• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.55
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• pp.1-11
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• 2000

Taguchi's robust design methodology has focus only a single characteristic or response, but the quality of most products is seldom defined by a characteristics, and is rather the composite of a family of characteristics which are often interrelated and nearly always measured in a variety of units. The multiple characteristics problem is how to compromise the conflicts among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. Also, We propose a decision model to incorporates the values assigned by a group of experts on different factors in weighting decision. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for weighting decision.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.251-254
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• 2000

The critical problem in dealing with multiple characteristics is how to compromise the conflict among the selected levels of the design parameters for each individual characteristic. In this study, First, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. Second, to solve the issue on the optimal design for multiple quality characteristics, this study modelled the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of the terms. The model will be used to determine the global optimal design parameters where there exists the conflict among the characteristics, which shows difference in optimal design parameters for the individual characteristics. Third, this paper propose a decision model to incorporates the values assigned by a group of experts on different factors in weighting decision of characteristic. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for weighting decision of characteristic.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.530-535
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• 2007

Disparity space image (DSI) technique is a method of establishing correspondence between a pair of images. It has a merit of generating a dense disparity map for each pixel. DSI has a cost function to be minimized, and it needs empirical weighting factors for occlusion penalty and match reward. This paper provides theoretical basis for the weighting factors, which depend on image noise and contrast between an object and background.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.295-302
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• 2003

A quantitative approach for the retrofit prioritization of bridges is developed based on the damage risk of seismic vulnerable components. In the developed approach, seismic damage risk is estimated in the probabilistic perspectives with an analytical bridge model, which can consider various phenomena found in the seismic behaviors of girder-type bridges and damage models of various vulnerable components. Based on the total cost due to failure of structural components, weighting factors are proposed. Finally, the ranking index and retrofit priority of bridges are estimated from the overall damage risk and weighting factors of bridges. As a result, the retrofit priority of four PSC girder bridges is evaluated by using the proposed approach. The vulnerable components in need of seismic retrofit are selected accordingly. From simulated results, the validity of the proposed approach is verified by comparison with the existing approach. In addition, the proposed approach is found to be appropriate in evaluating the priority of existing bridges.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.228-228
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• 1997

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1333-1341
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• 2009

Tactical path-finding in computer games is path-finding where a path is selected by considering not only basic elements such as the shortest distance or the minimum time spend but also tactical information of surroundings when deciding character's moving trajectory. One way to include tactical information in path-finding is to represent a heuristic function as a sum of tactical quality multiplied by a weighting factor which is.. determined based on the degree of its importance. The choice of weighting factors for tactics is very important because it controls search performance and the characteristic of paths found. In this paper. we propose a method for improving a heuristic function by adjusting weights based on the difference between paths on examples given by a level designer and paths found during the search process based on the CUITent weighting factors. The proposed method includes the search algorithm modified to detect search errors and learn heuristics and the perceptron-like weight updating formular. Through simulations it is demonstrated how different paths found by tactical path-finding are from those by traditional path-finding. We analyze the factors that affect the performance of learning and show the example applied to the real game environments.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Mass Spectrometry Letters
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• v.13 no.1
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• pp.11-19
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• 2022

Although translational research is referred to clinical chemistry measures, correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm have not been carefully considered in bioanalytical assays yet. The objective of this study was to identify steroidogenic roles in preeclampsia and verify accuracy of quantitative results by comparing two different linear regression models with weighting factor of 1 and 1/x². A liquid chromatography-mass spectrometry (LC-MS)-based adrenal steroid assay was conducted to reveal metabolic signatures of preeclampsia in both serum and placenta samples obtained 15 preeclamptic patients and 17 age-matched control pregnant women (33.9 ± 4.2 vs. 32.8 ± 5.6 yr, respectively) at 34~36 gestational weeks. Percent biases in the unweighted model (w_i = 1) were inversely proportional to concentrations (-739.4 ~ 852.9%) while those of weighted regression (w_i = 1/x²) were < 18% for all variables. The optimized LC-MS combined with the weighted linear regression resulted in significantly increased maternal serum levels of pregnenolone, 21-deoxycortisol, and tetrahydrocortisone (P < 0.05 for all) in preeclampsia. Serum metabolic ratio of (tetrahydrocortisol + allo-tetrahydrocortisol) / tetrahydrocortisone indicating 11β-hydroxysteroid dehydrogenase type 2 was decreased (P < 0.005) in patients. In placenta, local concentrations of androstenedione were changed while its metabolic ratio to 17α-hydroxyprogesterone responsible for 17,20-lyase activity was significantly decreased in patients (P = 0.002). The current bioanalytical LC-MS assay with corrected weighting factor of 1/x² may provide reliable and accurate quantitative outcomes, suggesting altered steroidogenesis in preeclampsia patients at late gestational weeks in the third trimester.

• Journal of Korean society of Dental Hygiene
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• v.16 no.3
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• pp.337-346
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• 2016

Objectives: This study aims to investigate and analyzed the priority of vocational core competency factors in dental hygienists in Gwangju. Methods: Expert survey was conducted and Analytic Hierarchy Process(AHP) was applied to evaluate the weighting factors. First, we established the vocational core competency defined in NCS as AHP analysis model. The vocational core competency has 10 categories and 34 sub-categories. Secondly, AHP survey was conducted by 195 dental hygienists in Gwangju. Finally, the weights representing relative importance of each factor were calculated by using AHP method. Results: The AHP analysis on 10 categories showed that the weighting of interpersonal skills(0.165) was higher than any other categories while that of numeracy(0.035) was at the bottom, and the analysis on sub-categories revealed that the most important factors in each categories included the teamwork skills(interpersonal skills), problem-solving capability(problem-solving skills), listening skills(communication skills), ethical community(professional ethics), ability to understand business(ability to understand organizational structure), applicable technical skills(technical skills), self-management skills(self-development capability), information processing capabilities(information capacity), ability to manage time(resource management capabilities) and basic math skills(numeracy). Conclusions: The results in this study can be used as basic data for the development of liberal arts curriculum for dental hygiene education.