Grey Correlation Analysis Research Articles

Global Warming: Predictive Models and Correlation Analysis

In recent years, the Earth's rising temperatures and the shrinking Antarctic ice cover have become increasingly evident. Understanding the factors driving global warming and developing strategies to mitigate it are critical. By applying the Pettitt change-point detection method, we analyzed March temperatures from the past decade and identified a significant shift occurring in March 2015. Forecasting models, including ARIMA and LSTM, predict similar temperature trends, with the LSTM model demonstrating slightly higher accuracy. Through grey correlation analysis and multiple linear regression, we identified CO2 as the most influential factor affecting global temperatures. Therefore, reducing CO2 emissions is crucial to mitigating global warming.

Multi-Objective Optimization of Steel Pipe Pile Cofferdam Construction Based on Improved Sparrow Search Algorithm

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more; the duration is shortened by 18 days, a reduction of 21%; and the total cost is reduced by CNY 220,000, saving 3% of the cost.

Optoelectronic effect of Al-based buffer layers on Al-doped ZnO thin transparent conductive films on flexible substrates

AZO (Al-doped ZnO) film is a transparent conductive film that offers a simple preparation process, low cost, non-toxicity, and excellent stability. It serves as a high-quality material extensively used in liquid crystal displays, solar cells, and other fields. In this study, AZO thin films were prepared on a PET substrate with an Al2O3 buffer layer using the magnetron sputtering method. The deposition parameters were optimized through the Taguchi method and Grey Correlation Analysis. The results demonstrate that the optimal deposition parameters for achieving superior photoelectric properties during the fabrication of AZO films are 100 W (radio frequency power), 1.0 Pa (sputtering pressure), 20 min (coating time), and 20°C (substrate temperature). Furthermore, the incorporation of an Al2O3 buffer layer enhances the quality of AZO film on PET substrate. This investigation successfully deposited uniform and highly efficient AZO thin films onto flexible PET substrates at low temperatures, thereby expanding their potential applications in flexible electronics.

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.

Research on the contribution of technological innovation efficiency and internal structure optimization of charging pile industry: A case study of China

In the context of resource scarcity and environmental protection, the new energy industry has garnered significant attention from various sectors. The charging pile (CP) industry, a crucial component of the new energy vehicle (NEV) industry's supply chain, requires improvements in both quantity and quality. This study examined the technological innovation efficiency (TIE) of the CP industry, considering two levels: pure technical efficiency (PTE) and scale efficiency (SE), and explored methods to optimize its internal structure. By combining the grey correlation analysis (GCA) method with the generalized data envelopment analysis (GDEA) model, a reliable evaluation indicator system was established. Empirical findings revealed that the PTE of technological innovation in the CP industry exhibited an upward trend from 2019 to 2022, achieving a GDEA-effective state. However, the SE of technological innovation shows a downward trend, indicating non-GDEA effectiveness. The contribution of CP companies to the PTE of technological innovation has shown a persistent and accelerating decline, while their contribution to SE, once negative, gradually shifts toward a positive influence. Consequently, the internal technological innovation structure of the CP industry necessitates optimization and adjustment to attain balance. This study provides recommendations from the perspectives of both the government and companies.

Study on optimization of thermal injection parameters in fractured reservoirs of HDR

ABSTRACT To improve the heat recovery of hot dry rock (HDR) and realize the efficient and sustainable development of geothermal energy, it is necessary to analyse the factors affecting the heat recovery performance of HDR fractured reservoir. In this paper, a numerical model of heat recovery in fractured reservoirs with HDR is established. The effects of injection parameters on the heat recovery performance of the Enhanced Geothermal System (EGS) are compared and analysed by means of the average temperature, heat extraction rate, production temperature and heat recovery power of matrix rocks at different locations. The influence degree of injection flow rate and injection temperature on production temperature was analysed by grey correlation analysis method. The results showed that reducing the injection temperature is beneficial to fully extract the heat energy in the rock mass. The injection flow rate is positively correlated with the heat extraction rate and negatively correlated with the extraction temperature. In this simulation scheme, the optimal results are as follows: injection temperature is 298.15K, injection flow is 12 kg/s, and the influence of injection flow on production temperature is greater than that of injection temperature. This study provides a theoretical basis for the development and utilization of geothermal energy in HDR and provides a reference for the design of engineering operation parameters.

