Grey Correlation Degree Research Articles

Application of the BP Neural Network Model of Gray Relational Analysis in Economic Management

According to the BP neural network algorithm, for the problem that the input variables of the BP network cannot be automatically optimized during the modeling process of the multivariable complex system, it is used to establish a neural network optimization algorithm according to the gray correlation analysis (GM2 BPANN). Using the data of China’s grain production forecast, the stepwise regression method and the gray GM (1, N) model method were compared and tested. The results show that the new model can comprehensively and extensively process a large number of input variables by using the concept of the gray correlation degree, without having to go through special subjective screening and hence improving the adaptability of the BP network, and at the same time, it can obtain better prediction accuracy and stability. Through the empirical test, the prediction ability of the three methods of regression, GM (1, N) gray system, and GM‐BPANN model is compared. It is proved that the GM‐BPANN optimization algorithm that combines the gray relational analysis and BP neural network method can enhance the multivariable processing ability and network adaptability of the BP network and has good prediction accuracy and stability.

Evaluation Algorithm of Ecological Energy-Saving Effect of Green Buildings Based on Gray Correlation Degree

The environmental protection attribute and energy-saving level of green buildings cannot be described by the traditional evaluation model. In order to solve the above problems, a new ecological energy-saving effect evaluation algorithm of green buildings based on gray correlation degree is designed. Based on the framework of building energy-saving index system, the environmental protection evaluation standards are divided and the results are used to screen the energy-saving indexes, so as to complete the establishment of green building ecological energy-saving index system and standards. Then, the evaluation set is established, and the evaluation scale of each layer of indicators is accurately located according to the weight value of each index. On this basis, the membership matrix is constructed. By calculating the index weight and determining the fuzzy synthesis operator, the rating process of the algorithm is improved and the analysis of the evaluation algorithm of environmental protection and energy conservation indicators of green building materials based on gray correlation degree is realized. The experimental results show that the designed algorithm has good stability of the fitting curve, can save energy, and has low cost.

Optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm

According to the characteristics of NC milling, an approach for optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm is proposed. Taking the carbon emission and processing time as the objectives, the cutting rate, feed per tooth, and cutting width as the optimization variables. A multi-objective optimization model of NC milling parameters is established. An non-dominated sorting gravity search algorithm (NSGSA) is used to solve the multi-objective model, and the position update backoff operation is introduced. Finally, taking NC machining process as an example, the multi-objective optimization results and the single objective optimization results are compared respectively, the actual data show that when the optimization objective is high efficiency and low carbon, the processing time and carbon emissions are 173 and 192 respectively. The comparison results show that the combination of processing parameters obtained by multi-objective optimization is the best, the optimal parameter combination obtained by NSGSA algorithm is verified by grey correlation analysis, and the grey correlation degree of the optimal solution set is 0.81, which is the largest in all solution sets. This approach can help the decision-makers flexibly select the corresponding milling parameters, and provide decision-makers with flexible selection decisions suitable for various scenarios.

Identification of low-voltage connection relation in distribution platform based on similarity of voltage curve and grey correlation degree of entropy weight

The correct low-voltage connection relationship in distribution area is of great significance to the safe operation and efficient management of power grid. As there are many reconstruction projects in the low-voltage platform area, the assets change frequently, and the interconnection perception ability of the low-voltage platform area is weak, which brings great difficulties to the identification of the user connection relationship. Traditional identification methods are heavy and inefficient. This paper proposes a method based on trend similarity and distance measure to identify low-voltage connection relation in distribution platform area. Firstly, Pearson correlation coefficient and discrete Fréchet distance are calculated to measure the trend similarity of voltage curve, and abnormal users are found out. GIS is used to search for adjacent stations, and finally, the correct station area of users is determined by analyzing the entropy weight grey correlation degree. The applicability and correctness of the proposed algorithm are verified by the application results of an example.

