A related degree-based frequent pattern mining algorithm for railway fault data

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

Influence of relative compaction and degree of saturation on the deformation characteristics of bentonite under freeze-thaw cycles

Bentonite,consisting of clay minerals of the montmorillonite group,has been widely used as an adsorbent and backfill material in nuclear waste disposal and groundwater remediation.It is challenging to use bentonite as a filling material in cold regions since bentonite is highly sensitive to thermal environmental changes,during which its bulk volume and microstructure change significantly.In this study,a series of one-dimensional and three-dimensional freeze-thaw tests were carried out within a closed system to investigate the influencing factors of the deformation of bentonite under freeze-thaw cycles.Results show that the initial soil water content greatly impacts bentonite's deformation during freeze-thaw cycles.For an initial higher degree of saturation(Sr),the expansion caused by the formation of ice lenses has a greater impact than the shrinkage induced by dehydration,ice-cementation,and so on.Conversely,bentonite tends to shrink at a lower degree of saturation during freezing.And the critical degree of saturation that determines bentonite's behavior of frost heave or frost shrinkage seems to be roughly 0.8.As the number of freeze-thaw cycles rises,initially uncompacted bentonite clay becomes more compacted,and initially compacted bentonite clay remains unchanged.

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.

Constitutive relation of an orthorhombic polycrystal with the shape coefficients

An orthorhombic polycrystal is an orthorhombic aggregate of tiny crystallites. In this paper, we study the effect of the crystalline mean shape on the constitutive relation of the orthorhombic polycrystal. The crystalline mean shape and the crystalline orientation arrangement are described by the crystalline shape function （CSF） and the orientation distribution function （ODF）, respectively. The CSF and the ODF are expanded as an infinite series in terms of the Wigner D-functions. The expanded coefficients of the CSF and the ODF are called the shape coefficients s^lm0 and the texture coefficients c^lmn respectively. Assuming that Ceff in the constitutive relation depends on the shape coefficients s^lm0 and the texture coefficients c^lmn by the principle of material frame-indifference we derive an analytical expression for C^eff up to terms linear in s^lmo and c^lmn and the expression would be applicable to the polycrystal whose texture is weak and whose crystalline mean shape has weak anisotropy. C^cff contains six unspecified material constants （λ, μ, c, s1, s2, s3）, five shape coefficients （s^2 00, s^2 20, s^4 00, s^4 20, s^4 40）, and three texture coefficients （c^4 99,c^4 20, c^4 40）, The results based on the perturbation approach are used to determine the five material constants approximately. We also find that the shape coefficients 2 and a s^2mo and s^4m0 are all zero if the crystalline mean shape is a cuboid. Some examples are given to compare our computational results.

Severity of the metabolic syndrome as a predictor of prediabetes and type 2 diabetes in first degree relatives of type 2 diabetic patients:A 15-year prospective cohort study

BACKGROUND Type 2 diabetes mellitus(T2DM)has high morbidity and mortality worldwide,therefore there is of paramount importance to identify the risk factors in the populations at risk early in the course of illness.A strong correlation between severity of metabolic syndrome(MetS)and HbA1c,fasting insulin and insulin resistance has been reported.Accordingly,the MetS severity score(or MestS Zscore)can potentially be used to predict the risk of T2DM progression over time.AIM To evaluate the association the of MestS Z-score in first degree relatives(FDRs)of T2DM with the risk of prediabetes and type 2 diabetes in future.METHODS A prospective open cohort study was conducted between 2003-2018.At baseline,the sample comprised of 1766 FDRs of patients with T2DM who had a normal glucose tolerance test.Relative risk(RR)and 95%confidence interval were calculated based on logistic regression.The receiver-operator characteristic analysis and area under the curve based on MetS Z-score were used to evaluate the risk of prediabetes and diabetes among the FDR population.RESULTS Baseline MetS Z-scores were associated with the its latest values(P<0.0001).Compared with individuals who were T2DM free at the end of follow up,those who developed T2DM had higher MetS Z-score at baseline(P<0.001).In multivariable logistic regression analyses for every unit elevation in MetS Z-score at the baseline,the RR for developing future T2DM and prediabetes was(RR=1.94,RR=3.84),(RR=1.5,RR=2.17)in total population and female group,respectively(P<0.05).The associations remained significant after adjusting the potential confounding variables.A cut off value of 0.97 and 0.94 was defined in the receiver-operator characteristic curve based on the MetS Z-score for differentiating female patients with diabetes and prediabetes from the normal population,respectively.CONCLUSION The MetS Z-score was associated with an increased risk of future T2DM.Appropriate interventions at earlier stages for preventing and attenuating MetS effects may be considered as an effective strategy for FDR as at-risk population.

