Target recognition based on modified combination rule

Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly.

An improved displacement-based adaptive pushover procedure based on factor modal combination rule

An accurate estimation of the applied load pattern is an essential component in each pushover procedure. Recently, a number of adaptive pushover methods have been proposed in which the effects of the higher modes as well as the progressive changes in the dynamic characteristics of structures are taken into account to compute the applied load pattern. The basic shortcoming of these advanced pushover methods is related to employing the quadratic modal combination rule, whereby the sign reversals of the modal load vectors are suppressed. In this study, an improved displacement-based adaptive pushover method is developed in which the applied load pattern is computed using the factor modal combination rule(FMC). In the proposed procedure, multiple load patterns, depending on the number of the modes considered, are determined in order to take into account the sign reversals of different modal load vectors. The accuracy of the proposed method is verifi ed for seven moment resisting frame buildings of 3, 9 and 20 stories with regularity or vertically geometric and mass irregularities subjected to 60 earthquake ground motion records. The results showed that the proposed methodology is capable of reproducing the peak dynamic responses with very good accuracy.

Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints

Currently,very few roof shape information for complex buildings is available on OSM.Moreover,additional data requirements(e.g.3D point clouds)limit the applicability of many roof reconstruction approaches.To mitigate this issue,we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints:the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints.First,it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm.Second,a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions.Then,a set of selection rules are defined to rank partitions,and the best ones are chosen for roof shape recommendation.Finally,a set of combination rules and a symmetry rule are defined.It enables to evaluate the probability of a footprint being a certain combination of roof shapes.Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17–45%and from a prior probability of 0.29–14.3%,removing 60%and 93%of the incorrect roof shape options,respectively.

An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

A study of acupoint selection rules of moxibustion and auricular point therapy in the treatment of urinary retention after anorectal surgery based on data mining

Based on the Traditional Chinese Medicine Inheritance Support System(V2.5),we explored the compatibility rules of moxibustion and auricular point prescriptions in the treatment of urinary retention after anorectal surgery,and further to obtain new prescriptions.Methods:We searched CNKI,Wanfang,VIP and CBM databases for the literature of moxibustion and auricular point therapy for urinary retention after anorectal surgery,collecting complete prescriptions and entering them into the Traditional Chinese Medicine Inheritance Support System(V2.5).The acupoint frequency,association rules,and combination rules were analyzed by data mining method of software integration.Results:126 moxibustion prescriptions were selected,and the acupoints for urinary retention after anorectal surgery in the literature were analyzed,including 10 acupoints with frequency≥6,9 acupoint combinations with frequency≥26,5 acupoints with association rules,6 core acupoint combinations and 3 new prescription.124 auricular point prescriptions were selected,and the relevant acupoints in the literature were analyzed,including 10 acupoints with frequency of use≥18,15 acupoint combinations with frequency≥40,12 acupoint combinations with association rules,6 core acupoint combinations,and 3 new prescriptions.The core points include Qihai(RN6),Sanyinjiao(SP6),Zhongji(RN3),Guanyuan(RN4).The auricular point therapy is more to select the tenderness points and acupoints that have an impact on the urinary system,mainly including“Pangguang”(CO9),“Shenmen”(TF4),“Shen”(CO10),“Sanjiao”(CO17),“Pizhixia”(AT4),“Niaodao”(HX3),“Fei”(CO14),“Jiaogan”(AH6a).Conclusion:Moxibustion for the treatment of urinary retention is mainly based on invigorating“Primordial Qi”and regulating the functions of“Middle Jiao”(refers to the area of the body just below the diaphragm and above the navel that helps produce delicate nutrients).The entropic clustering of complex systems excavated new prescriptions for the treatment of urinary retention after anorectal surgery,providing new ideas for future research and treatment.

Combining the Rule of Law with the Rule of Morality——General Secretary Xi Jinping's Important Discourse on the Relationship Between the Rule of Law and Rule by Virtue

Key points:Throughout the ages,rule of law has been an indispensable means of governing a state.However,as General Secretary Xi Jinping has pointed out,it is not enough to govern a state by the rule of law

交互自适应组合规则(英文)

Aiming at the invalidation of DS theory dealing with the evidence in a high conflict and reducing confidence level of DSm theory processing a low conflict,this paper proposes an interactive-adaptive combination rule. Adopting the angle similarity based on hyper-power set as the weight of generalized Dempster rule and PCR rule,the new rule adaptively processes various evidence combination issues. In this way,the rule can obtain not only the better fusion of decision making effect in a low conflict,but also the solution to the synthesis in a high conflict. Simulation analysis demonstrates the validity and applicability from this rule of combination.

New weighting factors assignment of evidence theorybased one vidence distance

Evidence theory has been widely used in the information fusion for its effectiveness of the uncertainty reasoning. However, the classical DS evidence theory involves counter-intuitive behaviors when the high conflict information exists. Based on the analysis of some modified methods, Assigning the weighting factors according to the intrinsic characteristics of the existing evidence sources is proposed, which is determined on the evidence distance theory. From the numerical examples, the proposed method provides a reasonable result with good convergence efficiency. In addition, the new rule retrieves to the Yager's formula when all the evidence sources contradict to each other completely.

New conflict representation model in generalized power space

The study on alternative combination rules in Dempster- Shafer theory （DST） when evidences are in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. The earlier researches have mainly focused on investigating the alternative which would be appropriate for the conflicting situation, under the assumption that a conflict is identified. However, the current research shows that not only the combination rule but also the classical conflict coefficient in DST are not correct to determine the conflict degree between two pieces of evidences. Most existing methods of measuring conflict do not consider the open world situation, whose frame of discernment is incomplete. To solve this problem, a new conflict representa- tion model to determine the conflict degree between evidences is proposed in the generalized power space, which contains two parameters： the conflict distance and the conflict coefficient of inconsistent evidences. This paper argues that only when the con- flict measure value in the new representation model is high, it is safe to say the evidences are in conflict. Experiments illustrate the efficiency of the proposed conflict representation model.

Using the Dempster-Shafer Theory of Evidence to Rank Documents

Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster＇s rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference （TREC） test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model （VSM）. Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision,