• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.6
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• pp.322-332
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• 2014

This paper proposes a robust lane detection algorithm for non-flat roads by combining a piecewise linear model and dynamic programming. Compared with other lane models, the piecewise linear model can represent 3D shapes of roads from the scenes acquired by monocular camera since it can form a curved surface through a set of planar road. To represent the real road, the planar roads are created by various angles and positions at each section. And dynamic programming determines an optimal combination of planar roads based on lane properties. Experiment results demonstrate the robustness of proposed algorithm against non-flat road, curved road, and camera vibration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.679-686
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• 2020

In this thesis, an enhanced method for the feature extraction of vocal source signals and score combination using an MCE-Based weight estimation of the score of multiple feature vectors are proposed for the performance improvement of speaker recognition systems. The proposed feature vector is composed of perceptual linear predictive cepstral coefficients, skewness, and kurtosis extracted with lowpass filtered glottal flow signals to eliminate the flat spectrum region, which is a meaningless information section. The proposed feature was used to improve the conventional speaker recognition system utilizing the mel-frequency cepstral coefficients and the perceptual linear predictive cepstral coefficients extracted with the speech signals and Gaussian mixture models. In addition, to increase the reliability of the estimated scores, instead of estimating the weight using the probability distribution of the convectional score, the scores evaluated by the conventional vocal tract, and the proposed feature are fused by the MCE-Based score combination method to find the optimal speaker. The experimental results showed that the proposed feature vectors contained valid information to recognize the speaker. In addition, when speaker recognition is performed by combining the MCE-based multiple feature parameter scores, the recognition system outperformed the conventional one, particularly in low Gaussian mixture cases.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.41-53
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• 2002

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation suggested by the first report of this project. According to the regions and consecutive durations, optimal design rainfalls were derived by the regional frequency analysis for L-moment in the second report of this project. Using the LH-moment ratios and Kolmogorov-Smirnov test, the optimal regional probability distribution was identified to be the Generalized extreme value (GEV) distribution among applied distributions. regional and at-site parameters of the GEV distribution were estimated by the linear combination of the higher probability weighted moments, LH-moment. Design rainfall using LH-moments following the consecutive duration were derived by the regional and at-site analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design rainfall were computed and compared in the regional and at-site frequency analysis. Consequently, it was shown that the regional analysis can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than at-site analysis in the prediction of design rainfall. Relative efficiency (RE) for an optimal order of L-moments was also computed by the methods of L, L1, L2, L3 and L4-moments for GEV distribution. It was found that the method of L-moments is more effective than the others for getting optimal design rainfall according to the regions and consecutive durations in the regional frequency analysis. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• Structural Engineering and Mechanics
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• v.56 no.6
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• pp.959-982
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• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.4
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• pp.623-633
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• 2020

Objective: Substituting starch with digestible fiber (dF) can improve digestive health of rabbits and reduce costs. Therefore, it is necessary to develop a criterion for dF and starch supply. Effects of the dietary dF-to-starch ratio on pellet quality, growth and cecal microbiota of Angora rabbits were evaluated. Methods: Five isoenergetic and isoproteic diets with increasing dF/starch ratios (0.59, 0.66, 0.71, 1.05, and 1.44) were formulated. A total of 120 Angora rabbits with an average live weight of 2.19 kg were randomly divided into five groups with four replicates. At the end of 40 day feeding trial, cecal digesta were collected to analyse microbiota. Results: The results showed that the dF/starch ratio had linear effects on pellet variables (p<0.01). When the dF/starch ratio was 1.44, the pellets had the lowest powder and highest durability. The dF/starch ratio had unfavorable linear effects on growth variables (p<0.001). When analyzed by quadratic regression, the optimal dF/starch ratios for average weight gain and feed/gain were 0.59 and 0.74, respectively. There were differences in wool yield, fiber length and fiber diameter caused by the dF/starch ratio (p<0.05), and the dF/starch ratios that ranged from 0.66 to 1.06 were appropriate for good results. The cecal microbiota operational taxonomic unit (OTU) number index in the 1.05 dF/starch treatment was higher than that in the 0.66 and 0.71 dF/starch treatments. The higher dF/starch ratio resulted in a higher cecal microbiota OTU number index (p<0.05). The proportion of Ruminococcus in the 0.71 dF/starch treatment was higher than that in the 0.59 dF/starch treatment (p<0.05) Conclusion: The most suitable dF/starch ratio for feed pellet quality is 1.44, and for rabbit growth the optimal range of ratios is from 0.59 to 0.74. With combination of the wool growth, output cost, and cecal microbiota, we suggest that a dietary dF/starch ratio ranging from 0.74 to 1.06 is optimal.

• Structural Engineering and Mechanics
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• v.33 no.1
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• pp.79-93
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• 2009

This paper investigates the possibility of controlling the response of typical portal frame structures to blast loading using a combination of semi-active and passive control devices. A one storey reinforced concrete portal frame is modelled using non-linear finite elements with each column discretised into multiple elements to capture the higher frequency modes of column vibration response that are typical features of blast responses. The model structure is subjected to blast loads of varying duration, magnitude and shape, and the critical aspects of the response are investigated over a range of structural periods in the form of blast load response spectra. It is found that the shape or length of the blast load is not a factor in the response, as long as the period is less than 25% of the fundamental structural period. Thus, blast load response can be expressed strictly as a function of the momentum applied to the structure by a blast load. The optimal device arrangements are found to be those that reduce the first peak of the structural displacement and also reduce the subsequent free vibration of the structure. Semi-active devices that do not increase base shear demands on the foundations in combination with a passive yielding tendon are found to provide the most effective control, particularly if base shear demand is an important consideration, as with older structures. The overall results are summarised as response spectra for eventual potential use within standard structural design paradigms.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.