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.

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.

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.

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.

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.

D-S证据理论应用中的一种验证方法

D-S证据理论是一种比概率论确定性弱的不确定性理论,它能将“不知道”和“不确定”两个认知学上的主要概念区别开来,在多传感器数据融合中具有广泛的应用前景。D-S证据理论在实际应用中却存在一个困难,当目标的个数较多时,需要计算的项数太多,容易造成漏项,引起计算错误。提出了一种确定计算项数的算法,作为验证计算结果的必要条件,并通过图解的方法找出需要计算的项。

改进的D-S证据理论在海上目标融合识别模型中的应用

船舶海上航行的过程中传感器获取到的信息间会存在冲突。本文对传统的D-S证据理论进行改进,通过权重来调整不同证据的可信度,然后利用Dempster合并法则实现目标信息的融合。最后通过对比实验证明本文改进算法的有效性和稳定性优于其他对比算法。

D-S证据理论应用中的一种图解方法

D-S证据理论是对概率论的进一步扩充,在多传感器数据融合中,有着广泛的应用。当利用D-S合成公式进行融合时,由于需计算的项数较多且不易直接判定具体该有哪些项,容易造成漏项或错项,引起计算错误。提出了一种直观、简单的图解法,可使需计算的项一目了然。

基于D-S证据理论的航空装备技术状态决策研究

要准确确定航空装备的维修内容和时机，在对维修类型进行决策时是以装备技术状态作为主要依据的。为了更全面、更准确地获得航空装备整机技术状态信息，提出基于D-S证据理论的航空装备技术状态决策模型。实例证明：经过多证据融合后，该模型可有效提高航空装备技术状态决策的准确性和可靠性，为武器装备技术状态决策提供了新思路。

A Renormalizable Theory of Quantum Gravity: Renormalization Proof of the Gauge Theory of Volume Preserving Diffeomorphisms

Inertial and gravitational mass or energy momentum need not be the same for virtual quantum states. Separating their roles naturally leads to the gauge theory of volume-preserving diffeomorphisms of an inner four-dimensional space. The gauge-fixed action and the path integral measure occurring in the generating functional for the quantum Green functions of the theory are shown to obey a BRST-type symmetry. The related Zinn-Justin-type equation restricting the corresponding quantum effective action is established. This equation limits the infinite parts of the quantum effective action to have the same form as the gauge-fixed Lagrangian of the theory proving its spacetime renormalizability. The inner space integrals occurring in the quantum effective action which are divergent due to the gauge group’s infinite volume are shown to be regularizable in a way consistent with the symmetries of the theory demonstrating as a byproduct that viable quantum gauge field theories are not limited to finite-dimensional compact gauge groups as is commonly assumed.

人工智能医疗侵权纠纷中的责任承担与举证分配

涉及人工智能医疗技术的民事医疗纠纷中的侵权责任与举证分配已成为愈发重要的法学课题。不同国家对于人工智能侵权责任的分配机制是不同的。在我国,人工智能医疗侵权责任具有医疗损害责任与产品责任两种属性。根据实体法上的法律要件分类进行举证责任的分配是民事案件的一般规则。鉴于人工智能医疗存在技术上的特殊性与证据偏在的情形,有必要对其适用危险领域理论的举证责任分配规则。法院需要在人工智能医疗纠纷中根据具体情形进行举证责任的倒置与举证责任的减轻;同时,法院也可依当事人申请进行证据调查活动,通过文书提出命令等手段平衡双方当事人的证据收集能力。

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.

Privacy-preserving Distributed Location Proof Generating System

The rapid development of location-based service(LBS) drives one special kind of LBS, in which the service provider verifies user location before providing services. In distributed location proof generating schemes, preventing users from colluding with each other to create fake location proofs and protecting user's location privacy at the same time, are the main technical challenges to bring this kind of LBS into practical. Existing solutions tackle these challenges with low collusion-detecting efficiency and defected collusion-detecting method. We proposed two novel location proof generating schemes, which inversely utilized a secure secret-sharing scheme and a pseudonym scheme to settle these shortcomings. Our proposed solution resists and detects user collusion attacks in a more efficient and correct way. Meanwhile, we achieve a higher level of location privacy than that of previous work. The correctness and efficiency of our proposed solution is testified by intensive security analysis, performance analysis, as well as experiments and simulation results.

一类决策模型的payoff-proof纳什均衡解

研究1种基于线性规划约束的多人决策模型。首先,运用线性规划和对偶理论给出了纳什均衡点存在的充分必要条件,进而证明了在这种模型下的纳什均衡点可以在多项式时间内求解。其次,在原有的纳什均衡定义的基础上,定义了1个强均衡的概念—payoff-proof纳什均衡,并对前述模型研究了payoff-proof纳什均衡的性质和存在性。

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.

Performance analysis of blockchain for civil aviation business data based on M/G/1 queuing theory

An Ethereum blockchain based on proof of stake ( PoS) consensus mechanism is used to achieve the data sharing within the civil aviation service platform for both airport group management and passengers. Considering the Gas consumption of Ethereum, the dynamic batch-service capacity constraint by the Block Gas Limit and the priority mechanism depending on the different Gas Price of transactions, M/ G/1 queuing theory with batch-service is used to construct the service model of transactions confirmation process in the proposed blockchain system, where the effects of transactions arrival rate, block capacity, service rate and number of nodes on the average confirmation time of transactions with different priority are analyzed, and eventually a performance analysis model of blockchain for civil aviation business data is proposed. The simulation results prove the usability and accuracy of the model, which can provide both theoretical basis for data sharing of civil aviation using Ethereum blockchain and the further optimization of transactions confirmation time.

从实践应用到证据法定:大数据证据的理论证成与规则建构

伴随着大数据时代的到来,大数据证据在司法实践中的证明价值愈加凸显。在肯定大数据证据实践证明价值的同时,囿于大数据证据在我国证据法地位的缺失、证据审查判断规则的缺位,使得大数据证据在法庭审查判断时表现混乱、在法庭质证时辩护效率低下,导致证据裁判规则规范侦查行为的价值功能失灵。从大数据证据的内生应用逻辑出发,大数据证据成为独立证据具有实践基础,包括大数据证据改变了传统证据的司法认知路径,推动了印证证明模式向综合验证模式的转向;大数据证据成为独立证据具有现实基础,包括电子数据无法与大数据证据等同、书证难以包容大数据证据、鉴定意见与大数据证据存在明显区分以及大数据证据与勘验、检查笔录和证人证言等法定证据类型存在诸多区别;大数据证据成为独立证据具有法理基础,从程序法的角度可以发现我国法定证据的分类存在着与取证型侦查行为密切相关的司法逻辑,即一种取证型侦查行为的诞生便会产生一种相对应的证据分类,结合当前我国新型犯罪侦查治理的需要,基于大数据搜索、大数据挖掘等新的取证型大数据侦查行为的出现,大数据证据成为独立证据类型也同样符合我国基于取证型侦查行为的诞生而伴生新的证据类型的法理基础。从大数据证据的真实性审查、合法性审查、科学性审查以及关联性审查等方面建构我国大数据证据的审查判断规则;以印证证明为基础,建构证据与证据之间、证据与案件事实之间、事实与事实之间,及其证据、事实与数据之间“网状式验证”的综合验证规则;以及建构包括绝对排除、相对排除为内容的大数据证据排除规则,可以有效推动我国大数据证据的基本理论证成与证据应用规则的完善。

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).

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.