• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.483-497
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• 2018

Image mosaicking, which combines several images into one image, is effective for analyzing images and important in various fields of spatial information such as a continuous image map. The crucial processes of the image mosaicking are optimal seamline determination and color correction of mosaicked images. In this study, the overlap regions were determined by SURF (Speeded Up Robust Features) for image matching. Based on the characteristics of the edges extracted by Canny filter, seamline candidates were selected from classified edges with their characteristics, and the edges were connected by using Dijkstra algorithm. In particular, anisotropic filter and image pyramid were applied to extract reliable seamlines. In addition, it was possible to determine seamlines effectively and efficiently by utilizing building and road layers from digital maps. Finally, histogram matching and seamline feathering were performed to improve visual quality of the mosaicked images.

• Tribology and Lubricants
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• v.38 no.6
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• pp.274-280
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• 2022

There is a problem that semiconductor probe pin has a short lifespan. In order to solve this problem, Ti having excellent conductivity was doped to tetrahedral amorphous carbon (ta-C) having excellent hardness and abrasion resistance. This experiment was planned through the Taguchi robust design to determine the effect of the control factor of the ta-C:Ti coating film. The effect and contribution of control factors such as Unbalanced Magnetron Sputter(UBM) discharge current, arc discharge current, temperature, and bias voltage on ta-C:Ti characteristics were analyzed from the perspective of electrical and mechanical characteristics. The UBM discharge current was set to 4, 6, and 8 A. The main control factor of thickness and resistance is the UBM discharge current, and the thickness increased and the resistance decreased as the current increased. The decrease in resistance is due to the increase in the Ti content of the ta-C:Ti coating film. The arc discharge current was set to 60, 80, and 100 A. The main control factor of hardness and wear is the arc discharge current, and as the current rises, the hardness increases and the wear area decreases. This is due to the increased ta-C content of the ta-C:Ti coating film. Since resistance and wear are important for Probe Pin, the optimal level is set from the perspective of resistance and wear and a confirmation experiment is conducted.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.17-31
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• 2004

This paper proposes an algorithm for the robust transmission of video in error Prone environment using multiple description codingby optimal split of DCT coefficients and rate-distortionoptimization framework. In MDC, a source signal is split Into several coded streams, which is called descriptions, and each description is transmitted to the decoder through different channel. Between descriptions, structured correlations are introduced at the encoder, and the decoder exploits this correlation to reconstruct the original signal even if some descriptions are missing. It has been shown that the MDC is more resilient than the singe description coding(SDC) against severe packet loss ratecondition. But the excessive redundancy in MDC, i.e., the correlation between the descriptions, degrades the RD performance under low PLR condition. To overcome this Problem of MDC, we propose a hybrid MDC method that controls the SDC/MDC switching according to channel condition. For example, the SDC is used for coding efficiency at low PLR condition and the MDC is used for the error resilience at high PLR condition. To control the SDC/MDC switching in the optimal way, RD optimization framework are used. Lagrange optimization technique minimizes the RD-based cost function, D+M, where R is the actually coded bit rate and D is the estimated distortion. The recursive optimal pet-pixel estimatetechnique is adopted to estimate accurate the decoder distortion. Experimental results show that the proposed optimal split of DCT coefficients and SD/MD switching algorithm is more effective than the conventional MU algorithms in low PLR conditions as well as In high PLR condition.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.6
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• pp.11-18
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• 2009

In this paper, a hierarchical link allocation algorithm between mobile stations (MSs) and the corresponding base station (BS) by an optimal utilization of relay stations (RSs) is proposed to improve throughput of RS systems. In the proposed hierarchical algorithm, each RS operates cognitive radio functions to sense the degree of satisfaction in the quality of services (QoSs) and then selects the candidate set of MSs to have links with the RS. Such information is reported to the BS, where an auction process is performed to get an optimal allocation of communication links between the MSs and the BS. To maximize system throughput, the proposed auction algorithm is conducted upon bidding prices of communication links, considering both the co-channel interference (CCI) information shared among adjacent cells and the QoS enhancement information for each MS collected from RSs. The BS then switches the communication links of the auction winner MSs through the corresponding RS. The computer simulation shows that the proposed algorithm enhances the user QoS more than the conventional algorithm, especially for RS systems with more users requiring higher QoS. The proposed algorithm has also been proved to have more robust performance than the conventional one when the traffic load is higher and the CCI becomes stronger.

