Grey Relational Degree Research Articles

Correlations between water quality and the structure and connectivity of the river network in the Southern Jiangsu Plain, Eastern China

Incorporating the structure and connectivity of the river network to seasonal variations and different land use patterns can help improve the understanding the complex relationship between water quality and environmental factors. The present study first employed the grey relational analysis (GRA) to examine any existing correlations between the water quality and the structure and connectivity of river networks in the Southern Jiangsu Plain in Eastern China. All grey relational degree results were greater than the distinguishing coefficient (ρ = 0.5), and their average value was 0.7551. The average grey relational degrees of the water quality parameters varied between 0.7389 and 0.7744, and those of the characteristic indicators of the river network ranged from 0.6874 to 0.8850. Seasonal variations and different land use patterns were then employed to further analyze these relationships. The average grey relational degrees in the urban, rural, and fringe regions were calculated to be 0.7231, 0.7530, and 0.7124 during the flood season, respectively, and 0.7331, 0.7432, and 0.7052 during the non-flood season. The results suggest strong correlations between the water quality and the structure and connectivity of the river network. The preponderance of the urban land weakened the original correlations more than that of the cultivated land, while the seasonal interactions of the cultivated and urban lands presented opposite. The GRA can be employed as an effective supplement for numerical modeling and statistical analysis of the incomplete data. In addition, the structure and connectivity of the river network should be taken in account to improve water quality.

Bidirectional Absolute GRA/GIA Model for Uncertain Systems: Application in Project Management

This paper extends the existing absolute grey relational analysis (Absolute GRA) model, also called the absolute degree grey incidence analysis (ADGIA) model. By working on the limitations of the existing Absolute GRA model, the proposed model, named Bidirectional Absolute GRA (BAGRA) model, is better equipped to handle uncertain systems being represented by uncertain data. It is suitable for simultaneous handling of both linear and nonlinear arrays of data with both consistent and inconsistent directions of motion. The model introduces a bidirectional grey relational degree that is a composite measure of both direction and strength of the relationship. The study also introduced two scales to test the appropriateness of collected data to measure the confidence level on the information extracted from that data. In the end, after the pilot testing, the model is being applied on an R&D project management case from a real world. The work is important both theoretically and practically, especially for the data analysts concerned with the uncertain relationships between different data arrays, in general, and for the grey systems analysts, in particular.

基于灰色关联度分析对合金收得率的研究

基于灰色关联度分析对合金收得率的研究

A combined model of dissolved oxygen prediction in the pond based on multiple-factor analysis and multi-scale feature extraction

As dissolved oxygen (DO) is an important indicator of water quality in aquaculture, an accurate prediction for DO can effectively improve quantity and quality of product. Accordingly, a novel hybrid dissolved oxygen prediction model, which combines the multiple-factor analysis and the multi-scale feature extraction, is proposed. Firstly, considering that dissolved oxygen is affected by complex factors, water temperature and pH are chosen as the most relevant environmental factors for dissolved oxygen, using grey relational degree method. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to decompose the dissolved oxygen, water temperature and pH data into several sub-sequences, respectively. Then, the sample entropy (SE) algorithm reconstructs the sub-sequences to obtain the trend component, random component and detail component. Lastly, regularized extreme learning machine (RELM), a currently effective and stable artificial intelligent (AI) tool, is applied to predict three components independently. The prediction models of random component, detail component and trend component are RELM1, RELM2 and RELM3 respectively. The dissolved oxygen, water temperature and pH of the random component forms the input layer of RELM1, and predicted value of dissolved oxygen in the random component is the output layer of RELM1. The input and output of RELM2 and RELM3 are similar to that of RELM1. Final prediction results are obtained by superimposing three components predicted values. One of the main features of the proposed approach is that it integrates the multiple-factor analysis and the multi-scale feature extraction using grey correlation analysis and EEMD. Its performance is compared with several outstanding algorithms. Results for experiment show that the proposed model has satisfactory performance and high precision.

