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Journal articles on the topic 'GRA-PCA'

Author: Grafiati

Published: 11 September 2023

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1

Hu, Guohua, Guoyan Meng, Qingshan Zhao, and Xiaoxia Zheng. "Emotion feature optimisation based on PCA-GRA analysis." International Journal of Computing Science and Mathematics 12, no. 4 (2020): 339. http://dx.doi.org/10.1504/ijcsm.2020.112673.

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2

Zheng, Xiaoxia, Guohua Hu, Qingshan Zhao, and Guoyan Meng. "Emotion feature optimisation based on PCA-GRA analysis." International Journal of Computing Science and Mathematics 12, no. 4 (2020): 339. http://dx.doi.org/10.1504/ijcsm.2020.10034918.

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3

Mishra, Yadvendra Kumar, Sanjay Mishra, and S. C. Jayswal. "Comparative Analysis of Grey Relational Analysis Integrated with the Principal Component Analysis and Analytic Hierarchy Process for Multiobjective Optimization of Inclined Laser Percussion Drilling in Carbon Fiber Reinforced Composites." Journal of Advanced Manufacturing Systems 21, no. 01 (February 11, 2022): 1–23. http://dx.doi.org/10.1142/s0219686721500475.

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Multiobjective optimization (MOO) helps to achieve simultaneous improvement of more than one output characteristic in machining processes where complex interaction between the input parameter exists. This study focuses on the comparative analysis of design of experiment (DoE)-based grey relational analysis (GRA) combined with principal component analysis (PCA) and analytic hierarchy process (AHP). Experiments were conducted with millisecond (ms) duration pulsed Nd: YAG laser using the Box–Behnken design (BBD) approach of the response surface methodology (RSM) at three different levels of input parameters. The output parameters, i.e., hole circularity at top (HCT), hole circularity at bottom (HCB), and hole taper (HT), were determined for various input parameters like pulse current (I), pulse width ([Formula: see text]), gas pressure ([Formula: see text]), workpiece thickness ([Formula: see text]), and incidence angle ([Formula: see text]) during laser percussion inclined hole drilling (LPIHD) in the carbon fiber reinforced polymer (CFRP) of three different thickness, i.e., 1[Formula: see text]mm, 3[Formula: see text]mm, and 5[Formula: see text]mm at incidence angles of 0, 10, and 20 degrees. Multiobjective function based on RSM has been developed for GRA-PCA and GRA-AHP and further optimizations were performed using the desirability approach of RSM. The analysis revealed that the angle of incidence is the most significant factor for controlling the output parameters. Interaction of pulse current and thickness ([Formula: see text]) has a major impact on output responses. The GRA-PCA approach gives the average improvement of 2%, 9%, and 37%, respectively, for HCT, HCB, and HT, whereas in the case of GRA-AHP, the corresponding improvements are only 1%, 6%, and 11%. Therefore, the GRA-PCA approach is a more effective tool for the MOO of LPIHD in CFRP.

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Jiang, Xuelian, Yueling Zhao, Ling Tong, Rui Wang, and Sheng Zhao. "Quantitative Analysis of Tomato Yield and Comprehensive Fruit Quality in Response to Deficit Irrigation at Different Growth Stages." HortScience 54, no. 8 (August 2019): 1409–17. http://dx.doi.org/10.21273/hortsci14180-19.

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To investigate the quantitative response of tomato yield and fruit quality to deficit irrigation applied at different growth stages, greenhouse experiments were conducted in 2017 and 2018. Three irrigation treatments (full irrigation and two-thirds or one-third of full irrigation) were applied to greenhouse-grown tomato plants at flowering and fruit development (stage 2) and at fruit maturation stage (stage 3). Grey relational analysis (GRA), the technique for order preference by similarity to an ideal solution (TOPSIS), and principal components analysis (PCA) were used to calculate the comprehensive fruit quality indexes, and combinatorial evaluation method was determined. The results showed that deficit irrigation significantly reduced evapotranspiration (ET) and tomato yield and that relative yield had a negative linear correlation with relative seasonal water deficit (1−ETi/ETc). However, deficit irrigation improved fruit quality, especially at stage 2. Total soluble solids, the total soluble sugar concentration, the sugar-to-acid ratio, and vitamin C in the tomatoes all increased significantly in plants that were deficit irrigated compared with fully irrigated plants, while organic acids and lycopene decreased in both years. There were linear correlations between fruit quality parameters and 1−ETi/ETc. The comprehensive quality index derived from GRA and PCA is reliable, and the comprehensive quality indexes given by GRA, PCA, and a combination of GRA and PCA showed positive linear correlation with 1−ETi/ETc. The comprehensive quality ranking showed that in both years, F2/3M1 (two-thirds full irrigation at stage 2) gave a better result and CK (full irrigation) the worst. An appropriate water deficit at the flowering and fruit development stage, which results in a trade-off between acceptable yield and improved fruit quality, is recommended. Our results provide a sound basis for tomato production that has a desirable balance between high yield and high fruit quality.

