Optimization of Tribological Performance of Al-6061T6-15% SiCp-15% Al₂O₃Hybrid Metal Matrix Composites Using Taguchi Method &Grey Relational Analysis

In this investigation, optimization of tribological performance parameters of Al-6061T6 alloy reinforced with SiC (15% by weight) and Al2O3 (15% by weight) particulates having particle size of 37 μm each has been presented. The wear and frictional properties of the hybrid metal matrix composites have been studied by performing dry sliding wear test using pin-on-disc wear tester. A L27 orthogonal array is selected for the analysis of the data. From the test results it is observed that sliding distance has the significant contribution in controlling the friction and wear behaviour of hybrid composites. A confirmation test is also carried out to verify the accuracy of the results obtained through the optimization. In addition an optical micrograph test is also performed on the wear tracks to study the wear mechanism.

The Evaluation on Ecological Agricultural Development in Sichuan Province on the Basis of Grey Relational Analysis

By using the grey relational model,14 indexes of ecological agricultural development in Sichuan Province from 1997 to 2008 are analyzed.According to the relational degree results,the ecological agricultural development in 12 years can be divided into three stages:the first stage is from 1997 to 2000,with the shape of inverted U;the second stage is from 2001 to 2004,belonging to transition stage with slow growth rate;the third stage is from 2005 to 2008,belonging to the stage of rapid growth.It draws conclusion that per capita grain yield is an important element for evaluating ecological agricultural construction.Besides,reducing the energy consumption of per unit production,improving labor productivity and increasing agricultural science and technology application and input on agricultural infrastructure construction are the engine for developing Sichuan ecological agriculture.

Residential Electricity Consumption Behavior Mining Based on System Cluster and Grey Relational Degree

In order to improve the utilization of the residential electricity consumption data which contains the information on the user’s electricity consumption habits, a residential electricity consumption behaviors mining algorithm model is constructed. Firstly, according to the attribute, the collected data can be divided into the global data and the phase data, then the appropriate global variables are selected to mine the user’s electricity consumption patterns in the near future on the system clustering algorithm. Based on the theory of grey relational analysis, combing phase data with the power modes to analyze the potential characteristics of residential electricity consumption behaviors deeply that verify the ability of latest power mode to predict household electricity consumption situation in the coming few days and the effect of dominant phase variables on the peak load shifting. Finally, from the actual data of a certain family, the proposed data mining algorithm is testified that it can effectively explore the electricity consumption behavior habits and characteristics of the family.

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.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Grey information coverage interaction relational decision making and its application

This study aims to reflect the information coverage grey number and the interaction between attributes in grey relational decision making. Therefore, a multi-attribute decision method based on the grey information coverage interaction relational degree(GIRD) is proposed. Firstly, this paper defines the information coverage grey number, and establishes the GIRD model by using the Choquet fuzzy integral and grey relational principle. It proves that the proposed model not only is the general and unified form of the point relational degree, interval relational degree, mixed relational degree and grey fuzzy integral relational degree, but also can effectively deal with the interaction between attributes. Further,a decision making example of evaluating the industrial operation quality for 14 cities in Hunan province of China is provided to highlight the implementation, availability, and feasibility of the proposed decision 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.

Study on Complex System Evaluation Method Based on PCA and Grey Relative Degree

The complexity and limited data samples in evaluation of complex system are obstacles for traditional evaluation method. Based on Grey relative degree theory and principal components analysis (PCA) method, a novel systemic evaluation method is put forward in this paper. Firstly, standardization method is modified for evaluation objective, and the method includes expression (2) and (3). Secondly, for few schemes of complex system, grey relative degree, expression (5), is substituted for relation coefficient. At last, validity of the method is verified by evaluating 3 schemes of a type of UUV.

Model calibration concerning risk coefficients of driving safety field model

Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.

Design of in-network caching scheme in CCN based on grey relational analysis

In-network caching is one of the most important issues in content centric networking （CCN）, which may extremely influence the performance of the caching system. Although much work has been done for in-network caching scheme design in CCN, most of them have not addressed the multiple network attribute parameters jointly during caching algorithm design. Hence, to fill this gap, a new in-network caching based on grey relational analysis （GRA） is proposed. The authors firstly define two newly metric parameters named request influence degree （RID） and cache replacement rate, respectively. The RID indicates the importance of one node along the content delivery path from the view of the interest packets arriving The cache replacement rate is used to denote the caching load of the node. Then combining hops a request traveling from the users and the node traffic, four network attribute parameters are considered during the in-network caching algorithm design. Based on these four network parameters, a GRA based in-network caching algorithm is proposed, which can significantly improve the performance of CCN. Finally, extensive simulation based on ndnSIM is demonstrated that the GRA-based caching scheme can achieve the lower load in the source server and the less average hops than the existing the betweeness （Betw） scheme and the ALWAYS scheme.