Experimental Study and Machine Learning-Based Prediction of the Abrasion Resistance of Manufactured Sand Concrete

To systematically analyze the impact of manufactured sand on the abrasion resistance of concrete, this paper investigates the correlation between sand type, sand ratio, stone powder content, compressive strength, and the abrasion resistance of manufactured sand concrete. Grey correlation analysis was conducted to assess the impact priority of each factor affecting the abrasion resistance, and prediction models for the abrasion resistance were developed using XGBoost, random forest, AdaBoost, and gradient boosting. The results indicate that compared to river sand concrete, C30 and C40 concrete prepared with limestone and diabase manufactured sand has 20% higher abrasion resistance due to the presence of stone powder and higher roughness and solidity. Within the range of 0.40 to 0.44, a lower sand ratio leads to higher abrasion resistance. For concrete prepared with manufactured sand containing 5% to 11% stone powder, the best abrasion resistance can be attained at a stone powder content of 9%, and microscopic analysis suggests the highest concrete density at this level. According to grey system theory, the influence of each affecting factor on the abrasion resistance follows the order: sand ratio > crushing value > roughness > compressive strength > stone powder content > 0.6. Compared to gradient boosting, random forest, and AdaBoost models, the XGBoost model adopted in this study showed relatively higher R2 and lower RMSE in both the training and testing sets, which proved its higher accuracy in predicting the abrasion loss of manufactured sand concrete. The machine learning models offer some guidance for predicting and enhancing the abrasion resistance of manufactured sand concrete in practical engineering.

Regional agricultural drought vulnerability prediction based on interpretable Random Forest.

Drought is one of the leading natural disasters in the world. It severely restricts the normal functioning of societies and economies. The purpose of this paper is to predict the drought vulnerability index in Henan Province so that timely measures can be taken to mitigate the impact of drought and to ensure the continued stability of people's lives and social development. First, the drought vulnerability index of Henan Province for 2010-2022 was calculated based on relevant literature and the regional situation of Henan Province. In addition, the main factors of sensibility and resilience are selected, the prediction indicator system is established, and the grey correlation analysis model is applied to select the top 10 main factors of drought vulnerability. Second, the Random Forest model was trained and simulated, and the drought vulnerability index of Henan Province was predicted for 2023-2025. Finally, the SHapley Additive exPlanations model was used to explain the specific effects of the influences of the Random Forest model. The interpretable Random Forest model not only accurately predicts the drought vulnerability index in Henan Province but also alleviates concerns about the "black box" problem of indirect interpretation of the Random Forest model. The results of the study show that the Average Percentage Error of the drought vulnerability index in Henan Province from 2010 to 2022 simulated by the Random Forest model is as low as 3.0457%, and the simulation accuracy is as high as 96.9643%. Drought vulnerability prediction for Henan Province was also made for the period 2023-2025, showing an increasing trend. Relevant sectors should enhance awareness and early warning of drought, improve resilience, and take effective measures to reduce the socio-economic impact of drought.

Grey correlation analysis of the main influencing factors of wax deposition of waxy crude oil and determination of pigging period based on dynamic analysis

Normal temperature gathering and transportation has significant advantages in reducing energy consumption and cost. Due to the high wax content of crude oil in carbonate reservoirs, obvious wax deposition will occur in the process of gathering and transportation at room temperature. In this paper, according to the component analysis results of crude oil on site, the HEATANALOGY wax deposition model was used to calculate the wax deposition of different gathering pipelines. The main influencing factors of wax deposition were analyzed by grey correlation method. Finally, according to the pigging mechanical balance model, the safe pigging period is derived, and the relationship diagram of different pipe diameters, throughput and ground temperature is drawn. The results show that the wellhead temperature has the greatest influence on the initial wax deposition position and the peak wax deposition position of the pipe section. The flow rate has the greatest influence on the maximum wax deposition thickness of the tube wall. According to the relationship between the working conditions and the pigging cycle, it can be found that with the increase of the throughput, the pigging cycle decreases first and then increases. With the increase of ground temperature, the pigging cycle increases.

