• Journal of Internet Computing and Services
• /
• v.9 no.1
• /
• pp.33-41
• /
• 2008

Due to the advantage of wavelet transform such as the compatibility with JPEG2000, multi-resolution decomposition, and application of HVS(Human Visual System), watermarking algorithm based on wavelet transform (DWT) is recently mast interesting research subject. However, mast of researches are focused on theoretical aspects for the robustness rather than practical usage, and. may be not suitable too much complicated to use in practice. In this paper, robust logo watermarking algorithm based on DWT is proposed for large and huge data processing. The proposed method embeds the logo watermark by mapping of $8{\times}8$ blocks in order of the number of '1' of the original image and the randomized watermark image with LFSR. The original image is transformed by 2 level wavelet. The experimental results shows that the watermark is embedded successfully, and the proposed algorithm has the valuable robustness from the image processing like JPEG compression, low pass filter, high pass filter and changes in brightness and contrast.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.7
• /
• pp.875-880
• /
• 2019

A RC4 stream cipher is widely used for security applications such as IEEE 802.11 WEP, IEEE 802.11i TKIP and so on, because it can be simply implemented to dedicated circuits and achieve a high-speed encryption. RC4 is also used for systems with limited resources like IoT, but there are performance limitations. RC4 consists of two stages, KSA and PRGA. KSA performs initialization and randomization of S-box and K-box and PRGA produces cipher texts using the randomized S-box. In this paper, we initialize the S-box and K-box in the randomization of the KSA stage to reduce the initialization delay. In the randomization, we use clusters to process swap operation between elements of S-box in parallel and can generate two cipher texts per clock. The proposed RC4 cipher hardware can initialize S-box and K-box without any delay and achieves about 2 times to 6 times improvement in KSA randomization and key stream generation.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.8
• /
• pp.54-63
• /
• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.5
• /
• pp.473-479
• /
• 2006

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.1023-1028
• /
• 2004

A micro-pressure wave is generated by the high-speed train which enters a tunnel, and it causes explosive noise and vibration at the exit. It is known that train speed, train-tunnel area ratio, nose slenderness and nose shape mainly influence on generating micro-pressure wave. So it is required to minimize it by searching optimal values of such train shape factors and tunnel condition. In this study, response surface model, one of approximation models, is used to perform optimization effectively and analyze sensitivity of design variables. Owen's randomized orthogonal array and D-optimal Design are used to construct response surface model. In order to increase accuracy of model, stepwise regression is selected. Finally SQP(Sequential Quadratic Programming) optimization algorithm is used to minimize the maximum micro-pressure wave by using built approximation model.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2003.12a
• /
• pp.299-306
• /
• 2003

본 논문에서는 현재 사용되고 있는 소수성 검정 알고리즘의 효율성을 비교하여 효과적인 알고리즘 사용에 관한 방향을 제시하려 한다. 현재 가장 일반적으로 사용하고 있는 Miller-Rabin 소수성검정법(Miller-Rabin primality test)에 대하여, Miller-Rabin 소수성 검정법 이외에 다른 확률적 소수성 검정법으로 제안된 Frobenius-Grantham 소수성 검정법(Frobenius-Grantham primality test) 이 있다. 그러나 합성수 판별에 대한 확률적 우세함에도 불구하고, Miller-Rabin 소수성 검정법을 대체하고 있지 못하는 이유는 시간복잡도(time complexity)가 Randomized polynomial time이기 때문에 같은 확률에 대한 평균 실행 속도가 Miller-Rabin 소수성 검정법보다 크게 효율적이지 못하기 때문이다. 또한, 2002년 Manindra Agrawal이 제시한 AKS 알고리즘(AKS algorithm)은 최초의 다항식 시간내 결정적 소수성 검정법(Polynomial time deterministic primality test)이지만, 시간 복잡도에서 다항식의 차수가 높기 때문에 현재 사용되고 있는 확률적 소수성 검정법(Probabilistic primality test)을 대체하지 못할 것으로 사료된다. 본 논문에서는 최근 발표된 소수성 검정법인 Frobenius-Grantham 소수성 검정법, AKS 알고리즘과 기존의 Miller-Rabin 소수성 검정법의 장단점을 비교·분석해 보고자 한다.

• Journal for History of Mathematics
• /
• v.18 no.4
• /
• pp.101-112
• /
• 2005

In this article, we introduce a generous but eccentric genius in mathematics, Paul Erdos. He invented probabilistic methods, pioneered in their applications to discrete mathematics, and estabilshed new theories, which are regarded as the greatest among his contributions to mathematical world. Here we introduce the probabilistic methods and random graph theory developed by Erdos and look at his life in glance with great respect for him.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.832-854
• /
• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.50.2-50.2
• /
• 2020

The dark matter (DM) only simulations have been exploited to study e.g. the large scale structures and properties of a halo. In a baryon side, the high-resolution hydrodynamic simulation such as IllustrisTNG has helped extend the physics of gas along with stars and DM. However, the expansive computational cost of hydrodynamic simulations limits the size of a simulated universe whereas DM-only simulations can generate the universe of the cosmological horizon size approximately. I will introduce a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements such as a refined error function in machine training and two-stage learning. By applying our machine to the DM-only simulation of a large volume, I then validate the pipeline that rapidly generates a galaxy catalog from a DM halo catalog using the correlations the machine found in hydrodynamic simulations. I will discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.

• Computers and Concrete
• /
• v.32 no.6
• /
• pp.607-613
• /
• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.