• Composites Research
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• v.13 no.5
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• pp.50-59
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• 2000

In this paper, two methods to suppress flutter of the composite panel are examined. First, in the active control method, a controller based on the linear optimal control theory is designed and control input voltage is applied on the actuators and a PZT is used as actuator. Second, a new technique, passive suppression scheme, is suggested for suppression of the nonlinear panel flutter. In the passive suppression scheme, a shunt circuit which consists of inductor-resistor is used to increase damping of the system and as a result the flutter can be attenuated. A passive damping technology, which is believed to be more robust suppression system in practical operation, requires very little or no electrical power and additional apparatuses such as sensor system and controller are not needed. To achieve the great actuating force/damping effect, the optimal shape and location of the actuators are determined by using genetic algorithms. The governing equations are derived by using extended Hamilton's principle. They are based on the nonlinear von Karman strain-displacement relationship for the panel structure and quasi-steady first-order piston theory for the supersonic airflow. The discretized finite element equations are obtained by using 4-node conforming plate element. A modal reduction is performed to the finite element equations in order to suppress the panel flutter effectively and nonlinear-coupled modal equations are obtained. Numerical suppression results, which are based on the reduced nonlinear modal equations, are presented in time domain by using Newmark nonlinear time integration method.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.81-90
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• 2005

When we transmit signals, which are quantized by the vector quantizer (VQ), through noisy channels, the overall performance of the coding system is very dependent on the employed quantization scheme and the channel error effect. In order to design an optimal coding system, the source and channel coding scheme should be jointly optimized as in the channel-optimized VQ. As a suboptimal approach, we may consider the robust VQ (RVQ). In RVQ, we consider developing an index assignment function for mapping the output of quantizers to channel symbols so that the effect of the channel errors is minimized. Recently, a VQ, which can reduce the encoding complexity and is called the sample-adaptive product quantizer (SAPQ), has been proposed. SAPQ has very similar quantizer structure as to the product quantizer (PQ). However, the quantization performance can be better than PQ. Further, the encoding complexity and the memory requirement for the codebooks are lower than the regular full-search VQ case. In this paper, SAPQ is employed in order to design an RVQ to channel errors by reducing the vector dimension. Discussions on the codebook structure of SAPQ and experiments are introduced in an aspect of robustness to noisy channels.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.187-196
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• 2011

Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.25-31
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• 2011

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction. Although PCA has been applied in many areas successfully, it is sensitive to outliers due to the use of sum-square-error. Several variants of PCA have been proposed to resolve the noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA2, however, still can fall into a local optimum due to equal initial membership values for all data points. Another reason comes from the fact that RF-PCA2 is based on sum-square-error although fuzzy memberships are incorporated. In this paper, a variant of RF-PCA2 called RF-PCA3 is proposed. The proposed algorithm is based on the objective function of RF-PCA2. RF-PCA3 augments RF-PCA2 with the objective function of PCA and initial membership calculation using data distribution, which make RF-PCA3 to have more chance to converge on a better solution than that of RF-PCA2. RF-PCA3 outperforms RF-PCA2, which is demonstrated by experimental results.

• The Journal of Information Technology
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• v.3 no.2
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• pp.73-85
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• 2000

The efficient algorithms are suggested in this study for solving the multicommodity network flow problems applied to Communications Systems. These problems are typical NP-complete optimization problems that require integer solution and in which the computational complexity increases numerically in appropriate with the problem size. Although the suggested algorithms are not absolutely optimal, they are developed for computationally efficient and produce near-optimal and primal integral solutions. We supplement the traditional Lagrangian method with a price-directive decomposition. It proceeded as follows. First, A primal heuristic from which good initial feasible solutions can be obtained is developed. Second, the dual is initialized using marginal values from the primal heuristic. Generally, the Lagrangian optimization is conducted from a naive dual solution which is set as ${\lambda}=0$. The dual optimization converged very slowly because these values have sort of gaps from the optimum. Better dual solutions improve the primal solution, and better primal bounds improve the step size used by the dual optimization. Third, a limitation that the Lagrangian decomposition approach has Is dealt with. Because this method is dual based, the solution need not converge to the optimal solution in the multicommodity network problem. So as to adjust relaxed solution to a feasible one, we made efficient re-allocation heuristic. In addition, the computational performances of various versions of the developed algorithms are compared and evaluated. First, commercial LP software, LINGO 4.0 extended version for LINDO system is utilized for the purpose of implementation that is robust and efficient. Tested problem sets are generated randomly Numerical results on randomly generated examples demonstrate that our algorithm is near-optimal (< 2% from the optimum) and has a quite computational efficiency.