The Journal of Grey System

Ordinal Multivariate Grey Incidence Model and Its Application on Early Warning of Construction Quality Risk

Ke Zhang, Min Ma, Feizhen Zhang, Yuxin Zhou, Chunyong She, Zheng Zhang

2024, 36(3): 1-10.

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Government supervision is the highest level of construction quality management system. Due to a large number of constructions in progress, timely and accurate risk early warning is imperative for improving the efficiency of supervision. Aiming at the small-scale, ordinal, and unequal length multivariate time series of government supervision data, this paper proposes a construction quality risk early warning method based on ordinal multivariate grey incidence analysis. Firstly, to measure the dynamic similarity between risk indicators of projects, the proximity grey incidence model based on ordinal dynamic time warping (DTW) and the similarity grey incidence model based on ordinal L1 norm DTW are constructed respectively. Then, the two models are integrated to construct a comprehensive similarity model for construction quality risk warning. Combining the comprehensive similarity and k-nearest neighbour (k-NN) algorithm, a method of construction quality risk level classification and early warning is constructed. Finally, the method is applied to the quality supervision of water conservancy and hydropower projects in Zhejiang Province, and the results show that the proposed method can effectively solve the problem of construction quality risk early warning based on small-scale and ordinal data.

Multi-steps Carbon Emission Forecasts Using a Novel Grey Multivariable Convolution Model

Song Ding, Juntao Ye, Zhijian Cai, Xing'ao Shen, Huahan Zhang

2024, 36(3): 11-24.

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The accurate forecasting of provincial carbon emissions is pivotal for China as it strives to meet its carbon neutrality goals. To this end, an improved grey multivariable convolution model has been developed, employing a unified new-information-based method for the preliminary accumulation of data. The particle swarm optimization (PSO) algorithm is then applied to determine the optimal parameters within this sophisticated model. Moreover, to identify the relevant factors for provincial carbon emissions, a comprehensive determination of these factors was conducted from two aspects: literature research and grey relational analysis. For validation, carbon emission data from two provinces are analyzed, and the model’s efficacy is thoroughly compared with five competitors across three different predictive horizons. The empirical results indicate that the proposed model has distinct advantages over the competing models. Additionally, the model’s robustness and comprehensive forecasting abilities for provincial carbon emissions are confirmed through detailed Monte Carlo simulations and parameter sensitivity analyses across various forecasting horizons.

Some Properties of Generalized Whiteness of Interval Grey Number

Li Li, Xican Li

2024, 36(3): 25-36.

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In order to mine the intrinsic information of interval grey number, the concept of the generalized whiteness of interval grey number is first given in this paper based on the generalized greyness of interval grey number. Then the static and dynamic properties of generalized whiteness on bounded background domain, infinite background domain and infinitesimal background domain are analyzed, and the concepts of the extreme white system and extreme white spot are given. Finally, the conservation law of the generalized whiteness of interval grey numbers is given, and the generalized whiteness is applied to the ranking of interval grey numbers. The results show that the generalized whiteness of interval grey number on the bounded background domain has the static properties of the relativity, normality, unity, opposition, connectivity, justice and graduality; while on the infinite background domain and the infinitesimal background domain, the generalized whiteness of interval grey number has the above static properties except the graduality. On the bounded background domain, the generalized whiteness changes with the expansion of the background domain and the value domain, while on the infinite background domain and the infinitesimal background domain, the static and dynamic properties of the generalized whiteness of interval grey number are the same, it is not affected by the expansion change of the background domain and value domain, and the generalized whiteness of interval grey numbers is conserved. The research results not only enrich the grey system theory, but also provide a theoretical basis for the analysis and utilization of interval grey numbers.

Grey Generalized Stochastic Petri-Bayesian Network Testability Model for High-reliability Complex Systems

Cuiping Niu, Zhigeng Fang, Shuyu Xiao, Youpeng Liu

2024, 36(3): 37-50.

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Aiming at the issues of paucity of fault information, complexity of functional logic relationships between fault modes, and uncertainty of fault information and its propagation path in the testability analysis of high-reliability complex systems, a grey generalized stochastic Petri-Bayesian network (Grey-GSPBN) testability model is proposed in this study. Firstly, typical failure modes and their severity are obtained through the failure modes, effects and analysis (FMECA) study, and the failure modes are coded and coloured accordingly to construct the generalized stochastic Petri network (GSPN) model. Then, the correlation matrix between failure modes and test points is established by using the reachability algorithm, based on which the equivalent isomorphic grey Bayesian network (GBN) model is established, and grey number theory is introduced to integrate multi-source grey information to determine the grey prior and posterior distribution matrix of testability indexes. Finally, the grey probabilistic testability evaluation matrix is calculated using GreyGSPBN model, and the testability indicators are analyzed. A certain liquid rocket engine system is taken as a case to verify the scientificity and superiority of the proposed model in the testability modelling of high-reliability complex systems, and the model can provide a valuable reference for engineering applications

Exploring Death Population Prediction and Cemetery Planning in Chongqing amid an Aging Population: A Grey Forecast Model Based on Interval Grey Number

Huaan Wu, Yuhua Jin, Yinhe Xue, Bo Zeng, Hui Wang

2024, 36(3): 51-62.