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Zhou, Yongqin, Yujia Chang, Yongchao Wang, and Ran Li. "Research on methods for extracting aging characteristics and health status of lithium-ion batteries based on small samples." Journal of Renewable and Sustainable Energy 14, no. 2 (March 2022): 024101. http://dx.doi.org/10.1063/5.0071686.

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The number of aging samples is small with regard to the small sample characteristics of lithium-ion battery aging data, and the extraction of effective aging features is difficult to perform, which reduces the generalization of the battery aging behavior model as well as the accuracy of battery state of health (SOH) estimation. According to the incremental capacity analysis, a gray relational analysis (GRA) method based on PCA (principal component analysis) was proposed in this study, which was combined with AdaBoost-SVR (support vector regression) to achieve accurate SOH measurement under small sample conditions. Accordingly, the combination of GRA and PCA was able to fully mine the aging characteristic information from limited battery aging samples while improving the generalization ability of the battery aging behavior model. Moreover, based on AdaBoost-SVR, it was able to perform adaptive weighted sampling on small data samples, and through multiple iterations of the SVR training model, the full use of small sample information was realized, ensuring the accuracy of SOH estimation. Finally, by employing multiple sets of battery aging training sets and test sets for experimental verification, the joint algorithm based on GRA-PCA and AdaBoost-SVR was proven to achieve the accurate extraction of battery aging characteristics and precise tracking of capacity decline.

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Ekici, Ergün, Ali Motorcu, and Gültekin Uzun. "Multi-objective optimization of process parameters for drilling fibermetal laminate using a hybrid GRA-PCA approach." FME Transactions 49, no. 2 (2021): 356–66. http://dx.doi.org/10.5937/fme2102356e.

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This study investigated the effects of drilling parameters and cutting tool coating conditions on the thrust force, surface roughness, and delamination factor in the drilling of fiber-reinforced carbon reinforced aluminum laminate (CARALL) composite, a commercial type of fibermetal laminate. Gray relational analysis (GRA) was used as a multi-objective optimization method to determine optimum processing parameters and principal component analysis (PCA) was used to determine the weights. According to the findings of this experimental study, the most effective control factors for the thrust force, surface roughnes, and delamination factor were the feed rate, tool coating condition-cutting speed interaction, and tool coating condition, with 93.87%, 66.504%, and 29.137% contribution rates, respectively. From the results of the GRA-PCA analysis, the optimum levels of the control factors were determined as 110 m/min cutting speed, 0.1 mm/rev feed rate, and the uncoated tool.

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LAKSHMI SRINIVAS, CH, P. UMAMAHESWAR RAO, T. SRINAG, and M. C. SEKHAR. "Grey Relational Analysis and Principal Component Analysis based optimization of process parameters in turning of EN-8 Steel." INCAS BULLETIN 14, no. 2 (June 10, 2022): 43–51. http://dx.doi.org/10.13111/2066-8201.2022.14.2.4.

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The present work investigates the optimum machining parameters while turning EN 8 steel by employing hybrid Grey relational analysis (GRA) and principal component analysis (PCA) techniques. Experiments were designed based on the central composite design (CCD) of the Response surface method (RSM). Experiments were conducted by varying machining parameters such as cutting speed, feed, and depth of cut. In this study, the surface roughness and the material removal rate (MRR) are measured during the experimentation. To determine the influence of cutting parameters, an Analysis of variance (ANOVA) was deployed. The optimal turning parameters are found to be speed 1170 rpm, feed 0.225 mm/rev, and depth of cut 1.238 mm. The results revealed that the optimization through hybrid GRA-PCA enhanced the output quality characteristics.

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8

Li, Zhiyong, Wanting Xi, Yuan Cao, and Shiping Pu. "A Dimensional Reduction Optimization Strategy for Line Voltage cascade Quasi-Z-Source Inverter Based on GRA-PCA-PSO." Journal of Physics: Conference Series 2488, no. 1 (May 1, 2023): 012053. http://dx.doi.org/10.1088/1742-6596/2488/1/012053.

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Abstract Compared with traditional inverters, line voltage cascade quasi-Z source inverter (LVC-QZSI) has many advantages, but its power quality problems limit its wide application to some extent. In this paper, voltage ripple and power loss of LVC-QZSI are selected as the optimization objectives to build a multi-objective optimization model of LVC-QZSI. In the optimization process, it is found that the dimension of decision variables is too high, which belongs to a kind of large-scale multi-objective optimization problem. To solve this problem, this paper proposes the dimension reduction and optimization strategy of particle swarm optimization based on gray relation analysis and principal component analysis (GRA-PCA-PSO). GRA-PCA is used to realize the dimension reduction of decision variables, and then PSO based on information entropy is used to optimize the multi-objective optimization model after dimension reduction. Finally, the original optimization parameter values are derived from the eigenvalue matrix obtained from PCA. Compared with the traditional method of decision variable grouping and co-evolution, it reduces the calculation cost and improves the optimization accuracy. Finally, we verify the reliability of the algorithm.

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Kumar, Anil, Arindam Kumar Chanda, and Surjit Angra. "Optimization of Stiffness Properties of Composite Sandwich using Hybrid Taguchi-GRA-PCA." Evergreen 8, no. 2 (June 2021): 310–17. http://dx.doi.org/10.5109/4480708.

