• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Journal of Electrochemical Science and Technology
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• v.7 no.4
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• pp.286-292
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• 2016

Chemically amplified electrochemical detection, redox-active probe being amplified its electrochemical anodic current by a sacrificial electron donor presenting in solution, holds great potential for simple and quantitative bioanalytical analysis. Herein, we report the chemically amplified electrochemical analysis that drastically enhanced a detection of ferrocenemethanol (analyte) by ferrocyanide (chemical amplifier) on 4-nitrophenyl grafted glassy carbon electrodes at $60^{\circ}C$. The glassy carbon electrode grafted with a 4-nitrophenyl group using an electrochemical reduction suppressed the oxidation of ferrocyanide and thus enabled detection of ferrocenemethanol with excellent selectivity. The ferrocenemethanol was detected down to an nM range using a linear sweep voltammetry under kinetically optimized conditions. The detection limit was improved by decreasing the concentration of the ferrocyanide and increasing temperature.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.6
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• pp.50-55
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• 2003

This study proposed the analysis of damage detection due to the change of the stiffness of structure by using the original and modified dynamic characteristics. The present approach allows the use of composite data which consist of eigenvalues and eigenvectors. The suggested method is applied to examples of a cantilever and 3 degree of freedom system by modifying the stiffness. The predicted damage detections are in good agreement with these from the structural reanalysis using the modified stiffness.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.69-79
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• 2017

In this paper, we propose our four-phase life cycle of P2P botnet with corresponding detection methods and the future direction for more effective P2P botnet detection. Our proposals are based on the intensive analysis that compares existing P2P botnet detection schemes in different points of view such as life cycle of P2P botnet, machine learning methods for data mining based detection, composition of data sets, and performance matrix. Our proposed life cycle model composed of linear sequence stages suggests to utilize features in the vulnerable phase rather than the entire life cycle. In addition, we suggest the hybrid detection scheme with data mining based method and our proposed life cycle, and present the improved composition of experimental data sets through analysing the limitations of previous works.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.64-70
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• 2005

In this literature, fault detection and classification of faulty induction motors are carried out through Z-index and frequency analysis. Above frequency analysis refer Fourier transformation and Wavelet transformation. Z-index is defined as the similar form of energy function, also the faulty and healthy conditions are classified through Z-index. For the detection and classification feature extraction for the fault detection of an induction motor is carried out using the information from stator current. Fourier and Wavelet transforms are applied to detect the characteristics under the healthy and various faulty conditions. We can obtain feature vectors from two transformations, and the results illustrate that the feature vectors are complementary each other.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.912-928
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• 2019

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.863-871
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• 2018

Existing vulnerability analysis tools are prone to missed detections, incorrect detections, and over-detection, which reduces accuracy. In this paper, cross-checking based on a vulnerability detection method using static and dynamic analysis is proposed, which develops and manages safe applications and can resolve and analyze these problems. Risks due to vulnerabilities are computed, and an intelligent vulnerability detection technique is used to improve accuracy and evaluate risks under the final version of the application. This helps the development and execution of safe applications. Through incorporation of tools that use static analysis and dynamic analysis techniques, our proposed technique overcomes weak points at each stage, and improves the accuracy of vulnerability detection. Existing vulnerability risk-evaluation systems only evaluate self-risks, whereas our proposed vulnerability risk-evaluation system reflects the vulnerability of self-risk and the detection accuracy in a complex fashion to evaluate relative. Our proposed technique compares and analyzes existing analysis tools, such as lists for detections and detection accuracy based on the top 10 items of SANS at CWE. Quantitative evaluation systems for existing vulnerability risks and the proposed application's vulnerability risks are compared and analyzed. We developed a prototype analysis tool using our technique to test the application's vulnerability detection ability, and to show that our proposed technique is superior to existing ones.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.379-392
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• 2015

A number of acceleration-based damage detection methods have been developed but they have not been widely applied in engineering practices because the acceleration response is insensitive to minor damage of civil structures. In this article, a damage detection approach using the long-gauge strain sensing technology and the principle component analysis technology is proposed. The Long gauge FBG sensor has its special merit for damage detection by measuring the averaged strain over a long-gauge length, and it can be connected each other to make a distributed sensor network for monitoring the large-scale civil infrastructure. A new damage index is defined by performing the principle component analyses of the long-gauge strains measured from the intact and damaged structures respectively. Advantages of the long gauge sensing and the principle component analysis technologies guarantee the effectiveness for structural damage localization. Examples of a simple supported beam and a steel stringer bridge have been investigated to illustrate the successful applications of the proposed method for structural damage detection.

• International Journal of Contents
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• v.9 no.3
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.