Application of Grey Correlation Degree Analysis in Broad Bean Strain Screening

[Objectives] The aim was to clarify the target and approach to purifying and selecting Baoshan Touxinlv broad bean. [Methods] In 2012,5 homozygous strains were selected successfully as the material,and completely randomized block arrangement was adopted with 3 repeated trials to analyze the effects of plant height,effective length of podding branches,branch number per plant,pod number per plant,grain number per pod and 100-grain weight on the yield of each plant in the system. [Results] The results showed that the effects of the various factors on the yield of Baoshan broad beans were in the order of grain number per pod 〉 effective length of podding branches 〉 100-grain weight 〉 pod number per plant 〉 branch number per plant 〉 plant height. [Conclusions]In breeding new strains of Baoshan broad beans,under the premise of ensuring the seed nature and quality of the original variety,priority should be given to the gain number per pod and the increment of effective length of podding branches,followed by the pod number per plant,branch number per plant,plant height,which is the best way to breed new strains of Baoshan Touxinlv broad beans.

Application of grey weighted related degree to the ambient air quality assessment

It is difficult to establish the integrated method to assess the ambient air quality because the atmospheric environmental system is composed of several environmental elements, which contains many contanination factors. In view of the typical grey system character of the monitored data in the ambient air, this paper introduces the weighted value to obtain related degree during applying the method of grey related degree analvsis to assess ambient air quality by each polluting factor producing different effects on the environment quality. This method could give more reasonable and reliable evaluating results. Taking the monitored data of Harbin, a provincial city of Heilongjiang, for example in this paper, the result of assessment by this method was coincide,It with the actual environmental quality of Harbin.

Application of Grey-correlated Spectral Region Selection in Analysis of Near-infrared Spectra

The optimal selection method of spectral region based on the grey correlation analysis was applied in the analysis of near-infrared（NIR） spectra. In order to compute ＂characteristic＂ spectral region, 160 samples of tobacco were surveyed by NIR. Next, the whole spectral region was randomly divided into six regions, and the values of association coefficients and correlation orders of different regions were computed for total sugar, reducing sugar and nicotine. Moreover, two regions that owned the largest value of association coefficient were regarded as ＂characteristic＂ spectral region of a model. Finally, the quantitative analysis models of different components were established via the partial least squares method, and the common selection methods of spectral region were compared. The simulation results indicate that the models to choose the spectral region based on grey correlation analysis are more effective than the common selection methods of spectral region, the optimized time of algorithm is shorter, the prediction precision of the models is higher and generalization ability for quantitative analysis results is stronger. This research can provide the support for the quantitative analysis models of NIR spectra and new idea for commercial analysis software of NIR. So, it has a high application value in the analysis of NIR spectra.

Analysis on Industry Structure Adjustment and Energy Consumption Based on Grey Theory——A Case Study of Qinghai Province

In view of the complexity and non-linearity of energy consumption system and the status quo of the development of energy in Qinghai Province,the relations between energy consumption and industrial structure is analyzed by using the quantitative analysis of grey relation degree by using the grey system theory.The relevancy degree among the primary industry,the secondary industry and the tertiary industry and living energy consumption are obtained,and then the trend of energy consumption in the following several years can be predicted.The results show that the secondary industry has the largest relevancy degree to the total energy consumption.In the end,according to the results of the research,several suggestions on how to saving energy are put forward.Firstly,the government should improve the high-tech industry and restrict the development of high-consumption and high-pollution industries.Secondly,the government should promote the low-carbon way of life;promote energy saving and control the energy consumption of the department of life.Thirdly,clean production should be actively promoted in the tertiary industry and the circular economy should be vigorously expanded.

生产中的模糊贝叶斯决策模型

生产中的模糊贝叶斯决策模型李容华（湖南财经学院）一、问题的提出在统计决策中，人们通常用先验概率作决策．这种决策是根据历史资料过经验判断而得到的．实践证明，这种决策难以准确地反映生产现实，具有局际性．我们如果能在其决策中补充现实中发生的新信息，新资料，...

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

In order to promote the application of Beijing-1 small satellite(BJ-1) remote sensing data,the multispectral and panchromatic images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining.An improved Intensity-Hue-Saturation(IHS) fusion algorithm is proposed to fuse panchromatic and multispectral images,in which intensity component and panchromatic image are combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree.This improved IHS fusion algorithm outper-forms traditional IHS fusion method to a certain extent,evidenced by its ability in preserving spectral information and enhancing spatial details.Dempster-Shafer(D-S) evidence theory was adopted to combine the outputs of three member classifiers to generate the final classification map with higher accuracy than that by any individual classifier.Based on this study,we conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological environment degradation in mining areas,and the proposed fusion algorithms at data and decision levels can integrate the advantages of multi-resolution images and multiple classifiers,improve the overall accuracy and produce a more reliable land cover map.

