Grey Theory Research Articles

Neural ordinary differential grey algorithm to forecasting MEVW systems

Because of the advantage of the gray theory for forecasting small sample time data, gray algorithm theory has definitely been extensively utilized since it has been proposed and is currently being widely developed for predicting frames particularly in small sample problems. This article presented a viewpoint called gray algorithm by neuron- based ordinary-differential equation (NODE), called NODGM (neuron-based ordinary-differential gray-mode). In this type, we learn prediction methods through a training process that includes whiting equations. Compared with other models, the structure and time series via the regularity of real-samples are required in advance, so this NODGM design can have a better feasibility of applications and also study the origins of data according to different samples. The purpose is obtaining a better design with high forecast effectiveness, this study uses NODGM to train the model, while Runge-Kutta method is used to have the forecast set and solve numerical framwork. This algorithmic design creates a favorable theoretical basis for the installation of new process and distributes the numerical dimensions of completely mechanically elastic vehicle wheels (MEVW) in practical simulations.

Influence of Dilution on the Mechanical Properties and Microstructure of Polyurethane-Cement Based Composite Surface Coating.

Polyurethane-cement composite are widely used in modern civil engineering, and the method of adding diluent is often used to adjust the construction process to adapt to the engineering environment. Studies have shown that the addition of diluent impacts the performance of polyurethane-cement based composite surface coatings, but there have been few reports on the influence of diluent content on the mechanical properties and microstructure of the coatings. To address this, polyurethane coatings with different diluent contents were prepared, and positron annihilation lifetime spectroscopy was used to test the microstructure of the coatings. The tensile strength and elongation at rupture were tested using a universal material testing machine, and the fracture interface morphology of each coating was observed by scanning electron microscopy. Finally, the correlation between the microstructure parameters and the mechanical properties of the coating was analyzed using grey relation theory. The results demonstrated that with the increase in diluent content, (i) the average radius of the free volume hole (R) and the free volume fraction (FV) of the coating both showed a trend of first decreasing and then increasing. The value of R was between 3.04 and 3.24 Å, and the value of FV was between 2.08 and 2.84%. (ii) The tensile strength of the coating increased first and then decreased, while the elongation at rupture decreased first and then increased. Among them, the value of tensile strength was between 3.23 and 4.02 MPa, and the value of elongation at fracture was between 49.34 and 63.04%. In addition, the free volume in polymers plays a crucial role in facilitating the migration of molecular chain segments and is closely related to the macroscopic mechanical properties of polymers. A correlation analysis showed that the R value of the coating had the greatest influence on its tensile strength, while FV showed a higher correlation with the elongation at rupture.

Cam Profile Design of Low-Speed Marine Diesel Engine Based on the Grey Theory

The camshaft profile is one of the important parameters of the engine, the reasonableness or otherwise of its design directly influences the engine’s power output, fuel consumption, emissions, noise and reliability. However, most existing cam profile optimization designs are based on the basic software operation, profile parameter selection and tuning lack a scientific theoretical basis. In order to address this issue, this study establishes a single valve kinematic model of the gas delivery mechanism of diesel engines by using the AVL-EXCITE Timing Drive, and performs a model-based multinomial kinematic acceleration cam profile design. Subsequently, an orthogonal design method is employed to obtain the parameter combinations that can satisfy the dynamic requirements. The research investigates the influence of cam profile design parameters (C 4, p, q, r, and s) on valve fullness, maximum jerk, cam contact stress, and valve seating force through a systematic study. Lastly, based on the grey correlation theory, a thorough analysis of the performance indices of cams with different profile parameter combinations is conducted. The optimization problem of the profile parameters with multiple performance indicators is transformed into a single-objective grey relational optimization problem, resulting in the optimal cam profile parameter combination of C4 =0.1, p=14, q=20, r=28 and s=36. The optimal method proposed in the paper is straightforward to implement and is applicable to the optimum design of cam profiles of the engine, which is of great importance for the optimal design of cam fabrication processes.

Research on the precise pointing of college students’ career based on logistic regression classification algorithm

Abstract This study analyzes the career development direction of college students through logistic regression and gray theory to provide more accurate career planning guidance for college students. Logistic regression classification algorithm and gray theory are used to study career influencing factors and predict future career development trends. The significant influence of academic performance, English proficiency, and civic and political cultivation on career direction is identified by analyzing the sample data of S college students. The model’s accuracy is significantly higher than random guessing, up to 79.8%. The results show that the combination of logistic regression and gray theory can effectively predict the career direction of college students, provide data-supported career guidance services for colleges and universities, and help students make more reasonable career planning.

