Grey Relational Analysis Research Articles

Research on interval prediction method of railway freight based on big data and TCN‐BiLSTM‐QR

AbstractWith the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

Research on the identification and detection technology of electricity theft in power distribution areas under a smart grid environment

Abstract With the extensive application of smart meters, power companies can access a wealth of customer electricity usage data, which has not yet been fully utilized. This paper proposes a substation area user identification and electricity theft detection method based on smart meter data, aiming to enhance the level of intelligent management of power grid enterprises and reduce operational costs. By using the grey relational analysis method, analyzing the measurement data collected by smart meters at different time points, it is possible to determine the substation area and phase to which the user belongs and to detect electricity theft behavior. Finally, through simulation, the effectiveness of the proposed method is verified.

Multi-objective optimization of Stinger PDC cutter breaking rock parameters under intrusion-cutting action using Taguchi-based gray relational analysis.

To enhance the efficiency of the Stinger Polycrystalline Diamond Compact (PDC) cutter in breaking hard rocks, this study focuses on optimizing the cutter intrusion-cutting rock breaking parameters. A numerical calculation model for the rotational breaking of granite by a Stinger PDC cutter was established. A comprehensive statistical examination was performed to assess the influence of various factors on intrusion ability (IA), tangential force (TF), and mechanical specific energy (MSE). The Taguchi method was used to determine the optimal settings for each factor, while analysis of variance was employed to assess the significance and relative impact of these factors on the target outcomes. In addition, the multi-objective function was optimized using the gray relational analysis method. The primary process parameters obtained for the various performance characteristics are the cone top angle (α), the cone top radius (r), the cutter diameter (d), the cutter back inclination angle (β), and weight on bit (P). The impact ratios of these parameters are 6.20%, 7.66%, 3.93%, 17.20%, and 65.02%, respectively. The optimal geometrical parameters are α = 60°, r = 2mm, and d = 15mm, while the optimal working parameters are β = 30° and P = 800 N. In the optimal case, IA and MSE were reduced by 55.335% and 15.809%, respectively, compared to the initial case. Despite a 15.706% increase in TF, the overall GRG increased for all three evaluation criteria, with an overall increase in efficiency of 18.229%. The results of this paper can provide guidance for the design of Stinger cutter PDC drill bits.

Emerging eco-friendly fiber-reinforced concrete with shaped synthetic aggregates using Taguchi grey relational analysis and utility concept

The present study assessed the applicability of employing Taguchi-based Grey relational analysis (T-GRA) and the Taguchi approach combined with the utility concept (T-UC) for enhancing the mix design parameters of fiber-reinforced synthetic aggregate concrete (FRSAC). Cement was supplemented with binders such as ground granulated blast furnace slag (GGBFS), metakaolin (MK), and lime powder (LP). The optimized mix proportion of FRSAC was determined to enhance the fresh and mechanical performance. Furthermore, this investigation used two weighting approaches: equally weighted and maximum deviation methods. An established L16 (45) Taguchi orthogonal array was used with five different factors, namely, cement replacement (40 %, 50 %, 60 % and 70 %), binder content (B1, B2, B3, B4), water-to-cement ratio (0.31, 0.32, 0.33, 0.34), fiber dosage (0.1 %, 0.2 %, 0.3 % and 0.4 %), and synthetic aggregate replacement (25 %, 50 %, 75 %, and 100 %) were employed to design the experiments. Then, T-GRA and T-UC were used to determine the optimum mix proportions for five response parameters: workability, compressive strength, split tensile strength, elastic modulus, and impact ductility index. Results indicated that the two methods, GRA and UC, can be successfully integrated with the Taguchi method to derive the optimized FRSAC mixture. Confirmation experiments were performed after determining the optimum mix design parameters, including cement replacement of 60 %, binder content of B4, the water-to-cement ratio of 0.34, fiber dosage of 0.1 %, and synthetic aggregate replacement of 75 %. The optimum FRSAC mix was examined for its fresh (workability) and hardened (compressive strength, split tensile strength, elastic modulus, impact ductility index) properties.

