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Strategies-challenges of engineering education
Published in J. P. Mohsen, Mohamed Y. Ismail, Hamid R. Parsaei, Waldemar Karwowski, Global Advances in Engineering Education, 2019
Because of the nature of student learning, it is usually difficult or even sometimes impossible to get the adequate or complete information for SO. Incomplete information is the fundamental reason for a learning problem of being “grey” which is decided based on the amount of available information. The grey elements, the grey relations represent the relations with incomplete information that an evaluator might have. The grey relational analysis (GRA) uses data based on the level of similarity and variability among all factors to establish their relation and compare them quantitatively. This is unlike the traditional statistical methods handling the relation between student learning-level assessment tools which require plenty of statistical data. GRA requires less data and can analyze many factors that can overcome the disadvantages of statistical method. Grey theory is similar to fuzzy set theory. It is an effective mathematical tool to deal with systems analysis characterized by imprecise and incomplete information. The theory is based on the degree of information known. The advantage of grey theory over fuzzy theory is that grey theory takes into account the condition of fuzziness; that is, grey theory can deal flexibly with the fuzziness situations.
A diagnosis method for diesel engine wear fault based on grey rough set and SOM neural network
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
Silin Qian, Shenghan Zhou, Wenbing Chang, Yiyong Xiao, Fajie Wei
Grey system theory is one of the important methods and techniques for studying uncertain systems. And grey relational analysis is a very active branch in the grey system theory, which basic idea is to divide the factors as sequence curve, and then through the similarity degree of geometric shapes to obtain the correlation degree of each factors (Gao et al. 2013). The closer the shape of the curve is, the greater the correlation of the corresponding sequence is determined.
4C, Graphene and CNT Hybrid Composites
Published in P. C. Thomas, Vishal John Mathai, Geevarghese Titus, Emerging Technologies for Sustainability, 2020
J. P. Ajithkumar, Xavior M. Anthony
Grey relational analysis used for optimizing the multi-performance characteristics. To normalize the parameter values, the data was pre-processed first. In the present study, linearly normalized parameters for flank wear, crater wear, and cutting tool temperature along with the generating range between zero to one are tabulated in Table 61.3. These parameters were normalized based on ‘smaller-the-better’ condition.
Optimization of process parameter in AI7075 turning using grey relational, desirability function and metaheuristics
Published in Materials and Manufacturing Processes, 2023
Dillip Kumar Mohanta, Bidyadhar Sahoo, Ardhendu Mouli Mohanty
Grey relational analysis has emerged as a useful method for assessing processes with many performance criteria in recent years. Complex multiple response optimization problems can be condensed into the optimization of a single response grey relational grade using grey relational analysis. The overall analyzed table has been stated in Table 3. The normalized value of original sequence for Surface roughness (Ra) and cutting fornce (Fz) has been calculated considering smaller is better in the prospects of low power consideration. Thereafter deviation sequence has been calculated and tabulated which quantify the degree of comparability sequence is the closest to the reference sequence. Next, Grey relational coefficient is being calculated,Eqn.2. At last grey relational grade is calculated, Eqn.3 is the weighted sum of individual grey relational coefficient for both the output parameter. It shows the quantification of grey relational space. So, there the rank has been shown in the ascending order to assign best parameter set for optimum output in the stated experiment. Table 3 shows that the grey relational grade is directly proportional to the multiple performance characteristics. Therefore, cutting speed of 80 mm/min (Level-1), feed of 0.05 mm/rev (Level-1) and depth of cut as1.20 mm (Level-3) is the optimal input setting as concluded using grey relational grade and rank.
An effective energy management strategy in hybrid electric vehicles using Taguchi based approach for improved performance
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
Geetha Anbazhagan, Santhakumar Jayakumar, Suresh Muthusamy, Suma Christal Mary Sundararajan, Hitesh Panchal, Kishor Kumar Sadasivuni
In the context of HEVs, the effect of the EMCS, vehicle design, and SOC of energy sources on vehicle quality characteristics plays a crucial role. Although in existing studies great contributions are made to the individual analysis of the abovementioned effects, several essential questions still need to be answered: which control factor most strongly influences the vehicle quality characteristics? What is the impact of control factors on the vehicle’s quality characteristics? And which one must be adopted for better vehicle performance? In search of answers, this paper addresses these questions using design of experiment (DoE) techniques. One such technique is the Taguchi method, which imparts a standard framework for designing and analyzing experiments that involve many control/process parameters (Hwang et al. 2005; Wang et al. 1999). It is a dynamic tool for organizing experimental plans using the least number of experiments by considering the orthogonal array (Tsui 1992). Because the Taguchi method can optimize only single quality characteristics, it is still challenging to deal with the optimization of multiple quality characteristics effectively. Many researchers focus on grey relational analysis (GRA) to correlate the complicated relationships among multiple quality characteristics in an effective way. Technique for order preference by similarity to ideal solution (TOPSIS) and operational competitiveness rating (OCRA) are the other similar methods in use. GRA is comparatively simpler and easier to compute and understand than the other techniques (Wang, Zhu, and Wu 1996; Wu 2002).
Long-term corrosion behaviour of 1060 aluminium in deep-sea environment of South China Sea
Published in Corrosion Engineering, Science and Technology, 2021
Wenshan Peng, Tigang Duan, Jian Hou, Weimin Guo, Kangkang Ding, Wenhua Cheng, Likun Xu
In order to analyse the influence degree of the main factors on the corrosion rate of 1060 aluminium in deep-sea water, the correlation degree between each factor (hydrostatic pressure, oxygen content, temperature, and conductivity) and the corrosion rate was calculated using grey correlation analysis method. According to the degree of relevance, the degree of influence of each factor on the corrosion rate is judged. The grey relational analysis is divided into five steps: determining the selected data, data non-dimensionalisation processing, correlation coefficient calculation, correlation degree calculation, and relevance ranking. According to the grey correlation analysis method [42], it is assumed that the corrosion rate data obtained by numerical calculation has a total of m groups, and the corrosion rate is used as a reference factor, and the calculated value is: where, j = 1,2,3, … ,m.