Transmission line ice disaster early warning method based on ice thickness prediction using GM(1, 6) model

The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the anti-ice work of transmission line.

Differences in Approach and Avoidance Motivation Sensitivities Predicting Participation and Performance in Strength Sport

Abstract Cusimano, K, Freeman, P, Moran, J, and Yamaguchi, M. Differences in approach and avoidance motivation sensitivities predicting participation and performance in strength sport. J Strength Cond Res 38(1): 180–184, 2024—Gray's Reinforcement Sensitivity Theory proposes that individual differences in behavior are due to the sensitivity to 2 brain systems: the behavioral inhibition system (BIS), which regulates aversive emotions to threatening stimuli, and the behavioral approach system (BAS), which regulates positive emotions toward rewarding or nonpunishing stimuli. The current study investigated whether BIS and BAS sensitivity predicts participation and performance in strength sports. A sample of 177 competitive strength athletes (male = 148; female = 29; mean age = 28.68; SD = 6.24 years) and 178 control participants (male = 89; female = 89; mean age = 29.39; SD = 7.42) completed the BIS/BAS scale, with strength athletes also providing their Wilks scores as a measure of sporting performance. Independent t tests showed significantly higher BIS (MD = 2.37, p = 0.003, 95% CI [0.79, 3.94], d = 0.31) and total BAS (MD = 11.71, p < 0.001, 95% CI [9.26, 14.15], d = 1.00) sensitivity in strength athletes than individuals in the control group. A 3-step hierarchical regression analysis revealed that the number of training years (β = 0.506, p < 0 .001), BIS (β = −203, p = 0.005) and BAS drive (β = 0.188, p = 0.012) made significant unique contributions to predicting the Wilks score, with no significant contributions of age, sex, BAS fun seeking, and BAS reward responsiveness. The findings indicate that the overall reward sensitivity (total BAS score) and reward seeking (BAS drive) are associated positively with participation and performance in strength sports, respectively. Given the association of these brain systems to addiction and other psychiatric disorders, the findings could have implications in psychiatric treatment and sporting recruitment.

Multi-source data-driven estimation of maximum carrying capacity of urban water storage facilities under extreme conditions

Abstract With the deepening of urbanization and rapid economic development, urban water storage systems face increasing challenges. In this paper, the behavioral mechanism of urban water storage system is deeply analyzed by using the system dynamics method, and a system dynamics model of the carrying capacity of urban water storage equipment is established. Further, based on the gray correlation theory, a prediction model of the carrying capacity of urban water storage equipment is constructed and accuracy is examined. The study estimated the maximum carrying capacity of urban water storage equipment through performance analysis. The results show that the relative error of the fitted data is deficient, indicating that the model is highly accurate. The empirical Analysis of the carrying capacity index of the urban economy and water environment pollution is high. The prediction results for 2030 show that the carrying capacity of water storage facilities in City M is 0.22, which is already slightly overloaded and faces a severe risk of overloading. The model proposed in this study can not only accurately predict the trend of the carrying capacity of water storage equipment, but also effectively predict the overloading problem of urban water storage equipment, which provides a scientific basis for the optimization and improvement of urban water storage equipment, and an essential support for the formulation of the city’s sustainable development strategy.

Integracija razvrstavanja sivih relacija te analize troškova i koristi za optimalno određivanje prioriteta održavanja asfaltnih kolnika

making problem. The common approach involves the prioritisation of road repairs with more extensive surface damages but this does not always align with the actual repair needs. To address this, a method based on the grey correlation theory was introduced to evaluate pavement performance. This method was employed to determine the optimal maintenance time for asphalt pavements. Once the sequence was established, the ideal maintenance schedule for the pre-maintenance sections was determined using a cost–benefit model. This evaluation method is particularly useful for quantitative analyses of the influences of pavement age and traffic volume on pavement performance parameters. The results demonstrate that grey relation sorting effectively weighs the factors and provides rankings for road segments requiring maintenance. A cost–benefit analysis model was used to assess the maintenance sections. This model can guide decisions regarding pre-maintenance sections and assess pavement conditions, thus resulting in outcomes that align better with real-world situations and human judgment. These findings are expected to be valuable for the long-term maintenance of asphalt pavements.

