• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• Journal of Power Electronics
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• v.16 no.1
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• pp.48-56
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• 2016

This paper proposes a method for detecting irreversible demagnetization using the harmonic analysis of back electromotive force (BEMF) in interior permanent magnet-type brushless DC motors. First, demagnetization patterns, such as equality, inequality, and weighted demagnetizations, are defined and classified by considering the possibility of demagnetization resulting from motor operating characteristics. Second, an available diagnostic model for the harmonic analysis of BEMFs is defined according to pole-slot coefficients because the characteristics of BEMFs under demagnetization conditions are affected by the combination of poles and slots. Third, BEMFs and their harmonic components under normal and demagnetization conditions are analyzed through simulation and experiment to verify the proposed demagnetization detection technique.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1209-1225
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• 2023

In this paper, we defined some new classes of analytic functions in conic domains. We investigate some important properties such as necessary and sufficient conditions, coefficient estimates, convolution results, linear combination, weighted mean, arithmetic mean, radii of starlikeness and distortion for functions in these classes. It is important to mentioned that our results are generalization of number of existing results in the literature.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.47-55
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• 2018

In this paper, we propose a novel face recognition(FR) method that takes advantage of combining weighted deep local features extracted from multiple Deep Convolutional Neural Networks(DCNNs) learned with a set of facial local regions. In the proposed method, the so-called weighed deep local features are generated from multiple DCNNs each trained with a particular face local region and the corresponding weight represents the importance of local region in terms of improving FR performance. Our weighted deep local features are applied to Joint Bayesian metric learning in conjunction with Nearest Neighbor(NN) Classifier for the purpose of FR. Systematic and comparative experiments show that our proposed method is robust to variations in pose, illumination, and expression. Also, experimental results demonstrate that our method is feasible for improving face recognition performance.

• 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.

• The Journal of Korean Academy of Prosthodontics
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• v.27 no.1
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• pp.83-102
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• 1989

This study was done to analyze the sound distortion, before and after insertion of the palatal palates. For this study, 4 healthy subjects (3 males and 1 female, each 24-year-old), who were born in Seoul were recruited from K university, and 3 type palatal plates were fabricated, each palatal thickness being 1.0mm, 2.5mm, dentoalveolar portion 2.5mm and elsewhere 1.0mm, named B,C,D-type repectively, and informants's sounds of /사(sa), 서(se), 소(so), 수(su), 스($s\.{+}$), 시(si)/ were recorded, without plate, and with palatal plates of different types, in succession. A series of analysis were adminstered through a 16 Bit IBM PC/AT using linear combination methods. These experiments were analyzed by the Cepstrum (Weighted and Euclidian), Log Area Ratio, Linear prediction correlation methods The findings led to the following conclusions : 1. It was confirmed that the same consonant, /ㅅ(s)/, variously distorted by the following vowel. 2. By and large, 시($s\.{+}$) was the most distorted in all conditions, and (sa), 소(so) were the least distorted in each condition. 3. There were no persistant correlation of the palatal plate types, and sound distortions of each informant were diverse with no regularities. 4. There were persistent correaltion to the Cepstrum (Weighted, Euclidian), Log Area Ratio. However, Linear prediction correlation has a different alteration pattern.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Investigative Magnetic Resonance Imaging
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• v.24 no.1
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• pp.38-45
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• 2020

Background: Differentiation of cerebellopontine angle (CPA) schwannoma from meningioma is often a difficult process to identify. Purpose: To identify imaging features for distinguishing CPA schwannoma from meningioma and to investigate the usefulness of susceptibility-weighted imaging (SWI) in differentiating them. Materials and Methods: Between March 2010 and January 2015, this study pathologically confirmed 11 meningiomas and 20 schwannomas involving CPA with preoperative SWI were retrospectively reviewed. Generally, the following MRI features were evaluated: 1) maximal diameter on axial image, 2) angle between tumor border and adjacent petrous bone, 3) presence of intratumoral dark signal intensity on SWI, 4) tumor consistency, 5) blood-fluid level, 6) involvement of internal auditory canal (IAC), 7) dural tail, and 8) involvement of adjacent intracranial space. On CT, 1) presence of dilatation of IAC, 2) intratumoral calcification, and 3) adjacent hyperostosis were evaluated. All features were compared using Chi-squared tests and Fisher's exact tests. The univariate and multivariate logistic regression analysis were performed to identify imaging features that differentiate both tumors. Results: The results noted that schwannomas more frequently demonstrated dark spots on SWI (P = 0.025), cystic consistency (P = 0.034), and globular angle (P = 0.008); schwannomas showed more dilatation of internal auditory meatus and lack of calcification (P = 0.008 and P = 0.02, respectively). However, it was shown that dural tail was more common in meningiomas (P < 0.007). In general, dark spots on SWI and dural tail remained significant in multivariate analysis (P = 0.037 and P = 0.012, respectively). In this case, the combination of two features showed a sensitivity and specificity of 80% and 100% respectively, with an area under the receiver operating characteristic curve of 0.9. Conclusion: In conclusion, dark spots on SWI were found to be helpful in differentiating CPA schwannoma from meningioma. It is noted that combining dural tail with dark spots on SWI yielded strong diagnostic value in differentiating both tumors.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.