• Title/Summary/Keyword: Combined features

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Combined Features with Global and Local Features for Gas Classification

  • Choi, Sang-Il
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.9
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    • pp.11-18
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    • 2016
  • In this paper, we propose a gas classification method using combined features for an electronic nose system that performs well even when some loss occurs in measuring data samples. We first divide the entire measurement for a data sample into three local sections, which are the stabilization, exposure, and purge; local features are then extracted from each section. Based on the discrimination analysis, measurements of the discriminative information amounts are taken. Subsequently, the local features that have a large amount of discriminative information are chosen to compose the combined features together with the global features that extracted from the entire measurement section of the data sample. The experimental results show that the combined features by the proposed method gives better classification performance for a variety of volatile organic compound data than the other feature types, especially when there is data loss.

Hybrid Pattern Recognition Using a Combination of Different Features

  • Choi, Sang-Il
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.11
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    • pp.9-16
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    • 2015
  • We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

A Novel Modeling and Performance Analysis of Imperfect Quadrature Modulator in RF Transmitter

  • Park, Yong-Kuk;Kim, Hyeong-Seok;Lee, Ki-Sik
    • Journal of Electrical Engineering and Technology
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    • v.7 no.4
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    • pp.570-575
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    • 2012
  • In a wireless communication RF transmitter, the output of a quadrature modulator (QM) is distorted by not only the linear imperfection features such as in/quadrature-phase (I/Q) input gain imbalance, local phase imbalance, and local gain imbalance but also the nonlinear imperfection features such as direct current (DC) offset and mixer nonlinearity related to in-band spurious signal. In this paper, we propose the unified QM model to analyze the combined effects of the linear and nonlinear imperfection features on the performance of the QM. The unified QM model consists of two identical nonlinear systems and modified I/Q inputs based on the two-port nonlinear mixer model. The unified QM model shows that the output signals can be expressed by mixer circuit parameters such as intercept point and gain as well as the imperfection features. The proposed approach is validated by not only simulation but also measurement.

Content-based image retrieval using a fusion of global and local features

  • Hee Hyung Bu;Nam Chul Kim;Sung Ho Kim
    • ETRI Journal
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    • v.45 no.3
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    • pp.505-517
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    • 2023
  • Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.

Vibration-based damage detection in beams using genetic algorithm

  • Kim, Jeong-Tae;Park, Jae-Hyung;Yoon, Han-Sam;Yi, Jin-Hak
    • Smart Structures and Systems
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    • v.3 no.3
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    • pp.263-280
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    • 2007
  • In this paper, an improved GA-based damage detection algorithm using a set of combined modal features is proposed. Firstly, a new GA-based damage detection algorithm is formulated for beam-type structures. A schematic of the GA-based damage detection algorithm is designed and objective functions using several modal features are selected for the algorithm. Secondly, experimental modal tests are performed on free-free beams. Modal features such as natural frequency, mode shape, and modal strain energy are experimentally measured before and after damage in the test beams. Finally, damage detection exercises are performed on the test beam to evaluate the feasibility of the proposed method. Experimental results show that the damage detection is the most accurate when frequency changes combined with modal strain-energy changes are used as the modal features for the proposed method.

Recognition of Handwritten Numerals using Hybrid Features And Combined Classifier (복합 특징과 결합 인식기에 의한 필기체 숫자인식)

  • 박중조;송영기;김경민
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.5 no.1
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    • pp.14-22
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    • 2001
  • Off-line handwritten numeral recognition is a very difficult task and hard to achieve high recognition results using a single feature and a single classifier, since handwritten numerals contain many pattern variations which mostly depend upon individual writing styles. In this paper, we propose handwritten numeral recognition system using hybrid features and combined classifier. To improve recognition rate, we select mutually helpful features -directional features, crossing point feature and mesh features- and make throe new hybrid feature sets by using these features. These hybrid feature sets hold the local and global characteristics of input numeral images. And we implement combined classifier by combining three neural network classifiers to achieve high recognition rate, where fuzzy integral is used for multiple network fusion. In order to verify the performance of the proposed recognition system, experiments with the unconstrained handwritten numeral database of Concordia University, Canada were performed. As a result, our method has produced 97.85% of the recognition rate.

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Combination of 18F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma

  • Shen Li;Yadi Li;Min Zhao;Pengyuan Wang;Jun Xin
    • Korean Journal of Radiology
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    • v.23 no.9
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    • pp.921-930
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    • 2022
  • Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. Materials and Methods: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. Results: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. Conclusion: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.

Consequence Analysis on the Leakage Accident of Hydrogen Fuel in a Combined Cycle Power Plant: Based on the Effect of Regional Environmental Features (복합화력발전소 내 수소연료 적용 시 누출 사고에 대한 피해영향범위 분석: 지역별 환경 특성 영향에 기반하여)

  • HEEKYUNG PARK;MINCHUL LEE
    • Transactions of the Korean hydrogen and new energy society
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    • v.34 no.6
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    • pp.698-711
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    • 2023
  • Consequence analysis using an ALOHA program is conducted to calculate the accidental impact ranges in the cases of hydrogen leakage, explosion, and jet fire in a hydrogen fueled combined cycle power plant. To evaluate the effect of weather conditions and topographic features on the damage range, ALOHA is executed for the power plants located in the inland and coastal regions. The damage range of hydrogen leaked in coastal areas is wider than that of inland areas in all risk factors. The obtained results are expected to be used when designing safety system and establishing safety plans.

Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors

  • Rongping Ye;Shuping Weng;Yueming Li;Chuan Yan;Jianwei Chen;Yuemin Zhu;Liting Wen
    • Korean Journal of Radiology
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    • v.22 no.1
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    • pp.106-117
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    • 2021
  • Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

Combined Hepatocellular-Cholangiocarcinoma : Recent Progressin Pathology and Classification (복합간세포-담관암종 : 병리와 분류)

  • Choi, Joon-Hyuk
    • Journal of Yeungnam Medical Science
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    • v.28 no.1
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    • pp.1-12
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    • 2011
  • Primary liver carcinomas have classified classified into hepatocellular carcinoma, cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma (CHC). CHC is a tumor containing unequivocal, intimately mixed elements of both hepatocellular carcinoma and cholangiocarcinoma. It forms a small but significant proportion of primary liver carcinomas. The origin and pathogenesis of CHC have not been well established. According to the 2010 WHO classification, CHCs are categorized into 2 groups: the classical type and a subtype with stem cell features. This review describes recent progress in pathology and classification of CHC.

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