Research on Open Circuit Fault Diagnosis Strategy for DFPMM Drive System Based on Grey Prediction Theory With the Introduction of Euler Algorithm

Research on Open Circuit Fault Diagnosis Strategy for DFPMM Drive System Based on Grey Prediction Theory With the Introduction of Euler Algorithm

Analysis of Teaching Capacity Building of College Teachers’ Innovation and Entrepreneurship Education under Comprehensive Multiple Fuzzy Evaluation System

Abstract How can we effectively promote and improve the quality of innovation and entrepreneurship education in colleges and universities? The key lies in enhancing the capacity of teachers to impart innovative entrepreneurial education. The article takes the requirements of innovation and entrepreneurship education and the connotation of teaching ability as the orientation and establishes the evaluation index system of dual innovation education teaching ability of college teachers by combining the CIPP model. The IAHP method and the improved CRITIC method, respectively, calculate the subjective and objective weights of each index in the index system, and then they introduce the game theory idea to amalgamate and assign weights to the index system Based on the gray fuzzy mathematical theory, a fuzzy comprehensive evaluation model of the teaching ability of innovation and entrepreneurship education among college teachers was established. Using University Y as the research object, data analysis was carried out to determine the differences between teachers’ dual entrepreneurship teaching abilities, comprehensive evaluation, and the application effectiveness of the evaluation system. The maximum mean value for teachers’ innovative entrepreneurship education teaching ability is 4,085, and the minimum skewness coefficient is only −0.475, suggesting a trend towards correct skewness. Teachers’ mastery of dual-creation theory is proportionately weighted at 25.52%, and the fuzzy comprehensive evaluation score is 81.97, which is a good grade. The evaluation index system, combined with the fuzzy comprehensive evaluation model, can help college teachers clarify the problems of dual-creation education, which is conducive to improving their dual-creation teaching ability.

A Study on the Extension of Chinese Traditional Culture in College Art Education Combined with Gray Correlation Analysis Methods

Abstract This study incorporates gray correlation analysis from gray statistical theory to assess the quality of traditional culture and art education. It establishes an evaluation index system and a gray correlation analysis model, examining the relationship between contemporary art education and traditional culture. The model is applied to identify the correlation, key characteristics, and principal influencing factors of traditional Chinese culture and art education in higher education institutions. Additionally, it examines the advantages of random sequences of factors through specific data processing techniques. The structural model tests the influencing factors on the quality of traditional culture and art education, with an analysis of path coefficients. The results showed that 97.4% of teachers chose traditional culture and art teaching resources, mainly by referring to the network aspect. The results of the weight distribution indicate that the teaching process U3 (0.3613) is the most important in traditional culture and art teaching, and the innovative activities in teaching content (0.8113) hold the largest weight. The top three gray correlation values of the indicators of the second-level factor layer are U43>U22>U32. The direct influence effect of excellent traditional culture permeating the teaching process is 0.232, which has the greatest influence on the quality of teaching. The direct impact of excellent conventional culture permeating teaching content is 0.184, which has the second largest influence on teaching quality. This paper successfully constructs a gray correlation model to analyze the relationship between traditional culture and contemporary art, which has high scientific validity.

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

The application of evidence-entropy weight gray incidence theory on the risk assessment of rockburst intensity in the Daxiangling tunnel

The risk assessment of rockburst intensity is significant for tunnel construction safety. First, the depth of the rockburst (X1), the uniaxial compressive strength of the rocks (X2), the brittleness coefficient of the rocks (X3), the stress coefficients of the rocks (X4), and the elastic energy index (X5) are adopted as the evidence body, and their essential certainty and reliability is determined using the entropy-gray correlation theory. Second, the synthetic certainty reliability of other samples is calculated based on the evidence theory. Relatively to the traditional gray extension model, it can improve the predictive accuracy and determine the certainty and reliability of different evidence bodies. The difference of importance between other evidence bodies can be reflected; and an interval scale can be taken into consideration in the evaluation process, so the proposed theory can reasonably predict the grade criterion which is interval form. Conclusion demonstrated that the suggested approach is entirely consistent with the actual investigation. The proposed model not only considers the unreliability or reliability of the problem but also solves some degrees of uncertainty and ambiguity of the datum; it enhances the predictive efficiency and provides a new way and thought for future risk assessment of rockburst intensity.

