• Title/Summary/Keyword: 가중치 함수

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A Study on the Traffic Accident Estimation Model using Empirical Bayes Method (Empirical Bayes Method를 이용한 교통사고 예측모형)

  • Gang, Hyeon-Geon;Gang, Seung-Gyu;Jang, Yong-Ho
    • Journal of Korean Society of Transportation
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    • v.27 no.5
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    • pp.135-144
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    • 2009
  • This study estimates the expected number of accidents in Kyungbuk Province to capitalize on experience gained from four years of accident history using the Empirical Bayes (EB) Method. The number of accidents of each site in Kyungbuk Province is recalculated using the Equivalent Property Damage Only (EPDO) method to reflect the severities of the accidents. A cluster analysis is performed to determine similar sites and a unique Safety Performance Function (SPF) is established for each site. The overdispersion parameter is built to correct the difference between the actual number of accidents and the underlying probability distribution. To adjust for varying traffic characteristics of each site, a relative weight is applied and eventually estimates the expected number of accidents. The results show that the highest accident sites are Kimcheon, Youngcheon, and Chilgok, but on the other hand the lowest is Gunwi.

Fuzzy Neural System Modeling using Fuzzy Entropy (퍼지 엔트로피를 이용한 퍼지 뉴럴 시스템 모델링)

  • 박인규
    • Journal of Korea Multimedia Society
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    • v.3 no.2
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    • pp.201-208
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    • 2000
  • In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

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Bias adjusted estimation in a sample survey with linear response rate (응답률이 선형인 표본조사에서 편향 보정 추정)

  • Chung, Hee Young;Shin, Key-Il
    • The Korean Journal of Applied Statistics
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    • v.32 no.4
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    • pp.631-642
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    • 2019
  • Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

Optimization Of Water Quality Prediction Model In Daechong Reservoir, Based On Multiple Layer Perceptron (다층 퍼셉트론을 기반으로 한 대청호 수질 예측 모델 최적화)

  • Lee, Hankyu;Kim, Jin Hui;Byeon, Seohyeon;Park, Kangdong;Shin, Jae-ki;Park, Yongeun
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.43-43
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    • 2022
  • 유해 조류 대발생은 전국 각지의 인공호소나 하천에서 다발적으로 발생하며, 경관을 해치고 수질을 오염시키는 등 수자원에 부정적인 영향을 미친다. 본 연구에서는 인공호소에서 발생하는 유해 조류 대발생을 예측하기 위해 심층학습 기법을 이용하여 예측 모델을 개발하고자 하였다. 대상 지점은 대청호의 추동 지점으로 선정하였다. 대청호는 금강유역 중류에 위치한 댐으로, 약 150만명에 달하는 급수 인구수를 유지 중이기에 유해 남조 대발생 관리가 매우 중요한 장소이다. 학습용 데이터 구축은 대청호의 2011년 1월부터 2019년 12월까지 측정된 수질, 기상, 수문 자료를 입력 자료를 이용하였다. 수질 예측 모델의 구조는 다중 레이어 퍼셉트론(Multiple Layer Perceptron; MLP)으로, 입력과 한 개 이상의 은닉층, 그리고 출력층으로 구성된 인공신경망이다. 본 연구에서는 인공신경망의 은닉층 개수(1~3개)와 각각의 레이어에 적용되는 은닉 노드 개수(11~30개), 활성함수 5종(Linear, sigmoid, hyperbolic tangent, Rectified Linear Unit, Exponential Linear Unit)을 각각 하이퍼파라미터로 정하고, 모델의 성능을 최대로 발휘할 수 있는 조건을 찾고자 하였다. 하이퍼파라미터 최적화 도구는 Tensorflow에서 배포하는 Keras Tuner를 사용하였다. 모델은 총 3000 학습 epoch 가 진행되는 동안 최적의 가중치를 계산하도록 설계하였고, 이 결과를 매 반복마다 저장장치에 기록하였다. 모델 성능의 타당성은 예측과 실측 데이터 간의 상관관계를 R2, NSE, RMSE를 통해 산출하여 검증하였다. 모델 최적화 결과, 적합한 하이퍼파라미터는 최적화 횟수 총 300회에서 256 번째 반복 결과인 은닉층 개수 3개, 은닉 노드 수 각각 25개, 22개, 14개가 가장 적합하였고, 이에 따른 활성함수는 ELU, ReLU, Hyperbolic tangent, Linear 순서대로 사용되었다. 최적화된 하이퍼파라미터를 이용하여 모델 학습 및 검증을 수행한 결과, R2는 학습 0.68, 검증 0.61이었고 NSE는 학습 0.85, 검증 0.81, RMSE는 학습 0.82, 검증 0.92로 나타났다.

