• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.109-118
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• 2011

In this research estimated accuracies that were results in all the area of filtering of the plane equation that was used by whole data set, and regional of filtering that was driven by the plane equation for each vertual Grid. All of this estimates were based by all the area of filtering that deduced the plane equation by multiple linear regression analysis that was used by ground data set. Therefore, accuracy of all the area of filtering that used whole data set has been dropped about 2~3% when average of accuracy of all the area of filtering was based on ground data set while accuracy of Regional of filtering dropped 2~4% when based on virtual Grid. Moreover, as virtual Grid which was set 3~4 cm was difference about 2% of accuracy from standard data. Thus, it leads conclusion of set 3~4 times bigger size in virtual Grid filtering over LiDAR scan gap will be more appropriated. Hence, the result of this research allow us to conclude that there was difference in average accuracy has been noticed when we applied each different approaches, I strongly suggest that it need to research more about real topography for further filtering accuracy.

• ETRI Journal
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• v.42 no.4
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• pp.585-595
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• 2020

Integration of multiple communication technologies in a smart grid (SG) enables employing cognitive radio (CR) technology for improving reliability and security with low latency by adaptively and effectively allocating spectral resources. The versatile features of the CR enable the smart meter to select either the unlicensed or the licensed band for transmitting data to the utility company, thus reducing communication outage. Demand response management is regarded as the control unit of the SG that balances the load by regulating the real-time price that benefits both the utility company and consumers. In this study, joint allocation of the transmission power to the smart meter and consumer's demand is formulated as a two stage multi-armed bandit game in which the players select their optimal strategies noncooperatively without having any prior information about the media. Furthermore, based on historical rewards of the player, a real-time pricing adaptation method is proposed. The latter is validated through numerical results.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.83-86
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• 2006

A grid-connected distributed generation system which consists of engine generator, dc link with multiple energy sources and inverter is proposed. All six of the stator leads of the generator, which is a surface mount permanent magnet machine, are brought out to the terminal of the generator. Three leads are connected to the inverter and the others are connected to the utility grid. In this proposed system the power from the engine-generator and the power from dc link can be controlled simultaneously by only one three-phase power converter. A control algorithm for the system is developed and verified by experiment results.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Journal of Power Electronics
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• v.16 no.3
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• pp.960-969
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• 2016

High-power three-level voltage-source converters are widely utilized in high-performance AC drive systems. In several ultra-power instances, the harmonics on the grid side should be reduced through multiple rectifications. A combined harmonic elimination method that includes a dual primary-side series-connected winding transformer and selective harmonic elimination pulse-width modulation is proposed to eliminate low-order current harmonics on the primary and secondary sides of transformers. Through an analysis of the harmonic influence caused by dead time and DC magnetic bias, a synthetic compensation control strategy is presented to minimize the grid-side harmonics in the dual primary side series-connected winding transformer application. Both simulation and experimental results demonstrate that the proposed control strategy can significantly reduce the converter input current harmonics and eliminates the DC magnetic bias in the transformer.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.9-16
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• 2006

The Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. An algorithm has been developed for robots which explore the environment to measure the physical properties (dust in this paper). While the robot is moving, it measures the amount of dust and registers the value in the corresponding grid cell. The robot moves from local maximum to local minimum, then to another local maximum, and repeats. To reach the local maximum or minimum, simple gradient following is used. Robust estimation of the gradient using perturbation/correlation, which is very effective when analytical solution is not available, is described. By introducing the probability of each grid cell, and considering the probability distribution, the robot doesn't have to visit all the grid cells in the environment still providing fast and efficient sensing. The extended algorithm to coordinate multiple robots is presented with simulation results.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1271-1280
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• 2020

The yield and quality of a micromanufacturing process are important management factors. In real-world situations, it is difficult to achieve a high yield from a manufacturing process because the products are produced through multiple nanoscale manufacturing processes. Therefore, it is necessary to identify the processes and equipment that lead to low yields. This paper proposes an analytical method to identify the processes and equipment that cause a defect in the plastic ball grid array (PBGA) during the manufacturing process using logistic regression and stepwise variable selection. The proposed method was tested with the lot trace records of a real work site. The records included the sequence of equipment that the lot had passed through and the number of faults of each type in the lot. We demonstrated that the test results reflect the real situation in a PBGA manufacturing process, and the major equipment parameters were then controlled to confirm the improvement in yield; the yield improved by approximately 20%.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Journal of Power Electronics
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• v.16 no.1
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• pp.297-309
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• 2016

Grid-connected inverters (GCIs) with an LCL output filter have the ability of attenuating high-frequency (HF) switching ripples. However, by using only grid-current control, the system is prone to resonances if it is not properly damped, and the current distortion is amplified significantly under highly distorted grid conditions. This paper proposes a synchronous reference frame equivalent proportional-integral (SRF-EPI) controller in the αβ stationary frame using the parallel virtual resistance-based active damping (PVR-AD) strategy for grid-interfaced distributed generation (DG) systems to suppress LCL resonance. Although both a proportional-resonant (PR) controller in the αβ stationary frame and a PI controller in the dq synchronous frame achieve zero steady-state error, the amplitude- and phase-frequency characteristics differ greatly from each other except for the reference tracking at the fundamental frequency. Therefore, an accurate SRF-EPI controller in the αβ stationary frame is established to achieve precise tracking accuracy. Moreover, the robustness, the harmonic rejection capability, and the influence of the control delay are investigated by the Nyquist stability criterion when the PVR-based AD method is adopted. Furthermore, grid voltage feed-forward and multiple PR controllers are integrated into the current loop to mitigate the current distortion introduced by the grid background distortion. In addition, the parameters design guidelines are presented to show the effectiveness of the proposed strategy. Finally, simulation and experimental results are provided to validate the feasibility of the proposed control approach.

• KIEAE Journal
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• v.16 no.6
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• pp.159-166
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• 2016

Purpose: The architectural structures in the engineering field include more than one material, and the heat transfer through these multiple materials becomes complicated. More or less, the analytic solutions obtained by the hand calculation can provide the limited information of heat transfer phenomena. However, the engineers have generally been forced to obtain reliable results than those of the hand calculation. The numerical calculation such as a finite volume methods with the unstructured grid system is only the suitable means of the analysis for the complex and arbitrary domains that consists of multiple materials. In this study, a new numerical code is developed to provide temperature distributions in the multiple material domains, and the results of this code are compared with the validation cases in ISO10211. Method: Finite volume methods with an unstructured grid is employed. In terms of numerical methods, the heat transfer conduction coefficient is not defined on the surface of the cell between different material cells. The heat transfer coefficient is properly defined to accurately mimic the heat transfer through the surface. The boundary conditions of heat flux considering radiation or heat convection are also developed. Result: The comparison between numerical results and ISO 10211 cases. We are confirmed that the numerical method provides the proper temperature distributions, and the heat transfer equation and its boundary conditions are developed properly.