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Description of short circuit current of outdoor photovoltaic modules by multiple regression analysis under various solar irradiance levels

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RENEWABLE ENERGY 2020年 147卷 895-902页

作者： Nakayama, Koichi Tsuji, Masaki Chantana, Jakapan Kawano, Yu Nishimura, Takahito Hishikawa, Yoshihiro Minemoto, Takashi Ritsumeikan Univ Dept Elect & Elect Engn 1-1-1 Nojihigashi Kusatsu Shiga 5258577 Japan Ritsumeikan Univ Res Org Sci & Technol 1-1-1 Nojihigashi Kusatsu Shiga 5258577 Japan Ritsumeikan Univ Ritsumeikan Global Innovat Res Org 1-1-1 Nojihigashi Kusatsu Shiga 5258577 Japan Natl Inst Adv Ind Sci & Technol Res Ctr Photovolta Tsukuba Ibaraki 3058568 Japan

Short-circuit current (I-SC) values of test photovoltaic (PV) modules, i.e., multi-crystalline silicon, heterostructure-with-intrinsic-thin-layer, single-crystalline silicon back-contact, CulnSe(2) (CIS), and CdTe modules, are descripted using multiple regression analysis based on environmental factors (solar irradiance, average photon energy (APE), and module temperature (T-mod)) under several solar irradiance levels. The APE is an index of the solar spectral irradiance distribution. PV module irradiance sensor (PVMS), single-crystalline silicon PV module, is used to investigate simultaneous solar irradiance (IrrT(PVMS)). It is disclosed that I-SC is primarily determined by IrrT(PVMS). Error between the estimated I(SC )and measured I(SC )of test PV modules is investigated. Consequently, precise I(SC )description (low error) is obtained when IrrT(PVMS) is utilized. The more precise description of the I(SC )for CIS and CdTe PV modules, having the bandgap (E-g) different from PVMS, is realized when adding APE environment factor even under low IrrT(PVMS) (>= 0 kW/m(2)), accumulated on both sunny day and cloudy day suggesting the enhancement of investigation opportunity. This is because APE minimizes spectral mismatch error caused by E-g difference between PVMS and test PV module. Moreover, the precision of I-SC description is further increased under enhanced IrrT(PVMS) of >= 0.5 kW/m(2) (on sunny day) due to stable solar irradiance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Short-circuit current PV module irradiance sensor Solar irradiance levels multiple regression analysis

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Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains

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BIOSCIENCE BIOTECHNOLOGY AND BIOCHEMISTRY 2017年第8期81卷 1598-1606页

作者： Nakamura, Ryo Nakano, Kumiko Tamura, Hiroyasu Mizunuma, Masaki Fushiki, Tohru Hirata, Dai Asahi Sake Brewing Co Ltd Dept Res & Dev Nagaoka Niigata Japan Hiroshima Univ Grad Sch Adv Sci Matter Dept Mol Biotechnol Hiroshima Res Ctr Hlth Aging HiHA Higashihiroshima Japan Ryukoku Univ Fac Agr Otsu Shiga Japan Koshien Jr Coll Dept Life Environm Nishinomiya Hyogo Japan

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

关键词： factor analysis multiple regression analysis palatability sake

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Prediction of process parameters for gas metal arc welding by multiple regression analysis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2000年第6期214卷 443-449页

作者： Lee, JI Rhee, S Hanyang Univ Dept Mech Engn Sungdong Gu Seoul 133791 South Korea

The purpose of this study is to find a prediction regression equation of the welding process parameters in order to obtain the desired geometry of the back-bead in butt welding, a form of gas metal are welding in which a groove gap exists. The regression model equation is obtained from welding process parameters through the correlation of the parameters of the back-bead, to which an inverse transformation is performed. From the parameters of the back-bead, the correlation between the welding process parameters was found in a regression model equation. The accuracy of the regression model was proven through the mean error rate for prediction under conditions of data for the analysis level and the verification level respectively. As a result, the maximum prediction error rate in the forward process was under 9.5 per cent, and the prediction error rate in the inverse process, which is the prediction for the welding process parameters, was under 6.5 per cent, proving that a regression equation of the model for the prediction system of the back-bead can be obtained through both processes.

