• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1165-1172
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• 2006

The rapid progress of the software has enabled individuals to copy and remark digital contents, which was only done by professionals. As a solution for the problems, contents producer needs to have certification and inspection of its contents and hold the proprietary right. A fragile watermarking method is able to detect the distortion and damage of watermarked image, but the watermark is also fragile on standardized image compression. That is the problem of fragile watermarking technique. We propose semi-fragile watermarking technique that is robust in the image processing such as JPEG compression used on computer, but it is fragile on the addition of noise and other attacks. In the proposed method, we can generate the watermarks of an image from the relationship between two block coefficients, also the generated watermark is inserted into insensible part of HVS(Human Visual System) after processing DCT(Discrete Cosine Transform) and using quantization. As a result, on the spatial domain, high detection of distortion has been possible even in slight changes, and could detect the corrupted blocks on image. Therefore, it is simple to judge the pixels at which some location has been changed in the space.

• Structural Engineering and Mechanics
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• v.43 no.3
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• pp.371-394
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• 2012

Accurate finite element (FE) models are needed in many applications of Civil Engineering such as health monitoring, damage detection, structural control, structural evaluation and assessment. Model accuracy depends on both the model structure (the form of the equations) and the model parameters (the coefficients of the equations), and can be generally improved through that process of experimental reconciliation known as model updating. However, modelling errors, including (i) errors in the model structure and (ii) errors in parameters excluded from adjustment, may bias the solution, leading to an updated model which replicates measurements but lacks physical meaning. In this paper, an application of ambient-vibration-based model updating to a large-scale benchmark prototype of a building structure is reported in which both types of error are met. The error in the model structure, originating from unmodelled secondary structural elements unexpectedly working as resonant appendages, is faced through a reduction of the experimental modal model. The error in the model parameters, due to the inevitable constraints imposed on parameters to avoid ill-conditioning and under-determinacy, is faced through a multi-model parameterization approach consisting in the generation and solution of a multitude of models, each characterized by a different set of updating parameters. Results show that modelling errors may significantly impair updating even in the case of seemingly simple systems and that multi-model reasoning, supported by physical insight, may effectively improve the accuracy and robustness of calibration.

• Smart Structures and Systems
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• v.6 no.4
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• pp.405-421
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• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Smart Structures and Systems
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• v.26 no.3
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• pp.345-360
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• 2020

Modern swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.21-30
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• 2014

The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

• Transactions on Electrical and Electronic Materials
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• v.1 no.4
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• pp.1-6
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• 2000

The mechanical strength of silicon carbide dose nor permit the use of diffusion as a means to achieve selective doping as required by most electronic devices. While epitaxial layers may be doped during growth, ion implantation is needed to define such regions as drain and source wells, junction isolation regions, and so on. Ion activation without an annealing cap results in serious crystal damage as these activation processes must be carried out at temperatures on the order of 1600$^{\circ}C$. Ion implanted silicon carbide that is annealed in either a vacuum or argon environment usually results in a surface morphology that is highly irregular due to the out diffusion of Si atoms. We have developed and report a successful process of using silicon overpressure, provided by silane in a CAD reactor during the anneal, to prevent the destruction of the silicon carbide surface, This process has proved to be robust and has resulted in ion activation at a annealing temperature of 1600$^{\circ}C$ without degradation of the crystal surface as determined by AFM and RBS. In addition XPS was used to look at the surface and near surface chemical states for annealing temperatures of up to 1700$^{\circ}C$. The surface and near surface regions to approximately 6 nm in depth was observed to contain no free silicon or other impurities thus indicating that the process developed results in an atomically clean SiC surface and near surface region within the detection limits of the instrument(${\pm}$1 at %).