• 전기학회논문지
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• 제59권4호
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• pp.693-699
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• 2010

General transformer's life is known as paper insulation' life. If a transformer is degraded by these aging factors, it is known that electrical, mechanical and chemical characteristics for transformer's oil-paper are changed. When the kraft paper is aged, the cellulose polymer chains break down into shorter lengths. It causes decrease in both tensile strength and degree of polymerization of paper insulation. The paper breakdown is accompanied by an increase in the content of furanic compounds within the dielectric liquid. In this paper it is aimed at analysis on correlation between aging characteristics for insulating diagnosis of thermally aged paper. For investigating the accelerated aging process of oil-paper samples accelerating aging cell was manufactured for estimating variation of paper insulation during 500 hours at $140^{\circ}C$ temperature. To derive the results, it was performed analysis such as tensile strength(TS), depolymerization(DP), dielectric strength(DS), relative permittivity, water content(WC) and furan compound(FC) for aged paper. Also for analyzing correlation between insulating degradation characteristics, we used linear regression method. As as results of linear regression analysis, there was a close correlation between TS and DP. WC, FC. But dielectric strength was a weak correlation with aging time.

• Structural Engineering and Mechanics
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• 제31권1호
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• 콘크리트학회논문집
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• 제18권4호
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• pp.507-515
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• 2006

본 연구에서는 프리스트레스트 콘크리트(PSC) 교량의 프리스트레스를 장기적으로 예측하는 기법을 제안하였다. 제안 기법에서는 구조시스템의 계측자료를 이용하여 비선형 회귀분석을 전개하는 통계적 기법을 적용하였다. 프리스트레스의 장기예측은 비선형 회귀분석을 통해 이루어진다. 제안기법을 실제의 PSC 박스 거더 교량의 프리스트레스 예측에 적용하기 위하여 텐던에 프리스트레스 도입후 계측을 수행하였다. 프리스트레스 도입후 약 150일까지 프리스트레스는 눈에 띄게 감소하며, 손실률은 $7{\sim}8%$로 나타났다. 수치해석결과는 현장의 계측횟수가 증가할수록 신뢰구간의 폭은 감소하는 것으로 나타났다. 따라서, 제안기법에 의해 PSC 구조물의 프리스트레스를 더욱 실제적으로 예측할 수 있으며, 예측결과는 구조물의 사용기간 동안 관리 한계치에 의한 프리스트레스 관리에 유용하게 활용될 수 있을 것으로 사료된다.

• 터널과지하공간
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• 제14권2호
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• pp.133-141
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• 2004

현장조사를 통해 암반사면의 안정성을 평가하기 위해서 여러 연구자들에 의해 평가법이 제안되었다. 그러나 기존의 평가법들은 제안자의 주관적 판단에 의해 평가항목의 선정과 가중치가 달리 적용되고 있어 평가법에 따라 안정성 평가결과도 서로 상이하게 나타나고 있다. 따라서 각 평가항목에 대한 가중치의 객관성을 확보하기 위해 로지스틱 회귀분석을 실시하여 안정성 평가법을 제안하였다.

• 대한임상검사과학회지
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• 제40권1호
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• pp.18-25
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• 2008

In classical titrimetric analyses, the major concern is the concentration of titrant, usually the aqueous solution of hydrochloric acid or sodium hydroxide, that could be changed as time goes by and it is accompanied with the inaccuracy of the resulting data. And the statistical approach, the nonlinear regression analysis, which is a well-known statistical method, was introduced to determine the accurate concentration of the titrant and the exact value of parameters, $K_a$, r, $C_a$, $C_b$, for 0.01 M aqueous solutions of analytes, sodium pyruvate, sodium acetate, sodium bicarbonate, ammonium hydroxide, ammonium chloride and acetic acid at $25^{\circ}C$. We used Gauss-Newton method for the linearlization of the nonlinear titration system and the two-parameter fitting showed appreciable convergent data for the parameters of the analytes set with the various range of $K_a$ value.

