Grey Relational Analysis Model Research Articles

Modified Deng’s Grey Relational Analysis Model for Panel Data and Its Applications in Assessing the Water Environment of Poyang Lake

Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The purpose of this paper is to improve the clustering accuracy of the existing Deng’s GRA model for panel data in order to overcome some of its shortcomings. Firstly, the existing Deng’s GRA model for panel data was tested based on the dataset LP1 of Robot Execution Failures. Then, according to the test results, the existing Deng’s GRA model for panel data is modified by means of Taylor’s formula, and the modified model is successfully validated by the dataset LP1 of Robot Execution Failures. Finally, as a practical application, the modified Deng’s GRA model for panel data is applied to assess the water environment of Poyang Lake over the past five years. Compared with other cluster methods, the results of the case study show that the modified Deng’s GRA model for panel data is applicable and also confirm the remarkable effectiveness of the Chinese government’s water quality regulation in Poyang Lake. Therefore, the modified Deng’s GRA model presented in this paper improves the clustering accuracy compared to the original model and can be applied well to the classification of data with a large dimension.

Decision-Making Framework for Improving Bank Performance in Emerging Markets: The Analysis of AHP-TOPSIS and AHP-GRA Models

Abstract This study utilizes the Analytic Hierarchy Process (AHP) in combination with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) models to thoroughly assess the performance of banks in China, India, Pakistan, and Thailand. The integrated results offer significant insights into the relative rankings of various banks in each country. In China, Bank of China Ltd (BOC) emerges as the top performer, setting a benchmark for others. Similarly, in India, the State Bank of India is consistently identified as the leading bank. The National Bank of Pakistan stands out as the top performer in Pakistan. In Thailand, despite minor deviations in results, Kasikornbank PCL (KBANK) consistently shows strong performance. The alignment of results between AHP-TOPSIS and AHP-GRA underscores the reliability of both models, providing stakeholders and decision-makers with a comprehensive understanding of bank performance. This enables them to identify benchmarks, leverage strengths, and address areas for improvement within each country’s banking sector.

Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and Bayesian model averaging

Creating precise groundwater level (GWL) prediction models is of crucial significance for the productive use, extended planning, and controlling of limited sub-surface water supplies. In this research, the accuracy of GWL forecasts in Bangladesh was enhanced for three weeks by utilizing ensembles of Machine Learning (ML) models. Six advanced ML-based models were developed and assessed using eight performance indices, and an Overall Ranking (OR) was provided by combining the rankings produced by Grey Relational Analysis (GRA), Variation Coefficient (COV), and Shannon’s Entropy (SE). The standalone forecasting models demonstrated excellent performance across the three forecasting horizons, with accuracy values ranging from 0.986 to 0.997 for one-step, 0.971 to 0.999 for two-step, and 0.960 to 0.997 for three-step forecasts at GT3330001. Results also revealed that three ranking techniques (SE, COV, and GRA), as well as their combined ranking (OR), produced different best-performing models at different prediction horizons for different observation wells. Weighted average ensembles of the prediction models were developed by calculating individual model weights using four ensemble modelling techniques: SE, COV, GRA, and Bayesian Model Averaging (BMA). The BMA-based ensemble technique outperformed three benchmark ensemble approaches, achieving R = 0.947, KGE = 0.925, IOA = 0.972, MAE = 0.062 m, and RMSE = 0.123 m for one-step-ahead forecasts at GT3330001. The findings exhibit a consistent trend across other forecasting horizons and observation wells. Finally, the Dempster-Shafer evidence theory was employed to rank the single and composite models. The ranking results demonstrated that the BMA-based ensemble consistently secured the top position (with the weight values of 0.997, 0.991, and 0.987 for one-week, two-weeks, and three-weeks forward forecasts at GT3330001) for all forecasting horizons and observation wells. This study shows that the BMA-based composite model can produce more accurate GWL projections at Bangladesh study location, with possible for application in other regions worldwide.

