Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

Risk Assessment of Agricultural Products Supply-Chain Finance Based on D-S Theory

Agricultural products supply-chain finance, as one of the solutions to the issue of “capital problems” of agriculture, countryside and farmers, has proposed a kind of characteristics model to assess the risk of agricultural production, processing and marketing, which can improve the issue of farmers and enterprises lacking of funds. This model is proposed on the basis of uncertain information processing method of D-S theory and its data combination rules, combined with the “discount rate” correction model, and it includes a risk assessment index system of agricultural products supply-chain finance, fully considering the five aspects of production, processing, marketing, cooperation of supply chain and collateral. At last, a taro supply chain is taken for example. And the risk assessment of its supply-chain finance based on this model has been discussed in detail. And the result has proved that the model and its algorithm are practical and feasible.

EARLY WARNING MODEL OF NETWORK INTRUSION BASED ON D-S EVIDENCE THEORY

Application of data fusion technique in intrusion detection is the trend of next- generation Intrusion Detection System (IDS). In network security, adopting security early warn- ing technique is feasible to effectively defend against attacks and attackers. To do this, correlative information provided by IDS must be gathered and the current intrusion characteristics and sit- uation must be analyzed and estimated. This paper applies D-S evidence theory to distributed intrusion detection system for fusing information from detection centers, making clear intrusion situation, and improving the early warning capability and detection efficiency of the IDS accord- ingly.

Ubiquitous Computing Identity Authentication Mechanism Based on D-S Evidence Theory and Extended SPKI/SDSI

Ubiquitous computing systems typically have lots of security problems in the area of identity authentication by means of classical PKI methods. The limited computing resources, the disconnection network, the classification requirements of identity authentication, the requirement of trust transfer and cross identity authentication, the bi-directional identity authentication, the security delegation and the simple privacy protection etc are all these unsolved problems. In this paper, a new novel ubiquitous computing identity authentication mechanism, named UCIAMdess, is presented. It is based on D-S Evidence Theory and extended SPKI/SDSI. D-S Evidence Theory is used in UCIAMdess to compute the trust value from the ubiquitous computing environment to the principal or between the different ubiquitous computing environments. SPKI-based authorization is expanded by adding the trust certificate in UCIAMdess to solve above problems in the ubiquitous computing environments. The identity authentication mechanism and the algorithm of certificate reduction are given in the paper to solve the multi-levels trust-correlative identity authentication problems. The performance analyses show that UCIAMdess is a suitable security mechanism in solving the complex ubiquitous computing problems.

Fault diagnosis method of hydraulic system based on fusion of neural network and D-S evidence theory

According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.

Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.

Study on Evaluation Method of Teaching Quality Based on Cloud Model and D-S Theory

The Evaluation of teaching quality is a complicated project. It’s a typical problem of uncertainty. A design scheme of teaching quality evaluation method based on cloud model and D-S theory is proposed with the school teaching evaluation index. With the experiences and knowledge of experts, evaluation index was described by cloud model and judgment matrix of AHP was revised by D-S theory. The weight of evaluation indexes was determined reasonably and objectively. Applied in the actual environment, the method can make an accurate and efficient evaluation with a certain practical value.

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

Application of evidence theory in information fusion of multiple sources in bayesian analysis

How to obtain proper prior distribution is one of the most critical problems in Bayesian analysis. In many practical cases, the prior information often comes from different sources, and the prior distribution form could be easily known in some certain way while the parameters are hard to determine. In this paper, based on the evidence theory, a new method is presented to fuse the information of multiple sources and determine the parameters of the prior distribution when the form is known. By taking the prior distributions which result from the information of multiple sources and converting them into corresponding mass functions which can be combined by Dempster-Shafer (D-S) method, we get the combined mass function and the representative points of the prior distribution. These points are used to fit with the given distribution form to determine the parameters of the prior distribution. And then the fused prior distribution is obtained and Bayesian analysis can be performed. How to convert the prior distributions into mass functions properly and get the representative points of the fused prior distribution is the central question we address in this paper. The simulation example shows that the proposed method is effective.

An Introduction to Convex Optimization Theory in Communication Signals Recognition

In this paper,convex optimization theory is introduced into the recognition of communication signals. The detailed content contains three parts. The first part gives a survey of basic concepts,main technology and recognition model of convex optimization theory. Special emphasis is placed on how to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model,which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations,in contrast to D-S evidence inference method,this new method can also generate reasonable recognition results. Moreover,this new method can deal with the form of sensor reports which is more general than that allowed by the D-S evidence inference method,and it has much lower computation complexity than that of D-S evidence inference method. In addition,this new method has better recognition result,stronger anti-interference and robustness. Therefore,the convex optimization methods can be widely used in the recognition of communication signals.