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Xue, Feng, Qian Huang, Chuanlei He, and Bharat Pathak. "Using an Entropy-GRA, TOPSIS, and PCA Method to Evaluate the Competitiveness of AFVs – The China Case." Promet - Traffic&Transportation 32, no. 5 (October 5, 2020): 655–66. http://dx.doi.org/10.7307/ptt.v32i5.3417.

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With the increase in severe environmental problems associated with fossil fuel vehicles, the development of Alternative Fuel Vehicles (AFVs) has led to their promotion and use in Chinese provinces and cities. The comprehensive evaluation of competitiveness of the AFV industry in Chinese cities is beneficial to analyse the effects and relationships of different factors to promote the sustainable development of the AFV industry and guide the growth paths of the cities. An industrial competitiveness evaluation index system is established based on the characteristics of AFVs, and the development of the AFV industry in ten typical cities in China is comprehensively evaluated based on the Grey Relative Analysis (GRA) Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) and Principal Component Analysis (PCA) methods. To evaluate the results, the entropy weighting method is used for the weight distribution, and the industrial competitiveness rankings of ten cities are obtained by the entropy-GRA, TOPSIS, PCA (EGTP) method. The results show that Beijing is ranked first, followed by Shanghai, and Qingdao is ranked last. By analysing the correlation between the evaluation methods and indicators, it is found that EGTP has a high correlation with the other three evaluation methods, which proves the rationality of the weighted linear combination of GRA and the other three methods. Indices C5 (pure electric car proportion) and C13 (average concentration of PM2.5) were outliers due to the small number of samples.

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11

Sahu, Nitin Kumar, Mukesh Kumar Singh, Bupe Getrude Mutono-Mwanza, and Atul Kumar Sahu. "Investigation of Machinability Characteristics of EDMed Inconel 825 Alloy under Multidimensional Parametric Modeling by Using Holistic Grey-PCA Statistical Models." Advances in Materials Science and Engineering 2022 (May 14, 2022): 1–29. http://dx.doi.org/10.1155/2022/3147586.

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The current social and industrial communities exceedingly demanded the materials that dealt with rich mechanical properties, i.e., the rich strength, hardness, reliability, high resistance against corrosion and oxidation, and high toughness and refractoriness. Recently, the researchers ascertained the comprehensive applications of these difficult to machine materials in the domain of automotive, aeronautical, nuclear industries etc. It is claimed that it is quite hard and expensive for machining these superadvanced materials by traditional machining operations. In the present study, worldwide promising nickel-based superalloy Inconel 825 material is used due to its outstanding mechanical and thermal properties at eminent temperatures and also having broad application in imperative engineering fields. The authors probed that machining cost and smart machinability index have become the gigantic concern in EDM operation; however, these can be minimized by adapting a conduit of evaluation of the optimum setting among multiple input parameters. It was a challenging task, which is respected by authors as the research gaps to be sorted out. To fulfill research gaps, the authors encountered imperative significant EDM input parameters, i.e., spark gap (Sg), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff), Peak Current (Ip), Servo feed (Sf), Depth of Cut (Dc) and difficulty index (Di) corresponding to output responses, i.e., power consumption (Pc), machining time (Mt), and material removal rate (MRR) for framing the machinability index/model for conducting experiments and collecting objectives/responses/outputs. Next, the authors conducted experiments using the Taguchi L27 orthogonal array model in the nonvibratory domain for recording output responses. Later, to potentially access the results, the authors integrated the computational Taguchi methodology with dual models which is called as Taguchi-grey relational analysis (T-GRA) and Taguchi-principal component analysis (T-PCA). The optimum setting condition among considered inputs is discussed in the conclusion section. Later, confirmatory test gratified the evaluated optimum settings which is yielding an improvement of 0.16262 and 0.34398 score in executing T-GRA and T-PCA, respectively. In continuation, the current research evidenced that pulse off time is the chief significant process parameter having p values 0.005 and 0.001 for T-GRA and T-PCA, respectively. It is also seen that the depth of cut is also another important significant process parameter having p values 0.061 and 0.073 for T-GRA and T-PCA, respectively. Moreover, the current research work also investigated the effect of variation of momentous input process parameters over the surface topography and their utility in improving surface integrity and eliminating the micropores and cracks.

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12

Payal, Himanshu, Sachin Maheshwari, and Pushpendra S. Bharti. "Parametric optimization of EDM process for Inconel 825 using GRA and PCA approach." Journal of Information and Optimization Sciences 40, no. 2 (February 17, 2019): 291–307. http://dx.doi.org/10.1080/02522667.2019.1578090.

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13

Wang, Kai, Kangnan Li, and Feng Du. "Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model." Geofluids 2021 (October 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/3508806.

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The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.

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Wang, Kai, Kangnan Li, and Feng Du. "Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model." Geofluids 2021 (October 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/3508806.

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The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.