A new failure analysis and validation scheme for shale gas pilot processes

Shale gas pilot processes are usually composed of skid-mounted assemblies of individual equipment, with weak process matching, unknown unit equipment wear, and high failure rates. We propose a solution using the grey relational analysis model, analytical hierarchy process model, and reliability analysis model to achieve failure analysis, prediction, and improvement validation, respectively. The probability and degree of influence relationships between the factors affecting the occurrence of failures are discussed. The practical application of the study area shows that: when the threshold value is set to 0.8833, it can match the grey correlation conclusion well, connect the logic of hierarchical analysis, and cluster the engineering problems; this study area needs to focus on preventing the problems of erosion damages and puncture leakage. Although the short life of the nozzle has a large impact on the overall process, but the frequency of occurrence is not high, the next step needs to continue to collect data, using other mathematical models to mine the fault data internal information.

Grey relation analysis and multiple criteria decision analysis method model for suitability evaluation of underground space development

Underground space is an important asset for urban development, but its development comes with inherent risks. Therefore, in order to evaluate the risk of underground space development, we utilized 3D modeling and grey relational analysis (GRA) for decision-making, and evaluated underground space development through attribute modeling. First, the underground space was categorized into 4 layers according to depth and 15 important influencing factors were identified. Then, the weights of these factors (soft soil thickness, liquefaction index of saturated sand, etc.) were determined using analytic hierarchy process (AHP), criteria importance through intercriteria correlation (CRITIC), and GRA. Finally, the study area was divided into 1,197,778 evaluation units, each measuring 30 m × 30 m. The suitability of each unit was evaluated using the Multiple Criteria Decision Analysis (MCDA) combined with the GRA method. The experimental results show a commendable performance compared to the combined “AHP + multilevel exponential superposition” approach. In addition, the computing efficiency is reduced by about 30%, the stability is improved by at least 20%, and the reliability is improved by about 10%. The study enhances the decision-making integration of GRA, MCDA and underground space, improves the efficiency and accuracy of underground space risk evaluation, and provides a valuable basis for the field related to underground space development. Code is available at https://github.com/NX-first/underground-space-engineering-development.git.

Systematic evaluation of integration between China's digital economy and sports industry: Two-stage grey relational analysis and vector autoregressive model.

The development of the digital economy constitutes a key component of China's endeavors to advance towards "Digital China." The sports industry functions as a new catalyst for high-quality economic growth. This study systematically evaluated the integration between these two sectors. First, we conducted two levels of grey relational analysis to assess their integration between 2016 and 2021. Second, we conducted a VAR analysis to determine whether their integration between 2009 and 2021 represents a causal relationship. At the macro level, the grey relational analysis reveals that the sports industry (grade = 0.770) ranked second among China's eight key economic sectors in terms of digital economy integration. At the meso level, a wide variation (ranging from 0.606 to 0.789) existed in the grade of integration between the digital economy and the sub-sectors of the sports industry. According to the VAR model, the digital economy does not Granger cause (p = 0.344) the growth of the sports industry. This study yielded two added values to the existing literature: First, there exists a sectoral imbalance in the digitization process; second, the explosive growth of the sports industry was not primarily caused by the digital economy. Accordingly, the "sports + digital" complex is still in the first wave of technological integration. We propose three policy recommendations, namely, sectoral synergistic development, overtaking via esports IP, and new economy and new regulation. Collectively, these findings provide updated insights for the digital transformation towards "building a leading sports nation" and "Digital China."

Navigating the path to dual carbon goals: Understanding the driving forces of energy transition welfare performance

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006–2014 and 2015–2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed.

