• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.366-381
• /
• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.8
• /
• pp.873-877
• /
• 2010

CAPTCHA is a security tool that prevents the automatic sign-up by a spam or a robot. This CAPTCHA usually depends on the smart readability of humans. However, the common and plain CAPTCHA with text-based system is not difficult to be solved by intelligent web-bot and machine learning tools. In this paper, we propose a new sub-image based CAPTCHA system totally different from the text based system. Our system offers a set of cropped sub-image from a whole digital picture and asks user to identify the correct orientation. Though there are some nice machine learning tools for this job, but they are useless for a cropped sub-images, which was clearly revealed by our experiment. Experiment showed that our sub-image based CAPTCHA is easy to human solver, but very hard to all kinds of machine learning or AI tools. Also our CAPTCHA is easy to be generated automatical without any human intervention.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.2
• /
• pp.148-161
• /
• 2015

The examples that used camera in the vehicle is increasing with the growth of the automotive market, and the importance of the image processing technique is expanding. In particular, the Lane Departure Warning System (LDWS) and related technologies are under development in various fields. In this paper, in order to improve the lane recognition rate more than the conventional method, we extract a Normalized Luminance Descriptor value and a Normalized Contrast Descriptor value, and adjust image gamma values to modulate Normalized Image Quality by using the correlation between the extracted two values. Then, we apply the Hough transform using the optimized accumulator cells to the four-channel ROI. The proposed algorithm was verified in 27 frame/sec and $640{\times}480$ resolution. As a result, Lane recognition rate was higher than the average 97% in day, night, and late-night road environments. The proposed method also shows successful lane recognition in sections with curves or many lane boundary.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.1
• /
• pp.107-114
• /
• 2017

Iris diagnosis is an alternative medicine to diagnose the disease of the patient by using different of the iris pattern, color and other characteristics. This paper proposed a disease prediction algorithm that using the iris regions that analyze iris change to using differential image of iris image. this method utilize as patient's health examination according to iris change. Because most of previous studies only find a sign pattern in a iris image, it's not enough to be used for a iris diagnosis system. We're developed an iris diagnosis system based on a iris images processing approach, It's presents the extraction algorithms of 8 major iris signs and correction manually for improving the accuracy of analysis. As a result, PNSR of applied edge detection image is about 132, and pattern matching area recognition presented practical use possibility by automatic diagnostic that presume situation of human body by iris about 91%.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.3
• /
• pp.289-300
• /
• 2010

Recently, diverse information which are location, call history, SMS history, photographs, and video can be collected constantly from mobile devices such as cellular phone, smart phone, and PDA. There are many researchers who study services for searching and abstraction of personal daily life with contextual information in mobile environment. In this paper, we introduce MyLifeBrowser which is developed in our previous work. Also, we explain LPS and correction of GPS coordinates as extensions of previous work and show LPS performance test and evaluate the performance of expanded keywords. MyLifeBrowser which provides searching personal information in mobile device and support of detecting related information according to a fragmentary keyword and common knowledge in ConceptNet. It supports the functionality of searching related locations using Bayesian network that is designed by the authors. In our experiment, we visualize real data through MyLifeBrowser and show the feasibility of LPS server and expanded keywords using both Bayesian network and ConceptNet.

• Journal of The Korean Society of Integrative Medicine
• /
• v.4 no.4
• /
• pp.21-32
• /
• 2016

Purpose: The purpose of this study was to identify whether task-related circuit exercise program combined with sensorimotor training for 4 weeks could improve the balance and gait in stroke patients. Method: Fifteen stroke patients who had agreed with the study were randomly divided into 3 groups categorized as task-related circuit exercise program combined with sensorimotor training group (experimental group 1, n=5), task-related circuit exercise program group (experimental group 2, n=5), and control subjects performed conventional physical therapy (control group, n=5). The balance and gait were assessed by BT-4 force platform system, Berg Balance Scale, 10meter Walk Test and Smart Step at before training and after training. Wilcoxon signed rank test was used to analyze change before and after intervention in intra-group. Kruskal Wallis H test, Mann-Whitney U test and Bonfferoni correction were used to analyze changes of all variables in inter-groups. Result: The experimental group 1 showed significant improvements in postural sway area, BBS scores, walking velocity and plantar pressures of affected foot, whereas the experimental group 2 showed significant improvements in BBS scores, and the control group were no significantly different in all variables following training. The changes of postural sway area and BBS scores in the experimental group 1 were significantly greater than them of the control group. The changes of postural sway area in the experimental group 1 was significantly greater than that of the experimental group 2. Conclusion: The result of this study suggest the task-related circuit exercise program combined with sensorimotor training is an effective intervention to improve balance and gait in stoke patients.