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Jin, Xinyu, Shimin Wu, Wenjuan Yu, Xinyi Xu, Mingquan Huang, Yongfang Tang, and Zhenyu Yang. "Wine Authentication Using Integration Assay of MIR, NIR, E-tongue, HS-SPME-GC-MS, and Multivariate Analyses: A Case Study for a Typical Cabernet Sauvignon Wine." Journal of AOAC INTERNATIONAL 102, no. 4 (July 1, 2019): 1174–80. http://dx.doi.org/10.5740/jaoacint.18-0327.

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Abstract Background: Cabernet Sauvignon wine enjoys large market in China, and its adulteration has become a well-known problem and challenge. Objective: This study aims to evaluate the capabilities of multiple techniques, including headspace–solid-phase microextraction–GC-MS (HS-SPME-GC-MS), electronic tongue (E-tongue) spectroscopy, mid-infrared (MIR) spectroscopy, and near-infrared (NIR) spectroscopy, to differentiate this popular imported wine in China. Methods: MIR spectroscopy, NIR spectroscopy, E-tongue spectroscopy, and HS-SPME-GC-MS were used. Multivariate analysis techniques were applied to further explore the instrumental determination data for the wine discrimination. Results: Joint use of MIR and NIR with Grey relational analysis (GRA), E-tongue with principal component analysis (PCA) and hierarchical cluster analysis, and HS-SPME-GC-MS with PCA allowed unanimous differentiation of the wines. Conclusions: The approach described herein offers both ecologically friendly and multiperspective mutual corroboration techniques for Cabernet Sauvignon wine discrimination. The integrative methodology could be used as a reference for wine authentication. Highlights: GRA was first applied to discriminate the wine samples. Mutual corroboration was verified by multivariate statistics combined with MIR, NIR, E-tongue, and SPME-GC/MS. Integrated techniques pointed to a unanimous authentication of the wine samples.

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Han, Xirui, Longbo Chang, Nan Wang, Weifu Kong, and Chengguo Wang. "Effects of Meteorological Factors on Apple Yield Based on Multilinear Regression Analysis: A Case Study of Yantai Area, China." Atmosphere 14, no. 1 (January 15, 2023): 183. http://dx.doi.org/10.3390/atmos14010183.

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Evaluating the impact of different meteorological conditions on apple yield and predicting the future yield in Yantai City is essential for production. Furthermore, it provides a scientific basis for the increase in apple yield. In this study, first, a grey relational analysis (GRA) was used to determine the quantitative relationship between different meteorological factors and meteorological yield which is defined as affected only by meteorological conditions. Then, the comprehensive meteorological factors extracted by a principal component analysis (PCA) were used as inputs for multiple linear regression (MLR). The apple yield accuracy was compared with the lasso regression prediction. Trend analysis showed that the actual apple yield increased annually, but the meteorological yield decreased annually over a long time. Correlation ranking illustrated that the meteorological yield was significantly correlated with the frost-free period, the annual mean temperature, the accumulated temperature above 10 °C, etc. The good consistency between GRA and MLR–PCA showed that the accumulated temperature above 10 °C, the March–October mean temperature, and the June–August mean temperature are key meteorological factors. In addition, it was found that the principal components F2, F4, and F5 were negatively correlated with meteorological yield, while the principal components F1 and F3 were positively correlated with meteorological yield. Moreover, the MLR–PCA model predicted the apple yield in 2020 as 47.256 t·ha−1 with a 7.089% relative error. This work demonstrates that the principal component regression model can effectively extract information about different meteorological factors and improve the model’s accuracy for analyzing key meteorological factors and predicting apple yield.

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Rathore, Suneel Kumar, Jyoti Vimal, and Dinesh K. Kasdekar. "Determination of optimum parameters for surface roughness in CNC turning by using GRA-PCA." International Journal of Engineering, Science and Technology 10, no. 2 (June 6, 2018): 37. http://dx.doi.org/10.4314/ijest.v10i2.5.

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Agrawal, Shashank, Sharad Agrawal, and Dinesh Kumar Kasdekar. "Optimization of MRR and Electrolyte Coating Thickness of ECM Parameters using PCA based GRA." Materials Today: Proceedings 5, no. 9 (2018): 18956–65. http://dx.doi.org/10.1016/j.matpr.2018.06.246.

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19

Syahzaqi, Idrus, Hani Brilianti Rochmanto, and Muhammad Ahsan. "SELECTING OPTIMAL PROCESS PARAMETERS OF Al2O3/C COMPOSITE USING GRA WITH PCA AND TAGUCHI’S QLF APPROACH." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (September 1, 2022): 1039–50. http://dx.doi.org/10.30598/barekengvol16iss3pp1039-1050.