An early warning model for vegetable pests based on multidimensional data

Based on the research of sensor networks, pest monitoring equipment and systematic research, we mainly studied the major pests of south China vegetables, such as Bemisia Tabaci, Beetles, Plutellaxy Lostella and Thrips Tabaci. Using the multi-sensor network system, we collected the multi-dimensional information of the number of pests, soil, environment, ecological climate and meteorological factors in real time, on which a multi-dimensional big data based vegetable pest early warning model was constructed. According to the number of pests, the model used k-means algorithm to classify the damage into four levels: Mild, Moderate, Moderately Severe, and Severe. The correlation coefficient and gray relational degree were used to find out the key factors between the number of pests and the multi-dimensional information such as the vegetable soil, environment, ecology climate and meteorology, and based on BP neural network model, the key impact factors were trained, we find out five key impact factors: Rainfall Volume, Carbon Dioxide Concentration, Soil Temperature, Air Temperature and Foliar Humidity from June 2016 to February 2017. Finally, the selected characteristic data were normalized and then learned by BP Neural Network. The results showed that the recognition rate of the pests in southern vegetables was 96.7%. The algorithm is proved to be of high availability, meets the needs of early warning of pests and has a broad application prospect.

The effects of rock fragment content on the erosion processes of spoil heaps: a laboratory scouring experiment with two soils

Spoil heaps on newly engineered landforms create extensive artificially accelerated erosion, especially when there are catchment areas above spoil heaps, erosion caused by runoff will be much greater than that induced by rainfall. This study investigated the erosional characteristics of clay loam and sandy loam spoil heaps and proposed an appropriate hydraulic parameter to simulate the variation in erosion rate. A laboratory scouring experiment was conducted using a soil pan (dimensions 5 m × 1 m × 0.5 m deep) with a discharging arrangement to test four samples of clay loam and sandy loam containing rock fragments (0%, 10%, 20%, and 30%) by mass. The slope of simulated spoil heaps was 53.2% with a discharging inflow rate of 15 L min−1. The rock fragments used were those commonly used in construction works, having a diameter of 2–3 cm and irregular shape. Twenty-four scouring tests for eight treatments with duplication were accomplished in total. Average erosion rates showed a negative linear correlation with rock fragment content in clay spoil heaps (R2 = 0.94) and a positive linear correlation in sandy loam spoil heaps (R2 = 0.92). Rill width evolution of clay loam spoil heaps mainly developed at the early scouring stage (0–15 min), and rills developed even more rapidly during later scouring times (30–60 min) in sandy loam spoil heaps. Grey relational analysis showed that sheer stress and stream power both had higher Grey relational degrees with erosion rate for both soils, regression analysis showed that stream power can efficiently describe the erosional process of clay loam and sandy loam for each rock fragment content, but sheer stress only did well in sandy loam heaps. Adding rock fragments to spoil heaps resulted in significantly opposite effects in the different soils; great attention should be paid to sandy loam spoil heaps due to their more severe erosion with increasing rock fragment content; stream power is an appropriate hydraulic parameter to simulate the soil erosion process of spoil heaps for both soil types.

A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting

Wind speed forecasting is fundamental to the dispatching, controllability, and stability of the power grid. As a challenging but essential work, wind speed forecasting has attracted significant attention from researchers and managers. However, traditional forecasting models sometimes fail to capture data features owing to the randomness and intermittency of wind speed, and the models always focus only on improving accuracy, which is one-sided. Motivated by these problems, in this study, a novel hybrid forecasting system consisting of three modules (a data preprocessing module, optimization module, and forecasting module) is developed to improve the forecasting accuracy and stability. In the data preprocessing module, an effective denoising technique is used to produce a smoother sequence. A fuzzy time series method optimized by a multi-objective differential evolution algorithm that balances the conflict between forecasting accuracy and stability is developed in the next two modules to perform the forecasting. Several experimental results covering different models and wind-speed time intervals all indicate that our proposed hybrid forecasting system can achieve both satisfactory accuracy and stability with a mean absolute percentage error of below 4% in wind speed forecasting. Moreover, the statistical hypothesis test, forecasting effectiveness and grey relational degree are discussed which also demonstrate the great performance of proposed system.