Identification of Time-Varying Modal Parameters for Thermo-Elastic Structure Subject to Unsteady Heating

A time-varying modal parameter identification method combined with Bayesian information criterion(BIC)and grey correlation analysis(GCA)is presented for a kind of thermo-elastic structures with sparse natural frequencies and subject to an unsteady temperature field.To demonstrate the method,the thermo-elastic structure to be identified is taken as a simply-supported beam with an axially movable boundary and subject to both random excitation and an unsteady temperature field,and the dynamic outputs of the beam are first simulated as the measured data for the identification.Then,an improved time-varying autoregressive(TVAR)model is generated from the simulated input and output of the system.The time-varying coefficients of the TVAR model are expanded as a finite set of time basis functions that facilitate the time-varying coefficients to be time-invariant.According to the BIC for preliminarily determining the scope of the order number,the grey system theory is introduced to determine the order of TVAR and the dimension of the basis functions simultaneously via the absolute grey correlation degree(AGCD).Finally,the time-varying instantaneous frequencies of the system are estimated by using the recursive least squares method.The identified results are capable of tracking the slow time-varying natural frequencies with high accuracy no matter for noise-free or noisy estimation.

Research on Flight Delay Based on Fuzzy Evaluation Algorithm

In recent years, since airspace restrictions and the volume of passenger traffic are increasing, the rate of flight delay is rising rapidly and the contradiction about it is outstanding. Flight delay event not only holds up passengers’ time, but also makes the airlines suffer a lot. So a scientific and reasonable guidance is necessary to reduce the delay effect. This paper firstly establishes a method to assess the degree of airport delays and get all factors which caused the flight delays quantification, and ultimately we offer a proposal to deal with the flow factor, which is the principal reason for flight delays.

Optimization of structural parameters for elliptical cross-section spiral equal-channel extrusion dies based on grey theory

The elliptical cross-section spiral equal-channel extrusion （ECSEE） process is simulated by using Deform-3D finite element software. The ratio m of major-axis to minor-axis length for ellipse-cross-section, the torsion angle u, the round-ellipse cross-section transitional channel L1, the elliptical rotation cross-section transitional channel L2 and the ellipse-round cross-section transitional channel L3 are destined for the extrusion process parameters. The average effective strain eave on cross-section of blank, the deformation uniformity coefficient a and the value of maximum damage dmax are chosen to be the optimize indexes, and the virtual orthogonal experiment of L16 （45） is designed. The correlation degree of the process factors affecting eave, a and dmax is analyzed by the numerical simulation results using the weights and grey association model. The process parameters are optimized by introducing the grey situation decision theory and the ECSEE optimal combination of process parameters is obtained： u of 120 , m of 1.55, L1 of 7 mm, L2 of 10 mm, and L3 of 10 mm. Simulation and experimental results show that the material can be refined with the optimized structural parameters of die. Therefore, the optimization results are satisfactory.

Establishment of Neural Network Prediction Model for Terminative Temperature Based on Grey Theory in Hot Metal Pretreatment

In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization. Thus, an optimization neural network model of terminative temperature in the process of dephosphorization by laying correlative degree weights to all input factors related was used. Then sim- ulation experiment of model newly established is conducted utilizing 210 data from a domestic steel plant. The results show that hit rate arrives at 56.45~~ when error is within plus or minus 5%, and the value is 100% when within ~10%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 6. 839o//oo. Thus, the simulation prediction fits the real perfectly, which accounts for that neural network model for terminative tempera- ture based on grey theory can reflect accurately the practice in dephosphorization. Naturally, this method is effective and nraeticahle.

Research on Planning for Integrated Development of Marine and Terrestrial Industries in the Blue Economic Zone of Shandong Peninsula

The measuring of the integration degree between marine and terrestrial industries is a crucial part in formulating strategies of industrial adjustment and industrial development for coastal regions. Taking the Blue Economic Zone of Shandong Peninsula as an example, this paper conducted grey correlation analysis on the correlation between the three industrial sectors in marine and terrestrial areas, as well as on the internal interaction between main industries in marine and terrestrial areas. In the end, the paper puts forward related suggestions on the integrated development of marine and terrestrial industries in the Blue Economic Zone of the Shandong Peninsula.

Dynamic trust evaluation model for task participants oriented to mobile crowd sensing

In the mobile crowd sensing(MCS)network environment,it is very important to establish an evolutionary process that can dynamically depict the trust degree of task participants.To address this issue,this paper proposes a dynamic trust evaluation model for task participants.Firstly,according to the security requirements and trust strategy of the perceived tasks,the attribute reduction algorithm(ARA)based on rough set is used to obtain the multi-attribute indexes that affect the participants’trust information.Removing the redundant attributes can avoid the lag of trust evaluation and reduce the time cost.Secondly,the grey correlation analysis method is used to solve the correlation degree between the target sequence and the comparison sequence on the trust attributes by integrating the multi-attribute decision-making method,which avoids the distortion of the trust evaluation caused by human subjective factors and improves the quality of the perceived data.Finally,a dynamic trust evaluation model for participants in complex sensing network environment is established.The simulation results show that the proposed model can not only dynamically depict the trust degree of participants in real time,but also have higher accuracy and less time cost.