• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1571-1581
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• 2016

A novel method is proposed to construct the voltage stability boundary of power system considering different Reactive Power Control Mode (RPCM) of Doubly-Fed Induction Generator (DFIG) Wind Power Plant (WPP). It can be used for reflecting the static stability status of grid operation with wind power penetration. The analytical derivation work of boundary search method can expound the mechanism and parameters relationship of different WPP RPCMs. In order to improve the load margin and find a practical method to assess the voltage security of power system, the approximate method of constructing voltage stability boundary and the critical points search algorithms under different RPCMs of DFIG WPP are explored, which can provide direct and effective reference data for operators.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2002-2007
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• 2006

The path search method in the telematics system should consider traffic flow of the roads as well as the shortest time because the optimal path with minimized travel time could be continuously changed by the traffic flow. The existing path search methods are not able to cope efficiently with the change of the traffic flow. The search method to use traffic information also needs more computation time than the existing shortest path search. In this paper, a method for efficiency improvement of path search is implemented and its performance is evaluated. The method employs the fixed grid for adjustable heuristic to traffic flow. Moreover, in order to simplify the computation of estimation values, it only adds graded decimal values instead of multiplication operation of floating point numbers with due regard to the gradient between a departure and a destination. The results obtained from the experiments show that it achieves the high accuracy and short execution time as well.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.1-9
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• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.206-209
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• 2005

Optimal path search systems to take continuously changed traffic flows into consideration is necessary in order to reduce the cost to get destination. However, to search optimal path in client terminals with low computing power yields high computational cost. Thus, a method with low cost and near optimal path as well is required. In this paper, we propose a path search method using variable heuristic for the sake of reducing operation time. The heuristic is determined by the change of the average speeds of cars located in grid which means a rectangle region.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2005.04a
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• pp.79-82
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• 2005

In this study, we have tested the feasibility of the convex non-linear objective model and the line search optimization method for the fluence map optimization (FMO). We've created the convex nonlinear objective function with simple bound constraints and attained the optimal solution using well-known gradient algorithms with an Armijo line search that requires sufficient objective function decrease. The algorithms were applied to 10 head-and-neck cases. The numbers of beamlets were between 900 and 2,100 with a 3 mm isotropic dose grid. Nonlinear optimization methods could efficiently solve the IMRT FMO problem in well under a minute with high quality for clinical cases.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.116-126
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• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

• Geophysics and Geophysical Exploration
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• v.18 no.2
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• pp.54-63
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• 2015

Petrophysical parameters such as porosity and fluid saturation which provide useful information for reservoir characterization could be estimated by rock physics model (RPM) using seismic velocity and resistivity. Therefore, accurate P-wave velocity and resistivity information have to be obtained for successful estimation of the petrophysical parameters. Compared with the individual inversion of electromagnetic (EM) or seismic data, the joint inversion using both EM and seismic data together can reduce the uncertainty and gives the opportunity to use the advantages of each data. Thus, more reliable petrophysical properties could be estimated through the joint inversion. In this paper, for the successful estimation of petrophysical parameters, we proposed an effective method which applies a grid-search method to find the porosity and fluid saturation. The relations of porosity and fluid saturation with P-wave velocity and resistivity were expressed by using RPM and the improved resistivity distribution used to this study was obtained by joint inversion of seismic and EM data. When the proposed method was applied to the synthetic data which were simulated for subsea reservoir exploration, reliable petrophysical parameters were obtained. The results indicate that the proposed method can be applied for detecting a reservoir and calculating the accurate oil and gas reserves.

• Journal of Welding and Joining
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• v.26 no.1
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• pp.63-68
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• 2008

This paper has aimed to prevent excessive heat input by controlling arc distribution and heat input capacity with pulse GTAW in order to improve weld quality in 0.08mm pressure gauge diaphragm and flange welding parts. A design of experiment was designed using Box-Behnken method to optimize a welding process. The pulse GTAW parameters such as pulse current, base current, pulse duty, frequency and welding speed were set to input variables while hydraulic pressure that represents welding characteristics in diaphragm and flange joint were set to output variables. Based on the test result, a second regression equation was obtained between input and output variables and turned out significant. Besides, an influence of parameters has been confirmed through response surface analysis using the second-order regression equation and optimum welding condition was obtained through a grid-search method. The optimum welding condition was set to pulse current 84.4(A), base current 29.6(A), pulse duty 58.8(%), frequency 10(%), and welding speed 596(mm/min). Then, decent bead shape was acquired with no excessive heat input under the $2.3kgf/cm^2$ of hydrostatic pressure.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.493-502
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• 2019

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.