• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.47-48
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• 2012

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.911-923
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• 2016

Machine learning becomes increasingly important in the big data era. Logistic regression is a type of classification in machine leaning, and has been widely used in various fields, including medicine, economics, marketing, and social sciences. Rhipe that integrates R and Hadoop environment, has not been discussed by many researchers owing to the difficulty of its installation and MapReduce implementation. In this paper, we present the MapReduce implementation of Gradient Descent algorithm and Newton-Raphson algorithm for logistic regression using Rhipe. The Newton-Raphson algorithm does not require a learning rate, while Gradient Descent algorithm needs to manually pick a learning rate. We choose the learning rate by performing the mixed procedure of grid search and binary search for processing big data efficiently. In the performance study, our Newton-Raphson algorithm outpeforms Gradient Descent algorithm in all the tested data.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.110-111
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• 2007

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.731-742
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• 2007

This paper presents a new nonlinear analysis algorithm, that is, adaptive Newton-Raphson iteration method, The presented algorithm is based on the existing Newton-Raphson method, and the concept of it can be summarized as calculating the equivalent load for stiffness(ELS) and adapting this to the initial global stiffness matrix which has already been calculated and saved in initial analysis and finally calculating the correction displacements for the nonlinear analysis, The key characteristics of the proposed algorithm is that it calculates the inverse matrix of the global stiffness matrix only once irresponsive of the number of load steps. The efficiency of the proposed algorithm depends on the ratio of the active Dofs - the Dofs which are directly connected to the members of which the element stiffness are changed - to the total Dofs, and based on this ratio by using the proposed algorithm as a complementary method to the existing algorithm the efficiency of the nonlinear analysis can be improved dramatically.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.435-439
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• 2007

This paper deals with RLS algorithm using Newton-Raphson method based adaptive forgetting factor for a passive telemetry RF sensor system in order to estimate the time variant parameter to be included in RF sensor model. For this estimation with RLS algorithm, phasor typed RF sensor system modelled with inductive coupling principle is used. Instead of applying constant forgetting factor to estimate time variant parameter, the adaptive forgetting factor based on Newton-Raphson method is applied to RLS algorithm without constant forgetting factor to be determined intuitively. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.198-201
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• 2007

본 논문에서는 변형된 Newton-Raphson 알고리즘과 LUT(Look Up Table)를 사용하는 역제곱근 연산기를 제안한다. Newton-Raphson 부동소수점 역수 알고리즘은 일정한 횟수의 곱셈을 반복하여 역수 제곱근을 계산하는 방식이다. 변형된 Newton-Raphson 알고리즘은 하드웨어 구현에 적합하도록 변환되었으며, LUT는 오차를 줄이기 위해 개선되었다. 제안된 연산기는 LUT의 크기를 최소화하고, 순환적인 구조가 아닌 2-stage pipeline 구조를 가진다. 또한 IEEE-754 부동소수점 표준을 기초로 하는 24-bit 데이터 형식을 사용해 면적과 속도 향상에 유리하여 휴대용 기기의 멀티미디어 분야의 응용에 적합하다. 본 역제곱근 연산기는 소수점 이하 8-bit의 정확도를 가지며 VHDL을 이용하여 설계되었다. 그 크기는 $0.18{\mu}m$ CMOS 공정에서 약 4,000 gate의 크기를 보였으며 150MHz에서 동작이 가능하다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.127-133
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• 2002

This paper presents an efficient improvement of the iterative eigenvalue calculation method of the AESOPS algorithm. The intuitively and heuristically approximated iterative eigenvalue calculation method of the AESOPS algorithm is transformed to the Second Order Newton Raphson Method which is generally used in numerical analysis. The equations of second order partial differentiation of external torque, terminal and internal voltages are derived from the original AESOPS algorithm. Therefore only a few calculation steps are added to transform the intuitively and heuristically approximated AESOPS algorithm to the Second Order Newton Raphson Method, while the merits of original algorithm are still preserved.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2968-2970
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• 2000

시계열(time series)이란 한 사상 또는 여러 사상에 대하여 시간의 흐름에 따라 일정한 간격으로 이들을 관측하여 기록한 자료를 말한다. 이러한 시계열은 어떠한 경제현상이나 자연현상에 관한 시간적 변화를 나타내는 역사적 계열(historical series)이므로 어느 한 시점에서 관측된 시계열자료는 그 이전까지의 자료들에 주로 의존하게 된다. 따라서 시계열분석을 통한 예측에서는 과거의 자료들을 분석하여 법칙성을 발견해서 이를 모형화하여 추정하고. 이 추정된 모형을 사용하여 미래에 관측될 값들을 예측하게 된다. 본 연구에서는 ARMA (p, q)모형 (autoregressive moving-average model)을 이용하여 시계열 데이터를 분석하며 계수의 추정에는 Levinson-Durbin 알고리듬과 Newton-Raphson Method를 이용한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.46-55
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• 2007

The Newton-Raphson's algorithm for finding a floating point reciprocal and inverse square root calculates the result by performing a fixed number of multiplications. In this paper, an improved Newton-Raphson's algorithm is proposed, that performs multiplications a variable number. Since the number of multiplications performed by the proposed algorithm is dependent on the input values, the average number of multiplications per an operation is derived from many reciprocal and inverse square tables with varying sizes. The superiority of this algorithm is proved by comparing this average number with the fixed number of multiplications of the conventional algorithm. Since the proposed algorithm only performs the multiplications until the error gets smaller than a given value, it can be used to improve the performance of a reciprocal and inverse square root unit. Also, it can be used to construct optimized approximate tables. The results of this paper can be applied to many areas that utilize floating point numbers, such as digital signal processing, computer graphics, multimedia, scientific computing, etc.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.249-256
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• 2000

In EIT(electrical impedance tomography), the internal resistivity(or conductivity) distribution of the unknown object is estimated using the boundary voltage data induced by different current patterns using various reconstruction algorithms. In this paper, we present a regularized modified Newton-Raphson(mNR) scheme which employs additional a priori information in the cost functional as soft constraint and the weighting matrices in the cost functional are selected based on the exponentially weighted least square criterion. The computer simulation for the 32 channels synthetic data shows that the reconstruction performance of the proposed scheme is improved compared to that of the conventional regularized mNR at the expense of slightly increased computational burden.