• Smart Structures and Systems
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• v.26 no.6
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Smart Media Journal
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• v.12 no.3
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• pp.77-84
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• 2023

Forest fires are representative natural disasters resulting from dramatic global climate change in these modern times. When forest formation is insufficient due to forest damage caused by fire, secondary damages such as landslides occur during the winter thawing period and heavy rains. In most countries, only a limited area is managed as CCTV-centered monitoring systems for forest management. For the landslide prediction, markers containing 3D spatial coordinates were located on the slopes of the danger areas in advance. Then 3D mapping and angle of repose were obtained by periodic drone imaging. The recognition range and angle of view of markers were defined, and a new method for predicting signs of landslides in advance was presented in this study.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.246-253
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• 2000

The optimal equations to predict the soil tempratures of twelve cities in the region of the southern part of the Korea such as Changhung, Cheju, Chinju, Kwangju, Masan, Miryang, Mokpo, Muan, Pusan, Sogwipo, Ulsan, Yoosu, were suggested as function of time and soil depth and the time dependent variation and soil depth dependent distribution of temperature were analyzed for the back data of the geothermal energy utilization system design and agricultural usages. The equation form is $T(x,\;t)\;=\;T_{m}\;-\;T_{so}{\cdot}Exp(-\xi){\cdot}cos{\omega}(t\;-\;t_{o}\;-\;x\;/\sqrt{2{\alpha}{\omega}}$) and it can predict the soil temperatures well with the correlation factor of 0.98 or upwards for most data. The range of mean soil temperature was $14.99~18.53^{\circ}C$ and soil surface temperature swing, 11.65~14.54 days, soil thermal diffusivity, $0.025~0.069\;m^2/day$ except Mokpo of $0.100\;m^2/day$, and phase shift, 19.66~27.81 days. During about thirty years from 1960s to 1990s, the mean soil temperature was increased by $0.04~1.25^{\circ}C$. The temperature difference depending on soil depth was not significant.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Wind and Structures
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• v.38 no.6
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• pp.445-459
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• 2024

To model the aeroelasticity in vortex-induced vibrations (VIV) of slender tubular towers, this paper presents an approach where the aerodynamic damping distribution along the height of the structure is calculated not only as a function of the normalized lateral oscillation but also considering the local incoming wind velocity ratio to the critical velocity (velocity ratio). The three-dimensionality of aerodynamic damping depending on the tower's displacement and the velocity ratio has been observed in recent studies. A contour map model of aerodynamic damping is generated based on the forced vibration tests. A sectional calculation procedure based on the spectral method is developed by defining the aerodynamic damping locally at each increment of height. The proposed contour map model of aerodynamic damping and the sectional calculation procedure are validated with full-scale measurement data sets of a rotorless wind turbine tower, where good agreement between the prediction and measured values is obtained. The prediction of cross-wind response of the wind turbine tower is performed over a range of wind speeds which allows the estimation of resulting fatigue damage. The proposed model gives more realistic prediction in comparison to the approach included in current standards.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.10a
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• pp.259-262
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• 2009

The influence of pre-strain in low-cycle fatigue behavior of Fe-18Mn-0.05Al-0.6C TWIP steel was studied by conducting axial strain-controlled tests. As-received plates were deformed by rolling with reduction ratios of 10 and 30%, respectively. A triangular waveform with a constant frequency of 1 Hz was employed for low cycle fatigue test at the strain amplitudes in the range of ${\pm}0.4{\sim}{\pm}0.6$ pct. The results showed that low-cycle fatigue life was strongly dependent on the amount of pre-strain as well as the strain amplitude. Increasing the amount of prestrain, the number of reversals to failure was significantly decreased at high strain amplitudes, but the effect was negilgible at low strain amplitudes. A new model for predicting fatigue life of pre-strained body has been devised adding a correction term of ${\Delta}E_{pre-strain}$ to the energy-based fatigue damage parameter.

• 한국연소학회:학술대회논문집
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• 2004.11a
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• pp.152-160
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• 2004

Studies to investigate the influence on hydraulic acoustic wave were conducted using pressure swirl atomizer under making frequency range from 0 to 60Hz using water as a propellant. Pressure oscillation from hydraulic sources gives a strong influences on atomization and mixing processes. The ability to drive these low frequency pressure oscillations makes spray characteristics changeable. The effect of pressure perturbation and its spray characteristics showed that low injector pressure with pressure pulsation gives more significantly than high injector pressure with pressure perturbation in SMD, spray cone angle, breakup length. Moreover, this data could be used for prediction of low combustion instability getting G factor.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.10a
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• pp.95-102
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• 1994

This paper presents a Cable Stayed Bridge Construction Management System, which consists of Structural System Identification Method (SSIM), Error Sensitivity Analysis and Optimum Error Adjustment & Prediction System. The 1st System Identification Method builds an error influence matrix using the linear superposition of each error modes. The 2nd SSIM also considers the second error mode term, which shows good error factor estimation. The optimal cable adjustment can be accomplished within the allowable range of both cable tension and camber. The Post processor, constituted with Motif and GL library on SGI platform, is useful for monitoring construction stage management by displaying construction data, adjustment and prediction results at each construction step.