• Multiscale and Multiphysics Mechanics
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• v.1 no.4
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• pp.327-340
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• 2016

Poly(${\epsilon}$-caprolactone) (PCL) diol, with good biodegradation and biocompatibility, is one of the widely used soft segments (SSs) in composing bio-polyester-urethanes (Bio-PUs), which show great potential in both biomedical and tissue engineering applications. Properties of Bio-PUs are tunable by combining SS monomers with different molecular weights, structures, modifications, and ratio of components. Although numbers of research have reported many Bio-PUs properties, few studies have been done at the molecular scale. In this study, we use molecular dynamic (MD) simulation to construct atomistic models for two commonly used PCL diol SSs with different molecular weights 1247.58 Da and 1932.42 Da. We compare the simulation results by using two widely used classical force fields for organic molecules: Consistent Valence Force Field (CVFF) and CHARMM General Force Field (CGenFF), and discuss the validity and accuracy. Melt density, volume, polymer conformations, transition temperature, and mechanical properties of PCL diols are calculated and compared with experiments. Our results show that both force fields provide accurate predictions on the properties of PCL diol system at the molecular scale and could help the design of future Bio-PUs.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.51-56
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• 2001

Recent researches have been focusing on jointly using lip motions and speech for reliable speech recognitions in noisy environments. To this end, this paper proposes the method of combining the visual speech recognizer and the conventional speech recognizer with each output properly weighted. In particular, we propose the method of autonomously determining the weights, depending on the amounts of noise in the speech. The correlations between adjacent speech samples and the residual errors of the LPC analysis are used for this determination. Simulation results show that the speech recognizer combined in this way provides the recognition performance of 83 ％ even in severely noisy environments.

• ETRI Journal
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• v.42 no.6
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• pp.886-898
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• 2020

Unusual data patterns or outliers can be generated because of human errors, incorrect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to create a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are presented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.757-768
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• 2015

Recently, climate change has affected short time concentrated local rainfall and unexpected heavy rain which is increasingly causing life and property damage. In this research, arithmetic average analysis, weighted average analysis, and principal component analysis are used for predicting flood risk. This research is foundation for application of predicting flood risk based on annals of disaster and status of urban planning. Results obtained by arithmetic average analysis, weighted average analysis, and principal component analysis using many factors affect on flood are compared. In case of arithmetic average analysis, each factor has same weights though it is simple method. In case of weighted average analysis, correlation factors are complex by many variables and multicollinearty problem happen though it has different weights. For solving these problems, principal component analysis (PCA) is used because each factor has different weights and the number of variables is smaller than other methods by combining variables. Finally, flood risk assessment considering flood control ability and urban environment risk in former research is predicted.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.793-810
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• 2022

Purpose: As improving the search performance of aviation security equipment is considered essential, this study proposes the need for research on how to find an optimized test sequence that can reduce test time and operator power during the search function test of explosive detection systems. We derive the weights and work difficulty adjustment factor required to find the optimized test order. Methods: First, after setting the test factors, the time of each test and the difficulty scale determined by the worker who performed the test directly were used to derive weights. Second, the work difficulty adjustment coefficient was determined by combining the basic weight adjustment factor and corresponding to the body part used by the test using objective rating. Then the final standard time was derived by calculating the additional weights for the changeability of the test factors. Results: The order in which the final standard time is minimized when 50 tests are performed was defined as the optimized order. 50 tests should be conducted without duplication and the optimal order of tests was obtained when compared to previously numbered tests. As a result of minimizing the total standard time by using Excel's solver parameters, it was reduced by 379.14 seconds, about 6.32 minutes. Conclusion: We tried to express it in mathematical formulas to propose a method for setting an optimized test sequence even when testing is performed on other aviation security equipment. As a result, the optimal test order was derived from the operator's point of view, and it was demonstrated by minimizing the total standard time.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.292-293
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• 2008

In this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images are hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.258-262
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• 2005

An adaptive beamforming system based on code filtering and differential correlation approaches is proposed. The differential correlation method was originally proposed for time delay estimation of direct sequence code division multiple access (DS-CDMA) systems under near-far ratio conditions and the code filtering correlation algorithm, on the other hand, was proposed for array response estimation in DS-CDMA systems under perfect power control. In this paper, by combining differential correlation concept with the code filtering beamforming technology, an accurate estimate of the beam forming weights and an enhanced performance of DS-CDMA systems under sever near-far ratio conditions is achieved. The system performance in terms of beam pattern and bit-error-rate (HER) shows that the proposed adaptive beamformer outperforms the conventional code filtering correlation technique.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.816-821
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• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• Journal of Environmental Impact Assessment
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• v.6 no.1
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• pp.93-103
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• 1997

LHR(Landfill Site Hazard Ranking Model) was developed for ranking the relative hazard of landfill sites by using the method of value-structured approach. LHR consists of combining a multiattribute decision-making method with a Qualitative risk assessment approach. A pairwise com parisian method was applied to determine weights of landfill site factors related. To determine the hazard of landfill site, hydrogeological factors, waste characteristics factors and receptors factors were evaluated by LHR. LHR can help decision-makers prioritization of remediation of landfill sites through the relatively convenient and concise evaluation method of landfill site features related. LHR focuses mainly on pathways of groundwater and surfacewater for evaluating landfill hazard to receptors including humans. To validiate the applicability of LHR, Nanjido Landfill site, Metropolitan Landfill site, and Hwasung Landfill site were evaluated.