A Manufacturing SCOS Model (MSCOS) Based on the Similarity of Parameter Sequences Between Tasks and Service Composition

Service composition and optimal selection (SCOS) is a core issue in cloud manufacturing (CMfg) when integrating distributed manufacturing services for complex manufacturing tasks. Generally, a set of recommended task parameter sequences (Tps) will be given when publishing manufacturing tasks. The similarity between the service composition parameter sequence (SCps) and Tps also reflects the rationality of the service composition. However, various evaluation models based on QoS have been proposed, ignoring the rationality between the Tps and SCps. Considering the similarity of the Tps and SCps in an evaluation model, we propose a manufacturing SCOS framework called MSCOS. The framework includes two parts: an evaluation model and an algorithm for both optimization and selection. In the evaluation model, based on the numerical proximity and geometric similarity between the Tps and SCps, improving the technique for order preference by similarity to an ideal solution (TOPSIS) with the grey correlation degree (GC), we propose the GC&TOPSIS (GTOPSIS). In the optimization and selection algorithm, an improved flower pollination algorithm (IFPA) is proposed to achieve optimization and selection based on polyline characteristics between the fitness values in the population. Experiments show that the MSCOS evaluation effect and optimal selection offer better performance than commonly used algorithms.

Micro-hole texture prepared on PCD tool by nanosecond laser

Plenty of experimental studies have shown that the performance of the cutting tools can be improved through fabrication micro-texture on the rake surface. In this paper, a nanosecond laser with wavelength of 1064 nm is used to create micro-holes on the rake surface of polycrystalline diamond (PCD) tool, the influence of laser process parameters (laser power, frequency and defocusing amount) on the diameter, depth and recast layer area of micro-hole is investigated by single factor and multi-objective optimization experiments. The diameter, depth and recast layer area of micro-hole are converted into a single gray correlation degree as the detection index of micro-hole quality by the gray correlation method combined with the principal component analysis. The mathematical model between laser process parameters and gray correlation degree (GRG) is established based on the response surface method (RSM), and the adequacy of the mode for gray correlation degree is studied by ANVOA, and the optimum process parameters are obtained and verified. The results show that the average relative error of the gray correlation model is 2.67%. Ultimately, the required micro-holes are prepared by laser power of 20 W, pulse frequency of 90 kHz, and defocusing amount of-0.5468 μm.

Application of Adaptive Local Iterative Filtering and Permutation Entropy in Gear Fault Recognition

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system.

A Method for Analyzing Correlation Grades of Factors Influencing the Behavior of Bolted Connections Based on the Grey Correlation Degree

A Method for Analyzing Correlation Grades of Factors Influencing the Behavior of Bolted Connections Based on the Grey Correlation Degree

Sustainability Assessment of Autonomous Regions in China Using GRA-SPA Method

Sustainability development is a core issue in autonomous regions’ construction and development. The paper evaluated the sustainability development of the five autonomous regions in Western China from 2010 to 2019. In order to further analyze the sustainable development level of the autonomous regions, it is compared with the three provinces with the largest GDP in Central China in the past three years, and similarly, with the three provinces in Eastern China. A new weighting method was proposed by combining the grey relational analysis (GRA) and set pair analysis (SPA) methods that not only analyze the correlation between indicators and ideal points but also analyze the status and development trend. The method can ensure the objectivity of indicator weight. Firstly, the ideal reference point is determined by the grey correlation degree between the indicator and the ideal positive point. Secondly, the indicator and the ideal reference point constitute a set pair system, and the relation number is used further to analyze the status and development trend of the indicator to determine the weight objectively. The sustainability results showed that the progress of the autonomous regions’ sustainable development in China was increased slowly in 2010–2019. For example, Ningxia and Xinjiang saw the slowest growth. The prime reason is that economic sustainability has declined severely. Although Inner Mongolia presented the highest increasing trends, the growth rate value was 0.75%. In contrast, other autonomous regions showed a negative growth trend. Regarding sustainable development in three dimensions, the economic sustainability performance of autonomous regions is not ideal, but the environmental sustainability performance is the most ideal. This conclusion implicates the necessity and urgency of improving the coordinated development of the three dimensions of autonomous regions in China.