Drilling performance of micro-textured twist drill bit for TI-6AL-4V alloy: Validated FEM and statistical approaches

The titanium alloy, particularly Ti-6Al-4V, is an extensively demanded material in diverse sectors because of its superior physical and chemical properties. Nevertheless, the inherent hardness of the material causes challenges during machining operations. The alloy's heat-resistance characteristic induces increased friction and heat generation on cutting tools, ultimately causing a decrease in the tool life. In an attempt to overcome this issue, numerous forms of surface texturing have been investigated with the aim of improving the tribological characteristics of the material. The aim of the present investigation was to enhance the cutting performance of drill bits by implementing micro-textured patterns, hence minimizing thrust force, torque, and tool temperature. The combined effect of three distinct micro-textured geometries on a drill bit's flank, flute, and margin surfaces, with the influence of coating conditions, on the performance of drilling operations was investigated. The Finite Element Method (FEM) was used to perform a range of varied drilling operations. The Grey Relation Analysis (GRA) and General Linear Model (GLM) were employed to assess and compare the drilling performances with respect to thrust force, torque, and drill bit temperature. It was found that the square-sectioned textures on the side face exhibited the most obvious influence on the drilling performance. In the meantime, the textures found on the flute face were identified as having a comparatively smaller influence. The ideal configuration for the drill bit entailed a flank face with a square sectioned texture, a flute face without any texture, a margin face with a square sectioned texture, and a hard coating. As a result, the current situation substantially reduced cutting forces, tool wear, and tool temperature by 51 %, 20.5 %, and 23.4 % compared to the drilling condition without textured drill bits.

A new grey relational analysis model of cross-sequences

PurposeThe purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.Design/methodology/approachThe definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.Findings(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.Practical implicationsThe grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.Originality/valueThe new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

Link-16 anti-jamming performance evaluation based on grey relational analysis and cloud model

Link-16 anti-jamming performance evaluation based on grey relational analysis and cloud model

Studying Foreign Trade and Economic Growth of Morocco using Regression and Grey Relational Analyses

The study conducts an empirical examination of the relationship between foreign trade and economic growth in Morocco, employing two distinct analytical models: The Dynamic Grey Relational Analysis (GRA) model and the Multiple Regression model. The empirical outcomes reveal that foreign trade exerts a statistically significant and positive influence on the economic growth of Morocco, a relationship consistently substantiated by both the GRA and regression models. Nevertheless, it is important to emphasize that the linkage between these variables is characterized by a high degree of dynamism, influenced by a diverse array of economic determinants. Furthermore, the results point to the primary pathway through which foreign trade impacts economic growth in Morocco, highlighting its role in fostering capital accumulation and technological progress. Consequently, this study implies that strategic policies oriented toward the promotion of foreign trade and foreign investment have the potential to significantly bolster sustained economic growth within the Moroccan context.

Study on Imagery Modeling of Electric Recliner Chair: Based on Combined GRA and Kansei Engineering

This study aims to integrate data-driven methodologies with user perception to establish a robust design paradigm. The study consists of five steps: (1) theoretical research—a review of the subject background and applications of Kansei engineering and gray relational analysis (GRA); (2) algorithmic framework research—the discussion delves into the intricate realm of Kansei engineering theory, accompanied by a thorough elucidation of the gray relational analysis (GRA) algorithmic framework, a crucial component in constructing a fuzzy logic model for product image modeling; (3) Kansei data collection—18 groups of perceptual words and six classic samples are selected, and the electric recliner chair samples are scored by the Kansei words; (4) Kansei data analysis—morphological analysis categorizes the electric recliner chair into four variables. followed by the ranking and key consideration areas of each area; (5) GRA fuzzy logic model verification—the GRA fuzzy logic model performs simple–complex (S-C) imagery output on 3D models of three modeling instances. By calculating the RMSE value of the seat image modeling design GRA fuzzy logic model, it is proven that the seat image modeling design GRA fuzzy logic model performs well in predicting S-C imagery. The subsequent experimental study results also show that the GRA fuzzy logic model consistently produces lower root mean square error (RMSE) values. These results indicate the efficacy of the GRA fuzzy logic approach in forecasting the visual representation of the electric recliner chair shape’s 3D model design. In summary, this research underscores the practical utility of the GRA model, harmoniously merged with perceptual engineering, in the realm of image recognition for product design. This synergy could fuel the extensive exploration of product design, examining perceptual engineering nuances in product modeling design.