Optimization of Magnetic Field-Assisted Laser Cladding Based on Hierarchical Analysis and Gray Correlation Method

Process parameters directly affect the quality of laser cladding. In this study, magnetic field-assisted laser cladding experiments were carried out on the surface of 300 M ultra-high-strength steel by setting laser energy density, magnetic field strength, and frequency as processing parameters. The optimization of laser cladding process parameters was investigated based on evaluating the quality of the laser cladding layer through hierarchical analysis and gray correlation analysis. Based on orthogonal test data, the correlation coefficients of the process parameters with the single objective function and the correlation degree of the multi-objective function were calculated by using the gray theory. Then the comprehensive objective optimization was carried out according to the gray correlation degree. The optimization problem with multiple process objectives was transformed into a single gray correlation degree optimization method to realize the optimization of process objectives and obtain the optimal combination of process parameters. The validation experiments indicate that the quality of the laser cladding layer can be greatly improved by employing optimal process parameters. The optimized laser cladding layer shows a reduced microstructure size and enhanced wear resistance, indicating the effectiveness of the optimization approach.

Effective passenger flow congestion propagation model for multi-mode rail transit networks

ABSTRACT During peak passenger flow periods, congestion propagation directly affects the operational safety and efficiency of multi-mode rail transit interconnections. By analyzing the key factors affecting congestion propagation, such as the train stop schedule, and considering parameters such as the basic reproduction number and propagation threshold, this study proposes a multi-mode rail transit susceptible-infected-recovered-susceptible (MRT-SIRS) epidemic model to analyze passenger flow congestion propagation. Simulation experiments and sensitivity analyses using data from the multi-mode rail transit in Beijing, China, were conducted to examine the influence mechanism of key factors on congestion propagation. The degree of influence of each factor was investigated using Gray correlation analysis. Each key factor, including the propagation and recovery rates, influences congestion propagation differently. The results of this study may provide theoretical support for the efficient operation and management of multi-mode rail transit systems.

Characterisation of surface morphology of high voltage DC relay contacts under arc erosion

Abstract High voltage DC relays are a crucial component in new energy vehicles. Due to the high failure rate caused by contact sticking (fusion welding), existing technologies can only analyze contact morphology after failure. There is an urgent need to determine the quantitative characterization value of contact surface morphology and the correlation between morphology, failure mechanisms, and working conditions. In this study, an Olympus DSX1000 microscope is used to extract surface topography data, and a quantitative topography characterization method based on a machine vision system is proposed to quantitatively analyze contact surface changes after arc erosion. Grey correlation analysis was applied to explore the relationship between electrical parameters such as voltage, current, and capacitance, and the contact morphology features. The results show that current and capacitance have the greatest influence on contact surface features after arc erosion, while voltage has a relatively smaller influence. This conclusion is consistent with the experimentally observed arc erosion features. Finally, an arc ion sputtering deposition model was developed to analyze the material transfer mode and its influence on the contact failure mechanism. The results provide a scientific basis for the analysis and design improvement of high voltage DC relays.

Based on Spectrum-Effect Relationship, Network Pharmacology, and Molecular Docking, the Material Basis and Mechanism of Antioxidant Effect of Miao Medicine Indigofera stachyoides Lindl Were Studied.