Complexity estimation of image sequence for automatic target track

In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches about the complexity of image sequence.To solve this problem,a criterion of evaluating image sequence complexity is proposed.Firstly,to characterize this criterion quantitatively,two metrics for measuring the complexity of image sequence,namely feature space similarity degree of global background(FSSDGB)and feature space occultation degree of local background(FSODLB)are developed.Here,FSSDGB reflects the ability of global background to introduce false alarms based on feature space,and FSODLB represents the difference between target and local background based on feature space.Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally,the image sequence complexity is not a linear sum of the single-frame image complexity.Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well.The nonlinear transformation based on median(NTM)is proposed to construct complexity of image sequence.The experimental results show that the proposed metric is more valid than other metrics,such as sequence correlation(SC)and interframe change degree(IFCD),and it is highly relevant to the actual performance of automatic target tracking algorithms.

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.

Optimizing soil dissolved organic matter extraction by grey relational analysis

Dissolved organic matter(DOM)in soil plays an important role in the fate and transport o f contaminants.It is typically composed of many compounds,but the effect of different extraction factors on the abundance of different DOM components is unknown.In this study,DOM was extracted from three soils(paddy field,vegetable field and forest soils)with various extraction time,liquid to solid ratios(LSRs).extractant types,and extractant concentrations.The LSR had a significant effect on DOM content,which increased by 0.5-4.0 times among the three soils when LSR increased from 2:1 to 10:1(P<0.05).Dissolved organic matter content increased by 4%-53%when extraction time increased from 10 to 300 min(P<0.05).Extractant concentration had different effects on DOM content depending on the extractant.Higher concentrations of KC1 promoted DOM extraction,while higher concentrations o f KH2PO4 inhibited DOM extraction.Therefore,grey relational analysis was used to further quantitatively evaluate the effect of extraction time,LSR,and extractant concentration on DOM,using KC1 as an extractant.For the paddy field and forest soils,the impact of these three factors on DOM extraction efficiency was in the following order:KC1 concentration>LSR>extraction time.However,the effect was different for the vegetable field soil:LSR>extraction time>KCI concentration.Taking all these factors into account,1.50 mol L^-1 KC1 and an LSR of 10:1 with a shaking time of 300 min was recommended as the most appropriate method for soil DOM extraction.

Analysis of synergy degree and its influencing factors in hydropower EPC project management

The use of engineering procurement construction (EPC) mode is currently a trend in hydropower engineering construction. The clarification of the internal relationship between hydropower EPC projects and the realization of synergy has great significance in improving management efficiency and implementation effect. In this work, a three-dimensional system and a system model of hydropower EPC project management synergy are constructed. The mechanism and factors that influence the degree of management synergy are analyzed on the basis of management synergy theory. Furthermore, the evaluation index system and the degree of synergy model are established, and grey relational analysis is utilized to identify the key factors that affect the synergy degree. Thus, this study aims to facilitate the hydropower EPC project management synergy, provide a quantitative method for synergy degree evaluation, and propose corresponding promotion strategies. Results show that the order degree of each subsystem presents a steady upward trend. Specifically, the order degree of the subsystem at the trial operation stage is low, which is the major restriction on the further improvement of the synergy degree of EPC project management. The key factors in improving the synergy level of hydropower EPC project management are mainly concentrated in the information and organization synergy subsystems, including the construction degree of information platform, the performance of functions, the timeliness of information transfer, and the functions of the information platform.

基于核和灰度的区间灰数的序关系

首先,根据区间灰数的核的概念,在同核区间灰数定义的基础上,讨论了基于核的区间灰数的等价关系和大小关系,并给出了一系列相关结论。其次,根据区间灰数的灰度的概念,在同灰度区间灰数定义的基础上,讨论了基于灰度区间灰数的等价关系和大小关系,也得到了一系列相关结论。最后,在同时考虑核和灰度的基础上,讨论了区间灰数的序关系,并得到区间灰数的偏序关系不是全序关系的结论,从而说明基于核和灰度的区间灰数代数系统不能定义大小关系,同时这也说明了基于核和灰度的灰数表示形式具有一定的局限性。

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.

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.

Factors influencing methane diffusion behaviors in micro-nano coal pores: a comprehensive study

Gas diffusion in the coal matrix plays a significant role in forecasting the production performance of coalbed methane(CBM)wells.To better understand methane diffusion behavior,a systematic study was performed on various rank coals with vitrinite reflectance(Ro,m)ranging from 0.46%to 2.79%.Multiple experiments,including coal petrographic analysis,field emission scanning electron microscopy(FESEM),low-temperature N2 adsorption/desorption,and mercury intrusion porosimetry(MIP),were conducted to quantitatively characterize the multiscale micro-nano pore system in different rank coals,which showed that the pore structure of coals exhibited a multimodal pore size and volume distribution.Isothermal adsorption-diffusion experiments using the volumetric method were also performed to understand the methane diffusion characteristics in the micro-nano pores of the coal reservoir.The applicability of the multiporous diffusion model is verified,and methane diffusion in the multi-scale pores of coal reservoirs exhibits the characteristics of early fast diffusion,transitional diffusion in the medium term,and slow diffusion in the later period.In addition,the factors affecting methane diffusion in coals were systematically analyzed,and gray relational analysis(GRA)was employed to analyze and identify the importance of these factors on methane diffusion.The results show the impact ranking of factors,in order from the most important to the least:particle size>moisture>surface area>pore volume>pressure>coal rank>temperature in all of three diffusion stages.These findings are helpful for forecasting production performance and enhancing the production efficiency of CBM.