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The aim of this study is to find the controlled factors affecting the mass density of the combined Al2O3/Cu. All experiments were carried out using powder metallurgy. Experiments were carried out with four controllable powder processing parameters, namely milling time, compaction pressure, sintering temperature, and holding time. The L18 mixed-level Taguchi Orthogonal Array was used for experimental because it is the basis for the analysis of the Taguchi method. In this research, statistical analysis is carried out using GRA with PCA and Quality Loss Function. The result was the best model based on the Quality Loss Function, because the method has the biggest determination coefficient value is 99,97% where the results is better than GRA with PCA. From the main effect table study, the optimal combination of parameters for response: mass density and hardness are A2B3C3D2 powder metallurgical process parameters, namely milling time of 360 minutes, compacting powder of 200 MPa, sintering of 7000C, and holding time of 20 minutes. The ANOVA results show that the compaction pressure has the most influential parameter that affects the response. The percentage contribution of compaction pressure is 87.09%. Based on ANOVA, the R-squared value is 99.97%, which means the tested factor variables can explain the density of the Al2O3/Cu composite by 99.70%. Therefore, only 18 experimental trials are needed to discover the reality of what will happen in the process.

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20

Gurpreet Singh Matharou et al.,, Gurpreet Singh Matharou et al ,. "Parametric Optimization of EDM Processes for Aluminum Hybrid Metal Matrix Composite using GRA-PCA Approach." International Journal of Mechanical and Production Engineering Research and Development 10, no. 3 (2020): 367–78. http://dx.doi.org/10.24247/ijmperdjun202034.

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Pandey, Gaurav Kumar, and Sanjeev Kumar Singh Yadav. "Multi-Response Optimization of Vibration Assisted Electrical Discharge Drilling Process using PCA based GRA approach." Materials Today: Proceedings 22 (2020): 2906–15. http://dx.doi.org/10.1016/j.matpr.2020.03.424.

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Pandey, Gaurav Kumar, and Sanjeev Kumar Singh Yadav. "Multi-objective optimization of vibration assisted electrical discharge drilling process using PCA based GRA method." Materials Today: Proceedings 26 (2020): 2667–72. http://dx.doi.org/10.1016/j.matpr.2020.02.561.

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Porwal, Rajesh Kumar, Vinod Yadava, and J. Ramkumar. "Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining." International Journal of Manufacturing, Materials, and Mechanical Engineering 4, no. 1 (January 2014): 1–21. http://dx.doi.org/10.4018/ijmmme.2014010101.

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Determination of material removal rate (MRR), tool wear rate (TWR) and hole taper (Ta) is a challenging task for manufacturing engineers from the productivity and accuracy point of view of the symmetrical and nonsymmetrical holes due to hole sinking electro discharge micro machining (HS-EDMM) process. Thus, mathematical models for quick prediction of these aspects are needed because experimental determinations of process performances are always tedious and time consuming. Not only prediction but determination of optimum parameter for optimization of process performance is also required. This paper attempts to apply a hybrid mathematical approach comprising of Back Propagation Neural Network (BPNN) for prediction and Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) for optimization with multiple responses of HS-EDMM of Invar-36. Experiments were conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters MRR, TWR and Ta. The results indicate that the hybrid approach is capable to predict process output and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different material.

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Kavimani, V., K. Soorya Prakash, Titus Thankachan, S. Nagaraja, A. K. Jeevanantham, and Jithin P. Jhon. "WEDM Parameter Optimization for Silicon@r-GO/Magneisum Composite Using Taguchi Based GRA Coupled PCA." Silicon 12, no. 5 (July 1, 2019): 1161–75. http://dx.doi.org/10.1007/s12633-019-00205-6.

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25

Andrade, Liz Hassad de, Jorge Junio Moreira Antunes, and Peter Wanke. "Performance of TV programs: a robust MCDM approach." Benchmarking: An International Journal 27, no. 3 (February 29, 2020): 1188–209. http://dx.doi.org/10.1108/bij-07-2019-0316.

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PurposeThe aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.Design/methodology/approachThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.FindingsBy comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.Practical implicationsTo determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).Originality/valueThis work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.

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Mehat, Nik Mizamzul, Shahrul Kamaruddin, and Abdul Rahim Othman. "A Study of Hybrid Optimization of Injection Moulding Process Parameters for Plastic Gear." Advanced Materials Research 591-593 (November 2012): 2135–38. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2135.

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This paper presents a hybrid optimization method for optimizing the injection moulding process parameters for plastic gear. This proposed method combines the Taguchi robust design, grey relational analysis (GRA) and principal component analysis (PCA) in designing the experiments and analyzing the behaviour of shrinkage in addendum circles (AC), dedendum circles (DC) and tooth thickness (TT) of the moulded gear. The proposed hybrid optimization method appears to be a necessity to overcome the shortcomings in multiple quality characteristics optimization as well as provides a statistical solution in defining a weight for each quality characteristic.

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Sharma, Satish Kumar, and Sachin Maheshwari. "Multi-Objective Optimization of HAZ Characteristics for Submerged Arc Welding of Micro-Alloyed High Strength Pipeline Steel using GRA-PCA Approach." Journal for Manufacturing Science and Production 16, no. 4 (December 1, 2016): 263–71. http://dx.doi.org/10.1515/jmsp-2016-0027.