• Smart Structures and Systems
• /
• v.26 no.3
• /
• pp.331-343
• /
• 2020

In this paper, based on the CPT, motion equations for a sandwich plate containing a core and two integrated face-sheets have derived. The structure rests on the Visco-Pasternak foundation, which includes normal and shear modules. The piezo-magnetic core is made of CoFe₂O₄ and also is subjected to 3D magnetic potential. Two face sheets at top and bottom of the core are under electrical fields. Also, in order to obtain more accuracy, the effect of flexoelectricity has took into account at face sheets' relations in this work. Flexoelectricity is a property of all insulators whereby they polarize when subject to an inhomogeneous deformation. This property plays a crucial role in small-scale rather than macro scale. Employing CPT, Hamilton's principle, flexoelectricity considerations, the governing equations are derived and then solved analytically. By present work a detailed numerical study is obtained based on Piezoelectricity, Flexoelectricity and modified couple stress theories to indicate the significant effect of length scale parameter, shear correction factor, aspect and thickness ratios and boundary conditions on natural frequency of sandwich plates. Also, the figures show that there is an excellent agreement between present study and previous researches. These finding can be used for automotive industries, aircrafts, marine vessels and building industries.

• Advances in nano research
• /
• v.5 no.4
• /
• pp.281-301
• /
• 2017

In this disquisition, an exact solution method is developed for analyzing the vibration characteristics of magneto-electro-elastic functionally graded (MEE-FG) beams by considering porosity distribution and various boundary conditions via a four-variable shear deformation refined beam theory for the first time. Magneto-electroelastic properties of porous FG beam are supposed to vary through the thickness direction and are modeled via modified power-law rule which is formulated using the concept of even and uneven porosity distributions. Porosities possibly occurring inside functionally graded materials (FGMs) during fabrication because of technical problem that lead to creation micro-voids in FG materials. So, it is necessary to consider the effect of porosities on the vibration behavior of MEE-FG beam in the present study. The governing differential equations and related boundary conditions of porous MEE-FG beam subjected to physical field are derived by Hamilton's principle based on a four-variable tangential-exponential refined theory which avoids the use of shear correction factor. An analytical solution procedure is used to achieve the natural frequencies of porous-FG beam supposed to magneto-electrical field which satisfies various boundary conditions. A parametric study is led to carry out the effects of material graduation exponent, porosity parameter, external magnetic potential, external electric voltage, slenderness ratio and various boundary conditions on dimensionless frequencies of porous MEE-FG beam. It is concluded that these parameters play noticeable roles on the vibration behavior of MEE-FG beam with porosities. Presented numerical results can be applied as benchmarks for future design of MEE-FG structures with porosity phases.

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

• Smart Structures and Systems
• /
• v.24 no.1
• /
• pp.53-65
• /
• 2019

Misaligned wind-wave and seismic loading render offshore wind turbines suffering from excessive bi-directional vibration. However, most of existing research in this field focused on unidirectional vibration mitigation, which is insufficient for research and real application. Based on the authors' previous work (Sun and Jahangiri 2018), the present study uses a three dimensional pendulum tuned mass damper (3d-PTMD) to mitigate the nacelle structural response in the fore-aft and side-side directions under wind, wave and near-fault ground motions. An analytical model of the offshore wind turbine coupled with the 3d-PTMD is established wherein the interaction between the blades and the tower is modelled. Aerodynamic loading is computed using the Blade Element Momentum (BEM) method where the Prandtl's tip loss factor and the Glauert correction are considered. Wave loading is computed using Morison equation in collaboration with the strip theory. Performance of the 3d-PTMD is examined on a National Renewable Energy Lab (NREL) monopile 5 MW baseline wind turbine under misaligned wind-wave and near-fault ground motions. The robustness of the mitigation performance of the 3d-PTMD under system variations is studied. Dual linear TMDs are used for comparison. Research results show that the 3d-PTMD responds more rapidly and provides better mitigation of the bi-directional response caused by misaligned wind, wave and near-fault ground motions. Under system variations, the 3d-PTMD is found to be more robust than the dual linear TMDs to overcome the detuning effect. Moreover, the 3d-PTMD with a mass ratio of 2% can mitigate the short-term fatigue damage of the offshore wind turbine tower by up to 90%.