• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4042-4059
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• 2020

The development of new multimedia techniques such as 3D printing is increasingly attracting the public's attention towards 3D objects. An optimized robust and imperceptible watermarking method based on Ant Colony Optimization (ACO) and Weber Law is proposed for 3D polygonal models. The proposed approach partitions the host model into smaller sub meshes and generates a secret watermark from the sub meshes using Weber Law. ACO based optimized strength factor is identified for embedding the watermark. The secret watermark is embedded and extracted on the wavelet domain. The proposed scheme is robust against geometric and photometric attacks that overcomes the synchronization problem and authenticates the secret watermark from the distorted models. The primary characteristic of the proposed system is the flexibility achieved in data embedding capacity due to the optimized strength factor. Extensive simulation results shows enhanced performance of the recommended framework and robustness towards the most common attacks like geometric transformations, noise, cropping, mesh smoothening, and the combination of such attacks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.201-204
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• 1997

This study is focussed on optimization problems which require allocating the restricted inventory to demand points and assignment of vehicles to routes in order to deliver goods for demand sites with optimal decision. This study investigated an integrated model using three step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations. we developed several sub-models such as; first, an inventory-allocation model, second a vehicle-routing model based on clustering and a heuristic algorithms, and last a vehicle routing scheduling model, a TSP-solver, based on genetic algorithm. Also, for each sub-models we have developed computer programs and by a sample run it was known that the proposed model to be a very acceptable model for the inventory-allocation and vehicle routing problems.

• Journal of Powder Materials
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• v.28 no.6
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• pp.491-496
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• 2021

The process optimization of directed energy deposition (DED) has become imperative in the manufacture of reliable products. However, an energy-density-based approach without a sufficient powder feed rate hinders the attainment of an appropriate processing window for DED-processed materials. Optimizing the processing of DED-processed Ti-6Al- 4V alloys using energy per unit area (E_eff) and powder deposition density (PDD_eff) as parameters helps overcome this problem in the present work. The experimental results show a lack of fusion, complete melting, and overmelting regions, which can be differentiated using energy per unit mass as a measure. Moreover, the optimized processing window (E_eff = 44~47 J/mm² and PDD_eff = 0.002~0.0025 g/mm²) is located within the complete melting region. This result shows that the Eeff and PDDeff-based processing optimization methodology is effective for estimating the properties of DED-processed materials.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.12
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• pp.9-20
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• 2012

This paper proposes the efficiency optimization control of interior permanent magnet synchronous motor(IPMSM) drive using series connected-fuzzy neural network PI(SC-FNPI) controller. The PI controller is generally used to control IPMSM drive in industrial field. However, the PI controller has problem which is falling control performance about parameter variation such as command speed, load torque and inertia due to fixed gain of PI controller. Therefore, to improve performance of PI controller, this paper proposes SC-FNPI controller adjusted input of PI controller by FNN controller according to operating conditions. Also, this paper proposes efficiency optimization control which is improving efficiency with minimize loss. The SC-FNPI controller proposed in this paper is compared control performance with conventional FNN and PI controller about command speed, load torque and inertia variation. And the efficiency optimization control is compared with $i_d=0$ control about loss and efficiency. The SC-FNPI controller proposed in this paper shows more excellent control performance for rising time, overshoot and steady-state error. Also efficiency optimization control is increased efficiency by reducing loss.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.20-28
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• 2016

This paper presents a generation rescheduling method for the enhancement of transient stability in power systems. The priority and the candidate generators for rescheduling are calculated by using the energy margin sensitivity. The generation rescheduling formulates the Lagrangian function with the fuel cost and emission such as NO_x and SO_x from power plants. The generation rescheduling searches for the solution that minimizes the Lagrangian function by using the Newton’s approach. While the Pareto optimum in the fuel cost and emission minimization has a drawback of finding a number of non-dominated solutions, the proposed approach can explore the non-inferior solutions of the multiobjective optimization problem more efficiently. The method proposed is applied to a 4-machine 6-bus system to demonstrate its effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2028-2041
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• 2019

Compressed sensing (CS) is a new theory. With regard to the sparse signal, an exact reconstruction can be obtained with sufficient CS measurements. Nevertheless, in practical applications, the transform coefficients of many signals usually have weak sparsity and suffer from a variety of noise disturbances. What's worse, most existing classical algorithms are not able to effectively solve this issue. So we proposed an efficient algorithm based on smoothed ${\ell}_0$ norm for sparse signal reconstruction. The direct ${\ell}_0$ norm problem is NP hard, but it is unrealistic to directly solve the ${\ell}_0$ norm problem for the reconstruction of the sparse signal. To select a suitable sequence of smoothed function and solve the ${\ell}_0$ norm optimization problem effectively, we come up with a generalized approximate function model as the objective function to calculate the original signal. The proposed model preserves sharper edges, which is better than any other existing norm based algorithm. As a result, following this model, extensive simulations show that the proposed algorithm is superior to the similar algorithms used for solving the same problem.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.1
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• pp.149-157
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• 2008

The reduction of leakage is a major issue of the South Korea water industry. The inclusion of pressure dependent leakage terms in network analysis allows the application of optimization techniques to identify the most effective means of reducing water leakage in distribution networks. This paper proposes a method to find optimal setting and location of control valve for reducing leakage efficiently. The proposed search method differs from previous methods for addressing optimal valve location problem and improves the GA simulation time with guaranteeing for getting the global optimal solution. The strength of this method has been demonstrated by means of case studies. This allows the procedure of optimization to be more robust and computational efficient.

• ETRI Journal
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• v.31 no.2
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• pp.121-128
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• 2009

In this paper, we describe a fixed-threshold sequential minimal optimization (FSMO) for structured SVM problems. FSMO is conceptually simple, easy to implement, and faster than the standard support vector machine (SVM) training algorithms for structured SVM problems. Because FSMO uses the fact that the formulation of structured SVM has no bias (that is, the threshold b is fixed at zero), FSMO breaks down the quadratic programming (QP) problems of structured SVM into a series of smallest QP problems, each involving only one variable. By involving only one variable, FSMO is advantageous in that each QP sub-problem does not need subset selection. For the various test sets, FSMO is as accurate as an existing structured SVM implementation (SVM-Struct) but is much faster on large data sets. The training time of FSMO empirically scales between O(n) and O($n^{1.2}$), while SVM-Struct scales between O($n^{1.5}$) and O($n^{1.8}$).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.3
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• pp.61-67
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• 2008

Proxy MIPv6 (PMIPv6) is that network-based mobility management protocol that network supports mobile node's mobility on behalf of the MN. In PMIPv6, when two MNs located within the same PMIPv6 domain want to communicate each other, sub-optimal path is established between the two MN. Route optimization method for PMIPv6 is proposed to resolve the problem. However, the method still suffer from the performance degradation due to out-of-sequence packet problem. In this paper, we propose the route optimization mechanism in PMIPv6 based on Flush message to resolve the out-of-sequence packet problem. The proposed mechanism is evaluated by performing simulations, and the simulation results show that the proposed one gives better performance.