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AbstractIn any type of welding, its heat affected zone (HAZ) is the most critical section where the probability of hydrogen or solidification cracking is prominent. Continuous efforts are being applied by researchers and welders to minimize HAZ and its alterations. For the same purpose, by varying the factors of submerged arc welding (SAW) process which directly influences the heat input and cooling rate of the weld, experiments are carried out on the plates of micro-alloyed high strength pipeline steel. Voltage, welding speed, contact tube to work distance, wire feed rate and preheating temperature are taken as process variables. The experiments are designed according to the central composite rotatable design approach of response surface methodology (RSM). Multi-objective optimization of HAZ characteristics (HAZ area and its hardness) is carried out using grey relational analysis (GRA). To un-correlate the correlated characteristics of HAZ, principal component analysis (PCA) approach is coupled with GRA. The results of the confirmatory test have shown an improvement of 28.51 % and 5.94 % in area and hardness value of HAZ respectively at optimal setting combination of SAW process parameters.

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K. Siva Prasad et al.,, K. Siva Prasad et al ,. "Multi-Objective Optimization of Machining Parameters on Drilling of GFRP Composites using Hybrid GRA-PCA Techniques." International Journal of Mechanical and Production Engineering Research and Development 10, no. 3 (2020): 12217–26. http://dx.doi.org/10.24247/ijmperdjun20201168.

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Kumar, Anil, Arindam Kumar Chanda, and Surjit Angra. "Application of hybrid Taguchi-GRA-PCA and ANOVA in optimisation of deformation properties of sandwich structure." International Journal of Nanotechnology 18, no. 11/12 (2021): 951. http://dx.doi.org/10.1504/ijnt.2021.119220.

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Umamaheswarrao, P., D. Ranga Raju, K. N. S. Suman, and B. Ravi Sankar. "Achieving Optimal Process Parameters during Hard Turning of AISI 52100 Bearing Steel Using Hybrid GRA-PCA." Key Engineering Materials 818 (August 2019): 87–91. http://dx.doi.org/10.4028/www.scientific.net/kem.818.87.

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In the present work hard turning of AISI 52100 steel has been performed using Polycrystalline cubic boron nitride (PCBN) tools. The input parameters considered are cutting speed, feed, depth of cut, nose radius and negative rake angle and the measured responses are machining force and workpiece surface temperature. Experiments are planned as per Central Composite Design (CCD) of Response Surface Methodology (RSM). The effect of input parameters and their interactions are discussed with main effects plot. Further, the multi-objective optimization scheme is proposed by adopting Grey Relational Analysis (GRA) coupled with Principle Component Analysis (PCA). Results demonstrated that speed is the most significant factor affecting the responses followed by negative rake angle, feed, depth of cut, and nose radius. The optimum cutting parameters obtained are cutting speed 1000 rpm, feed 0.02 mm/rev, depth of cut 0.5 mm, Nose radius 1 mm and Negative rake angle 5o.

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Dhar, Nikhil Ranjan, Shanta Saha, and Prianka B. Zaman. "Hybrid Taguchi-GRA-PCA approach for multi-response optimisation of turning process parameters under HPC condition." International Journal of Machining and Machinability of Materials 22, no. 3/4 (2020): 281. http://dx.doi.org/10.1504/ijmmm.2020.10028424.

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Zaman, Prianka B., Shanta Saha, and Nikhil Ranjan Dhar. "Hybrid Taguchi-GRA-PCA approach for multi-response optimisation of turning process parameters under HPC condition." International Journal of Machining and Machinability of Materials 22, no. 3/4 (2020): 281. http://dx.doi.org/10.1504/ijmmm.2020.107059.

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Bhosale, Shrikrushna B., Sumit Bhowmik, and Amitava Ray. "Experimental analysis and parametric optimization of drilling process for ferrous clay composite using GRA-PCA approach." Journal of Materials Research and Technology 10 (January 2021): 376–89. http://dx.doi.org/10.1016/j.jmrt.2020.12.032.

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Kaushik, Narinder, and Sandeep Singhal. "Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiCp matrix composite." Production & Manufacturing Research 6, no. 1 (January 2018): 171–89. http://dx.doi.org/10.1080/21693277.2018.1479666.

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Yellapragada, Naga Venkata Sai Ram, Venkata Sai Kumar Madala, Sameer Kumar Devarakonda, Raqheeb Sadiq Mahaboob Ali Shaik, Nageswara Rao Annamdasu, Kondala Rao Dasari, and Hasheer Shaik Mohammad. "Application of Taguchi – PCA/GRA Method to Optimize the Wear Behaviour of Polyester/Carbon Fibre Composites." Revue des composites et des matériaux avancés 33, no. 2 (April 30, 2023): 65–73. http://dx.doi.org/10.18280/rcma.330201.

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Wang, Shiyun, Xiaonan Liang, and Jiaoyue Wang. "Parameter assignment for InVEST habitat quality module based on principal component analysis and grey coefficient analysis." Mathematical Biosciences and Engineering 19, no. 12 (2022): 13928–48. http://dx.doi.org/10.3934/mbe.2022649.