Indigofera stachyoides Lindl (IS.Lindl, Xuerensen in Chinese) is a traditional medicine frequently utilized by ethnic minorities; nevertheless, the chemicals responsible for these effects have not been identified. Ultraperformance liquid chromatography (UPLC) was utilized to establish the fingerprints of various origins. Free radical scavenging in 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) and 2,2-biazobis(3-ethyl-phenylpropylthiazole-6-sulfonate) diammonium salt (ABTS) assays was used to evaluate the antioxidant activity. Partial least-squares regression analysis (PLSR) and gray correlation analysis (GRA) were utilized to determine the spectral-effect relationship in order to screen the antioxidant pharmacodynamic components. The corresponding targets were obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Integrated Traditional Chinese Medicine (ITCM). Disease-related targets for antioxidants were collected from GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. The PPI interaction network analysis, GO enrichment analysis, and KEGG pathway analysis were established using the online analysis platform, and Autodock software assisted in aligning the components with important targets. The fingerprint profile revealed 32 common peaks, and eight standards were identified using standard comparison, with similarity ranging from 0.920 to 0.995. The primary antioxidant components include proanthocyanidin B1, epicatechin, and proanthocyanidin B3. Important targets include EGFR, CASP3, IL6, PTGS2, and TNF. Important signaling pathways include the AGE-RAGE signaling pathways in diabetic complications, the MAPK signaling pathway, the IL-17 signaling pathway, and the pathways in cancer. The results of molecular docking technology showed that the main active ingredients of the drug could bind well to the core target. In this study, we successfully established the spectral-effect relationship of IS.Lindl and clarified the effective substances of IS.Lindl. Through network pharmacology and molecular docking methods, it was clear that IS.Lindl plays an antioxidant role through multicomponent, multitarget, and multipathway synergy.

Evaluation system of vocational education construction in China based on linked TOPSIS analysis

In response to the task of vocational education reform in the new era, in order to cultivate innovative skilled talents, it is necessary to improve traditional evaluation methods to enhance their efficiency. This study proposes an evaluation model for vocational education construction that combines grey relational analysis and approximate ideal point method (TOPSIS). By selecting indicators and combining subjective analytic hierarchy process and objective entropy weight method for weight allocation, the weights of the indicators are determined using subjective analytic hierarchy process and objective entropy weight method. Through experimental analysis, the results showed that among the 11 analysis indicators, the weight of policy environment indicators was the highest at 0.124, while the weight of employment rate and contract signing rate indicators was the lowest at 0.050. The above results indicate that the TOPSIS comprehensive evaluation system based on grey correlation analysis has better application effects in practical scenarios and is more suitable for the development requirements of vocational education.

Simulation of lunar soil compressive strength performance test based on grey correlation analysis

Detecting water ice in the permanent shadow area of the lunar south pole is an important task of China's Chang'e-7. For this purpose, a simulation study was carried out under the earth environment, and a test sample of polar water-containing simulated lunar soil was prepared. The mechanical properties of the lunar soil simulant were tested under ultra-low temperature conditions, and the changes in water content, relative density, temperature, loading rate, and base material ratio with compressive strength were analyzed. The results show that the compressive strength of the frozen lunar soil simulant increases with the increase of water content, increases with the increase of density, and increases with the decrease of temperature. It is less affected by the change of loading rate. The order of compressive strength affected by different base material ratios is: 100%anorthosite, <90%anorthosite, +10%basalt <70%, +30%anorthosite <100%, basalt, lunar soil simulant. Finally, the correlation between the basic physical properties of lunar soil simulant such as water content, relative density, temperature, loading rate, and ratio and compressive strength is explored through grey correlation analysis. The correlation order is: water content> density> temperature> ratio> loading rate. This paper prepared and tested the mechanical properties of hydrated lunar soil, and proposed a more systematic and standardized implementation plan. It serves as the basis for studying the properties of hydrated lunar soil in the polar region, and provides a theoretical basis and reference for subsequent lunar soil simulation research.

VMD-HPCA-GRU ultra-short-term wind power forecasting based on COOT algorithm

In order to improve the prediction accuracy of ultra-short-term wind power, a combined prediction model of variational mode decomposition (VMD), hierarchical principal component analysis (HPCA) and gated recurrent unit neural network (GRU) based on COOT algorithm optimization is proposed. Firstly, the energy difference method is used to determine the number of sub-modes of variational mode decomposition, so that the original power sequence with strong nonlinearity is decomposed into a group of relatively stable sub-modes. Secondly, the correlation value between high-dimensional meteorological characteristics and power sequence is calculated by grey correlation analysis and sorted and layered. The first principal component of each layered characteristic variable is extracted by principal component analysis to achieve dimensionality reduction of high-dimensional meteorological characteristics. Finally, the COOT algorithm is introduced to optimize the hyperparameters of the gated recurrent unit prediction model, accelerate the model convergence speed, and improve the prediction accuracy of the model. The measured data of a wind farm in Guizhou are simulated and analyzed. The results show that compared with the prediction results of the traditional GRU model, the root mean square error, mean absolute error, and mean absolute percentage error of the proposed method are reduced 67.41%、72.25%、45.69%, and the prediction accuracy is higher than that of the other four combined prediction models, which effectively improves the prediction accuracy of ultra-short-term wind power.

