• Journal of KIISE:Computer Systems and Theory
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• v.31 no.8
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• pp.453-464
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• 2004

In order to solve the well-known drawback of reduced flexibility that is associate with ASIC implementations, this paper proposes a novel arithmetic unit over GF(2$^{m}$ ) for field programmable gate arrays (FPGAs) implementations of elliptic curve cryptographic processor. The proposed arithmetic unit is based on the binary extended GCD algorithm and the MSB-first multiplication scheme, and designed as systolic architecture to remove global signals broadcasting. The proposed architecture can perform both division and multiplication in GF(2$^{m}$ ). In other word, when input data come in continuously, it produces division results at a rate of one per m clock cycles after an initial delay of 5m-2 in division mode and multiplication results at a rate of one per m clock cycles after an initial delay of 3m in multiplication mode respectively. Analysis shows that while previously proposed dividers have area complexity of Ο(m$^2$) or Ο(mㆍ(log$_2$$^{m}$ )), the Proposed architecture has area complexity of Ο(m), In addition, the proposed architecture has significantly less computational delay time compared with the divider which has area complexity of Ο(mㆍ(log$_2$$^{m}$ )). FPGA implementation results of the proposed arithmetic unit, in which Altera's EP2A70F1508C-7 was used as the target device, show that it ran at maximum 121MHz and utilized 52% of the chip area in GF(2$^{571}$ ). Therefore, when elliptic curve cryptographic processor is implemented on FPGAs, the proposed arithmetic unit is well suited for both division and multiplication circuit.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.81-97
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• 2003

In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL$_3$, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL$_3$, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.569-589
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• 2018

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3121-3131
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• 2014

Vulnerabilities related to memory have been known as major threats to the security of a computer system. Actually, the number of attacks using memory vulnerability has been increased. Accordingly, various memory protection mechanisms have been studied and implemented on operating system while new attack techniques bypassing the protection systems have been developed. Especially, buffer overflow attacks have been developed as Return-Oriented Programing(ROP) and Jump-Oriented Programming(JOP) called Code Re-used attack to bypass the memory protection mechanism. Thus, in this paper, I analyzed code re-use attack techniques emerged recently among attacks related to memory, as well as analyzed various detection mechanisms proposed previously. Based on the results of the analyses, a mechanism that could detect various code re-use attacks on a binary level was proposed. In addition, it was verified through experiments that the proposed mechanism could detect code re-use attacks effectively.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1372-1381
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• 2017

In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.

• Elastomers and Composites
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• v.39 no.4
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• pp.318-323
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• 2004

Cloud-point and bubble-point curves for poly(ethylene-co-13.8 mol% octene) ($PEO_{13.8}$) and Poly(ethylene-co-15.3 mol% octene) ($PEO_{15.3}$) were determined up to $150^{\circ}C$ and 450 bar in hydrocarbons which have different molecular size and structure. Whereas ($PEO_{15.3}$+ n-pentane) system has cloud-point and bubble-point type transitions, ($PEO_{15.3}$+ n-propane) and ($PEO_{15.3}$+ n-butane) systems do only cloud-point type transition. In cyclo-pentane, -hexane, -heptane, and -octane, $PEO_{15.3}$ has a bubble-point transition. ($PEO_{13.8}$+ n-butane) mixture has a critical mixture concentration at 5 wt% PEO. (PEO + hydrocarbon) mixtures exhibit LCST type behavior. Solubility of PEO increases with hydrocarbon size due to increasing dispersion interaction which is favorable to dissolve PEO.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of Preventive Medicine and Public Health
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• v.54 no.4
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• pp.230-237
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• 2021

Objectives: This study explored changes in individuals' behavior in response to social distancing (SD) levels and the "no gatherings of more than 5 people" (NGM5) rule in Korea during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Using survey data from the COVID-19 Behavior Tracker, exploratory factor analysis extracted 3 preventive factors: maintenance of personal hygiene, avoiding going out, and avoiding meeting people. Each factor was used as a dependent variable. The chisquare test was used to compare differences in distributions between categorical variables, while binary logistic regression was performed to identify factors associated with high compliance with measures to prevent transmission. Results: In men, all 3 factors were significantly associated with lower compliance. Younger age groups were associated with lower compliance with maintenance of personal hygiene and avoiding meeting people. Employment status was significantly associated with avoiding going out and avoiding meeting people. Residence in the capital area was significantly associated with higher compliance with personal hygiene and avoiding venturing out. Increasing SD levels were associated with personal hygiene, avoiding going out, and avoiding meeting people. The NGM5 policy was not significantly associated with compliance. Conclusions: SD levels, gender, age, employment status, and region had explanatory power for compliance with non-pharmaceutical interventions (NPIs). Strengthening social campaigns to inspire voluntary compliance with NPIs, especially focused on men, younger people, full-time workers, and residents of the capital area is recommended. Simultaneously, efforts need to be made to segment SD measures into substrategies with detailed guidance at each level.

• Convergence Security Journal
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• v.18 no.1
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• pp.13-18
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• 2018

Efficiency is one of the most important factors in cryptographic systems. Cheon et al. proposed a new exponent form for speeding up the exponentiation operation in discrete logarithm based cryptosystems. It is called split exponent with the form $e_1+{\alpha}e_2$ for a fixed element ${\alpha}$ and two elements $e_1$, $e_2$ with low Hamming weight representations. They chose $e_1$, $e_2$ in two unbalanced subsets $S_1$, $S_2$ of $Z_p$, respectively. We achieve efficiency improvement making $S_1$, $S_2$ balanced subsets of $Z_p$. As a result, speedup for exponentiations on binary fields is 9.1% and speedup for scalar multiplications on Koblitz Curves is 12.1%.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.17-25
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• 2019

Accurate tracking of the sinkholes that are appearing frequently now is an important method of protecting human and property damage. Although many sinkhole detection systems have been proposed, it is still far from completely solved especially in-depth area. Furthermore, detection of sinkhole algorithms experienced the problem of unstable result that makes the system difficult to fire a warning in real-time. In this paper, we proposed a method of sinkhole tracking by deep learning and data association, that takes advantage of the recent development of CNN transfer learning. Our system consists of three main parts which are binary segmentation, sinkhole classification, and sinkhole tracking. The experiment results show that the sinkhole can be tracked in real-time on the dataset. These achievements have proven that the proposed system is able to apply to the practical application.