Novel combinatorial algorithm for the problems of fuzzy grey multi-attribute group decision making

To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Empirical Analysis on the Relations between Rural Residents' Consumption and Economic Growth——A Case of Liaoning Province

Liaoning is a granary province with a large agricultural population and great market potential. Expanding rural residents' consumption becomes a necessity for enlarging domestic demand, solving three agriculture problems and promoting sustainable and rapid economic development. The research shows that since invigorating old industrial base in Liaoning, the contribution rate of rural residents' consumption to economic motivation is low and unstable, which has become one of the choke points for the development of economy. By using the grey correlation method, the influences of rural residents' consumption in different periods to GDP per capita are analyzed, the results show that the consumption level of rural residents were increasing, but their contribution rate on economic growth showed the descending tend. The residential expenses stay in the major position of consumption expenses; the education and entertainment products and service consumption play an important role; the growth of transportation and telecommunication is slow; the expenses on medical care are low and its contribution rate on economic growth is relatively weak. The countermeasures on developing rural economy, increasing rural residents' income, improving rural consumption environment, accelerating rural infrastructure construction, constructing and perfecting rural social security system and expanding rural consumption credit market are put forward to expand rural residents' consumption demand and realize the sustainable development of economy.

Relational Analysis of Yanbian's Primary Industry of Forestry and Forestry Industry

In this paper,we use grey relational analysis method to systematically analyze the relational degree between Yanbian's forestry industry and three industries of forestry,and conclude that Yanbian's primary industry of forestry shows a downward trend in the development of forestry industry,so it is necessary to transform the traditional primary industry of forestry for the better development.

Correlative Analysis of Yanbian’s Primary Forestry Industry and Sub-industries

By selecting three indicators(timber cultivating and planting,timber harvesting,forest products),this paper uses grey relational degree to analyze the correlation between Yanbian’s primary forestry industry and sub-industries. Results show that there is a decline in the relational degree of three indicators concerning Yanbian’s primary forestry industry,but the relational degree of timber harvesting is still high and the relational degree of forest products is slightly increased.

A Power Load Prediction by LSTM Model Based on the Double Attention Mechanism for Hospital Building

This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.

Reliability analysis on civil engineering project based on integrated adaptive simulation annealing and gray correlation method

Dynamic reliability is a very important issue in reliability research. The dynamic reliability analysis for the project is still in search of domestic and international research in the exploration stage. By now, dynamic reliability research mainly concentrates on the reliability assessment; the methods mainly include dynamic fault tree, extension of event sequence diagram and Monte Carlo simulation, and et al. The paper aims to research the dynamic reliability optimization. On the basis of analysis of the four quality influence factors in the construction engineering, a method based on gray correlation degree is employed to calculate the weights of factors affecting construction process quality. Then the weights are added into the reliability improvement feasible index （RIFI）. Furthermore, a novel nonlinear programming mathematic optimization model is established. In the Insight software environment, the Adaptive Simulated Annealing （ASA） algorithm is used to get a more accurate construction subsystem optimal reliability under different RIFI conditions. In addition, the relationship between construction quality and construction system reliability is analyzed, the proposed methods and detailed processing can offer a useful reference for improving the construction system quality level.