• Current Optics and Photonics
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• 제4권5호
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• pp.411-420
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• 2020

Non-contact three-dimensional (3D) measuring technology is used to identify defects in miniature products, such as optics, polymers, and semiconductors. Hence, this technology has garnered significant attention in computer vision research. In this paper, we focus on shape from focus (SFF), which is an optical passive method for 3D shape recovery. In existing SFF techniques using interpolation, all datasets of the focus volume are approximated using one model. However, these methods cannot demonstrate how a predefined model fits all image points of an object. Moreover, it is not reasonable to explain various shapes of datasets using one model. Furthermore, if noise is present in the dataset, an error will be generated. Therefore, we propose an algorithm based on polynomial regression analysis to address these disadvantages. Our experimental results indicate that the proposed method is more accurate than existing methods.

• 농촌계획
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• 제2권2호
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• pp.103-108
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• 1996

In rural planning, the cost estimation of project is a key factor for planning. Therefore, development of reliable cost estimation method is essential. Recently, new techniques are suggested for determination of project cost using historical cost data. In this study, a multiple-regression analysis was used to determine the cost of the farm land consolidation. The results demonstrated that multiple regression analysis using historical cost data can be applicable to project cost estimation.

• 한국재료학회지
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• 제29권12호
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• pp.781-789
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• 2019

The prediction of Jominy hardness curves and the effect of alloying elements on the hardenability of boron steels (19 different steels) are investigated using multiple regression analysis. To evaluate the hardenability of boron steels, Jominy end quenching tests are performed. Regardless of the alloy type, lath martensite structure is observed at the quenching end, and ferrite and pearlite structures are detected in the core. Some bainite microstructure also appears in areas where hardness is sharply reduced. Through multiple regression analysis method, the average multiplying factor (regression coefficient) for each alloying element is derived. As a result, B is found to be 6308.6, C is 71.5, Si is 59.4, Mn is 25.5, Ti is 13.8, and Cr is 24.5. The valid concentration ranges of the main alloying elements are 19 ppm < B < 28 ppm, 0.17 < C < 0.27 wt%, 0.19 < Si < 0.30 wt%, 0.75 < Mn < 1.15 wt%, 0.15 < Cr < 0.82 wt%, and 3 < N < 7 ppm. It is possible to predict changes of hardenability and hardness curves based on the above method. In the validation results of the multiple regression analysis, it is confirmed that the measured hardness values are within the error range of the predicted curves, regardless of alloy type.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3482-3497
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• 2021

Artificial intelligence has emerged as the core of the 4th industrial revolution, and large amounts of data processing, such as big data technology and rapid data analysis, are inevitable. The most fundamental and universal data interpretation technique is an analysis of information through regression, which is also the basis of machine learning. Ridge regression is a technique of regression that decreases sensitivity to unique or outlier information. The time-consuming calculation portion of the matrix computation, however, basically includes the introduction of an inverse matrix. As the size of the matrix expands, the matrix solution method becomes a major challenge. In this paper, a new algorithm is introduced to enhance the speed of ridge regression estimator calculation through series expansion and computation recycle without adopting an inverse matrix in the calculation process or other factorization methods. In addition, the performances of the proposed algorithm and the existing algorithm were compared according to the matrix size. Overall, excellent speed-up of the proposed algorithm with good accuracy was demonstrated.

• 한국작물학회지
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• 제43권2호
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• pp.101-104
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• 1998

This study was conducted to establish a rapid analysis method for determining protein and moisture contents of pea. Ninety and eighty pea (Pisum sativum L.) lines were analyzed to determine protein and moisture contents, respectively using near-infrared reflectance spectroscopy. Simple correlations (${\gamma}$) of protein content in a ground sample and an intact grain sample by an automatic regression method were 0.978 and 0.910, respectively. Simple correlations by partial least square regression/principal component analysis (PLS/PCA) methods were 0.982 and 0.925, respectively. Standard error of performance (SEP) in protein content was the lowest value, 0.446 in ground sample by PLS/PCA methods. Simple correlation of moisture content was the highest at 0.871 in ground samples. when using a standard regression method. Accuracy for the moisture content was slightly lower than for protein content. It was concluded that the NIRS method would be applicable only for rapid determination of protein content in pea.