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.

ADRC BASED INPUT DISTURBANCE REJECTION FOR MINIMUM-PHASE PLANTS WITH UNKNOWN ORDERS AND/OR UNCERTAIN RELATIVE DEGREES

The capability of ADRC is studied for linear time-invariant SISO minimum-phase systems with unknown orders, uncertain relative degrees, and unknown input disturbances. It is proved that ADRC can reject the unknown input disturbance and guarantee the close-loop stability for the plants with unknown but bounded relative degrees. Meanwhile, some close-loop performances can be achieved. The influence of the sensor noise is also discussed. And it is demonstrated by numerical examples that one ADRC with fixed parameters can be applied to a group of plants of different orders, relative degrees, and parameters.

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.

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.

Comprehensive evaluation method for superstructures of prestressed concrete girder bridges

A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A normal relative function about the evaluation indices of each element is developed to calculate the relative degree, and for each element there are no sub-level elements. When evaluating the elements in the sub-item level or the index level of the model, the weights of elements pertain to one adopted element, taking into account their degrees of deterioration. Since the relative degrees and structure evaluation scales on the damage conditions are applied to characterize the superstructure of damaged prestressed concrete girder bridges, this method can evaluate the prestressed structure in detail, and the evaluation results agree with the Code for Maintenance of Highway Bridges and Culvers （JTG Hll--2004 ）. Finally, a bridge in Jilin province is taken as an example, using the method developed to evaluate its damage conditions, which gives an effective way for bridge engineering.

Similarity measure on intuitionistic fuzzy sets

Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of many entropy definitions. By considering different fuzzy entropy definitions, fuzzy entropy on IFSs is designed and discussed. Similarity measure was also presented and its usefulness was verified to evaluate degree of similarity.

Decoupling Control Method Based on Neural Network for Missiles

In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.

Impacts of future climate change(2030-2059)on debris flow hazard:A case study in the Upper Minjiang River basin,China

An increase in extreme precipitation events due to future climate change will have a decisive influence on the formation of debris flows in earthquake-stricken areas. This paper aimed to describe the possible impacts of future climate change on debris flow hazards in the Upper Minjiang River basin in Northwest Sichuan of China, which was severely affected by the 2008 Wenchuan earthquake. The study area was divided into 1285 catchments, which were used as the basic assessment units for debris flow hazards. Based on the current understanding of the causes of debris flows, a binary logistic regression model was used to screen key factors based on local geologic, geomorphologic, soil,vegetation, and meteorological and climatic conditions. We used the weighted summation method to obtain a composite index for debris flow hazards, based on two weight allocation methods: Relative Degree Analysis and rough set theory. Our results showed that the assessment model using the rough set theory resulted in better accuracy. According to the bias corrected and downscaled daily climate model data, future annual precipitation(2030-2059) in the study area are expected to decrease, with an increasing number of heavy rainfall events. Under future climate change, areas with a high-level of debris flow hazard will be even more dangerous, and 5.9% more of the study area was categorized as having a high-level hazard. Future climate change will cause an increase in debris flow hazard levels for 128 catchments, accounting for 10.5% of the total area. In the coming few decades, attention should be paid not only to traditional areas with high-level of debris flow hazards, but also to those areas with an increased hazard level to improve their resilience to debris flow disasters.

Path following control of underactuated ships based on nonswitch analytic model predictive control

A path following controller is developed for underactuated ships with only surge force and yaw moment available to follow a predefined path.The proposed controller is based on nonswitch analytic model predictive control.It is shown that the optimal control law for a nonlinear path following system with ill-defined relative degree is continuous and nonsingular.The problem of ill-defined relative degree is solved.The path-following ability of the nonlinear system is guaranteed.Numerical simulations are provided to demonstrate the effectiveness of the proposed control law.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

Nonlinear dynamic response analysis of supercavitating vehicles

A finite element model for the supercavitating underwater vehicle was developed by employing 16-node shell elements of relative degrees of freedom.The nonlinear structural dynamic response was performed by introducing the updated Lagrangian formulation.The numerical results indicate that there exists a critical thickness for the supercavitating plain shell for the considered velocity of the vehicle.The structure fails more easily because of instability with the thickness less than the critical value,while the structure maintains dynamic stability with the thickness greater than the critical value.As the velocity of the vehicle increases,the critical thickness for the plain shell increases accordingly.For the considered structural configuration,the critical thicknesses of plain shells are 5 and 7 mm for the velocities of 300 and 400 m/s,respectively.The structural stability is enhanced by using the stiffened configuration.With the shell configuration of nine ring stiffeners,the maximal displacement and von Mises stress of the supercavitating structure decrease by 25% and 17% for the velocity of 300 m/s,respectively.Compared with ring stiffeners,longitudinal stiffeners are more significant to improve structural dynamic performance and decrease the critical value of thickness of the shell for the supercavitating vehicle.