State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis

An accurate battery state-of-health (SOH) monitoring is crucial to guarantee safe and reliable operation of electric vehicles (EVs). In this paper, an incremental capacity analysis (ICA) method for battery SOH estimation is proposed. This uses grey relational analysis in combination with the entropy weight method. First, an interpolation method is employed to obtain incremental capacity (IC) curves. The health indexes are then extracted from the partial IC curves for grey relational analysis, and the entropy weight method is used to evaluate the significance of each health index. The battery SOH is assessed by calculating the grey relational degree between the reference and comparative sequences. Experimental tests are conducted on two battery cells with the same specifications to verify the efficacy of the proposed method. The results show that the maximum estimation error is limited to within 4%, thus proving its effectiveness.

Research on the Evaluation Method of Green Construction Project Based on Grey Entropy Correlation

This paper proposes a bid evaluation method based on grey entropy correlation degree for green construction projects. The first, the evaluation index system of a green construction project is constructed based on the technical requirements of a green construction project. The grey entropy method is also used to eliminate the influence of subjective empowerment on the evaluation result. This removal is intended to build the evaluation model based on the grey relational degree of entropy weight, and select the winning bidder by calculating the correlation degree of each bidder and the ideal scheme. Finally, an engineering example shows this method can be applied to the process of evaluating bids of green construction projects. This method can help the tenderer select the winner of the green construction project more objectively and reasonably.

Evaluation Model of Building Materials Suppliers Considering Carbon Emissions

In the study of AHP and grey relational degree model, this paper establishes four secondary indicators such as product competitiveness, enterprise performance, enterprise cooperation ability, enterprise environmental assessment and twelve tertiary indicators. Then the weight index system is determined by using the analytic hierarchy process, and the warrants of environmental assessment are added considering carbon emissions and other factors. Last the grey correlation analysis is used to determine the supplier correlation. In this paper, the mathematical model is used to carry out an example analysis, so as to evaluate suppliers, and then select low-carbon, economic, high-quality suppliers.

NEW HYBRID FMADM MODEL FOR MOBILE COMMERCE IMPROVEMENT

Internet of things (IoT) can provide an extensive scope of services via smart devices to promote the convenience of life. With advances being made in smart phones, enterprises are increasingly considering expanding their customer base through mobile commerce services. To promote m-commerce improvement, enterprises should organize an excellent m-commerce environment and attempt to realize user needs in the era of IoT. In a fuzzy environment of the real world, objective decision-making for m-commerce improvement is usually a FMADM problem involving feedback-effect and interdependence among the dimensions and criteria. But, many traditional decision models cannot conduct the complicated interrelationships among dimensions and criteria. This study proposes an improvement model that can promote m-commerce improvement towards achieving the aspiration level in fuzzy environment. The proposed hybrid model conducts the feedback-effect and dependence among attributes, and it combines the FDEMATEL technique, FDANP, and MFGRA methods. The empirical case study was conducted to prove the utility of the new hybrid FMADM model in evaluating an m-commerce environment. Comparative results exhibited that the proposed approach is superior to the traditional method and that it can obtain most real grey relational degree that can be used for establishing the best performance improvement strategy in reality.

Soil pH value grey relation estimation model based on hyper-spectral

PurposeThe purpose of this paper is to establish the grey relational estimating model of soil pH value based on hyper-spectral data.Design/methodology/approachAs to the uncertainty of the factors affecting the soil pH value estimation based on hyper-spectral, the grey weighted relation estimation model was set up according to the grey system theory. Then the linear regression correction model is established according to the difference and grey relation degree information between the estimated samples and their corresponding pattern. At the same time, the model was applied to Hengshan county of Shanxi province.FindingsThe results are convincing: not only that the linear regression correction model of grey relation estimating pattern of soil pH value based on hyper-spectral data is valid, but also the model’s estimating accuracy is higher, which the corrected average relative error is 0.2578 per cent, and the decision coefficient R2=0.9876.Practical implicationsThe method proposed in the paper can be used at soil pH value hyper-spectral inversion and even for other similar forecast problem.Originality/valueThe paper succeeds in realising both the soil pH value hyper-spectral grey relation estimating pattern based on the grey relational theory and the correction model of the estimating pattern by using the linear regression.