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China’s population is steadily aging, contributing to the increase in the number of deceased people and the growing disparity between supply and demand for cemeteries. To provide theoretical support and data reference for cemetery planning, this study considers Chongqing, a city with a high rate of aging population, as an example to apply the interval grey number model to model and predict the size of the death population in Chongqing. The following conclusions are drawn: (1) The accuracy of the interval grey number prediction model in simulating the size of the death population in Chongqing exceeds 98%, indicating that the model employed in the study is suitable for medium- to long-term prediction; (2) The prediction results show that the annual death scale of registered population in Chongqing will range between 220 and 330 thousand from 2022 to 2030, with a fluctuating upward trend; (3) According to the size of the death population predicted, the cemetery market in Chongqing will experience a shortage of supply within 10 years. Therefore, in order to ensure a balance between the supply and demand of cemeteries in Chongqing, the government should actively promote the concept of green funerals and reduce the demand for cemeteries. Alternatively, it is also necessary to accelerate the planning and construction of cemeteries to avoid the predicament of people wanting to be buried without a tomb.

Risk-transmission Mechanism of Industry Chain under a Multi-parameter Grey-GERT Network

Lan Xu, Yingying Shang

2024, 36(3): 63-73.

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Aiming at the risk of local obstruction or rupture in the operation process of the industry chain, a Grey-GERT network model of industry chain risk transmission is constructed based on the effect of input resources of each link of the industry chain, and the key links and their degree of risk in the process of industry chain network value transmission are identified and analysed to reveal the risk transmission mechanism of the industry chain. Finally, an empirical study is conducted on China’s integrated circuit industry chain to verify the feasibility and effectiveness of the proposed model and to propose targeted control measures for the key links and their value transmission risks. The results show that the proposed model can effectively solve the problem of incomplete information on multiple transmission parameters in industry chain network activities, thoroughly analyse the risk transmission mechanism of the industry chain, and provide theoretical support for strengthening the risk control of the industry chain.

A New Optimized Grey Forecasting Model with Polynomial Term and Its Application

An Wang, Yaoguo Dang, Junjie Wang

2024, 36(3): 74-85.

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China’s total energy consumption and production rank first in the world. However, China’s energy structure is not reasonable. Therefore, accurate prediction of future energy trends is of great significance for the Chinese government to adjust the energy structure. In this paper, we propose an optimized Grey Euler model with polynomial term, which is abbreviated as OSGEM(1,1,N), to forecast the total energy consumption and production of China in comparison with the commonly used prediction models. The data from 2002 to 2018 are used to simulate the parameters in the proposed model, and the data from 2019 to 2021 are used to test the improved approach. The results show that the OSGEM(1,1,N) model outperforms the other models. Finally, the OSGEM(1,1,N) model is used to forecast the total energy consumption of China from 2022 to 2025 and different results from the previous research results have been obtained.

Predicting Solar Array Power Output on a Spacecraft Using a Fractional-Order Grey Model and Particle Swarm Optimization

Liang Ren, Yuanhe Gao, Feng Yang, Yongcong He

2024, 36(3): 86-97.

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During eclipse periods, the spacecraft relies on electricity, which its solar arrays produce and store in batteries. Forecasting a solar array’s power output employed during space missions is of significant importance. Varying space environments and satellite loads, which are characterized by significant randomness and uncertainty, affect the generated power of the spacecraft’s solar array. These challenges pose difficulties in power prediction. To address these issues and achieve a more accurate estimation of the solar array’s generated power during a space mission, this study develops a metabolic model termed TDGM(1, 1, r) that incorporates an enhanced accumulating fractional-order, optimizing it within the discrete grey TDGM(1,1) model’s framework with three parameters. The optimization model’s objective function is defined as the mean absolute percentage error (MAPE) within the modeling context. In order to minimize MAPE, the differential equation’s order and accumulation number are determined using a particle swarm approach. The TDGM(1, 1, r) demonstrates superior forecasting performance in comparison to the classical GM(1,1) and Grey–Markov models. These findings indi-cate the superiority of TDGM(1,1,r) over GM(1,1) and Grey–Markov, with improvements of 84.2% and 81.2% for MAPE (from 1.83% to 0.29% and from 1.54% to 0.29%). The metabolic TDGM(1,1,r) employing the particle swarm algorithm (PSO) is better suited for short-term predictions. Finally, relevant suggestions for future development of the prediction model are proposed.

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