Enhancing Mechanical Performance of Friction Stir Welded AZ31 Magnesium Alloy with Nano-TiC Reinforcements Using Grey Relational Analysis

Enhancing Mechanical Performance of Friction Stir Welded AZ31 Magnesium Alloy with Nano-TiC Reinforcements Using Grey Relational Analysis

3D DEM investigation on macro-meso mechanical responses of gas hydrate-bearing sediments in process of unloading confining pressure

Well drilling, gas production, and reservoir stimulation can disrupt stress states of gas hydrate-bearing sediments (GHBSs) surrounding wellbores, potentially inducing wellbore instability due to confining pressure unloading. Therefore, it is very important to know about the macro-meso mechanical responses of GHBSs under an unloading confining pressure path (UCPP). Herein, a discrete element method (DEM) was used to perform triaxial tests on three-dimensional numerical GHBSs under the UCPP and a conventional drained path (CDP) to bridge the knowledge gap. Results show that GHBSs under the UCPP are more prone to failure and dilatation than under the CDP, due to the reduced bearing capacity of contact force chains, relatively unstable particle assembly, and rapid cementation breakage development. Additionally, the stress path has little effect on the cohesion and friction angle of GHBSs. Furthermore, under the UCPP, the failure and dilatation risk of GHBSs are positively correlated with unloading confining pressure rate, inversely related to hydrate saturation and initial effective confining pressure. The grey relational analysis illustrates that compared to hydrate saturation and initial effective confining pressure, the unloading confining pressure rate minimally affects the unloading confining pressure effect. These findings can serve for experimental studies and field applications (e.g., optimizing drilling mud density, sand production rate, and fracturing fluid flowback rate) concerning the unloading confining pressure responses of GHBSs.

Deciphering the anti-diabetic potential of Dipsacus asperoides root: Identification of active compounds and quality assessment

The plant Dipsacus asperoides C. Y. Cheng et T. M. A., has been traditionally utilized as a medicinal plant in China due to its roots known as “Dipsaci Radix”. It is suggested that Dipsaci Radix exhibits antihyperglycemic, hypolipidemic, and antioxidant properties, along with down-regulating effects on advanced glycation end-products (AGEs) formation. However, there are currently no reliable reports on the effective components derived from Dipsaci Radix for anti-diabetes. In this study, the methanol extract and its corresponding ethyl acetate (EA) fraction of Dipsaci Radix were investigated as potential natural sources of antidiabetic agents and antioxidants by in vivo methods. The existence of phenolic components in extract and fraction enhances their antioxidant and antidiabetic properties. This is evidenced by the fact that all isolated phenolic compounds exhibited such properties, with half of them (Compound 2, 4, 5, 8, 10) displaying multidirectional inhibitory effects. Quality evaluation of these active components using high performance liquid chromatography-ultraviolet (HPLC-UV) technology combined with grey relational analysis provides further information regarding the spectrum-effect relationship between active components and their activities. Among them, the presence of the active ingredient “5-O-caffeoylquinic acid” significantly contributes to the antidiabetic and antioxidant properties of Dipsaci Radix. This holds promising applications in traditional health products and cosmeceuticals.

Using the Taguchi Method and Grey Relational Analysis to Optimize Ventilation Systems for Rural Outdoor Toilets in the Post-Pandemic Era

This study addresses the issue of poor air quality and thermal comfort in rural outdoor toilets by proposing a ventilation system powered by a building-applied photovoltaic (BAPV) roof. A numerical model is established and validated through comparison with the literature and experimental data. Based on a consensus, four influential variables, namely, inlet position, outlet height, supply air temperature, and ventilation rate, are selected for optimization to achieve multiple objectives: reduction in ammonia concentration, a predicted mean vote (PMV) value of 0, minimization of age of air, and energy consumption. The present study represents a pioneering effort in integrating the Taguchi method, computational fluid dynamics (CFD), and grey relational analysis to concurrently optimize the influential variables for outdoor toilet ventilation systems through design and simulation. The results indicate that all four variables exhibit nearly equal importance. Ventilation rate demonstrates a dominant effect on ammonia concentration and significantly impacts the age of air and energy consumption, while supply air temperature noticeably influences PMV. The optimal scheme features an inlet at center top position, an outlet height of 0.2 m, a supply air temperature of 12 °C and a ventilation rate of 20 times/h. This scheme improves ammonia concentration by 18.9%, PMV by 6.8%, and age of air by 30.0% at a height of 0.5 m, while achieving respective improvements by 18.9%, 5.5%, and 22.2% at a height of 1.5 m. The BAPV roof system generates an annual electricity output of 582.02 kWh, which covers the energy consumption of 358.1 kWh for toilet ventilation, achieving self-sufficiency. This study aims to develop a zero-carbon solution for outdoor toilets that provides a safe, comfortable, and sanitary environment.