Correlation between Breteau index and mosquito oviposition index in surveillance for Aedes albopictus larvae density

Objective To observe the trend of continuous density of Aedes albopictus larvae by using Breteau Index (BI) and the Mosquito Oviposition Index (MOI), and compare the grey correlation, spearman correlation and risk grade consistency of the two density indexes in different habitats. Methods The densities of Aedes albopictus in Wuxing district (place A) and in Nanxun district (place B) in Huzhou were monitored every ten days by using BI and MOI from April to December in 2020. The correlation between BI and MOI were analyzed by grey correlation and spearman correlation. The two density indexes were classified according to the risk assessment criteria of Aedes vector surveillance guidelines. And the consistency of risk grade of two methods was compared by Kappa test. Results The density of Aedes albopictus indicated by BI and MOI increased first and then decreased from April to December. When the density was very low or very high (BI 20), the BI was higher than MOI. When the density index was between 5 and 20, the BI and MOI fluctuated. The BI was higher than the MOI from April to July. However, the MOI was higher than the BI from August to October. The grey correlation degree of the BI and the MOI were 0.601 and 0.682 in urban and rural residential areas respectively in place A and 0.696 and 0.671 in urban and rural residential areas respectively in place B, and the spearman correlation coefficients of the BI and the MOI were 0.461 and 1.000 in urban and rural residential areas in place A and 0.531 and 0.444 respectively in place B, the differences were all significant (P Conclusion The density of Aedes albopictus larvae increased and decreased seasonally. There was a certain correlation between BI and MOI in the surveillance for the seasonal density of Aaedes Aalbopictus larvae, but the consistency of risk level was not good. The sensitivity of BI is better than that of MOI when the density of Aedes is very low or high.

A joint grey relational analysis based state of health estimation for lithium ion batteries considering temperature effects

A reliable and efficient state-of-health (SOH) estimation is essential to prolong service life of lithium-ion batteries (LIBs), optimize power management strategies and reduce cost. In this paper, a joint grey relational analysis (JGRA) based SOH estimation method considering temperature effects is proposed to explore the degradation mechanism of LIBs at different temperature and a generic temperature regressive model is developed. JGRA is used to calculate joint grey correlation degree (JGCD) of the incremental capacity (IC) curves of aged batteries to fresh battery due to their geometric proximity. Experiments including life cycle tests and capacity tests are conducted on three lithium cobalt batteries at different temperatures to verify the performance of the proposed method. The results show that the proposed method can evaluate SOH of LIBs at different temperatures within 2.41% error bound.

Multi-attribute group decision-making method based on time-series q-rung orthopair fuzzy sets

The q-rung orthopair fuzzy set is a powerful and useful tool to deal with uncertainty, but in actual decision-making process, decision-makers are usually required to analyze the actual problem dynamically. Therefore in this paper, we consider the time-series q-rung orthopair fuzzy decision making. First, we introduce the new cosine similarity measure of q-ROFS which combines the cosine similarity measure and the Euclidean distance measure. Then, we combine the advantages of projection method and grey correlation degree, establishing the nonlinear programming model to calculate the weights of attributes. Furthermore, we use the exponential decay model to get the weights formulas of q-ROFS at different times. Then we replace the distance function with grey relational projection and extend TOPSIS method. Based on these, we propose a new MAGDM approach to deal with time-series q-rung orthopair fuzzy problem not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate effectiveness and feasibility of the new method.

In situ cadmium removal from paddy soils by a reusable remediation device and its health risk assessment in rice

Passivation is widely used in the remediation of cadmium (Cd) polluted soils. However, this technique could only reduce the bioavailability of soil Cd rather than remove Cd from the soils. Herein, it is crucial to develop an efficient in-situ technique to remove soil Cd, and further to uncover its effect and mechanism on crops and soil environment. In this study, a soil pot experiment was employed to investigate the effects of a well-designed reusable device on in situ Cd removal from the two paddy soils (red soil and brown soil), rice (Oryza sativa L) growth, and the associated health risk of Cd. The device contained three typical soil Cd amendments including cation exchange resin (CER), biochar (BC), and steel slag (SS). Each of the soil amendments was combined with slow-release fertilizer (SRF) or conventional compound fertilizers (CF). The results indicated that the device with CER, BC, and SS decreased soil Cd by 12.6%, 16.2%, and 19.8%, respectively. Thus, it reduced the rice Cd accumulation by 30.6%, 16.1%, and 29%, and increased the rice yield by 70.3%, 42.6%, and 55.4%, respectively. This could be stemmed from the improvement of photosynthesis and the reduction of oxidative damage, as well as the increase in the relative abundance of soil beneficial microbial community. Moreover, the grey correlation degrees and human health risk index (HRI) indicated that the in-situ removal device could diminish the health risk of Cd in rice. The findings reported in this work could provide a new solution to remediate heavy metal contaminated soils.

