• Smart Structures and Systems
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• v.20 no.5
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• pp.595-605
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• 2017

The present article reported the thermal buckling strength of the sandwich shell panel structure and subsequent improvement of the same by embedding shape memory alloy (SMA) fibre via a general higher-order mathematical model in conjunction with finite element method. The geometrical distortion of the panel structure due to the temperature is included using Green-Lagrange strain-displacement relations. In addition, the material nonlinearity of SMA fibre due to the elevated thermal environment also incorporated in the current analysis through the marching technique. The final form of the equilibrium equation is obtained by minimising the total potential energy functional and solved computationally with the help of an original MATLAB code. The convergence and the accuracy of the developed model are demonstrated by solving similar kind of published numerical examples including the necessary input parameter. After the necessary establishment of the newly developed numerical solution, the model is extended further to examine the effect of the different structural parameters (side-to-thickness ratios, curvature ratios, core-to-face thickness ratios, volume fractions of SMA fibre and end conditions) on the buckling strength of the SMA embedded sandwich composite shell panel including the different geometrical configurations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4024-4041
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• 2022

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.461-467
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• 2020

In noisy and multi-speaker environments, the performance of speech recognition is unavoidably lower than in a clean environment. To improve speech recognition, in this paper, the signal of the speaker of interest is extracted from the mixed speech signals with multiple speakers. The VoiceFilter model is used to effectively separate overlapped speech signals. In this work, clustering by Probabilistic Linear Discriminant Analysis (PLDA) similarity score was employed to detect the speech signal of the interested speaker, which is used as the reference speaker to VoiceFilter-based separation. Therefore, by utilizing the speaker feature extracted from the detected speech by the proposed clustering method, this paper propose a speaker diarization system using only the mixed speech without an explicit reference speaker signal. We use phone-dataset consisting of two speakers to evaluate the performance of the speaker diarization system. Source to Distortion Ratio (SDR) of the operator (Rx) speech and customer speech (Tx) are 5.22 dB and -5.22 dB respectively before separation, and the results of the proposed separation system show 11.26 dB and 8.53 dB respectively.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.579-586
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• 2001

Digital watermarking is to embed imperceptible mark into image, video, audio, and text data to prevent the illegal copy of multimedia data. arbitrary modification, and also illegal sales of the copies without agreement of copyright ownership. In this paper, we study for the embedding and extraction of watermark key using wavelet in the luminance signal in order to implement the system to protect the copyright for image MPEG. First, the original image is analyzed into frequency domain by discrete wavelet transform. The RSA(Rivest, Shamir, Aldeman) public key of the coded target is RUN parameter of VLD(variable length coding). Because the high relationship among the adjacent RUN parameters effect the whole image, it prevents non-authorizer not to possess private key from behaving illegally. The Results show that the proposed method provides better moving picture and the distortion more key of insert than direct coded method on low-frequency domain based DCT.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.32-40
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• 2011

Reversible data hiding, which can recover the original image without any distortion after the extraction of the hidden data, has drawn considerable attention in recent years. However, underflow and overflow problems have occurred occasionally in the embedded image. To overcome these problems, we propose a new reversible data hiding algorithm which embeds a compressed location map used to identify these underflow and overflow points. In addition, the proposed algorithm allows for multilevel data hiding to increase the hiding capacity. The simulation results demonstrate that the proposed algorithm generates good performances in the PSNR, the embedding capacity, and the size of side information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4389-4411
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• 2018

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.205-211
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• 2019

Steganalysis is a technique that aims to detect and recover data hidden by steganography. Steganalytic methods detect hidden data by analyzing visual and statistical distortions caused during data embedding. However, for recovering the hidden data, they need to know which steganographic methods the hidden data has been embedded by. Therefore, we propose a hierarchical convolutional neural network (CNN) structure that identifies a steganographic method applied to an input image through multi-level classification. We trained four base CNNs (each is a binary classifier that determines whether or not a steganographic method has been applied to an input image or which of two different steganographic methods has been applied to an input image) and connected them hierarchically. Experimental results demonstrate that the proposed hierarchical CNN structure can identify four different steganographic methods (LSB, PVD, WOW, and UNIWARD) with an accuracy of 79%.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.45.1-45.1
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• 2016

Spatially rotating magnetic fields have been observed in the solar wind and in the Earth's magnetopause as well as in reversed field pinch (RFP) devices. Such field configurations have a similarity with extended current layers having a spatially varying plasma pressure instead of the spatially varying guide field. It is thus expected that magnetic reconnection may take place in a rotating magnetic field no less than in an extended current layer. We have investigated the spontaneous evolution of a collisionless plasma system embedding a rotating magnetic field with a two-and-a-half-dimensional electromagnetic particle-in-cell (PIC) simulation. In magnetohydrodynamics, magnetic flux can be decreased by diffusion in O-lines. In kinetic physics, however, an asymmetry of the velocity distribution function can generate new magnetic flux near O- and X-lines, hence a dynamo effect. We have found that a magnetic-flux-reducing diffusion phase and a magnetic-flux-increasing dynamo phase are alternating with a certain period. The temperature of the system also varies with the same period, showing a similarity to sawtooth oscillations in tokamaks. We have shown that a modified theory of sawtooth oscillations can explain the periodic behavior observed in the simulation. A strong guide field distorts the current layer as was observed in laboratory experiments. This distortion is smoothed out as magnetic islands fade away by the O-line diffusion, but is soon strengthened by the growth of magnetic islands. These processes are all repeating with a fixed period. Our results suggest that a rotating magnetic field configuration continuously undergoes deformation and relaxation in a short time-scale although it might look rather steady in a long-term view.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1946-1956
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• 2001

In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.