New insights into the controlling factors of nitrate spatiotemporal characteristics in groundwater of Dagu aquifer in Qingdao, China

Identifying spatiotemporal variation of groundwater NO3-N and its primary controlling factors are vital for groundwater protection. This study, under the data scarce conditions and based on time series monitoring data in Dagu aquifer, applied methods including hydrochemical ion ratio, multiple linear regression, support vector regression and grey relational analysis and dedicated to revealing primary controlling factors of temporal variation patterns of groundwater NO3-N. The results showed that agricultural and manure fertilizer are the main sources of NO3-N in north and central area (vegetable farming area), and that domestic sewage discharge and manure fertilizer are the main sources of NO3-N in south area (residential and grain planting area). In addition, results identified the dominant influencing factors of variation of NO3-N in different regions, with human wastewater discharge, nitrogen load amount and water-table depth being the dominant factors of variations of NO3-N in north area, human wastewater discharge being the main factor of variations of NO3-N in central area, and irrigation water and human wastewater being the leading factors of variations of NO3-N in south area. Moreover, types of controlling factors can influence the seasonal variations of NO3-N. NO3-N in vegetable farming area that dominantly affected by fertilization generally shows higher concentration and larger variation range of concentration during summer and autumn than that during spring. NO3-N which mainly affected by human wastewater discharge and manure inputs shows minimal seasonal variation of mean concentration. NO3-N in grain area influenced by irrigation could show more significant variations during spring and autumn than that during summer. The conclusions can enhance understandings of major influencing factors on NO3-N variation in local aquifer. Importantly, the dominant roles of water-table depth and irrigation in NO3-N variation of N2 site (vegetable planting area) and S5 site (grain planting area), respectively, were highlighted.

Research on the development relationship between safety production indicators and economic and social indicators in China

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research.

Friction stir welding of aluminum AA6061 & AA8011 using optimization approach based on both Taguchi and grey relational analysis

Friction stir welding of aluminum AA6061 & AA8011 using optimization approach based on both Taguchi and grey relational analysis

An Integrated Pythagorean Neutrosophic DEMATEL- Grey Relational Analysis Framework for Assessing Recent Technological Advancements in Maternal Healthcare

This study proposes a novel methodology that integrates the Decision making trial and evolution laboratory (DEMATEL) technique and Grey relational analysis (GRA) in a Pythagorean neutrosophic environment. This multi-criteria decision-making approach uses the DEMATEL technique to analyze the weights of the criteria and Grey relational analysis to prioritize the alternatives. The proposed approach is utilized to evaluate the most recent technological advancements in maternity healthcare, along with a pertinent numerical example.

Optimization of Agricultural Tractor Engine Hood Forming Quality Based on Neural Network Genetic Algorithm Function

To improve the forming quality of agricultural tractor engine hood and effectively solve the problems of wrinkling and cracking during the drawing forming process, DYNAFORM 5.9 finite element software was used for numerical simulation of the part. Based on a comprehensive scoring method of orthogonal experiments and grey relational analysis, the critical process parameters affecting the “maximum thickening rate” and “maximum thinning rate” were determined. Latin hypercube sampling was used to randomly sample the key process parameters, and the sampled samples served as the data basis for the optimization stage of the neural network genetic algorithm function; an optimization model of the neural network genetic algorithm function was established, with the key process parameters, and the “maximum thickening rate” and “maximum thinning rate” as inputs and outputs respectively, to construct a nonlinear mapping relationship and optimize the process parameters. The study showed that the blank holder force and die clearance had the greatest impact on the “maximum thickening rate” and “maximum thinning rate”, identifying them as key process parameters; the optimal process parameter combination was the blank holder force of 462.71 kN, the die clearance of 1.09 mm, and corresponding “maximum thickening rate” and “maximum thinning rate” of 3.97% and 25.96%, respectively. Overall, this research provides a practical optimization strategy to solve the quality issues of the engine hood, offering a theoretical basis for its actual production and processing with significant practical application value.

Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings

This paper deals with the topic of reducing drag force acting on aircraft wings by incorporating novel winglet designs, such as multi-tip, bird-type, and twisted. The high-speed NASA common research model (CRM) was selected as the baseline model, and winglet designs were retrofitted while keeping the projected wingspan constant. Computational analysis was performed using RANS coupled with the Spalart–Allmaras turbulence model to determine aerodynamic coefficients, such as CL and CD. It was observed that the multi-tip and bird-type designs performed exceptionally well at a low angle of attack (0°). A parametric study was conducted on multi-tip winglets by tweaking the parameters such as sweep angle (Λ), tip twist (Є), taper ratio (λ), and cant angle (Φ). The best combination of parameters for optimal aerodynamic performance while maintaining the wing root bending moment was determined using both the Taguchi method and Taguchi-based grey relational analysis (T-GRA) coupled with principal component analysis (PCA). Also, the percentage contribution of each parameter was determined by using the analysis of variance (ANOVA) method. At the design point, the optimized winglet design outperformed the baseline design by 18.29% in the Taguchi method and by 20.77% in the T-GRA coupled with the PCA method based on aerodynamic efficiency and wing root bending moment.

Optimization of Dissimilar AA5052 and AA2219 Alloy Joints in Friction Stir Welding Using Taguchi Grey Relational Analysis

Optimization of Dissimilar AA5052 and AA2219 Alloy Joints in Friction Stir Welding Using Taguchi Grey Relational Analysis

An experimental investigation on copper square electrode wear in electric discharge machining of Hastelloy B2

Hastelloy B2 is a high-performance nickel-molybdenum alloy and has been used in micro reactor, pump, pipe, and corrosion-resistant equipment. Square holes are required in the above application components. Although square holes are easily produced by spending more electrodes wear in Electrical Discharge Machining (EDM). Moreover, the square hole with small electrode wear achieved is a challenging task in thermal machining. Hence, in this work, the effects of EDM process parameters on electrode taper angle, electrode radius, electrode wear length and cutting time of Hastelloy B2 are studied. Taguchi based VišekriterijumskoKompromisnoRangiranje (VIKOR) method is used to find the optimum process parameters and compared with Taguchi based Grey Relational Analysis (GRA) method for validation. The results showed that current and pulse duration greatly affects the electrode wear out surface and edges. The square electrodes with sharp edges and surfaces are fully transformed into hemispherical shapes at electrode ends due to an unstable spark with diffusion of heat. Also, the VIKOR method provides an alternative multi-criteria optimization method that enhances the process performance over the grey relational analysis due to comparability, uniqueness, assessment and prediction.

Phosphorus prediction in the middle reaches of the Yangtze river based on GRA-CEEMDAN-CNLSTM-DBO

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse

The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

A Data-Driven Comprehensive Evaluation Method for Electromagnetic Suspension Maglev Control System

As new advanced vehicles, the safety and stability of electromagnetic suspension maglev trains have always been a subject of concern. This study introduces the improved R index and τ-distance index into the performance evaluation of the suspension control system, respectively assessing the stability of the suspension gap and the smoothness of train operation, combining them with grey relational analysis to achieve data-driven comprehensive evaluation. Furthermore, feasibility tests on the Fenghuang Maglev Express validate the effectiveness and superiority of the comprehensive evaluation method based on measured data. Experimental results demonstrate that the data-driven comprehensive evaluation method, through designing specialized evaluation metrics and increasing assessment dimensions, effectively evaluates the performance of the suspension system control loop. Compared to a traditional error integral comprehensive performance index, it offers greater comprehensiveness and accuracy, along with real-time state-monitoring capabilities.