• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.11-21
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• 2009

The NTRU cryptosystem proposed by Hoffstein et al. in 1990s is a public key cryptosystem based on hard lattice problems. NTRU has many advantages compared to other public key cryptosystems such as RSA and elliptic curve cryptosystems. For example, it guarantees high speed encryption and decryption with the same level of security, and there is no known quantum computing algorithm for speeding up attacks against NTRD. In this paper, we analyze the security of NTRU against the simple power analysis (SPA) attack and the statistical power analysis (STPA) attack such as the correlation power analysis (CPA) attack First, we implement NTRU operations using NesC on a Telos mote, and we show how to apply CPA to recover a private key from collected power traces. We also suggest countermeasures against these attacks. In order to prevent SPA, we propose to use a nonzero value to initialize the array which will store the result of a convolution operation. On the other hand, in order to prevent STPA, we propose two techniques to randomize power traces related to the same input. The first one is random ordering of the computation sequences in a convolution operation and the other is data randomization in convolution operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.17-23
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• 2006

In recent years, RFID has received much attention because of spread usage in industrial applications including factory, material flow, logistics and defense areas. However, there is only CRC-16 for error detection in ISO/IEC 18000-6 Protocols prepared for 860-960 MHz RFID, high error rates are expected in cases of high level of security and noisy envirionment. In this paper, we propose a usage of convolution code as a method for satisfying the high level of security requirement and system error performance.'1'he signal control function is implemented in a microprocessor with RF modulation and the convolutional encoding and Viterbi decoding are implemented in an FPGA chip.'The frame error rates are measured with and without convolution coding under the channel conditions of line-of- sight and non line-of-sight, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.10 s.89
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• pp.977-982
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• 2004

In this paper, a dispersive anisotropic perfectly matched layer(APML) is proposed using piecewise linear recursive convolution(PLRC) for finite difference time domain(FDTD) methods. This proposed APML can be utilized for the analysis of a nonlinear dispersive medium as absorbing boundary condition(ABC). The formulation is simple modification to the original AMPL and can be easily implemented. Also it has some advantages of the PLRC approach-fast speed, low memory cost, and easy formulation of multiple pole susceptibility. We applied this APML to 2-D propagation problems in dispersive media such as Debye and Lorentz media The results showed good absorption at boundaries.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1408-1416
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• 1990

A powerful and efficient method for finding approximate solutions to initial-boundary-value problems in the transient coupled thermoelasticity is formulated in time domain using the finite element technique with time-marching strategy. The final system equations can be derived by the Guritin's variational principle using the definition of convolution integral. But, the finite element formulation for the equations of motion is modified by differentiating in time. Numerical results to some test problems are compared with analytical and other sophisticated approximate solutions. Stable responces are observed in all the given examples irrespective of incremental time steps and mesh shapes. In addition, it is shown that good numerical results are obtained even in coarser mesh or larger time step comparing to other numerical methods.

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.118-128
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• 2018

In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1192-1196
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• 2010

As the space for installation of automotive exhaust system is limited, the space should be optimized with relation to the endurance and shape of the system. Geometric nonlinear analysis was used and deflection of bellows was assumed 6mm. Obtained results are as follows; (1) The Von-Mises stress of bellows is increased with increase of thickness or radius of bellows linearly. (2) As the principal stress varies according to the radius of convolution, it is necessary to decide the optimal radius of convolution.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• Journal of KSNVE
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• v.10 no.4
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• pp.679-685
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• 2000

A procedure is presented for calculating the magnitude and shape of impact pulses in a vibro-impact system when an arbitrary input force is applied to a point in the system. The procedure utilizes the condition that the displacements of two contacting point in the primary and secondary system are the same during a contacting period. The displacements of those points are calculated numerically through the convolution integral which involve the impulse response functions and applied forces. The validity of the calculation procedure is demonstrated by using it to calculated the impact forces of a simple system where a theoretical solution is known and also of systems for which other researchers have published results. The agreement between the results derived by the numerical method and the theoretical and also some published results is good.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2E
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• pp.51-59
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• 2009

Mutual radiation impedance becomes more important in the design and analysis of acoustic transducers for higher power, better beam pattern, and wider bandwidth at low frequency sonar systems. This review paper focused on literature survey about the researches of mutual radiation impedance in the acoustic transducer arrays over 60 years. The papers of mutual radiation impedance were summarized in terms of transducer array structures on various baffle geometries such as planar, cylindrical, spherical, conformal, spheroidal, and elliptic cylindrical arrays. Then the computation schemes of solving conventional quadruple integral in the definition of mutual radiation impedance were surveyed including spatial convolution method, which reduces the quadruple integral to a double integral for efficient computation.