• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.94-99
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• 2005

Though the technology on the ultra-precise machining has been developed intensively, the high speed and high precision for large machining range is still very hard to achieve. The linear motor system fur the universal machining center is proper fur high speed and high precision, but it has drawback of sensitivity to disturbance. In this research, two degrees of freedom controller based on the zero phase error tracking controller (ZPETC) and disturbance observer are proposed to improve the tracking performance and dynamic stiffness of linear motor system. The proposed controller is verified in simulations and experiments on a nano-positioner system, and the experimental result shows that the tracking performance improved. In addition, the PID optimization method is proposed for the commercialized controller such as the PMAC based system. The tracking as well as impedance is included in the cost function of optimization.

• Earthquakes and Structures
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• v.12 no.4
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• pp.389-395
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• 2017

In this paper a simple approach is presented for spectral matching of ground motions utilizing the wavelet transform and a recently developed metaheuristic optimization technique. For this purpose, wavelet transform is used to decompose the original ground motions to several levels, where each level covers a special range of frequency, and then each level is multiplied by a variable. Subsequently, the vibrating particles system (VPS) algorithm is employed to calculate the variables such that the error between the response and target spectra is minimized. The application of the proposed method is illustrated through modifying 12 sets of ground motions. The results achieved by this method demonstrate its capability in solving the problem. The outcomes of the VPS algorithm are compared to those of the standard colliding bodies optimization (CBO) to illustrate the importance of the enhancement of the algorithm.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.649-658
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• 2008

This paper investigates the optimal nesting system for a board. A hybrid method is used to search the optimal solution for rectangular nesting problem. This method is composed of heuristic approach algorithm. An engineer's experience of board nesting in which a loss occurred to sheet because of various individual error and diffidence. So, item layout at resource sheet were evaluated in engineering algorithm logic in which specially designed was installed. The nesting system consists of Lisp and Visual Basic. The system was controlled by AutoCAD so as to best item batch path test.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.58-67
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• 2022

In this paper, we propose an approach resolving inaccuracy of the low-cost redundant manipulator workspace with low encoder and low stiffness. When the manipulators are manufactured with low-cost encoders and low-cost links, the robots can run into workspace inaccuracy issues. Furthermore, trajectory generation based on conventional forward/inverse kinematics without taking into account inaccuracy issues will introduce the risk of end-effector fluctuations. Hence, we propose an optimization for the trajectory generation method based on the DDPG (Deep Deterministic Policy Gradient) algorithm for the low-cost redundant manipulators reaching the target position in Euclidean space. We designed the DDPG algorithm minimizing the distance along with the jacobian condition number. The training environment is selected with an error rate of randomly generated joint spaces in a simulator that implemented real-world physics, the test environment is a real robotic experiment and demonstrated our approach.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.377-384
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• 2023

The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNs on the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory; the common part of the associated data set is mined by exchanging common information among the cluster head nodes, and the independent parts are updated within each cluster head node. To solve the non-convergence problem in the distributed computing, the approximate term is introduced into the optimization objective function to make the sub-optimization problem strictly convex. And the decompressed sensing signal reconstruction problem is addressed by the alternating direction multiplier method to realize the distributed signal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is located according to the characteristic information of the cross-power spectrum. The proposed method can accurately acquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance the communication quality of the experimental object.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.115-124
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• 2010

Stereo matching techniques are categorized in two major schemes, local and global matching techniques. In global matching schemes, several investigations are introduced, where cost accumulation is performed in multiple matching lines. In this paper, we introduce a new multi-line stereo matching techniques which expands a conventional single-line matching scheme to multiple one. Matching cost is based on simple normalized cross correlation. We expand the scan-line optimization technique to a multi-line scan-line optimization technique. The proposed technique first generates a reliability image, which is iteratively updated based on the previous reliability measure. After some number of iterations, the reliability image is completed by a hole-filling algorithm. The hole-filling algorithm introduces a disparity score table which records the disparity score of the current pixel. The disparity of an empty pixel is determined by comparing the scores of the neighboring pixels. The proposed technique is tested using the Middlebury and CMU stereo images. The error analysis shows that the proposed matching technique yields better performance than using conventional global matching algorithm.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.202-209
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• 2019

In this study, an acoustic full-waveform inversion using Adam optimizer was proposed. The steepest descent method, which is commonly used for the optimization of seismic waveform inversion, is fast and easy to apply, but the inverse problem does not converge correctly. Various optimization methods suggested as alternative solutions require large calculation time though they were much more accurate than the steepest descent method. The Adam optimizer is widely used in deep learning for the optimization of learning model. It is considered as one of the most effective optimization method for diverse models. Thus, we proposed seismic full-waveform inversion algorithm using the Adam optimizer for fast and accurate convergence. To prove the performance of the suggested inversion algorithm, we compared the updated P-wave velocity model obtained using the Adam optimizer with the inversion results from the steepest descent method. As a result, we confirmed that the proposed algorithm can provide fast error convergence and precise inversion results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.263-269
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• 2006

An optimization model was developed for groundwater development and management in coastal areas. The optimization model consists of coastal groundwater flow model and optimization techniques. The objective of this work is to validate sharp-interface model which is one of major components of the optimization model. A laboratory experimental model is built to simulate freshwater lens, i.e., layer of freshwater floating on top of saltwater, phenomena. Experimental results for the position of fresh-saltwater sharp-interface and the salinity in well are compared with numerical results. Average ratio of relative error is estimated approximately between 2.91% and 4.39%. And the numerical results are in good agreement with the laboratory results of water quality in well in addition to the position of sharp-interface. Accordingly the evaluation of coastal groundwater flow using sharp-interface model can produce reasonable results.

• Computers and Concrete
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• v.34 no.2
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• pp.151-168
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• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.173-178
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• 2012

In the generated model for the recognition vocabulary, tri-phones which is not make preparations are produced. Therefore this model does not generate an initial estimate of parameter words, and the system can not configure the model appear as disadvantages. As a result, the sophistication of the Gaussian model is fall will degrade recognition. In this system, we propose the error correction system using out-of vocabulary rejection algorithm. When the systems are creating a vocabulary recognition model, recognition rates are improved to refuse the vocabulary which is not registered. In addition, this system is seized the lexical analysis and meaning using probability distributions, and this system deactivates the string before phoneme change was applied. System analysis determine the rate of error correction using phoneme similarity rate and reliability, system performance comparison as a result of error correction rate improve represent 2.8% by method using error patterns, fault patterns, meaning patterns.