Quality analysis of range hood impeller automated assembly based on generalized grey relational degree

PurposeThis paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.Design/methodology/approachFirst, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.FindingsThe research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.Practical implicationsThe newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.Originality/valueThis study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

Parametric optimization of hole taper control in ultraviolet nanosecond laser micro-drilling of copper foil

While hole taper is inevitably formed in the laser micro-drilling of through micro-holes, the formation propensity is largely determined by the interaction between laser processing parameters. In the present work, we perform numerical simulations and experiments to investigate the ultraviolet nanosecond laser micro-drilling of 1 mm diameter through micro-holes on copper foil with a thickness of 280 μm, with an emphasis on the coupled influence of laser processing parameters on both the hole taper and circularity. Firstly, the characteristics of formed micro-hole quality are experimentally characterized, and the formation mechanisms of hole taper are further revealed by finite element simulations of multi-pulse laser ablation. Then, the relational degree of individual laser processing parameters in determining the hole taper and circularity is discovered by experimental investigation based on the gray relational analysis model. Finally, the coupled effect between significant parameters of laser power and repeat scanning number on the hole taper and circularity is further investigated by utilizing the response surface methodology, which derives the optimum laser processing parameters for manufacturing 1 mm diameter through micro-hole with a taper degree of 2.556° and a circularity of 0.992 on the copper foil.

A practical decision-making framework for highway transportation scheme selection based on the triangular fuzzy neutrosophic information

In recent years, due to the further development of the market economy, the internal competition in the large-cargo transportation industry has become increasingly fierce, and the profit space has been greatly compressed. Therefore, large-cargo logistics enterprises are paying more and more attention to the research of highway transportation route plan. The highway transportation scheme selection is looked as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers (TFNN) grey relational analysis (TFNN-GRA) method is established based on the classical grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs) with completely unknown weight information. In order to obtain the weight values, the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs. Then, combining the traditional fuzzy GRA model with TFNSs information, the TFNN-GRA method is set up and the computing steps for MADM are established. Finally, a numerical example for highway transportation scheme selection was established and some comparisons are established to study the advantages of TFNN-GRA. The main contributions of this paper are established as follows: (1) the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs; (2) the TFNN-GRA method is established with completely unknown weight information. (2) the TFNN-GRA method is established and the computing steps for MADM are established. (3) Finally, a numerical example for highway transportation scheme selection was established and some comparisons is employed to study advantages of TFNN-GRA method.

Comprehensive evaluation of the effectiveness of endemic disease prevention and control in Jiangsu Province, China, 2013-2022.

As a strong economic and populous province in China, Jiangsu is home to four endemic diseases. Despite efforts in the past decade, the prevention and control of these four endemic diseases are not uniform because of the different etiological chains and influencing factors of these diseases. Among the evaluation methodologies for endemic disease control, only one method is currently available for each disease. In this study, we selected 14 indicators to comprehensively evaluate the effectiveness of endemic disease control in Jiangsu between 2013 and 2022. We improved the method for calculating the weights of the indicators and established a fuzzy comprehensive evaluation model based on the weighted Technique for Order Preference by Similarity to an Ideal Solution model and a weighted grey relational analysis model. The results of the comprehensive evaluation showed that the progress of endemic disease control in Jiangsu was not always in line with our expectations of improvement, with the top five years of better control occurring in 2015, 2013, 2021, 2022, and 2014. The results of the sensitivity analysis confirm the reliability and accuracy of these findings. We discovered that measures such as the reform of the salt industry, use of thyroid ultrasound, and new water supply projects for residents in Jiangsu affected the progress of endemic disease prevention and control. The tracking of endemic disease status should consider the potential effects of changes in policies implemented in other industries on endemic disease prevention and control. Additionally, the results of this study provide a theoretical basis for enhancing prevention and control practices in other regions of China.