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.

Relationship between Housing Price and Land Price Based on Grey Relational Analysis

In recent Years, China＇s real estate market has been rapid developed, and real estate has become a hot spot of consumption and investment. In some large and medium-sized cities there has been a rapid rise in housing prices. The rapid rise in housing prices has led to difficulties in the purchase of houses in some cities and towns, and this phenomenon has aroused the attention and con- cern of all walks of life. Housing is the basic human life needs. Housing problem is not only an economic problem, but also a social problem. The relationship between house price and land price and the effective control of housing prices have become the focus of government and scholars. Thus, grey relational analysis is used to ana- lyze the relationship between housing prices and land prices, and the grey relational coefficients are calculated.

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.

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.

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.

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.

Damage Identification in Beam-Type Structures Using Pseudo Strain Energy Density and Grey Relation Coefficient

Based on pseudo strain energy density (PSED) and grey relation coefficient (GRC), an index is proposed to locate the damage of beam-type structures in time-domain. The genetic algorithm (GA) is utilized to identify the structural damage severity of confirmed damaged locations. Furthermore, a systematic damage identification program based on GA is developed on MATLAB platform. ANSYS is employed to conduct the finite element analysis of complicated civil engineering structures, which is embedded with interface technique. The two-step damage identification is verified by a finite element model of Xinxingtang Highway Bridge and a laboratory beam model based on polyvinylidens fluoride (PVDF). The bridge model was constructed with 57 girder segments, and simulated with 58 measurement points. The damaged segments were located accurately by GRC index regardless of damage extents and noise levels. With stiffness reduction factors of detected segments as variables, the GA program evolved for 150 generations in 6 h and identified the damage extent with the maximum errors of 1% and 3% corresponding to the noise to signal ratios of 0 and 5%, respectively. In contrast, the common GA-based method without using GRC index evolved for 600 generations in 24 h, but failed to obtain satisfactory results. In the laboratory test, PVDF patches were used as dynamic strain sensors, and the damage locations were identified due to the fact that GRC indexes of points near damaged elements were smaller than 0.6 while those of others were larger than 0.6. The GA-based damage quantification was also consistent with the value of crack depth in the beam model.

Interactive group decision making procedure based on uncertain multiplicative linguistic preference relations

Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express their linguistic preference information over alternatives.The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one,and then a simple approach is developed to determine the experts＇ weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations.Furthermore,a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations,in which a possibility degree formula and a complementary matrix are used to rank the given alternatives.Finally,the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.

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.

Program evaluation and its application to equipment based on super-efficiency DEA and gray relation projection method

For the gray attributes of the equipment program and its difficulty to carry out the quantitative assessment of the equipment program information, the gray relation projection method is simply reviewed. Combining the super-data envelopment analysis（DEA） model and the gray system theory, a new super-DEA for measuring the weight is proposed, and a gray relation projection model is established to rank the equipment programs. Finally, this approach is used to evaluate the equipment program. The results are verified valid and can provide a new way for evaluating the equipment program.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Pore Size Distribution and Its Quantitative Relationship with Strength of Corundum Based Castables

A series of corundum based castables with 0,2%,4%,6%,and 8% α-Al2O3 micropowders were prepared using tabular alumina aggregates （6-3,3-1 and ≤1 mm） and fines （≤0.088 and ≤0.045 mm）,calcium aluminate cement,and α-Al2O3 micropowders （d50=1.754 μm） as starting materials. Cold mechanical strength and pore size distribution of the castables specimens after heat treatment at 110,1 100 and 1 500 ℃ were tested,respectively. The quantitative relationship between strength and apparent porosity,and that between strength and median pore diameter were verified by Atzeni equation. The correlation between interval of pore size and mechanical strength of specimens was also studied by means of gray relational theory. The results show that：（1） the pore size distribution of castables is strongly influenced by both micropowders filling and matrix sintering; the addition of micropowders decreases median pore diameter while the sintering process increases it; （2） when adding a constant correction term,Atzeni equation can substantially describe the quantitative relationship between median pore diameter and strength of castables specimens after heat treatment at the same temperature; the significant differences of the gray relational degree between the interval of pore size and castables strength are characterized; it is also found that for the same interval of pore size,the gray relational degree isaffected by the heat treatment temperature; the pore size interval 〈0.5 μm has the highest gray relational degree with the strength at 110-1 500 ℃.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.