Ill-conditioned stable inversion arising from singularly perturbed zero dynamics

The ill-conditioned stable inversion is studied for slightly nonminimum phase systems whose zero dynam- ics is singularly perturbed, that is, the relative degree is ill-defined. For these systems, we show that there exists an inherent limitation in the bandwidth of a reference trajectory to be tracked when a well-conditioned feedforward input via stable inversion is sought. We assert that, when the violation of this limitation occurs, the so-called reference trajectory redesign is called for. Our analysis results can provide an explicit assessment as well as useful guidance for the reference trajectory redesign if needed.

Assessment on Agricultural Drought Risk Based on Variable Fuzzy Sets Model

Drought is one of the major natural disasters causing huge agricultural losses annually. Regional agricultural drought risk assessment has great significance for reducing regional disaster and agricultural drought losses. Based on the fuzzy characteristics of agricultural drought risk, variable fuzzy sets model was used for comprehensively assessing agricultural drought risk of Liaoning Province in China. A multi-layers and multi-indices assessment model was estab-lished according to variable fuzzy sets theory, and agricultural drought risk of all 14 prefecture-level cities was respec-tively estimated in terms of dangerousness, vulnerability, exposure and drought-resistibility. By calculating the combi-nation weights of four drought risk factors, agricultural drought risk grade of each city was obtained. Based on the as-sessment results, the spatial distribution maps of agricultural drought risk were drawn. The results shows that eastern cities have lower drought dangerousness than western cities in Liaoning Province totally. Most cities are located in low drought vulnerability region and high drought exposure region. Because of frequent and severe drought since 2000, most cities are located in lower drought-resistibility region. Comprehensive agricultural drought risk presents apparent spatial characteristics, escalating from the east to the west. Drought dangerousness is the most important factor influencing comprehensive agricultural drought risk. Through the spatial distribution maps of drought risk, decision makers could find out drought situation and make decisions on drought resistance conveniently.

Simulation of Dynamic Electromagnetic Interference Environment for Unmanned Aerial Vehicle Data Link

In order to test the anti-interference ability of an Unmanned Aerial Vehicle(UAV) data link in a complex electromagnetic environment,a method for simulating the dynamic electromagnetic interference of an indoor wireless environment is proposed.This method can estimate the relational degree between the actual face of an UAV data link in an interface environment and the simulation scenarios in an anechoic chamber by using the Grey Relational Analysis(GRA) theory.The dynamic drive of the microwave instrument produces a real-time corresponding interference signal and realises scene mapping.The experimental results show that the maximal correlation between the interference signal in the real scene and the angular domain of the radiation antenna in the anechoic chamber is 0.959 3.Further,the relational degree of the Signal-toInterference Ratio(SIR) of the UAV at its reception terminal indoors and in the anechoic chamber is 0.996 8,and the time of instrument drive is only approximately 10 μs.All of the above illustrates that this method can achieve a simulation close to a real field dynamic electromagnetic interference signal of an indoor UAV data link.

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.

Fuzzy Optimum Model of Semi-Structural Decision for Lectotype Optimization of Offshore Platforms

In the process of concept design of offshore platforms, it is necessary to select the best from feasible alternatives through comparison and filter. The criterion set, used to evaluate and select the satisfying alternative, consists of many qualitative and quantitative factors. Therefore, the selection is a problem of multicriteria and semi-structural decision-making. Different from traditional methods in semi-structural decision-making, a new framework and methodology is presented in this paper for evaluation of offshore platform alternatives, First, the criterion set is established for the evaluation of alternatives. Next, the approach is studied to construct the relative membership degree matrix, in which both qualitative and quantitative factors are consistent with the uniform calculating standard. And then a new weight-assessing method is developed for calculation of the weights based on the relative membership degree matrix. Finally, a multi-hierarchy fuzzy optimum model is adopted to select the satisfying offshore platform alternative. A case study shows that the new framework and methodology are scientific, reasonable and easy to use in practice.

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.

Multi-objective Fuzzy Optimization Algorithm for Separation-Recycle System

Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.

Application of fuzzy optimal selection of similar slopes to the evaluation of slope stability

The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.

NUMERICAL ANALYSIS OF BIFURCATION BUCKLING FOR ROTATIONALLY PERIODIC STRUCTURES UNDER ROTATIONALLY PERIODIC LOADS

By considering the characteristics of deformation of rotationally periodic structures under rotationally periodic loads, the periodic structure is divided into some identical substructures in this study. The degrees-of-freedom (DOFs) of joint nodes between the neighboring substructures are classified as master and slave ones. The stress and strain conditions of the whole structure are obtained by solving the elastic static equations for only one substructure by introducing the displacement constraints between master and slave DOFs. The complex constraint method is used to get the bifurcation buckling load and mode for the whole rotationally periodic structure by solving the eigenvalue problem for only one substructure without introducing any additional approximation. The finite element (FE) formulation of shell element of relative degrees of freedom (SERDF) in the buckling analysis is derived. Different measures of tackling internal degrees of freedom for different kinds of buckling problems and different stages of numerical analysis are presented. Some numerical examples are given to illustrate the high efficiency and validity of this method.