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<abstract> <p>The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model is a concise approach to evaluate the status of habitat quality for supporting ecosystem management and decision making. Assigning parameters accurately in the InVEST model is the premise for effectively simulating habitat quality. The purpose of this study is to propose an available method for assigning the important parameters in the Habitat Quality module of InVEST. Herein, the methods of principal component analysis (PCA) and grey relational analysis (GRA) were utilized to assign the weights of threat factors and the sensitivity of each habitat type to each threat factor, respectively. Through a case study of the habitat quality of Fuzhou City, we find that using PCA and GRA methods to assign parameters is feasible. Generally, the habitat quality of Fuzhou City in 2015 and 2018 was above the fair suitable level, and the proportion of fair suitable and good suitable habitats was about 83%. The areas with higher habitat quality were mainly concentrated in forest, wetland and grassland ecosystems. The spots with lower habitat quality were scattered all over the main urban areas of districts and counties, and their periphery. GDP per capita and population density were the main factors that affect the habitat quality of Fuzhou City. Narrowing the economic imbalance gap is an important way to reduce population shift and relieve the pressure of the urban environment in economically developed areas. This study is expected to provide an effective method for assigning parameters in the InVEST Habitat Quality Module and support regional ecosystem conservation.</p> </abstract>

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Vijay Praveen, D., D. Ranga Raju, and M. V. Jagannadha Raju. "Assessment of Optimal Parameters of Wire EDM on Ni-Coated Al2O3p/AA7075 MMCs Using PCA Coupled GRA." Arabian Journal for Science and Engineering 46, no. 8 (April 13, 2021): 7953–66. http://dx.doi.org/10.1007/s13369-021-05579-7.

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Chakraborty, Sadananda, Souren Mitra, and Dipankar Bose. "An investigation on dimensional accuracy and surface topography in powder mixed WEDM using RSM and GRA-PCA." Materials Today: Proceedings 44 (2021): 1524–30. http://dx.doi.org/10.1016/j.matpr.2020.11.734.

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Kharwar, Prakhar Kumar, and Rajesh Kumar Verma. "Machining performance optimization in drilling of multiwall carbon nano tube/epoxy nanocomposites using GRA-PCA hybrid approach." Measurement 158 (July 2020): 107701. http://dx.doi.org/10.1016/j.measurement.2020.107701.

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Umamaheswarrao, P., D. Ranga Raju, KNS Suman, and B. Ravi Sankar. "Multi objective optimization of process parameters for hard turning of AISI 52100 steel using Hybrid GRA-PCA." Procedia Computer Science 133 (2018): 703–10. http://dx.doi.org/10.1016/j.procs.2018.07.129.

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Khan, Zahid A., Arshad Noor Siddiquee, Sachin Maheshwari, and Brijpal Singh. "Optimal design of flux for submerged arc weld properties based on RSM coupled with GRA and PCA." International Journal of Manufacturing Technology and Management 34, no. 1 (2020): 97. http://dx.doi.org/10.1504/ijmtm.2020.10027161.

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Singh, Brijpal, Zahid A. Khan, Arshad Noor Siddiquee, and Sachin Maheshwari. "Optimal design of flux for submerged arc weld properties based on RSM coupled with GRA and PCA." International Journal of Manufacturing Technology and Management 34, no. 1 (2020): 97. http://dx.doi.org/10.1504/ijmtm.2020.105820.

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Du, Zhe, Liyuan Zhang, Xiaolin Xie, Denghui Li, Xinping Li, Zhihong Zhang, and Jing Pang. "Application of Grey Relational Analysis and Multiple Linear Regression to Establish the Cutting Force Model of Oil Peony Stalk." Mathematical Problems in Engineering 2022 (April 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/2341766.

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Oil peony is an important oil crop, which has high quality and oil content. In order to improve the cutting quality and harvest efficiency of oil peony, the cutting equipment of the pruning machine and harvester is a key component. Also, the accurate prediction of cutting force of oil peony stalk is one of the essential processes for the design of the cutting equipment. In this article, to accurately predicted the cutting force of the stalk, the physical property parameters and chemical components were considered as influencing factors, which were used to establish the model of mechanical property parameter of oil peony stalk. The physical property parameters of oil peony stalk included the stalk diameter, internode distance, fresh weight, dry weight, relative moisture content, volume, fresh density, and dry density. The chemical components of the stalk were cellulose, hemicellulose, and lignin. Besides, the modeling methods, which were the partial least squares regression (PLSR), principal component analysis (PCA) couple with multiple linear regression (MLR), and grey relational analysis (GRA) couple with MLR, were used to optimize the multiple parameters (physical property parameters and chemical components). The results showed that the internode distance and relative moisture content had significant effects on the cutting force of oil peony stalk. The R c 2 and R p 2 values of the GRA (0.5) + MLR method were 0.801 and 0.820, and RMSEC and RMSEP values were 2.862N and 4.715N, respectively. Consequently, the GRA + MLR method could be used to predict the cutting force of oil peony stalk, which was an important basis for the design of precision cutting equipment.

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Ba, Huaiqiang, Luxin Guo, Haiyang Huan, Shibo Zhang, and Zhiwei Lin. "Multi-Objective Optimization of Epoxy Resin Adhesive for Pavement Toughened by Self-Made Toughening Agent." Polymers 15, no. 8 (April 19, 2023): 1946. http://dx.doi.org/10.3390/polym15081946.

