2015 18th International Conference on Information Fusion (Fusion)最新文献

ComTrustO: Composite trust-based ontology framework for information and decision fusion ComTrustO:用于信息和决策融合的基于信任的复合本体框架

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.32210

A. Oltramari, Jin-Hee Cho

引用次数: 11

Information fusion with belief functions: A comparison of proportional conflict redistribution PCR5 and PCR6 rules for networked sensors 基于信念函数的信息融合:网络化传感器比例冲突再分配规则PCR5与PCR6的比较

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23211

R. Ilin, Erik Blasch

引用次数: 8

A graph-based evidence theory for assessing risk 基于图表的风险评估证据理论

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.32199

Riccardo Santini, Chiara Foglietta, S. Panzieri

{"title":"A graph-based evidence theory for assessing risk","authors":"Riccardo Santini, Chiara Foglietta, S. Panzieri","doi":"10.5281/ZENODO.32199","DOIUrl":"https://doi.org/10.5281/ZENODO.32199","url":null,"abstract":"The increasing exploitation of the internet leads to new uncertainties, due to interdependencies and links between cyber and physical layers. As an example, the integration between telecommunication and physical processes, that happens when the power grid is managed and controlled, yields to epistemic uncertainty. Managing this uncertainty is possible using specific frameworks, usually coming from fuzzy theory such as Evidence Theory. This approach is attractive due to its flexibility in managing uncertainty by means of simple rule-based systems with data coming from heterogeneous sources. In this paper, Evidence Theory is applied in order to evaluate risk. Therefore, the authors propose a frame of discernment with a specific property among the elements based on a graph representation. This relationship leads to a smaller power set (called Reduced Power Set) that can be used as the classical power set, when the most common combination rules, such as Dempster or Smets, are applied. The paper demonstrates how the use of the Reduced Power Set yields to more efficient algorithms for combining evidences and to application of Evidence Theory for assessing risk.","PeriodicalId":297288,"journal":{"name":"2015 18th International Conference on Information Fusion (Fusion)","volume":"101 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120823386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Classification of incomplete patterns based on the fusion of belief functions 基于信念函数融合的不完全模式分类

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23204

Zhunga Liu, Q. Pan, J. Dezert, Arnaud Martin, G. Mercier

{"title":"Classification of incomplete patterns based on the fusion of belief functions","authors":"Zhunga Liu, Q. Pan, J. Dezert, Arnaud Martin, G. Mercier","doi":"10.5281/ZENODO.23204","DOIUrl":"https://doi.org/10.5281/ZENODO.23204","url":null,"abstract":"The influence of the missing values in the classification of incomplete pattern mainly depends on the context. In this paper, we present a fast classification method for incomplete pattern based on the fusion of belief functions where the missing values are selectively (adaptively) estimated. At first, it is assumed that the missing information is not crucial for the classification, and the object (incomplete pattern) is classified based only on the available attribute values. However, if the object cannot be clearly classified, it implies that the missing values play an important role to obtain an accurate classification. In this case, the missing values will be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM) techniques, and the edited pattern with the imputation is then classified. The (original or edited) pattern is respectively classified according to each training class, and the classification results represented by basic belief assignments (BBA's) are fused with proper combination rules for making the credal classification. The object is allowed to belong with different masses of belief to the specific classes and meta-classes (i.e. disjunctions of several single classes). This credal classification captures well the uncertainty and imprecision of classification, and reduces effectively the rate of misclassifications thanks to the introduction of meta-classes. The effectiveness of the proposed method with respect to other classical methods is demonstrated based on several experiments using artificial and real data sets.","PeriodicalId":297288,"journal":{"name":"2015 18th International Conference on Information Fusion (Fusion)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132857039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Environment perception using grid occupancy estimation with belief functions 基于信念函数的网格占用估计环境感知

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23208

J. Dezert, J. Moras, B. Pannetier

{"title":"Environment perception using grid occupancy estimation with belief functions","authors":"J. Dezert, J. Moras, B. Pannetier","doi":"10.5281/ZENODO.23208","DOIUrl":"https://doi.org/10.5281/ZENODO.23208","url":null,"abstract":"Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the safety (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy state of each cell represents a small piece of information of the surrounding area of the vehicle. The state of each cell must be estimated from sensors measurements and classified in order to get a complete and precise perception of the dynamic environment where the vehicle moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors and Dempster-Shafer rule of combination. Recently we have shown that PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache Theory) did improve substantially the quality of grid map with respect to other techniques, especially when the quality of available information is low, and when the sources of information appear as conflicting. In this paper, we go further and we analyze the performance of the improved version of PCR6 with Zhang's degree of intersection. We will show through different realistic scenarios (based on a LIDAR sensor) the benefit of using this new rule of combination in a practical application.","PeriodicalId":297288,"journal":{"name":"2015 18th International Conference on Information Fusion (Fusion)","volume":"161 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129048692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Generic object recognition based on the fusion of 2D and 3D SIFT descriptors 基于二维和三维SIFT描述子融合的通用目标识别

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23200

Miaomiao Liu, Xinde Li, J. Dezert, C. Luo

引用次数: 12

Information fusion with topological event spaces 拓扑事件空间的信息融合

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23214

R. Ilin, Jun Zhang

引用次数: 0

On the quality estimation of optimal multiple criteria data association solutions 多准则数据关联最优解的质量估计

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23202

J. Dezert, K. Benameur, L. Ratton, J. Grandin

引用次数: 4

Two novel methods for BBA approximation based on focal element redundancy 基于焦元冗余的两种新的BBA逼近方法

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.23206

Deqiang Han, J. Dezert, Yi Yang

引用次数: 9

Modified PCR rules of combination with degrees of intersections 改进了交叉度组合的PCR规则

2015 18th International Conference on Information Fusion (Fusion) Pub Date : 2015-07-06 DOI: 10.5281/ZENODO.48919

F. Smarandache, J. Dezert

引用次数: 17