关键词： gas metal arc welding gap of groove width of back-bead depth of back-bead laser vision sensor image processing multiple regression analysis inverse transformation

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A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network

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OPTICS AND LASERS IN ENGINEERING 2000年第3期34卷 149-158页

作者： Lee, J Um, K Yong In Songdam Coll Dept Comp Aided Automat Yongin 449710 Kyunggi Do South Korea Hanyang Univ Dept Mech Engn Sungdong Gu Seoul 133791 South Korea

This research was done on the basis of prediction that there is a relationship between welding parameters and geometry of the back-bead in are welding which is a gap. multiple regression analysis and artificial neural network were used as methods for predicting the geometry of the back-bead. The multiple regression analysis and the artificial neural network were formed, and the analysis data or verification data which were used in the formation process of the multiple regression, and the training data or test data which were used in the formation process of the artificial neural network, were used to perform the prediction of the back-bead. Through this research, it was found that the error rate predicted by the artificial neural network was smaller than that predicted by the multiple regression analysis, in terms of the width and depth of the back-bead. It was also found that between the two predictions, the prediction of the width of the back-bead was superior to the prediction of the depth in both methods. (C) 2001 Published by Elsevier Science Ltd.

关键词： artificial neural network multiple regression analysis the geometry of back-bead

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Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

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INFRARED PHYSICS & TECHNOLOGY 2018年 91卷 220-232页

作者： Shrivastava, Prashant Kumar Pandey, Arun Kumar Jaypee Univ Engn Technol Dept Mech Engn Guna MP India Bundelkhand Inst Engn & Technol Dept Mech Engn Kanpur Rd Jhansi Uttar Pradesh India

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Inconel-718 Laser cutting Parametric optimization Hybrid approach multiple regression analysis Genetic algorithm

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Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2013年 38卷 161-169页

作者： Jang, Hyongdoo Topal, Erkan Curtin Univ Western Australian Sch Mines Dept Min Engn Kalgoorlie WA Australia

Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R-2) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.

关键词： Blasting Overbreak Underground mine Artificial neural network multiple regression analysis

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Estimation of maturity of compost from food wastes and agro-residues by multiple regression analysis

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BIORESOURCE TECHNOLOGY 2006年第16期97卷 1979-1985页

作者： Chikae, Miyuki Ikeda, Ryuzoh Kerman, Kagan Morita, Yasutaka Tamiya, Eiichi Japan Adv Inst Sci & Technol Sch Mat Sci Nomi Ishikawa 9231292 Japan

The composting process of food wastes and tree cuttings was examined on four composting types composed from two kinds of systems and added mixture of microorganisms. The time courses of 32 parameters in each composting type were observed. The efficient composting system was found to be the static aerated reactor system in comparison with the turning pile one. Using the multiple regression analysis of all the data (159 samples) obtained from this study, some parameters were selected to predict the germination index (GI) value, which was adopted as a marker of compost maturity. For example, using the regression model generated from pH, NH4+ concentration, acid phosphatase activity, and esterase activity of water extracts of the compost, GI value was expressed by the multi-linear regression equation (p < 0.0001). High correlations between the measured GI value and the predicted one were made in each type of compost. As a result of these observations, the compost maturity might be predicted by only sensing of the water extract at the composting site without any requirements for a large-size equipment and skill, and this prediction system could contribute to the production of a stable compost in wide-spread use for the recycling market. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： composting food waste tree cutting maturity multiple regression analysis

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Thermodynamic Stabilities of PdRuM (M = Cu, Rh, Ir, Au) Alloy Nanoparticles Assessed by Wang-Landau Sampling Combined with DFT Calculations and multiple regression analysis

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BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2021年第10期94卷 2484-2492页

作者： Nanba, Yusuke Koyama, Michihisa Shinshu Univ Res Initiat Supra Mat 4-17-1 Wakasato Nagano 3808553 Japan Kyoto Univ Open Innovat Inst Sakyo Ku Kyoto 6068501 Japan