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Epoxy resin adhesive for pavement is often insufficient in flexibility and toughness. Therefore, a new type of toughening agent was prepared to overcome this shortcoming. To achieve the best toughening effect of a self-made toughening agent on an epoxy resin adhesive, its ratio to the epoxy resin needs to be optimally selected. A curing agent, a toughening agent, and an accelerator dosage were chosen as independent variables. The epoxy resin’s adhesive tensile strength, elongation at break, flexural strength, and flexural deflection were used as response values to establish a single-objective prediction model of epoxy resin mechanical property indexes. Response surface methodology (RSM) was used to determine the single-objective optimal ratio and analyze the effect of factor interaction on epoxy resin adhesive’s performance indexes. Based on principal component analysis (PCA), multi-objective optimization was performed using gray relational analysis (GRA) to construct a second-order regression prediction model between the ratio and gray relational grade (GRG) to determine the optimal ratio and to validate it. The results showed that the multi-objective optimization using response surface methodology and gray relational analysis (RSM-GRA) was more effective than the single-objective optimization model. The optimal ratio of epoxy resin adhesive was 100 parts of epoxy resin, 160.7 parts curing agent, 16.1 parts toughening agent, and 3.0 parts accelerator. The measured tensile strength was 10.75 MPa, elongation at break was 23.54%, the bending strength was 6.16 MPa, and the bending deflection was 7.15 mm. RSM-GRA has excellent accuracy for epoxy resin adhesive ratio optimization and can provide a reference for the epoxy resin system ratio optimization design of complex components.

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Li, Qiaoyang, Guiming Chen, Ziqi Li, Yi Zhang, and Lingliang Xu. "Evaluation of missile electromagnetic launch system based on effectiveness." Journal of Intelligent & Fuzzy Systems 41, no. 6 (December 16, 2021): 6517–26. http://dx.doi.org/10.3233/jifs-210353.

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To solve the problems of strong infrared radiation, poor continuous combat capability of the system, serious ablation of the launching device, and environmental pollution of the existing missile launching system, electromagnetic launch system (EMLS) has been studied for missile launch system. Combining the situation that the current research on missile electromagnetic launch system (MEMLS) mainly focuses on the key technical points and the deficiencies in the previous research on MEMLS, this paper establishes an effectiveness prediction model based on GRA-PCA-LSSVM, and discusses the investment efficiency of the system based on DEA. The experimental results prove that the established model is reasonable, effective and superior, and provides a reference for the further improvement and development of MEMLS.

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Ravi Sankar, B., P. Umamaheswarrao, K. Rajasekhara Babu, and M. Pardhasaradhi. "Multi Objective Optimization of Joining Dissimilar AA5083& AA6061 Alloys Using Friction Stir Welding- Integrated Taguchi and Grey Systems Approach." Materials Science Forum 978 (February 2020): 133–39. http://dx.doi.org/10.4028/www.scientific.net/msf.978.133.

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In this study, multi objective optimization for improving the joining characteristics of dissimilar AA5083-AA6061 alloys during FSW has been presented. Tool rotational speed, feed and tilt angle are the input parameters whereas tensile strength and hardness are the responses. Experiments are planned and conducted as per Taguchi L9 orthogonal array. Main effects plot and contour plot discloses the parametric influence over the responses. Hybrid GRA and PCA were deployed as tools to perform multi objective optimization. Results pointed out the fact that tilt angle played vital role in affecting the responses followed by feed and tool rotational speed. The optimum parametric settings obtained are speed 710 rpm, Feed 50 mm/rev, and Tilt angle 2o.

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SIVAKOTESWARARAO, KATTA, G. CHAITANYA, and SHANKAR B. RAVI. "MULTI-OBJECTIVE OPTIMIZATION OF A PROCESS PARAMETER IN TURNING OF TITANIUM ALLOY USING GRA, PCA, AND RSM METHOD." i-manager's Journal on Material Science 6, no. 4 (2019): 33. http://dx.doi.org/10.26634/jms.6.4.14816.

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Vijayan, D., and V. S. Rao. "Friction Stir Welding of Age-Hardenable Aluminum Alloys: A Parametric Approach Using RSM Based GRA Coupled With PCA." Journal of The Institution of Engineers (India): Series C 95, no. 2 (April 2014): 127–41. http://dx.doi.org/10.1007/s40032-014-0116-2.

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Senthilkumar, N., B. Deepanraj, K. Vasantharaj, and V. Sivasubramanian. "Optimization and performance analysis of process parameters during anaerobic digestion of food waste using hybrid GRA-PCA technique." Journal of Renewable and Sustainable Energy 8, no. 6 (November 2016): 063107. http://dx.doi.org/10.1063/1.4972884.

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Saha, Abhijit, and Subhas Chandra Mondal. "Multi-objective Optimization of Welding Parameters in MMAW for Nano-structured Hardfacing Material Using GRA Coupled with PCA." Transactions of the Indian Institute of Metals 70, no. 6 (August 18, 2016): 1491–502. http://dx.doi.org/10.1007/s12666-016-0945-1.

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