• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.96-97
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• 2020

랜덤워크 기반 노드 랭킹 방식 중 하나인 RWR(Random Walk with Restart) 기법은 희소행렬 벡터 곱셈 연산과 벡터 간의 합 연산을 반복적으로 수행하며, RWR 의 수행 시간은 희소행렬 벡터 곱셈 연산 방법에 큰 영향을 받는다. 본 논문에서는 CSR5(Compressed Sparse Row 5) 기반 희소행렬 벡터 곱셈 방식과 CSR-vector 기반 희소행렬 곱셈 방식을 채택한 GPU 기반 RWR 기법 간의 비교 실험을 수행한다. 실험을 통해 데이터 셋의 특징에 따른 RWR 의 성능 차이를 분석하고, 적합한 희소행렬 벡터 곱셈 방안 선택에 관한 가이드라인을 제안한다.

• Bulletin of the Korean Mathematical Society
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• v.21 no.2
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• pp.99-106
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• 1984

J. Yeh has recently introduced the concept of conditional Wiener integrals which are meant specifically the conditional expectation E$^{w}$ (Z vertical bar X) of a real or complex valued Wiener integrable functional Z conditioned by the Wiener measurable functional X on the Wiener measure space (A precise definition of the conditional Wiener integral and a brief discussion of the Wiener measure space are given in Section 2). In [3] and [4] he derived some inversion formulae for conditional Wiener integrals and evaluated some conditional Wiener integrals E$^{w}$ (Z vertical bar X) conditioned by X(x)=x(t) for a fixed t>0 and x in Wiener space. Thus E$^{w}$ (Z vertical bar X) is a real or complex valued function on R$^{1}$. In this paper we shall be concerned with the random vector X given by X(x) = (x(s$_{1}$),..,x(s$_{n}$ )) for every x in Wiener space where 0=s$_{0}$ $_{1}$<..$_{n}$ =t. In Section 3 we will evaluate some conditional Wiener integrals E$^{w}$ (Z vertical bar X) which are real or complex valued functions on the n-dimensional Euclidean space R$^{n}$ . Thus we extend Yeh's results [4] for the random variable X given by X(x)=x(t) to the random vector X given by X(x)=(x(s$_{1}$).., x(s$_{n}$ )).

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.106-111
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• 2002

In this paper, a new three phase RPWM(Random PWM)with fixed switching frequency is proposed. In the proposed RPWM, each of three phase pulses is located randomly in each switching interval. Based on the space vector modulation technique, the duty ratio of the pulses is calculated. Along with the the randomization of the PWM pulses, we can obtain the effects of spread spectra of voltage, current as in the case of randomly changed switching frequency. To verify the validity of the proposed RPWM, the experimental study was tried. Along with the randomization of PWM pulses, the space vector modulation is also executed in the C167 micro-controller. The experimental results show that the voltage and current harmonics are spread to a wide band area and that the audible acoustic noise is reduced by the proposed RPWM method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.221-230
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• 2002

This paper presents a new moving object segmentation algorithm using markov random field. The algorithm is based on signal detection theory. That is to say, motion of moving object is decided by binary decision rule, and false decision is corrected by markov random field model. The procedure toward complete segmentation consists of two steps: motion detection and object segmentation. First, motion detection decides the presence of motion on velocity vector by binary decision rule. And velocity vector is generated by optical flow. Second, object segmentation cancels noise by Bayes rule. Experimental results demonstrate the efficiency of the presented method.

• Geomechanics and Engineering
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• v.32 no.3
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• pp.271-291
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• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.

• Journal of IKEEE
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• v.18 no.3
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• pp.305-312
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• 2014

IIt takes a lot of time and needs the workloads to verify the RTL code used in complex system like a nuclear control system which is required high level reliability using simple testbench. UVM has a layered testbench architecture and it is easy to modify the testbench to improve the code coverage. A test vector can be easily constructed in the UVM, since a constrained random test vector can be used even though the construction of testbench using UVM. We showed that the UVM testbench is easier than the verilog testbench for the analysis and improvement of code coverage.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.938-949
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• 2015

In this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.449-457
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• 2017

In this paper, we analyze the security and performance of the Quantis Quantum random number generator in terms of cryptography through experiments. The Quantis' post-processing is designed to output full-entropy via bit-matrix-vector multiplication based on mathematical background, and we used the min-entropy estimating test of NIST SP 800-90B so as to verify whether the output is full-entropy. Quantis minimizes the effect on the random bit rate by using an optimization technique for bit-matrix-vector multiplication, and compared the performance to conditioning functions of NIST SP 800-90B by measuring the random bit rate. Also, we have distinguished what is in Quantis' post-processing to the standard model of NIST in USA and BSI in Germany, and in case of applying Quantis to cryptographic systems in accordance with the CMVP standard, it is recommended to use the output of Quantis as the seed of the approved DRBG.

• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.783-791
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• 1998

A random vector X on $R^{d}$ with the following properties is constructed: the distribution of X is infinitely divisible and not selfdecomposable, but every linear transformation of X to a lower-dimensional space has a selfdecomposable distribution.