• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.673-686
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• 2023

Determining the properties of pile from cone penetration test (CPT) is costly, and need several in-situ tests. At the present study, two novel hybrid learning models, namely PSO-RF and HHO-RF, which are an amalgamation of random forest (RF) with particle swarm optimization (PSO) and Harris hawks optimization (HHO) were developed and applied to predict the pile set-up parameter "A" from CPT for the design aim of the projects. To forecast the "A," CPT data along were collected from different sites in Louisiana, where the selected variables as input were plasticity index (PI), undrained shear strength (S_u), and over consolidation ratio (OCR). Results show that both PSO-RF and HHO-RF models have acceptable performance in predicting the set-up parameter "A," with R² larger than 0.9094, representing the admissible correlation between observed and predicted values. HHO-RF has better proficiency than the PSO-RF model, with R² and RMSE equal to 0.9328 and 0.0292 for the training phase and 0.9729 and 0.024 for testing data, respectively. Moreover, PI and OBJ indices are considered, in which the HHO-RF model has lower results which leads to outperforming this hybrid algorithm with respect to PSO-RF for predicting the pile set-up parameter "A," consequently being specified as the proposed model. Therefore, the results demonstrate the ability of the HHO algorithm in determining the optimal value of RF hyperparameters than PSO.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.721-733
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• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.2
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• pp.47-58
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• 2001

The purpose of this study is to present methods for determining the k most vital arcs (k-MVAs) in shortest path problem (SPP) using evolutionary algorithms. The problem of finding the k-MVAs in SPP is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the shortest distance between two specified nodes. Generally, the problem of determining the k-MVAs in SPP has been known as NP-hard. Therefore, to deal with problems of the real world, heuristic algorithms are needed. In this study we present three kinds of evolutionary algorithms for finding the k-MVAs in SPP, and then to evaluate the performance of proposed algorithms.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.792-796
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• 2009

Determining the optimal number of cluster is an important issue in research area of data clustering. It is choosing the cluster validity method and finding the cluster number where it optimizes the cluster validity. In this paper, an efficient heuristic for determining optimal number of cluster using clustering balance is proposed. The experimental results using k-means at artificial and real-life data set show that proposed algorithm is excellent in aspect of time efficiency.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.120-130
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• 2000

The purpose of this study is to present a method for determining the k most vital arcs in shortest path problem using an evolutionary algorithm. The problem of finding the k most vital arcs in shortest path problem is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the total length of shortest path. Generally, the problem determining the k most vital arcs in shortest path problem has known as NP-hard. Therefore, in order to deal with the problem of real world the heuristic algorithm is needed. In this study we propose to the method of finding the k most vital arcs in shortest path problem using an evolutionary algorithm which known as the most efficient algorithm among heuristics. The method presented in this study is developed using the library of the evolutionary algorithm framework and then the performance of algorithm is analyzed through the computer experiment.

• Transactions of Materials Processing
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• v.11 no.4
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• pp.363-370
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• 2002

In this study, an optimal design technique was investigated for determining appropriate dimensions of components of the die set used in the extrusion process. For this, an axi-symmetric elastic finite element program for the analysis of deformation of the shrink fitted die set was developed with the Lagrange multiplier method to implement the constraint condition of shrink fit of stress ring. By coupling the rigid-viscoplastic analysis of extrusion process by CAMPform and elastic analysis of the die set, the optimization study was made by employing optimization program DOT. Considering the various assembly conditions, optimal design was determined for a single stress ring case. It is construed that the proposed design method can be beneficial for improving the tool life of cold extrusion die set at practice.

• Transactions of Materials Processing
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• v.18 no.4
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• pp.290-295
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• 2009

The roll gap set-up in the finishing mill is one of the most important technologies in the hot plate rolling process. As the target thickness can be obtained by the correct set-up of the roll gap, improving the roll gap set-up technology is very critical for plate thickness accuracy. The main cause of thickness variation in hot plate mills is the non-uniform temperature distribution along the length of the slab. The objective of this study is to adjust the roll gap set-up for the thickness accuracy of plate in hot rolling process considering top-end temperature drop. Therefore this study has concentrated on determining the correct amounts of thickness variation according to top-end temperature drop and roll gap to compensate thickness variation. The control method of roll gap set-up which can improve the thickness accuracy was proposed. The off-line simulation of compensated roll gap significantly decreases top-end thickness variation.

• 전기의세계
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• v.20 no.5
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• pp.19-22
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• 1971

This paper deals with the state-variable analysis of the arbitrary RLC lumped linear time-invariant networks. A formulation technique for determining a set of state equation using Bryant-Bashkow A Matrix and by means of the procedure setting up the terminal equation is discussed.

• Food Science and Preservation
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• v.7 no.2
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• pp.155-159
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• 2000

Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).

• Journal of Industrial Technology
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• v.22 no.B
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• pp.89-93
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• 2002

When testing products before shipment to the customer, quality characteristics are measured to decide whether or not their values are between the specification limits. Unfortunately, this testing procedure can lead to incorrect decisions because of gauge error. That is, good products can erroneously be qualified as bad, and bad products as good, and this has consequences for producer's and consumer's risk. In cases of such as this, the compressed gauge limit can be used to achieve the desired product quality level dictated by the manufacturer or the customer. A compressed gauge limit is a limit set by the manufacturer on a test gauge that is tighter than the specification limit established by the customer. The compressed gauge limits should be set at levels to achieve the defect levels desired by the customer and simultaneously minimize the loss of good product that is rejected due to errors in the gauges. In this article, the models for determining the defect levels and the losses obtained by adding compressed gauge limits will be developed. A response surface model approach is utilized which allows an optimal operating condition to be generated relatively easily.