Things (IoT) is facing growing security challenges due to its vulnerability. It is imperative to address the security issues using lightweight and efficient encryption schemes in resource-limited IoT. In this paper, we propose an enhanced message authentication encryption (MAE) scheme based on physical-layer key generation (PKG), which uses the random nature of wireless channels to generate and negotiate keys, and simultaneously encrypts the messages and authenticates the source. The proposed enhanced MAE scheme can greatly improve the security performance via dynamic keyed primitives construction while consuming very few resources. The enhanced MAE scheme is an efficient and lightweight secure communication solution, which is very suitable for resource-limited IoT. Theoretical analysis and simulations are carried out to confirm the security of the enhanced MAE scheme and evaluate its performance. A one-bit flipping in the session key or plain texts will result in a 50%-bit change in the ciphertext or message authentication code. The numerical results demonstrate the good performance of the proposed scheme in terms of diffusion and confusion. With respect to the typical advanced encryption standard (AES)-based scheme, the performance of the proposed scheme improves by 80.5% in terms of algorithm execution efficiency.

• Journal of the Korean Society of Visualization
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• v.22 no.2
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• pp.44-54
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• 2024

This study investigates the effectiveness of using a plasma actuator for active control of turbulent flow around a finite square cylinder. The primary objective is to analyze the impact of plasma actuators on flow separation and wake region characteristics, which are critical for reducing drag and suppressing vortex-induced vibrations. Direct Numerical Simulation (DNS) was employed to explore the flow dynamics at various operational parameters, including different actuation frequencies and voltages. The proposed methodology employs a neural network trained using the Proximal Policy Optimization (PPO) algorithm to determine optimal control policies for plasma actuators. This network is integrated with a computational fluid dynamics (CFD) solver for real-time control. Results indicate that this deep reinforcement learning (DRL)-based strategy outperforms existing methods in controlling flow, demonstrating robustness and adaptability across various flow conditions, which highlights its potential for practical applications.