• Journal of Applied Biological Chemistry
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• v.66
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• pp.81-89
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• 2023

The purpose of this study is to demonstrate the potential enhancement of the flavonoid contents from Agastache rugosa, which can be obtained as raw materials for functional products in the food medicine industry by identifying important factors for efficient preparation to save costs and time in terms of economic factors. For this reason, response surface methodology using Box-Behnken design was used to optimize the extraction conditions for the maximum yield of seven major compounds from A. rugosa. The optimum conditions were obtained with an ethanol concentration of 60.0%, a temperature of 50 ℃, and an extraction time of 33.6 min, meaning that the regression analysis fits the experimental data well. Under these conditions, the seven major compounds 1-7 had observed values of 2.169, 2.135, 0.697, 2.485, 0.105, 1.247, and 0.551%, respectively. These results show that the observed values are in good agreement with the predicted values in the regression model. This process for optimization study exhibited a basic protocol for obtaining stable ingredients from A. rugosa that are appropriate for the development of effective functional products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2512-2545
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• 2024

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.63-70
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• 2016

Deep drawing is a forming process in which a blank of sheet metal is radially drawn into a forming die by the mechanical action of a punch and converted to required shape. Deep drawing involves complex material flow conditions and force distributions. Radial drawing stresses and tangential compressive stresses are induced in flange region due to the material retention property. These compressive stresses result in wrinkling phenomenon in flange region. Normally blank holder is applied for restricting wrinkles. Tensile stresses in radial direction initiate thinning in the wall region of cup. The thinning results into cracking or fracture. The finite element method is widely applied worldwide to simulate the deep drawing process. For real-life simulations of deep drawing process an accurate numerical model, as well as an accurate description of material behavior and contact conditions, is necessary. The finite element method is a powerful tool to predict material thinning deformations before prototypes are made. The proposed innovative methodology combines two techniques for prediction and optimization of thinning in automotive sealing cover. Taguchi design of experiments and analysis of variance has been applied to analyze the influencing process parameters on Thinning. Mathematical relations have been developed to correlate input process parameters and Thinning. Optimization problem has been formulated for thinning and Genetic Algorithm has been applied for optimization. Experimental validation of results proves the applicability of newly proposed approach. The optimized component when manufactured is observed to be safe, no thinning or fracture is observed.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Steel and Composite Structures
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• v.53 no.4
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• pp.377-399
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• 2024

Accurately predicting the bearing capacity of steel and concrete piles is a critical factor in the design and safety of deep foundations. This study presents a novel application of hybrid machine learning models, specifically Invasive Weed Optimization with Multilayer Perceptron (IWOMLP) and Harris Hawks Optimization with Multilayer Perceptron (HHOMLP), for enhancing the prediction of pile bearing capacity. These hybrid models integrate evolutionary optimization algorithms with neural networks, aiming to improve prediction accuracy by addressing the nonlinearities and complexities in pile-soil interaction. The study compares the performance of IWOMLP and HHOMLP against conventional machine learning methods such as Simple Linear Regression, Gaussian Processes, Random Forest, and others. The training and testing phases evaluate the models based on various error metrics, including R², RMSE, MAE, and additional advanced metrics. The key innovation in this research lies in combining optimization techniques with neural networks, which significantly enhances the model's ability to predict complex geotechnical properties. The primary goal of this work is to develop a reliable, data-driven approach for accurate pile capacity prediction, providing a more precise tool for geotechnical engineers to improve decision-making in foundation design. Results indicate that the hybrid models, particularly IWOMLP, outperform traditional approaches, achieving higher R² and lower RMSE values. This research demonstrates the potential of hybrid models to advance geotechnical engineering practices by delivering more accurate and reliable predictions.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.1-6
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• 2012

To achieve the forest fire management goals such as early detection and quick suppression, fire resources should be allocated at high probability area where forest fires occur. The objective of this study was to develop and validate models to estimate spatially distributed probabilities of occurrence of forest fire. The models were builded by exploring relationships between fire ignition location and forest, terrain and anthropogenic factors using logistic regression. Distance to forest, cemetery, fire history, forest type, elevation, slope were chosen as the significant factors to the model. The model constructed had a good fit and classification accuracy of the model was 63%. This model and map can support the allocation optimization of forest fire resources and increase effectiveness in fire prevention and planning.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.258-267
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• 2016

Three-dimensional integrated circuits (3D ICs) experience die-to-die variations in addition to the already challenging within-die variations. This adds an additional design complexity and makes variation estimation and full-chip optimization even more challenging. In this paper, we show that the industry standard on-chip variation (AOCV) tables cannot be applied directly to 3D paths that are spanning multiple dies. We develop a new machine learning-based model and methodology for an accurate variation estimation of logic paths in 3D designs. Our model makes use of key parameters extracted from existing GDSII 3D IC design and sign-off simulation database. Thus, it requires no runtime overhead when compared to AOCV analysis while achieving an average accuracy of 90% in variation evaluation. By using our model in a full-chip variation-aware 3D IC physical design flow, we obtain up to 16% improvement in critical path delay under variations, which is verified with detailed Monte Carlo simulations.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.241-250
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• 2020

In this study, the methodology of the springback prediction of automotive parts applied 3rd generation AHSS was investigated using the response surface model analysis based on a regression model, and the meta model analysis based on a Kriging model. To design the learning data set for constructing the springback prediction models, and the experimental design was conducted at three levels for each processing variable using the definitive screening designs method. The hat-shaped member, which is the basic shape of the member parts, was selected and the springback values were measured for each processing type and processing variable using the finite element analysis. When the nonlinearity of the variables is small during the hat-shaped member forming, the response surface model and the meta model can provide the same processing parameter. However, the accuracy of the springback prediction of the meta model is better than the response surface model. Even in the case of the simple shape parts forming, the springback prediction accuracy of the meta model is better than that of the response surface model, when more variables are considered and the nonlinearity effect of the variables is large. The efficient global optimization algorithm-based Kriging is appropriate in resolving the high computational complexity optimization problems such as developing automotive parts.

• Proceedings of the KWS Conference
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• 2004.05a
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• pp.39-41
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• 2004

This study is to optimize the condition of pulse parameters using the response surface method in micro pulse TIG welding of pleated type metallic filter. The input parameters used were pulse current, base current, pulse duty, frequency and welding speed and the hydraulic pressure was used as the output parameter. The central composite design was designed using second order regression model, As the results, the optimal welding condition to manufacture the pleated type metallic filter was obtained.