Research on Evaluation of Core Competence of Listed Liquor Companies Based on Grey Relational Degree

Abstract The Grey Relational Degree method was applied to evaluating the core competence of the ten listed liquor companies. The study found that: the most critical factor affecting the strength of the core competence of listed liquor companies is the technological innovation capability; in general, the core competence of the ten listed liquor companies is weak, with an average Grey Relational Degree of 0.752, indicating more potentials for improvement; in addition, with the exception of Kweichou Moutai, the other nine listed liquor companies are all in an uncoordinated state regarding the development of the six capabilities, and they usually have one or several capabilities lagged behind.

Multi-objective optimisation and fast decision-making method for working fluid selection in organic Rankine cycle with low-temperature waste heat source in industry

In China, the utilisation of low-temperature waste heat (especially at temperatures lower than 100 °C) plays a significant role in increasing the energy-consumption efficiency in the industry. The organic Rankine cycle (ORC) is considered as a promising method to recover the aforementioned part of the waste heat. In the study, six potential candidates, namely R141b, R142b, R245ca, R245fa, R600a, and R601a were screened from 12 dry or adiabatic organic working fluids based on their thermodynamic performances in the ORC. A multi-objective optimisation (MOO) was performed for the thermodynamic performance (exergy efficiency, EXE) and economic performance (levelised energy cost, LEC) by using non-dominated sorting genetic algorithm-II (NSGA-II). The Pareto frontiers were obtained for the six candidates with the algorithm, and each optimal compromise solution was accurately obtained with the fuzzy set theory. Based on the EXE and LEC of the optimal compromise solution, the total cost and power generation efficiency for the six candidates were determined. This was used to obtain an explicit evaluation index in economic performance, namely static investment payback period (SIPP), to identify that the R245ca corresponded to the most cost-effective working fluid with the shortest SIPP. This suggests R245ca was the fastest to cover the investment and cost of the ORC system. Furthermore, a fast decision-making method was introduced to select the optimal working fluid based on the grey relational analysis (GRA) by considering key physical property parameters of the working fluids. The results suggest that any potential working fluid to recover low-temperature waste heat in the ORC can be evaluated by the simplified grey relational degree (SGRD) proposed in the study.

Method of Radar Power Control under Search State

To improve radar radio frequency stealth performance, a method of radar power control in search condition is proposed. Taking constraint condition of search frame period and detecting probability into consideration, target function of lowest radiation energy based on grey relational degree is set up, which combines detecting performance and radiation energy. An improved particle swarm optimization (IPSO) algorithm, which adjusts weight self-adaptively, changes learning factors asynchronously and introduces compensating particle to search the blind area, is proposed to solve the target function. The simulation shows that the proposed method can realize lower radiation energy on the premise of ensuring the detecting probability. In addition, it has better performance of radio frequency stealth.

Evaluating Water Consumption Based on Water Hierarchy Structure for Sustainable Development Using Grey Relational Analysis: Case Study in Chongqing, China

With the economic development, the demand for water resources has been increasing dramatically during the last several decades. The sustainable development of water resources has become a major challenge in our society. As the largest economic center in west China, Chongqing was chosen as a typical unit to investigate this issue by using statistical data of fifteen years. In this study, the complexity of the water resource system was simplified through hierarchical structure analysis. Then, grey relational analysis was used to measure hierarchical correlation degree. The correlation between the levels of water consumption was analyzed, especially between water consumption and socio-economic indicators. Based on the result of hierarchical grey evaluation, three conclusions were drawn: (1) from the water consumption-oriented aspect, the correlation rankings, from high to low, are production water use, domestic water use, and eco-environmental water use respectively; (2) from industrial structure aspect, the secondary industry has the highest grey relational degree, which is followed by the primary industry (agriculture); and (3) from the economic and social indicators aspect, many significant factors are highly related to water consumption, such as precipitation, urbanization rate, total population, GDP, the proportion of output value of the three industries and residential water price. In this paper, to achieve the goal of the strictest system of water resource management during the 13th Five-Year Period, the corresponding policy suggestions are proposed for the municipal government of Chongqing.

Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm), is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product), industrial structure (the proportion of the second industry added value), energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm) model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an empirical analysis for energy consumption prediction. Four models, ISFLA-LSSVM, SFLA-LSSVM (Least Squares Support Vector Machine Optimized by Shuffled Frog Leaping Algorithm), LSSVM (Least Squares Support Vector Machine), and BP(Back Propagation) neural network (Back Propagation neural network), are selected to compare with the EEMD-ISFLA-LSSVM model based on the evaluation indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), which fully prove the practicability of the EEMD-ISFLA-LSSVM model for energy consumption forecasting in China. Finally, the EEMD-ISFLA-LSSVM model is adopted to forecast the energy consumption in China from 2018 to 2022, and, according to the forecasting results, it can be seen that China’s energy consumption from 2018 to 2022 will have a trend of significant growth.

Interval-valued Pythagorean fuzzy GRA method for multiple-attribute decision making with incomplete weight information

In this paper, the concept of multiple-attribute group decision-making (MAGDM) problems with interval-valued Pythagorean fuzzy information is developed, in which the attribute values are interval-valued Pythagorean fuzzy numbers and the information about the attribute weight is incomplete. Since the concept of interval-valued Pythagorean fuzzy sets is the generalization of interval-valued intuitionistic fuzzy set. Thus, due the this motivation in this paper, the concept of interval-valued Pythagorean fuzzy Choquet integral average (IVPFCIA) operator is introduced by generalizing the concept of interval-valued intuitionistic fuzzy Choquet integral average operator. To illustrate the developed operator, a numerical example is also investigated. Extended the concept of traditional GRA method, a new extension of GRA method based on interval-valued Pythagorean fuzzy information is introduced. First, utilize IVPFCIA operator to aggregate all the interval-valued Pythagorean fuzzy decision matrices. Then, an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method is established, to get the weight vector of the attributes. Based on the traditional GRA method, calculation steps for solving interval-valued Pythagorean fuzzy MAGDM problems with incompletely known weight information are given. The degree of grey relation between every alternative and positive-ideal solution and negative-ideal solution is calculated. To determine the ranking order of all alternatives, a relative relational degree is defined by calculating the degree of grey relation to both the positive-ideal solution and negative ideal solution simultaneously. Finally, to illustrate the developed approach a numerical example is to demonstrate its practicality and effectiveness.

Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

Comprehensive Evaluation of Coal-Fired Power Units Using Grey Relational Analysis and a Hybrid Entropy-Based Weighting Method.

In recent years, coal-fired power plants contribute the biggest part of power generation in China. Challenges of energy conservation and emission reduction of the coal-fired power plant encountering with a rapid growth due to the rising proportion of renewable energy generation in total power generation. Energy saving power generation dispatch (ESPGD) based on power units sorting technology is a promising approach to meet the challenge. Therefore, it is crucial to establish a reasonable and feasible multi-index comprehensive evaluation (MICE) framework for assessing the performance of coal-fired power units accessed by the power grid. In this paper, a hierarchical multiple criteria evaluation system was established. Except for the typical economic and environmental indices, the evaluation system considering operational flexibility and power quality indices either. A hybrid comprehensive evaluation model was proposed to assess the unit operational performance. The model is an integration of grey relational analysis (GRA) with analytic hierarchy process (AHP) and a novel entropy-based method (abbreviate as BECC) which integrates bootstrap method and correlation coefficient (CC) into entropy principle to get the objective weight of indices. Then a case study on seven typical 600 megawatts coal-fired power units was carried out to illustrate the proposed evaluation model, and a weight sensitivity analysis was developed in addition. The results of the case study shows that unit 4 has the power generating priority over the rest ones, and unit 2 ranks last, with the lowest grey relational degree. The weight sensitivity analysis shows that the environmental factor has the biggest sensitivity coefficient. And the validation analysis of the developed BECC weight method shows that it is feasible for the MICE model, and it is stable with an ignorable uncertainty caused by the stochastic factor in the bootstrapping process. The elaborate analysis of the result reveals that it is feasible to rank power units with the proposed evaluation model. Furthermore, it is beneficial to synthesize the updated multiple criteria in optimizing the power generating priority of coal-fired power units.