• Geomechanics and Engineering
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• v.33 no.6
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• Korean Journal of Remote Sensing
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• v.14 no.3
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• pp.250-261
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• 1998

The purpose of this paper is to extract fast DEM (Digital Elevation Model) using satellite images. DEM extraction consists of three parts. First part is the modeling of satellite position and attitude, second part is the matching of two images to find corresponding points of them and third part is to calculate the elevation of each point by using the results of the first and second part. The position and attitude modeling of satellite is processed by using GCPs. A area based matching method is used to find corresponding points between the stereo satellite images. The elevation of each point is calculated using the exterior orientation parameters obtained from modeling and conjugate points from matching. In the DEM generation system, matching procedure holds most of a processing time, therefore to reduce the time for matching, a new fast matching algorithm using gradient correlation and fast similarity measure calculation method is proposed. In this paper, the SPOT satellite images, level 1A 6000$\times$6000 panchromatic images are used to extract DEM. The experiment result shows the possibility of fast DEM extraction with the satellite images.

• Journal of computational fluids engineering
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• v.10 no.2
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• pp.38-47
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• 2005

Substructuring methods are often used in finite element structural analyses. In this study a multi-level substructuring(MLSS) algorithm is developed and proposed as a possible candidate for finite element fluid solvers. The present algorithm consists of four stages such as a gathering, a condensing, a solving and a scattering stage. At each level, a predetermined number of elements are gathered and condensed to form an element of higher level. At the highest level, each sub-domain consists of only one super-element. Thus, the inversion process of a stiffness matrix associated with internal degrees of freedom of each sub-domain has been replaced by a sequential static condensation of gathered element matrices. The global algebraic system arising from the assembly of each sub-domain matrices is solved using a well-known iterative solver such as the conjugare gradient(CG) or the conjugate gradient squared(CGS) method. A time comparison with CG has been performed on a 2-D Poisson problem. With one domain the computing time by MLSS is comparable with that by CG up to about 260,000 d.o.f. For 263,169 d.o.f using 8 x 8 sub-domains, the time by MLSS is reduced to a value less than $30\%$ of that by CG. The lid-driven cavity problem has been solved for Re = 3200 using the element interpolation degree(Deg.) up to cubic. in this case, preconditioning techniques usually accompanied by iterative solvers are not needed. Finite element formulation for the incompressible flow has been stabilized by a modified residual procedure proposed by Ilinca et al.[9].

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.9 s.240
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• pp.1049-1056
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• 2005

A conservative pressure-based finite-volume numerical method has been developed for computing flow and heat transfer by using an unstructured grid system. The method admits arbitrary convex polyhedra. Care is taken in the discretization and solution procedures to avoid formulations that are cell-shape-specific. A collocated variable arrangement formulation is developed, i.e. all dependent variables such as pressure and velocity are stored at cell centers. Gradients required for the evaluation of diffusion fluxes and for second-order-accurate convective operators are found by a novel second-order accurate spatial discretization. Momentum interpolation is used to prevent pressure checkerboarding and the SIMPLE algorithm is used for pressure-velocity coupling. The resulting set of coupled nonlinear algebraic equations is solved by employing a segregated approach, leading to a decoupled set of linear algebraic equations fer each dependent variable, with a sparse diagonally dominant coefficient matrix. These equations are solved by an iterative preconditioned conjugate gradient solver which retains the sparsity of the coefficient matrix, thus achieving a very efficient use of computer resources.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1899-1909
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• 2015

Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians’ effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10- fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent- Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.11 no.3
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• pp.135-140
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• 1999

To simulate the wave transformation by an energy dissipation region, a numerical model is suggested by discretizing the elliptic mild-slope equation. Generalized conjugate gradient method is used as solution algorithm to apply parabolic approximation to open boundary condition. To demonstrate the applicabil-ity of the numerical procedure suggested, the wave scattering by a circular damping region is examined. The feature of reflection in front of the damping region is captured clearly by the numerical solution. The effect of the size of dissipation coefficient is examined for a rectangular damping region. The recovery of wave height by diffraction occurs very slowly with distance behind the damping region.

• Tribology and Lubricants
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• v.36 no.5
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• pp.290-296
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• 2020

When two layers of carbon nanotube (CNT) arrays are loaded to mate, the free ends of individual CNTs come into contact at the interface of the two layers. This leads to a higher contact resistance due to a smaller contact region. However, when the free CNT ends of one array penetrate into the mating array, the contact region increases, effectively lowering the contact resistance. To explore the penetration of mating CNTs, we perform molecular dynamic simulations of a simple unit cell model, incorporating four CNTs in the lower array layer coupled with a single moving CNT on the upper layer. The interaction with neighboring CNTs is modelled by long-range carbon bond order potential (LCBOP I). The model structure is optimized by energy minimization through the conjugate gradient method. A NVT ensemble is used for maintain a room temperature during simulation. The time integration is performed through the velocity-Verlet algorithm. A significant vibrational motion of CNTs is captured when penetration is not available, resulting in a specific vibration mode with a high frequency. Due to this vibrational behavior, the random behaviors of CNT motion for predicting the penetration are confirmed under the specific gap distances between CNTs. Thus, the probability of penetration is examined according to the gap distance between CNTs in the lower array and the aspect ratio of CNTs. The penetration is significantly affected by the vibration mode due to the van der Waals forces between CNTs.

• Computers and Concrete
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• v.19 no.2
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• pp.217-226
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• 2017

This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in $20{\pm}2^{\circ}C$ waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% $MgSO_4^{-2}$, 2% $MgSO_4^{-2}$, and 4% $MgSO_4^{-2}$ for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.523-533
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• 2000

In this paper, we present the hardware implementation of a smart antenna test-bed for a real -time performance analysis of the beam-forming algorithm operating in a wide-band CDMA environments of the WLL(Wireless Local Loop) standard. The test-bed introduced in this paper includes an external PC and signal generating module as well as the beam-forming module in order to perform, analyze, and evaluate the performance of the proposed smart antenna system. In the beam-forming module, the optimal weight vector is provided by the modified CGM algorithm. The computed weight vector is transferred back to the external PC for the performance analysis based on the off-line processing. From our analysis obtained in the hardware of the test-bed, it is concluded that the proposed smart antenna system for the WLL system is appropriate for enhancing the communication quality and capacity tremendously at the cell-site of the CDMA environment.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.