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 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.

基于云模型和改进D-S证据理论的浓香型白酒发酵质量评估方法

黄水是浓香型白酒发酵特有的副产物,黄水参数一定程度反映了发酵质量。基于黄水的酸度、还原糖、酒精度等关键参数,采用云模型与改进的D-S(Dempster-Shafer)证据理论,实现对白酒发酵质量的定量评估。基于黄水关键参数建立隶属度云模型,使用云模型判定样本在各发酵质量判定区间的隶属度情况。同时对D-S证据理论的冲突系数计算方式进行改进,使得信息融合结果更具代表性。提出一种浓香型白酒发酵质量的综合评估方法,降低了人工判别的主观性。

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.

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.

Ionothermal Synthesis, Crystal Structure and Antibacterial Activities of a New 3d-4f Hetero-metallic Compound Containing Two Kinds of Ligands

A new 3d-4f（CuⅡ-CeⅢ） hetero-metallic compound containing two kinds of ligands, namely [CuⅡ（H2pdc）（phen）（H2O）]（HⅢ3O）2[CeⅢ（pdc）3][CuⅡ（phen）Ce（pdc）3]（1, H2 pdc = pyridine-2,6-dicarboxylic acid, phen = 1,10-phenanthroline）, has been synthesized by an ionothermal method using the ionic liquid 3-butyl-1-methylimidazolium bromide（[Bmim]Br） as solvent, and characterized by elemental analysis, energy-dispersive X-ray spectroscopy（EDS）, IR, XPS and single-crystal X-ray diffraction. The structure reveals that 1 belongs to the triclinic system, space group P1 with a = 12.044（7）, b = 14.841（8）, c = 22.305（13） A, α = 85.802（12）, β = 85.471（12）, γ = 89.174（11）°, Z = 2, V = 3964（4） A3, Dc = 1.804 g·cm-3, F（000） = 2140, μ = 1.757 mm-1, the final R = 0.0734, wR = 0.1094 and S = 1.013. The compound can be viewed as a two-dimensional layered structure composed by 3d-4f hetero-nuclear anions [Cu（phen）Ce（pdc）3]-, coordination cations [Cu（H2pdc）（phen）（H2O）]2＋, coordination anions [Ce（pdc）3]3- and protonated water molecules via hydrogen bonding interactions and π-π stacking. Moreover, the antimicrobial activities of 1 have been also investigated. The results indicated that its inhibitory activity is slightly higher than that of penicillin against Candida albicans.

基于D-S证据理论的β因子估算方法

β因子法是定量评估共因失效的常用方法,为解决IEC 61508中评分表法估算β时可能存在不确定性的问题,通过分析估算过程中的不确定性类型,提出一种基于D-S证据理论的因子估算方法。该方法首先建立β不同取值范围的辨识框架,然后对被分析系统进行评估,并分配辨识框架中各基元的初始BPA(基本信任分配函数),最后通过证据理论融合各专家评分意见得到由信任函数和似然函数构成的β因子不确定区间。其中针对传统证据合成规则难以处理高度冲突证据的不足,提出了基于改进折扣系数的证据理论合成方法,该方法在证据融合之前,首先计算证据间的相似度以获取每个证据的支持度,同时结合专家权重,按一定的规则融合两者形成权重并将其作为折扣系数对初始BPA进行加权修正,以降低证据间的冲突程度。实例结果表明:与传统证据合成规则、Murphy法、折扣系数法相比,所提出的方法对目标结果的识别速度优于其他方法,且合成结果的不确定度0.0804是所有方法中最低的。

基于D-S证据理论的短期负荷预测模型融合

在各种预测模型融合时确定各种模型的权重直接影响到预测精度。对3种不同的神经网络负荷预测模型分别建立了权重提取和权重融合的数学模型,并运用证据理论对3种预测模型的权重进行融合。通过对历史预测数据的分析,提取了证据理论的融合样本,并将信度函数的多重融合结果作为负荷预测模型权重,得到权重融合后待预测日的负荷预测结果。将权重融合模型的预测结果与单一模型的预测结果进行比较,结果表明权重融合后的模型具有更高的预测精度,提高了负荷预测的准确性。

基于D-S证据理论的多指纹数据融合

为解决脱皮、有伤痕等低质量指纹识别困难、识别率低及稳定性差问题。提出将数据融合技术应用于指纹识别中。采用验证性的识别方法及离散性的特征提取手段,运用D-S证据理论,给出一个多指纹图像融合的辨识框架,计算益集元素的逻辑,得到多指纹融合的可信度,由此来辨别身份的真伪。通过仿真实验,表明此方法实用、有效,能提高系统的识别率及稳定性。可广泛应用于刑侦、金融等对安全有严格要求的领域。

一种基于冲突证据的D-S诊断研究

D-S证据理论已广泛地应用到故障诊断中,其中的基本置信分配是进行D-S故障诊断的基础。在导弹武器等复杂系统的D-S诊断中,所需的基本置信分配时有矛盾的现象。此时,一般的Dempser合成公式对证据的合成已无能为力,因此,迫切需要适用矛盾证据的合成方法。虽然人们做了不少研究,但利用优化理论所提出的D-S合成方法,由于其优点而受到人们的关注。但是这种方法由于没有考虑故障诊断的实际需求,在D-S诊断中显得不足。为此,文中提出了一种改进的D-S诊断优化合成方法。通过验证,表明改进后的D-S诊断合成方法具有更好的合理性和有效性。

基于D-S证据理论应用层DDOS攻击检测

提出一种基于D-S证据理论的应用层DDOS攻击检测算法,算法选取用户数据流作为输入焦元,设计了主/辅mass函数,选取用户浏览网页的时间行为和网页浏览热点跳转概率构造主mass函数,利用高耗资源请求比率构造辅助mass函数,进行规则的二次合成.实验结果表明算法具有可行性,能有效提高检测效率.

基于改进D-S证据理论的信息系统风险评估的研究

针对信息安全系统中存在数据不完备、风险项随机性强、模糊性强的特点,在以往的基于D-S证据理论的风险评估模型的基础上,提出“集中率”的方法,对风险项之间的证据冲突进行了有效的处理;然后利用改进后的Dempster证据合成法则对各风险项进行合成,减少了整个系统安全风险评估中的不确定因素;最后,给出了针对信息系统安全风险评估层级模型的实例分析,验证了该方法,实例表明,提出的改进方法可行有效.

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.

Hydrothermal Synthesis and Crystal Structure of a New Three-dimensional Heterometallic Coordination Polymer

A new 3d-4fheterometallic polymer {[Sm2Cu（PDC）2（SO4）2（H20）6]·2H2O}n 1 has been synthesized by Sm2O3, Cu（SO4）2·5H2O and pyridine-3,5-dicarboxylic acid under hydrothermal conditions. The compound crystallizes in triclinic system, space group Pi, with a = 6.352（7）, b = 10.040（10）, c = 10.315（11） A, α = 94.958（14）, β = 95.556（7）, γ = 99.747（14）°, V = 641.7（12）A3, Z = 1, M, = 1030.63, Dc= 2.651 Mg/m3,μ = 5.615 mm-1, F（000） = 491, the final R = 0.0491 and wR = 0.1345 for 2098 observed reflections with I 〉 2σ（I）. The compound is a three-dimensional network structure in which infinite lanthanide-carboxylate chains are linked by [Cu（$04）2]2- metalloligands to form a mixed-metal coordination network.

A Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.

A generalized evidence distance

How to efficiently measure the distance between two basic probability assignments（BPAs） is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.