Optimization method of microwave drying process parameters for rice

In this work, the optimal process to improve the efficiency of microwave drying of rice is studied. The optimal conditions for reducing the crack rate and improving the head rice rate are analyzed according to the principle of the orthogonal experiment. The optimization method of the rice microwave drying process parameters is proposed to simultaneously consider the two indicators, namely, crack and head rice rates, based on the grey system theory. The grey correlation degree analysis is carried out on the process conditions of the microwave drying rice. The result shows that the main factor affecting the integrity rate of the rice grain is the vacuum degree, and the main factor affecting the crack rate is the loading density. The grey correlation degree curve of head rice and crack rate fluctuates greatly, and the correlation between the two indexes is high. According to the grey correlation degree, the optimal parameters are microwave power of 500 W, vacuum degree of 0.02 MPa, and loading density of 1289.89 kg/m3. The head rice and crack rate are both optimized when drying under the parameters. The results provide a guiding method for the study of the correlation degree between drying indexes and provide a method basis for the further selection of the best drying process.

Quality Evaluation of Different Varieties of Fresh-edible Waxy Corns Based on Entropy Weight Method and Grey Interconnect Degree Analysis

The quality characteristics of 17 fresh eating waxy corn varieties were studied based on their quality and structure characteristics. The former included the content of moisture, starch, amylose and amylopectin, total sugar, dietary fiber, crude protein and crude fat, while the latter included the value of hardness, elasticity, cohesion, adhesiveness and chewiness. In order to screen out the core evaluation index, correlation analysis and principal component analysis were applied in this process. The entropy weight method was to assign weight to each index. And the grey correlation degree method was to comprehensively evaluate the quality. The results showed that there were differences in quality of different fresh-edible waxy corns. The texture properties had a good correlation with amylose and amylopectin. Four core evaluation indicators of fresh waxy corn were determined by principal component analysis; amylopectin, total sugar, moisture and crude fat. The weight value of amylopectin was the highest by the entropy method. The varieties with better comprehensive quality were ‘Wannuo2000’, ‘Sukenuo1702’ and ‘Sukenuo1505’ with the grey correlation analysis. The results would provide theoretical support for the evaluation of comprehensive quality and screening of fresh waxy corn variety.

Research on Mathematical Model of Box Office Forecast through BP Neural Network and Big Data Technology

With the rapid development of China’s film industry, the public’s enthusiasm for watching films has growing, and the contribution rate of the film industry to the economy has increasing year by year. Based on the research at home and abroad, this paper takes the box office of China’s films as the dependent variable, selects a series of indicators of China’s film box office for grey correlation analysis, and selects seven related indicators as independent variables, including the number of fans of heroine, the number of fans of hero, the box office of the first week, the number of fans of director, Baidu Index, average ticket price and film type. The influencing factors calculated by grey correlation degree are taken as the input of BP neural network, and the box office of China’s films is taken as the output of the network to predict the box office of China’s films. The results show that the prediction model based on BP neural network is more accurate than the classical Multiple Linear Regression model. This model can predict to a certain extent, and the prediction effect is good. It can predict the overall trend of film box office and provide reference for investment enterprises.

Landslide risk assessment based on combination weighting-improved TOPSIS

Landslide risk assessment is of great significance to landslide disaster monitoring and prevention. A landslide hazard evaluation method based on combination weighting and improved TOPSIS method is proposed. TOPSIS based on grey correlation degree is applied to landslide hazard evaluation, and a new closeness degree is constructed to evaluate landslide hazard level. The subjective and objective weights of evaluation indexes are obtained by G1 method and entropy weight method, which improves the accuracy of TOPSIS evaluation results. The case analysis shows that the method has high accuracy, certain rationality and effectiveness, and can provide a reference for landslide disaster prevention.

Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

To simultaneously reduce automobile exhaust pollution to the environment and satisfy the demand for high-quality gasoline, the treatment of fluid catalytic cracking (FCC) gasoline is urgently needed to minimize octane number (RON) loss. We presented a new systematic method for determining an optimal operation scheme for minimising RON loss and operational risks. Firstly, many data were collected and preprocessed. Then, grey correlative degree analysis and Pearson correlation analysis were used to reduce the dimensionality, and the major variables with representativeness and independence were selected from the 367 variables. Then, the RON and sulfur (S) content were predicted by multiple nonlinear regression. A multi-objective nonlinear optimization model was established with the maximum reduction in RON loss and minimum operational risk as the objective function. Finally, the optimal operation scheme of the operating variable corresponding to the sample with a RON loss reduction greater than 30% in 325 samples was solved in Python.

Research on Grey Relational Clustering Model of Multiobjective Human Resources Based on Time Constraint

The existing grey relational clustering method has limitations in the application of multidimensional sequences and cannot directly calculate the grey correlation degree between unequal-length sequences. In this paper, by introducing the multidimensional dynamic DTW distance into the existing 3D grey relational model, a new grey relational analysis model that can be applied to multidimensional data is proposed, which is based on DTW distance. The model does not require one-to-one correspondence of data points but evaluates the similarity of its geometric curves by calculating the shortest distance between sequences. In addition, since the traditional grey correlation clustering method is implemented, the method first extracts the reference sequence from the observation sequence and then calculates the similarity between the observation objects by calculating the grey correlation degree between each sequence and the reference sequence, so each object only needs to be calculated once. The experimental results show that the multidimensional grey correlation degree based on DTW distance and the grey relational clustering model oriented to multidimensional data are more accurate than other existing methods. Finally, the grey relation clustering method of multidimensional data is used to analyze the multiobjective human resource grey relational clustering model under time constraints, and the validity of the model is verified.

Effects of potassium-solubilizing bacteria promoting the growth of Lycium barbarum seedlings under salt stress.

We investigated the effects of potassium-releasing bacteria on physiological and bioche-mical characteristics of Lycium barbarum (Cultivar Ningqi 1) under salt stress, with an experiment with treatments following randomized block design. The treatments included control (CK), 100 mmol·L-1 NaCl stress (NaCl), 100 mmol·L-1NaCl stress+KSBGY01 bacteria (NaCl-M1), 100 mmol·L-1NaCl stress+KSBGY02 bacteria (NaCl-M2), and 100 mmol·L-1NaCl stress+KSBGY01+KSBGY02 (NaCl-M3). We measued chlorophyll content, polyphenol content, superoxide anion (O2-·) content, hydrogen peroxide (H2O2) content, soluble sugar content, antioxidant enzyme activity and sucrose metabolic enzyme activity of Lycium barbarum seedlings. Results showed that the presence of potassium bacteria increased the values of flavonoids (FLAV), fluorescence excitation than anthocyanins relative index (FERARI), anthocyanins (ANTH-RB), nitrogen balance index (NBI-G), decreased the contents of O2-· and H2O2, and improved soluble sugar content, catalase (CAT) activity, sucrose phosphate synthase (SPS) activity, sucrose synthase (SS) activity and invertase (INV) activity of leaves in Lycium barbarum seedlings under salt stress. Among all the treatments, the highest values of ANTH-RB and NBI-G, soluble sugar content, and activities of CAT, SPS, SS, and INV presented in NaCl-M2 treatment, the highest values of SPAD, FLAV, and FERARI presented in NaCl-M3 treatment, the highest activity of superoxide dismutase (SOD) presented in NaCl-M1 treatment, the highest activity of glutathione peroxidase (GSH-Px) presented in NaCl treatment, and the highest peroxisome (POD) activity presented in CK. The 14 significant physiological and biochemical indicators in the leaves of L. barbarum seedling were analyzed by grey system correlation degree method. Our results suggested that the weighted correlation degree of phy-siological and biochemical indices of L. barbarum inoculated potassium-solubilizing bacteria was higher than that under CK and NaCl treatments. The highest weighted correlation was observed in NaCl-M2 treatment. Therefore, adding KSBGY02 potassium-solubilizing bacteria could alleviate the salt stress for L. barbarum seedlings.