Grey angle cosine relational degree model based on generalized greyness of interval grey number

PurposeAim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.Design/methodology/approachFirstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.FindingsThe results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.Practical implicationsThe research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.Originality/valueThe paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

Application of Gray Expansion Model in Energy Economic Analysis and Load Forecasting

Objective and effective prediction of energy consumption can not only optimize the energy consumption structure, but also provide important information for the government to formulate energy conservation and emission reduction measures. With the development of new energy sources and changes in the global energy consumption structure, historical energy data that are too old may no longer be reliable for forecasting, which leads to a decrease in the amount of information on energy, and the gray theory, which is applicable to “poor information”, has gained attention. Firstly, the optimization of energy economic objectives and transformation path methods at this stage is clarified; then, the DEA-Malmqusit model is used to improve the shortcomings of the traditional model that can only compare different cross-sections at the same time node, and to evaluate and analyze the full-factor multi-indicators of energy enterprises in terms of technological empowerment, environmental dynamics, and economic output efficiency; finally, the LEAPS-based energy system consumption and load capacity prediction model. The results show that the traditional algorithm is not accurate enough and has some deviation when the energy raw data fluctuates a lot. The algorithm proposed in this paper still gives a better prediction, predicting a city’s carbon emission to be 65,240,100 tons in 2024, with a 3.6% increase in energy output year by year.

Application of Hybrid ANFIS Tool for Laser Beam Welding of Inconel 625 Alloy

Over the past decade, there has been a steady rise in the application of laser sources; this has led to reduced costs and enhanced output. One aspect that has played a part and helped to the quickening of this phenomena is the invention of new techniques such as laser beam welding systems. These developments in technology have made it feasible to produce both tiny and large parts. The usage of nickel alloy can provide considerable benefits in a wide variety of contexts. The advantages acquired from combining difficult materials, such as metal alloys, are responsible for these benefits. Manufacturing equipment for the chemical, nuclear, maritime, aeronautical, and automotive industries all rely on components made from Inconel 625. It can withstand potentially corrosive situations without losing its outstanding mechanical qualities. Investigation of nickel alloy weldments is required for the improvement of the production method. The goal of this piece is to develop an ANFIS (Adaptive Neuro Fuzzy Inference System) based on Grey theory that can reliably forecast LBW variables. The values predicted by the model were compared with the results of the experiments, and it was discovered that the values found in the experiments are closely related to the values anticipated by the model. The manufacturer can make decisions that are more in accordance with the available facts due to the evidence provided by the performance investigation.

Grey correlation analysis and molecular simulation study on modification mechanism of red mud mixed manganese slag

In order to promote the virtuous cycle of economic society, minimize the harm of red mud and manganese slag and maximize the efficiency of processing and utilization of red mud and manganese slag; in order to analyze the influence of red mud and manganese slag on the performance of matrix asphalt, this paper conducts multiple groups of compound proportion tests after fully mixing red mud and manganese slag, and finally determines the optimal compound proportion of 1：1.5. The red mud manganese slag compound modified asphalt binder was prepared by adding the powder-oil ratio of 0, 3%, 6%, 9%, 12%, 15%, 18%, 21% and 24% to the matrix asphalt by the external mixing method. Through the determination of three indexes of modified asphalt binder, the modification effect of different dosage on matrix asphalt is analyzed. From a large number of experimental data, it is concluded that when the mixing ratio is 15%, the three indexes of modified asphalt binder are the best. In this paper, the grey correlation theory is used to analyze the correlation between the dosage of the compound modifier and the penetration, ductility and softening point of the matrix asphalt. It is concluded that the addition of the compound modifier has the most obvious influence on the penetration at 10 ℃, followed by the penetration at 20 ℃, the softening point, the penetration at 25 ℃, the penetration at 30 ℃, the penetration at 15 ℃, the delay at 5 ℃ and the delay at 15 ℃. The adsorption process of red mud compound manganese slag and asphalt was qualitatively simulated by molecular dynamics theory, and the interaction energy between red mud compound manganese slag modified asphalt binder and matrix asphalt was quantitatively calculated, and the modification mechanism of modified asphalt was analyzed.