Triggered Asynchronous Cooperative Spectrum Sensing Scheme Based on Dempster-Shafer Theory

Aiming at solving the problems such as time consuming and application limiting presented in the existing synchronous cooperative spectrum sensing schemes,a triggered asynchronous scheme based on Dempster-Shafer(D-S) theory was proposed.Sensing asynchronously,each cognitive user calculated the confidence measure functions with double threshold spectrum sensing method.When the useful report was received by the fusion center,a fusion process would be triggered.Then the sensing results were fused together based on D-S theory.The analysis and simulation results show that the proposed scheme can improve the spectrum sensing efficiency and reduce the calculation amount of the fusion center compared with the existing schemes.

Origin of Magnetic Fields of Stellar Objects in the Universe Based on the 5D Projection Theory

Beginning with a 5D homogeneous universe [1], we have provided a plausible explanation of the self-rotation phenomenon of stellar objects previously with illustration of large number of star samples [2], via a 5D-4D projection. The origin of such rotation is the balance of the angular momenta of stars and that of positive and negative charged e-trino pairs, within a 3D ⊗1D?void of the stellar object, the existence of which is based on conservation/parity laws in physics if one starts with homogeneous 5D universe. While the in-phase e-trino pairs are proposed to be responsible for the generation of angular momentum, the anti-phase but oppositely charge pairs necessarily produce currents. In the 5D to 4D projection, one space variable in the 5D manifold was compacted to zero in most other 5D theories (including theories of Kaluza-Klein and Einstein [3] [4]). We have demonstrated, using the Fermat’s Last Theorem [5], that for validity of gauge invariance at the 4D-5D boundary, the 4th space variable in the 5D manifold is mapped into two current rings at both magnetic poles as required by Perelman entropy mapping;these loops are the origin of the dipolar magnetic field. One conclusion we draw is that there is no gravitational singularity, and hence no black holes in the universe, a result strongly supported by the recent discovery of many stars with masses well greater than 100 solar mass [6] [7] [8], without trace of phenomena observed (such as strong gamma and X ray emissions), which are supposed to be associated with black holes. We analyze the properties of such loop currents on the 4D-5D boundary, where Maxwell equations are valid. We derive explicit expressions for the dipolar fields over the whole temperature range. We then compare our prediction with measured surface magnetic fields of many stars. Since there is coupling in distribution between the in-phase and anti-phase pairs of e-trinos, the generated mag-netic field is directly related to the angular momentum, leading to the result that the magnetic field can be expressible in terms of only the mechanical variables (mass M, radius R, rotation period P)of a star, as if Maxwell equations are “hidden”. An explanation for the occurrence of this “un-expected result” is provided in Section (7.6). Therefore we provide satisfactory answers to a number of “mysteries” of magnetism in astrophysics such as the “Magnetic Bode’s Relation/Law” [9] and the experimental finding that B-P graph in the log-log plot is linear. Moreover, we have developed a new method for studying the relations among the data (M, R, P) during stellar evolution. Ten groups of stellar objects, effectively over 2000 samples are used in various parts of the analysis. We also explain the emergence of huge magnetic field in very old stars like White Dwarfs in terms of formation of 2D Semion state on stellar surface and release of magnetic flux as magnetic storms upon changing the 2D state back to 3D structure. Moreover, we provide an explanation, on the ground of the 5D theory, for the detection of extremely weak fields in Venus and Mars and the asymmetric distribution of magnetic field on the Martian surface. We predict the equatorial fields B of the newly discovered Trappist-1 star and the 6 nearest planets. The log B?−?log P graph for the 6 planets is linear and they satisfy the Magnetic Bode’s relation. Based on the above analysis, we have discovered several new laws of stellar magnetism, which are summarized in Section (7.6).

System Reliability Analysis Method Based on T-S FTA and HE-BN

For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.

Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence

In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer(D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.

Network Security Situation Evaluation Based on Modified D-S Evidence Theory

With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.

Analysis of Early-Warning Threshold for Metro Construction Collapse Risk Based on D-S Evidence Theory and Rough Set

The existing early-warning system in metro construction are generally based on traditional single-sensor data and simple analytic model, which makes it difficult to deal with the complex and comprehensive environment in metro construction. In this paper, the framework of early-warning threshold for metro construction collapse risk based on D-S evidence theory and rough set is built. By combining the primary data fusion collected based on rough set with the secondary data fusion which is based on D-S evidence theory, the integration of multiple information in metro construction is realized and the risk assessment methods are optimized. A case trial based on Hangzhou metro construction collapse accident is also carried out to exemplify the framework. The empirical analysis guarantees the completeness and independence of the prediction information, and realizes the dynamic prediction of the variation trend of metro construction collapse risk.

Comprehensive Evaluation of Electric Power Prediction Models Based on D-S Evidence Theory Combined with Multiple Accuracy Indicators

A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed.To obtain the preferred models,this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data,and carries out a comprehensive evaluation.First,according to Dempster-Shafer(D-S)evidence theory,a new accuracy indicator based on the relative error(RE)is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time.Next,a new dimensionless method is proposed,which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator,to avoid the conflict between pieces of evidence caused by the minimum value of zero.On this basis,the evidence fusion is used to obtain the comprehensive evaluation value of each model.Then,the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described.Finally,the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Assessment of Power Quality Based on D-S Evidence Theory

Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer(D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed,and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally,the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.