• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.151-156
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• 2018

Because of its large differences in writing style, context-independency and high recognition accuracy requirement, free handwritten digital identification is still a very difficult problem. Analyzing the characteristic of handwritten digits, this paper proposes a new handwritten digital identification method based on combining structural features. Given a handwritten digit, a variety of structural features of the digit including end points, bifurcation points, horizontal lines and so on are identified automatically and robustly by a proposed extended structural features identification algorithm and a decision tree based on those structural features are constructed to support automatic recognition of the handwritten digit. Experimental result demonstrates that the proposed method is superior to other general methods in recognition rate and robustness.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.11
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• pp.30-41
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• 2010

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM. The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.

• Structural Engineering and Mechanics
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• v.54 no.5
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• pp.891-908
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• 2015

For a systematic study on wind-induced vibration characteristics of large hyperbolic cooling towers with different feature sizes, the pressure measurement tests are finished on the rigid body models of three representative cooling towers with the height of 155 m, 177 m and 215 m respectively. Combining the refined frequency-domain algorithm of wind-induced responses, the wind-induced average response, resonant response, background response, coupling response and wind vibration coefficients of large cooling towers with different feature sizes are obtained. Based on the calculating results, the parametric analysis on wind-induced vibration of cooling towers is carried out, e.g. the feature sizes, damping ratio and the interference effect of surrounding buildings. The discussion shows that the increase of feature sizes makes wind-induced average response and fluctuating response larger correspondingly, and the proportion of resonant response also gradually increased, but it has little effect on the wind vibration coefficient. The increase of damping ratio makes resonant response and the wind vibration coefficient decreases obviously, which brings about no effect on average response and background response. The interference effect of surrounding buildings makes the fluctuating response and wind vibration coefficient increased significantly, furthermore, the increase ranges of resonant response is greater than background response.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Smart Structures and Systems
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• v.19 no.1
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• pp.11-20
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• 2017

For structural damage detection of shear buildings, this paper proposes a new concept using structural element mass-stiffness vector (SEMV) based on special mass and stiffness distribution characteristics. A corresponding damage identification method is developed combining the SEMV with the cross-model cross-mode (CMCM) model updating algorithm. For a shear building, a model is assumed at the beginning based on the building's distribution characteristics. The model is updated into two models corresponding to the healthy and damaged conditions, respectively, using the CMCM method according to the modal parameters of actual structure identified from the measured acceleration signals. Subsequently, the structural SEMV for each condition can be calculated from the updated model using the corresponding stiffness and mass correction factors, and then is utilized to form a new feature vector in which each element is calculated by dividing one element of SEMV in health condition by the corresponding element of SEMV in damage condition. Thus this vector can be viewed as a damage detection feature for its ability to identify the mass or stiffness variation between the healthy and damaged conditions. Finally, a numerical simulation and the laboratory experimental data from a test-bed structure at the Los Alamos National Laboratory were analyzed to verify the effectiveness and reliability of the proposed method. Both simulated and experimental results show that the proposed approach is able to detect the presence of structural mass and stiffness variation and to quantify the level of such changes.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.27-33
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• 2010

In this study, we conducted the classification experiments with GMM (Gaussian Mixture Model) from combining the extracted features by using microphone, Gyro sensor and Acceleration sensor in 9 different environment types. Existing studies of Context Aware wanted to recognize the Environment situation mainly using the Environment sound data with microphone, but there was limitation of reflecting recognition owing to structural characteristics of Environment sound which are composed of various noises combination. Hence we proposed the additional application methods which added Gyro sensor and Acceleration sensor data in order to reflect recognition agent's movement feature. According to the experimental results, the method combining Acceleration sensor data with the data of existing Environment sound feature improves the recognition performance by more than 5%, when compared with existing methods of getting only Environment sound feature data from the Microphone.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1334-1337
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• 2007

A steel pallet to carry lighter logistic articles is developed based on the DFSS(design for Six Sigma) methodology. Combining the conventional DFSS(Design For Six Sigma) methodology with that of VE(Value Engineering) is the novel feature of this paper to achieve maximum cost reduction. In this paper, systematical steps to achieve the required structural spec's are presented by conventional DMEDI(Define-Measure-Explore-Develop-Implement) process. To imply the target costing, evaluation of functions consisting of the pallet has been performed by value methodology. Then best design concept is selected in the Explore step, following structural optimization utilizing FEM. Finally the performance of prototype is investigated by pilot test in the Implement step. The developed steel pallet is being commercialized in the fields of automated ware house.

• Smart Structures and Systems
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• v.20 no.6
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• pp.683-696
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• 2017

In this study, the reliability of nonlinear ultrasonic modulation based fatigue crack detection is improved using a feature-level data fusion approach. When two ultrasonic inputs at two distinct frequencies are applied to a specimen with a fatigue crack, modulation components at the summation and difference of these two input frequencies appear. First, the spectral amplitudes of the modulation components and their spectral correlations are defined as individual features. Then, a 2D feature space is constructed by combining these two features, and the presence of a fatigue crack is identified in the feature space. The effectiveness of the proposed fatigue crack detection technique is experimentally validated through cyclic loading tests of aluminum plates, full-scale steel girders and a rotating shaft component. Subsequently, the improved reliability of the proposed technique is quantitatively investigated using receiver operating characteristic analysis. The uniqueness of this study lies in (1) improvement of nonlinear ultrasonic modulation based fatigue crack detection reliability using feature-level data fusion, (2) reference-free fatigue crack diagnosis without using the baseline data obtained from the intact condition of the structure, (3) application to full-scale steel girders and shaft component, and (4) quantitative investigation of the improved reliability using receiver operating characteristic analysis.

• Bulletin of the Korean Chemical Society
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• v.24 no.5
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• pp.569-572
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• 2003

The majority of dyes belong to the chromophoric class known as donor-acceptor systems. The essential structural feature of such systems is the presence of one or more electron donating groups conjugated to one or more electron withdrawing groups via an unsaturated bridge. The pyrazolin-5-one system is an effective electron acceptor residue, and can also act as a weak electron donor. In our experiments, the various symmetrical and unsymmetrical H-chromophores were synthesized in the indoxyl, imidazo[1,2-a]pyridin-2-one, pyrazolin-5-one, and pyridin-2,6-dione residues, resulting in cross-conjugated donor-acceptor systems. And the visible light absorption was then associated with the migration of electron density from the donor region of the molecule to the acceptor region. Also, it was useful to prepare related non-cross-conjugated donor acceptor chromophores by combining these residues with other electron donor or acceptor moieties. For convenience such chromophores are referred to as merocyanines.