Multi-objective grey correlation analysis based on CFRP Helical Milling simulation model

Multi-objective grey correlation analysis based on CFRP Helical Milling simulation model

Whether higher risk indicates severe loss in the flood disaster assessment: A comparative analysis in the Hubei province of central China

Study regionHubei province, Yangtze River Basin, Central China Study focusRisk and loss analysis constitutes a critical element of flood disaster management. Nevertheless, the relationship between risk and loss has been infrequently examined in the literature. Here we developed a comparative analysis framework for assessing flood risk and loss in Hubei Province. The risk model incorporates 22 indicators in terms of hazard, exposure, vulnerability and emergency response & recovery capability. Indicator weights were determined through a combination of objective and subjective weighting methods based on the game theory. Flood risk and loss levels were classified using fuzzy comprehensive evaluation and grey correlation analysis respectively. New hydrological insightsAnnual risk and loss maps exhibit a spatial pattern of higher levels in the eastern regions and lower levels in the western regions. However, the relationship between risk and loss was not as well as expected during designated flood periods. While regions impacted by disasters typically demonstrated higher risk levels, the risk in the regions unaffected by flood disasters appeared to be overestimated. By analyzing flood risk and loss across various temporal scales, this research contributes a novel perspective for the evaluation of flood risk models.

Capacity analysis of short-distance continuous diversion areas on mountainous urban expressways.

The short-distance continuous diversion area plays a crucial role within mountainous urban expressway systems, significantly enhancing the efficiency of specialized road sections through capacity analysis. This study develops a capacity calculation model tailored to the diversion area's unique characteristics and principal capacity-influencing factors. Initially, the research focuses on a specific short-distance continuous diversion area of a mountainous urban expressway, employing video trajectory tracking technology to gather trajectory data. This data serves as the basis for analyzing road and traffic characteristics. Subsequently, the model computes the capacity influenced by eight variables, including diversion point spacing and deceleration lane length, using VISSIM simulation experiments. A gray correlation analysis identifies key factors, which guide the establishment of the model's fundamental structure through two-factor surface fitting results. Mathematical statistical methods are then applied to resolve the model's parameters, culminating in a robust capacity calculation model. The findings reveal that diversion point spacing, along with primary and secondary diversion ratios, significantly influence capacity. Notably, the capacity exhibits a marked quadratic polynomial relationship with the primary diversion ratio and diversion point spacing, and a linear relationship with the secondary diversion ratio. The model's validity is confirmed through a case study at the diversion area north of Huacun Interchange in Chongqing Municipality, where the discrepancy between calculated and actual capacities is under 5%, underscoring the model's high accuracy. These results offer valuable theoretical and methodological support for the planning, design, and traffic management of diversion areas.

Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method

This paper introduces a multi-objective optimization problem (MPO) for the forming process parameters of disc forgings using grey relational analysis (GRA) and the response surface methodology (RSM). Firstly, an experimental design based on the Box–Behnken design (BBD) principle was established, and simulations were performed in Deform to obtain response data. Secondly, GRA was used to transform the MPO into a grey relational degree (GRD) problem, and the entropic weight method was integrated to ascertain the influence weights of each variable on GRD. Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. Finally, the optimal process parameter combination was determined through the particle swarm optimization algorithm, which included a friction coefficient of 0.3, an initial temperature of 1250 °C, and a downward pressing speed of 7.5 mm/s. The results of the experimental investigation indicate that optimized process parameters significantly reduce the forming load, equivalent stress, and damage value, effectively enhancing the overall quality of forged parts.