Unique features of alloy nanoparticles (NPs) originate from the configuration of elements within NPs;solid solution and segregated configurations show different properties even with the same overall composition of elements. The configuration space of an alloy NP is exponentially expanded by an increase of the constituent elements. Besides, the configurational entropy in an alloy NP cannot be analytically estimated due to the heterogeneous surface. Revealing the stable configuration and the corresponding entropy in the extensive configuration space is difficult. Herein, Wang-Landau sampling, combined with density functional theory (DFT) calculations and multiple regression analysis, was used to assess the thermodynamic stabilities of PdRuM (M = Cu, Rh, Ir, Au) ternary alloy NPs. Specifically, the excess energies calculated by DFT were subjected to multiple regression analysis, and the obtained regression equations were used for Wang-Landau sampling. The thus-obtained configurational densities of states allowed us to estimate thermodynamic quantities, and hence, to predict stable configurations at a finite temperature. We conclude that the developed method is well suited to probing the stable configurations of multinary alloy NPs at a finite temperature.

关键词： Alloy nanoparticle multiple regression analysis Wang-Landau sampling

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Estimation of melanin and hemoglobin in skin tissue using multiple regression analysis aided by Monte Carlo simulation

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JOURNAL OF BIOMEDICAL OPTICS 2004年第4期9卷 700-710页

作者： Nishidate, I Aizu, Y Mishina, H Muroran Inst Technol Dept Mech Syst Engn Muroran Hokkaido 0508585 Japan Muroran Inst Technol Div Sci Composite Funct Muroran Hokkaido 0508585 Japan

To estimate the concentrations of melanin and blood and the oxygen saturation in human skin tissue, we propose a method using a multiple regression analysis aided by a Monte Carlo simulation for diffuse reflectance spectra from the skin tissue. By using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, the multiple regression analysis gives regression coefficients. The concentrations of melanin and blood are determined from the regression coefficients using conversion vectors that are estimated numerically in advance, while the oxygen saturation is obtained directly from the regression coefficients. Numerical and experimental investigations were performed for layered skin tissue models and phantoms. Measurements of human skin were also carried out to monitor variations in the melanin and blood contents and oxygenation during cuff occlusion. The results confirmed the usefulness of the proposed method. (C) 2004 American Institute of Physics.

关键词： skin tissue oxygen saturation hemoglobin melanin multiple regression analysis diffuse reflectance Monte Carlo simulation absorbance

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Durability assessment of some Cappadocian tuffs using factor analysis, multiple regression analysis, and analytical hierarchy process

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2022年第1期81卷 1-28页

作者： Deniz, B. Ertas Topal, T. Disaster & Emergency Management Author Ankara Turkey Middle East Tech Univ METU Dept Geol Engn Ankara Turkey

Tuff located in Central Anatolia (Cappadocia) has been used as a building stone in many historical monuments, churches, and mosques. Although some of the tuffs used in those historical structures show good performances, the others yield poor performances with their physical and mechanical properties varying in large ranges. This study aims to assess the durability of the tuffs using factor analysis (FA), multiple regression analysis (MRA), and analytical hierarchy process (AHP). In current study, thirteen tuff samples from nine different quarries located in the region of Cappadocia were examined by using the physical and mechanical characteristics of the tuffs. Aging tests namely wetting-drying, freezing-thawing, and salt crystallization were performed on the fresh tuff samples. For mineralogical and petrographic analyses, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were applied. Principal component analysis (PCA) was used for selecting the most effective properties (variables) of the tuffs. PCA indicated that effective porosity, dry unit weight, dry sonic velocity, uniaxial compressive strength, and salt crystallization weight loss are the most effective variables of the tuffs for the purpose of durability assessment. Considering the analyzed tuffs, field evaluations, and laboratory performances, one tuff belongs to high durability class;the others fall into moderate durability class tuffs. Comparison of the results from the statistical analysis results and the field performances of the tuffs indicate that AHP and MRA methods can predict the long-term durability of the tuffs better than the FA method.

关键词： Tuff Factor analysis multiple regression analysis Analytical hierarchy process Cappadocia Turkey

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