Research on Subsidence Prediction Method of Water-Conducting Fracture Zone of Overlying Strata in Coal Mine Based on Grey Theory Model

The development height and settlement prediction of water-conducting fracture zones caused by coal seam mining play an important role in the stability of overburden aquifers and the safety of roadways. Based on the engineering geological data of the J60 borehole in the Daliuta Coal Mine and the mining conditions of the 2−2 coal seam, China, this study established a similar material test model of mining overburden. The deformation characteristics of overlying strata in the mining process of coal seam were studied by using distributed optical fiber sensing technology, and the development height of water flowing fractured zone was determined. According to the equidistant sampling characteristics of Brillouin optical time domain reflection technology and the principle of the grey theory model, the settlement prediction model of the water-conducting fracture zone was established. By analyzing and comparing the prediction accuracy of the GM (1, 1) model, grey progressive model, and metabolic model, the optimal method for settlement prediction of the water-conducting fracture zone was discussed. The results show that, for the metabolic model, with the increase in the number of test sets and the decrease in the number of prediction sets, the mean square error ratio c and the small error probability p of the prediction accuracy evaluation parameters display a downward trend. The accuracy is related to the sudden change in the settlement of the water-conducting fracture zone caused by the breaking of the key stratum of the overlying rock. The optimal time of test sets selected for the best settlement prediction model is 7~8, and that of prediction sets selected is 5~6. For the GM (1, 1) model and the grey progressive model, the prediction accuracy of mining overburden subsidence is grade 4, which is not suitable for settlement prediction of water-flowing fractured zones.

Exploring the short-term water damage characteristics of asphalt mixtures: The combined effect of salt erosion and dynamic water scouring

In icy or snowy conditions, improper application and uneven dispersion of deicing salts on roadways can lead to saltwater accumulation, subsequently causing roadway scouring when vehicles transit. This study aims to assess the combined influence of salt erosion and dynamic water scouring on short-term water damage of asphalt mixtures. A custom-designed dynamic water scouring simulation apparatus is used, employing three diverse deicing salt solutions as agents for dynamic water scouring. Numerous iterations of dynamic water scouring tests are conducted on two types of graded asphalt mixtures. Post-scouring, alterations in air void content and water stability are evaluated through void content tests, Marshall water immersion tests, and freeze-thaw splitting tests. Additionally, the GM (1,N) damage prediction model from gray theory is applied for data modeling and prediction. The results reveal a significant impact of the combined effects of salt erosion and dynamic water scouring on the short-term performance of asphalt mixtures. In the early phase, dynamic water scouring primarily influences the asphalt composite, augmenting micro-cracks and causing aggregate disintegration. The intermediate phase sees the disintegration of the asphalt membrane by deicing salts, disrupting adhesion between asphalt and aggregate, and progressively leading to the emergence of new micro-cracks. During later stages, dynamic water scouring continues to deteriorate the asphalt composite, building on the erosion instigated by deicing salts, resulting in the expansion of newly formed micro-cracks and further aggregate disintegration. Among the three deicing salts, CH4N2O exhibits the most pronounced impact on short-term water damage, followed by NaCl, while CH2CH3OH has the least effect. Increasing the fine aggregate proportion in the asphalt mixture enhances water damage resistance. The GM (1,N) damage prediction model effectively anticipates air void content and water stability of asphalt mixtures. The research outcomes offer theoretical and technical support for understanding the durability and service lifespan of asphalt pavements in cold regions.

Analysis of new urbanization level measurement in Heilongjiang Province

In recent years, Heilongjiang Province has made significant contributions to the country based on its resource advantages, but the contradictions and problems in sustainable development have become increasingly prominent. In order to better study the economic upgrading and transformation of resource-based cities in Heilongjiang Province, this article focuses on the new urbanization and industrial structure optimization level of resource-based cities in Heilongjiang Province, Construct and analyze the measurement index system for the level of new urbanization and industrial structure optimization, respectively. Under the concept of regional coordinated development, an evaluation index system for new urbanization and industrial structure was constructed. The coefficient of variation method was used to calculate the weights of various indicators for the new urbanization system and industrial structure optimization system. The grey correlation theory was used to analyze the correlation between the selected indicators for characterizing the level of new urbanization and industrial structure optimization, aiming to make the correlation between the internal indicators of the system more reasonable, By promoting the coordinated development of new urbanization and industrial structure optimization, resource-based cities can achieve economic upgrading and transformation, thereby achieving sustainable economic development. The research results indicate that policy recommendations for coordinated development between the two are proposed.