• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Earthquakes and Structures
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• v.26 no.1
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• pp.59-75
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• 2024

This paper presents a dam-reservoir interaction model that includes, water compressibility, sloshing of surface water, and radiation damping at the far-end reservoir, to investigate the influence of concrete deterioration on seismic behavior along with seismic performance of gravity dams. Investigations on seismic performance of the dam body have been conducted using the linear time-history responses obtained under six real and 0.3 g normalized earthquake records with time durations from 10 sec to 80 sec. The deterioration of concrete is assumed to develop due to mechanical and chemical actions over the dam lifespan. Several computer programs have been developed in FORTRAN 90 and MATLAB programming languages to analyze the coupled problem considering two-dimensional (2D) plane-strain condition. According to the results obtained from this study, the dam structure shows critical responses at the later ages (75 years) that could cause disastrous consequences; the critical effects of some earthquake loads such as Chi-Chi with 36.5% damage and Loma with 56.2% damage at the later ages of the selected dam body cannot be negligible; and therefore, the deterioration of concrete along with its effects on the dam response should be considered in analysis and design.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.17-52
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• 2024

In this paper, the authors explore the modeling and control of a point absorber wave energy converter, which is connected to the electric grid via a power converter that is based on a linear permanent magnet synchronous generator (LPMSG). The device utilizes a buoyant mechanism to convert the energy of ocean waves into electrical power, and the LPMSG-based power converter is utilized to change the variable frequency and voltage output from the wave energy converter to a fixed frequency and voltage suitable for the electric grid. The article concentrates on the creation of a predictive control system that regulates the speed, voltage, and current of the LPMSG, and the modeling of the system to simulate its behavior and optimize its design. The predictive model control is created to guarantee maximum energy output and stable grid connection, using Matlab Simulink to validate the proposed strategy, including control side generator and predictive current grid-side converter loops.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.51-59
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• 2024

This study presents a method for health monitoring of rotating objects for mobility based on multiple recursive least squares(RLS) algorithms. The performance degradation of the rotating objects causes low handing / low driving performances and even fatal accidents. Therefore, health monitoring algorithm of rotating objects is one of the important technologies for mobility fail-safe and maintenance areas. In order for health monitoring of rotating objects, four recursive least squares algorithms with forgetting factor were designed in this study. The health monitoring algorithm proposed in this study consists of two steps such as uncertainty estimation and parameter changes estimation. In order to improve estimation accuracy, time delay function was applied to the estimated signals based on the first order differential equation and forgetting factors used for the RLS were reasonably tuned. The health monitoring algorithm was constructed in Matlab/Simulink environment and simulation-based performance evaluation was conducted using DC motor model. The evaluation results showed that the proposed algorithm estimates the actual parameter differences reasonably using velocity and current information.

• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.341-349
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• 2002

The target controlled infusion(TCI) pump system is a logical approach to the development of improved administration techniques of an intravenous anaesthetic agent. The principle of TCI system is based on an understanding of the pharmacokinetic properties, three or four compartment model. The TCI system is optimal and flexible control of the plasma drug concentration. But the clinical goal is always to achieve a therapeutic drug effect, not a therapeutic concentration. So we developed the algorithm to target the concentration at the site of drug effect rather than the concentration in the plasma. If impulse drug is inputted into body, the decline of plasma concentration with time is shown, resulting in the expression of the differential equation. Therefore, we must reformulate our three-compartment model as four-compartment model with the effect compartment. And we tested plasma targeting and effect targeting algorithm by computer simulation using four-compartment model. So we developed the TCI capable of applying all intravenous drugs by adjusting individual pharmacokinetic parameters independently.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.7-14
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• 2015

To implement an automatic picking system (APS) in distribution center with high precision and high dynamics, this paper presents a high gain observer-based robust speed controller design for a linear induction motor (LIM) drive. The force disturbance as well as the mechanical parameter variations such as the mass and friction coefficient gives a direct influence on the speed control performance of APS. To guarantee a robust control performance, the system uncertainty caused by the force disturbance and mechanical parameter variations is estimated through a high gain disturbance observer and compensated by a feedforward manner. While a time-varying disturbance due to the mass variation can not be effectively compensated by using the conventional disturbance observer, the proposed scheme shows a robust performance in the presence of such uncertainty. A Simulink library has been developed for the LIM model from the state equation. Through comparative simulations based on Matlab - Simulink, it is proved that the proposed scheme has a robust control nature and is most suitable for APS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.884-891
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• 2020

The collapse of bridges can cause massive casualties and economic losses. Therefore, it is thus essential to evaluate the structural safety of bridges. For this task, structural reliability analysis, considering various bridge-related uncertainty factors, is often used. This paper proposes a new computational platform to perform structural reliability analysis for bridges and evaluate their structural safety under various loading conditions. For this purpose, a software package of reliability analysis, Finite Element Reliability Using MATLAB (FERUM), was integrated with MIDAS/CIVIL, which is a widely-used commercial software package specialized for bridges. Furthermore, a graphical user interface (GUI) control module has been added to FERUM to overcome the limitations of software operation. In this study, the proposed platform was applied to a simple frame structure, and the analysis results of the FORM (First-Order Reliability Method) and MCS (Monte Carlo simulation), which are representative reliability analysis methods, were compared. The proposed platform was verified by confirming that the calculated failure probability difference was less than 5%. In addition, the structural safety of a pre-stressed concrete (PSC) bridge was evaluated considering the KL-510 vehicle model. The proposed new structural reliability analysis platform is expected to enable an effective reliability-based safety evaluation of bridges.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.660-665
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• 2018

Research into underwater wireless networks that enable the monitoring and controlling of the ocean environments has been continuing for disaster prevention and military proposes, as well as for the exploitation of ocean resources throughout the world. A research group led by Hoseo university has been studying a distributed underwater monitoring and controlling network. In this study, we developed an interpolator for acoustic communication between an underwater base station controller and underwater base station, which is included in this network. The underwater acoustic communication provided by this network defines four links whose sampling rates are different. Low power consumption is one of the most important requirements. Therefore, we adopted CIC interpolators, which are known to act as filters with a low power consumption, and some CIC interpolators with an appropriate changing rate were selected depending on the link. However, these interpolators have a large passband drop and wide transition region. To solve these problems, we added a compensator and half-band filter. After verifying the algorithm by using Matlab, we designed and verified it with Verilog-HDL in a ModelSim environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.227-230
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• 2007

If we use the microwave of SAR, we can observe on the ocean in spite of bad weather, day and night time. Sea surface images on the ocean of SAR have a lot of information on the atmospheric phenomena related to surface wind vector. Information of wind speed which is extracted from SAR images is used variously. Wind direction data and sigma nought value are put in the CMOD which can extract wind information in order to estimate sea surface wind from SAR images. Wind spectrum which is extracted from SAR always presents opposed two points of $180^{\circ}$ because of applying to 2D-FFT. These ambiguities should be decided by position of land, wind direction or numerical model. Previously, we converted into sigma nought after extracting Digital Number from RadarSat-1 SAR using ENVI4.0, thus, it took a long time because every process was manual. Therefore, we converted sigma nought by matlab code after making matlab code. After that, we are extracting wind direction from sigma nought. Now, to decide wind direction needs further study because wind direction has $180^{\circ}$ ambiguity.