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Experimental Design of S box and G function strong with attacks in SEED-type cipher (SEED 형식 암호에서 공격에 강한 S 박스와 G 함수의 실험적 설계)

  • 박창수;송홍복;조경연
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.8 no.1
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    • pp.123-136
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    • 2004
  • In this paper, complexity and regularity of polynomial multiplication over $GF({2^n})$ are defined by using Hamming weight of rows and columns of the matrix ever GF(2) which represents polynomial multiplication. It is shown experimentally that in order to construct the block cipher robust against differential cryptanalysis, polynomial multiplication of substitution layer and the permutation layer should have high complexity and high regularity. With result of the experiment, a way of constituting S box and G function is suggested in the block cipher whose structure is similar to SEED, which is KOREA standard of 128-bit block cipher. S box can be formed with a nonlinear function and an affine transform. Nonlinear function must be strong with differential attack and linear attack, and it consists of an inverse number over $GF({2^8})$ which has neither a fixed pout, whose input and output are the same except 0 and 1, nor an opposite fixed number, whose output is one`s complement of the input. Affine transform can be constituted so that the input/output correlation can be the lowest and there can be no fixed point or opposite fixed point. G function undergoes linear transform with 4 S-box outputs using the matrix of 4${\times}$4 over $GF({2^8})$. The components in the matrix of linear transformation have high complexity and high regularity. Furthermore, G function can be constituted so that MDS(Maximum Distance Separable) code can be formed, SAC(Strict Avalanche Criterion) can be met, and there can be no weak input where a fixed point an opposite fixed point, and output can be two`s complement of input. The primitive polynomials of nonlinear function affine transform and linear transformation are different each other. The S box and G function suggested in this paper can be used as a constituent of the block cipher with high security, in that they are strong with differential attack and linear attack with no weak input and they are excellent at diffusion.

A New Directionally Weighted Demosaicing (방향성을 고려한 새로운 디모자이킹)

  • Jung, Tae-Young;Jeong, Je-Chang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.12C
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    • pp.1004-1009
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    • 2010
  • ost digital cameras use single sensor array with color filter array to reduce size and cost. However images taken by single sensor array have only one color component per pixel, to obtain a color image missing two color components need to be reconstructed. This reconstructing process is called as demosaicking. This paper propose a new directional demosaicking method and proposed method achieves better image quality with enhanced weighting function. With comparing objective and subjective performance, we show proposed method achieves better performance than the conventional methods.

Adaptive Watermarking for MP3 Copyright Protections Using Psychological Acoustics (심리음향 분석을 이용한 MP3 저작권 보안을 위한 적응적 워터마킹)

  • Lee, Kyeong-Hwan
    • The Journal of the Acoustical Society of Korea
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    • v.32 no.1
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    • pp.64-70
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    • 2013
  • In this paper, we suggest a new audio watermarking method for audio contents copyrights that can efficiently provide protection from MP3 compression attacks. Watermarks were inserted at the coefficients repeatedly from low frequencies to high frequencies after DCT transform in commonly used Cox's spread spectrum method. Because the methods using arbitrary coefficients are not effective, we use the new weight functions that make small losses for the watermark coefficients during attacks, using psychological acoustics. In the results of various sound clips, the suggested method had overall better outcomes than the Cox's method by preserving watermarks and reducing distortions of the original sounds.

Design Optimization of Liquid Rocket Engine Using Genetic Algorithms (유전알고리즘을 이용한 액체로켓엔진 설계 최적화)

  • Lee, Sang-Bok;Lim, Tae-Kyu;Roh, Tae-Seong
    • Journal of the Korean Society of Propulsion Engineers
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    • v.16 no.2
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    • pp.25-33
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    • 2012
  • A genetic algorithm (GA) has been employed to optimize the major design variables of the liquid rocket engine. Pressure of the main combustion chamber, nozzle expansion ratio and O/F ratio have been selected as design variables. The target engine has the open gas generator cycle using the LO2/RP-1 propellant. The gas properties of the combustion chamber have been obtained from CEA2 and the mass has been estimated using reference data. The objective function has been set as multi-objective function with the specific impulse and thrust to weight ratio using the weight method. The result shows about 4% improvement of the specific impulse and 23% increase of the thrust to weight ratio. The Pareto frontier line has been also obtained for various thrust requirements.

A Pruning Algorithm of Neural Networks Using Impact Factors (임팩트 팩터를 이용한 신경 회로망의 연결 소거 알고리즘)

  • 이하준;정승범;박철훈
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.2
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    • pp.77-86
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    • 2004
  • In general, small-sized neural networks, even though they show good generalization performance, tend to fail to team the training data within a given error bound, whereas large-sized ones learn the training data easily but yield poor generalization. Therefore, a way of achieving good generalization is to find the smallest network that can learn the data, called the optimal-sized neural network. This paper proposes a new scheme for network pruning with ‘impact factor’ which is defined as a multiplication of the variance of a neuron output and the square of its outgoing weight. Simulation results of function approximation problems show that the proposed method is effective in regression.

Prediction of Radiative Heat Transfer in a Three-Dimensional Gas Turbine Combustor with the Finite-Volume Method (유한체적법에 의한 복잡한 형상을 갖는 3차원 가스터빈 연속기내의 복사열 전달 해석)

  • Kim, Man-Yeong;Baek, Seung-Uk
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.20 no.8
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    • pp.2681-2692
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    • 1996
  • The finite-volume method for radiation in a three-dimensional non-orthogonal gas turbine combustion chamber with absorbing, emitting and anisotropically scattering medium is presented. The governing radiative transfer equation and its discretization equation using the step scheme are examined, while geometric relations which transform the Cartesian coordinate to a general body-fitted coordinate are provided to close the finite-volume formulation. The scattering phase function is modeled by a Legendre polynomial series. After a benchmark solution for three-dimensional rectangular combustor is obtained to validate the present formulation, a problem in three-dimensional non-orthogonal gas turbine combustor is investigated by changing such parameters as scattering albedo, scattering phase function and optical thickness. Heat flux in case of isotropic scattering is the same as that of non-scattering with specified heat generation in the medium. Forward scattering is found to produce higher radiative heat flux at hot and cold wall than backward scattering and optical thickness is also shown to play an important role in the problem. Results show that finite-volume method for radiation works well in orthogonal and non-orthogonal systems.