Reinforcement mechanism of nano-SiO2 and polypropylene fibers on concrete abrasion resistance subject to dynamic sandy water

Abrasion resistance of concrete is essential for the safety assessment of hydraulic structures subject to sandy water scouring. In this study, ten types of concrete specimens were designed to investigate the effect of nano-SiO2 (NS) and polypropylene fibers (PF) on the abrasion resistance and mechanical properties of concrete. In view of the discreteness of the results, grey theory, Support vector machine (SVR) and artificial neural network (ANN) model were selected to explore the relationship between abrasion resistance and factors involving NS, PF, compressive strength, splitting tensile strength and curing age. The results revealed that the appropriate addition of NS and PF promote the hydration process and remarkably enhanced the performance of concrete. And the grey relation analysis indicated that the optimal content of NS and PF were 3 % and 0.9 kg/m3, respectively. Compared with GM (1, N) and SVR, ANN model benefited from more comprehensive consideration of the effects of various factors to predict abrasion resistance more reliably. The results of grey relation analysis and ANN model showed that there was a close correlation between the mechanical properties, admixtures and the abrasion resistance.

Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model

Accurate assessment of the state of health of lithium-ion batteries using relevant factors is crucial for the maintenance of lithium-ion batteries in electric vehicles. Firstly, data features are extracted from University of Maryland public dataset and dataset is pre-processed. Secondly, the extracted features were analysed using a grey relational analysis model to identify the most significant factors affecting the state of health. Thirdly, this paper proposed an adaptive variable fractional order multivariate grey prediction model to accurately estimate the state of health of lithium-ion batteries. The comparative results demonstrate the overall superiority of the proposed model.

Novel grey dynamic trend relational analysis models with different types of accumulation delay effects for time-delay systems

In order to analyze the unknown accumulation delay relationship between the given inputs and outputs, one basic model and four extended models are proposed to simulate the different accumulation delay processes. The basic model called grey dynamic trend relational analysis model calculates the similarity between two sequences based on their trends. On the basis of the basic model, four extended models are developed to simulate the different accumulation delay effects. Two types of the accumulation delay effects, called driven-type and yielded-type accumulation delay effects, are put forward for different time-delay relationships. Each type of the accumulation delay effects designs two weight distributions for analyzing the different time-delay processes. Two optimization models based on grid-search are given to determine the lag parameters in the four extended models. The pseudo-codes of intelligent algorithms are provided for the optimization problems. To explain how it can be employed in practice, the new approaches for the time-delay systems are applied to analyze the relationships between the air pollution and its driven factors and results in the Yangtze River Economic Belt. The comparing results show that the novel models can handle the time-delay systems with accumulation delay effects and can determine the delay processes.

Green coal mining under buildings by overburden grout injection for coalmine sustainable development of central China

Coal will occupy the main position in China's energy structure for a long time, and the negative externalities of its exploitation have a serious impact on the ground surface and its appurtenances. With the proposal of the dual carbon strategy, the coal-based energy determines that green and safe coal mining should be the priority direction of China's energy development. Taking Xinyi coalmine, which is mined in unstable coal seams with large mining depth, as the research area, the surface response characteristic and subsidence law under the different mining degrees were clarified. Meanwhile, the damage mechanism of buildings was revealed, which was from no obvious damage to Grade Ⅳ under the extremely insufficient mining to subcritical mining. Based on the sustainable development and green production in coalmines, the overburden grout injection technique under buildings that does not affect the normal production was proposed, and its technical principle was described. A weighted grey relational analysis model was established, and obtained that the panel width was the main factor affecting the overburden failure height under subcritical mining. According to the definition of overburden failure degree proposed by the author, the feasibility of overburden grout injection technique under buildings was analyzed and the key parameters, such as slurry diffusion radius, borehole position and depth, grouting system and technology, were determined and successfully applied. The engineering application shows that the maximum surface subsidence after grouting is 253 mm, and the building damage is within the Grade I. Meanwhile, 5.82 Mt of coal resources under the buildings have been liberated, which realizes high quality coal mining, low environmental damage, green and low-carbon, and also provides a reference for the sustainable development of coal enterprises, especially for the exhausted coalmines that recover coal pillars.