Dezert-Smarandache Theory Research Articles

An Improved Focal Element Control Rule

The computational complexity of evidence theory is a hot issue in current research. Dezert-Smarandache theory (DSmT) introduces conflicting focal element, which makes the calculation complexity increases sharply. This paper starts with the focal element control rule used by the approximate calculation method mostly. The examples show that the improved rule is effective and feasible in both Shafer model and DSm model.

Two-level combination approach for solving conflict evidences

Considering that the combination of Dempster rule in Dempster-Shafer Theory (DST) has disadvantages while dealing with highly conflict evidences,a new two-level combination method for fusing conflict evidences was proposed.Distinguishing between highly conflict evidence and lowly conflict evidence,the method adopted the PCR6 rule based on Dezert-Smarandache Theory (DSmT) on the first level of combination to solve high conflict and used Dempster rule to keep fast convergence speed and good calculation capability on the second level,which could manage conflict evidence with various degrees effectively and reasonably.The validity of the proposed method was demonstrated by calculation examples.

A Static Evidential Network for Context Reasoning in Home-Based Care

In home-based care, reliable contextual information of remotely monitored patients should be generated by correctly recognizing the activities to prevent hazardous situations of the patient. It is difficult to achieve a higher confidence level of contextual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, generated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fusion process (SEFP) as a context-reasoning method. The context-reasoning method processes sensor data with an evidential form based on the Dezert-Smarandache theory (DSmT). The DSmT approach reduces ambiguous or conflicting contextual information in multisensor networks. Moreover, we compare SEFP based on DSmT with traditional fusion processes such as Bayesian networks and the Dempster-Shafer theory to understand the uncertainty analysis in decision making and to show the improvement of the DSmT approach compared to the others.

Case Retrieval in Medical Databases by Fusing Heterogeneous Information

A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

A New Hybrid Method for Mobile Robot Dynamic Local Path Planning in Unknown Environment

In this paper, a hybrid approach for efficiently planning smooth local paths for mobile robot in an unknown environment is presented. The single robot is treated as a multi-agent system, and the corresponding architecture with cooperative control is constructed. And then a new method of information fusion namely DSmT (Dezert-Smarandache Theory) which is an extension of the DST (Dempster-Shafer Theory) is introduced to deal with the error laser readings. In order to make A* algorithm suitable for local path planning, safety guard district search method and optimizing approach for searched paths are proposed. Also, the parameters of internal Proportional-Integral-Derivative (PID) controller in the goto agent are adjusted through practical experiments for the use of smoothing the path searched by optimized A* algorithm. Finally, two kinds of experiments are carried out with Pioneer 2-DXe mobile robot: one uses the hybrid method proposed in this paper, the other uses artificial potential field (APF) which is the classical algorithm for local path planning. The experimental results reveal the validity and superiority of the hybrid method for dynamic local path planning. The approach presented in this paper provides an academic support for path planning in dynamic environment with moving objects in the field.

An Introduction to DSMT in Information Fusion

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or highly conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and para- doxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the e±ciency and the generality of this new theory.

A Particle Filtering and DSmT Based Approach for Conflict Resolving in case of Target Tracking with Multiple Cues

In this paper, we propose an efficient and robust method for multiple targets tracking in cluttered scenes using multiple cues. Our approach combines the use of Monte Carlo sequential filtering for tracking and Dezert-Smarandache theory (DSmT) to integrate the information provided by the different cues. The use of DSmT provides the necessary framework to quantify and overcome the conflict that might appear between the cues due to the occlusion. Our tracking approach is tested with color and location cues on a cluttered scene where multiple targets are involved in partial or total occlusion.

An approximate reasoning method in Dezert-Smarandache Theory

With the increment of focal elements number in discernment framework, the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottleneck problem to block the wide application and development of DSmT. Aiming at this difficulty, in this paper, a kind of fast approximate reasoning method in hierarchical DSmT is proposed. Presently, this method is only fit for the case that there are only singletons with assignment in hyper-power set. These singletons in hyper-power set are forced to group through bintree or tri-tree technologies. At the same time, the assignments of singletons in those different groups corresponding to each source are added up respectively, in order to realize the mapping from the refined hyper-power set to the coarsened one. And then, two sources with the coarsened hyper-power set are combined together according to classical DSm Combination rule (DSmC) and Proportional Conflict Redistribution rule No. 5 (PCR5). The fused results in coarsened framework will be saved as the connecting weights between father and children nodes. And then, all assignments of singletons in different groups will be normalized respectively. Tree depth is set, in order to decide the iterative times in hierarchical system. Finally, by comparing new method with old one from different views, the superiority of new one over old one is testified well.

Qualitative reasoning by computing with words in Herrera-Martínez’s linguistic model

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information (by linguistic labels) besides information expressed quantitatively (by numbers), since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martinez’s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory (DSmT) framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation (q1). The linguistic information which expresses the expert’s qualitative beliefs is expressed by means of mixed 2 Tuples (equidistant linguistic labels with a numeric biased value). Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based (q2 DSmT) fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.

Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models (i.e., a distribution function model (DFM) and an improved Herrera-Martinez’s model) jointly with the fusion rules developed in Dezert-Smarandache Theory (DSmT), in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert’s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.

Fusion of imprecise qualitative information

In this paper, we present a new 2-tuple linguistic representation model, i.e. Distribution Function Model (DFM), for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) framework. Such new approach allows to preserve the precision and efficiency of the combination of linguistic information in the case of either equidistant or unbalanced label model. Some basic operators on imprecise 2-tuple labels are presented together with their extensions for imprecise 2-tuple labels. We also give simple examples to show how precise and imprecise qualitative information can be combined for reasoning under uncertainty. It is concluded that DSmT can deal efficiently with both precise and imprecise quantitative and qualitative beliefs, which extends the scope of this theory.

Approach to Model Interest for a Planetary Rover through Dezert-Smarandache Theory

In this paper, we propose an approach for assigning an interest level to the goals of a planetary rover. Assigning an interest level to goals allows the rover autonomously to transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. The fusion yields an “interest map” that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analyzed, with limited human intervention, and reallocates its goals autonomously. The Dezert–Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us directly to model the behavior of the scientists that have to evaluate the relevance of a particular set of goals. The paper shows an application of the proposed approach to the generation of a reliable interest map.

A new approach of simultaneous faults diagnosis based on random sets and DSmT

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults’ model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is proposed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

Efficient combination rule of Dezert-Smarandache theory

The Dezert-Smarandache theory (DSmT) is a useful method for dealing with uncertainty problems. It is more efficient in combining conflicting evidence. Therefore, it has been successfully applied in data fusion and object recognition. However, there exist shortcomings in its combination rule. An efficient combination rule is presented, that is, the evidence's conflicting probability is distributed to every proposition based on remaining the focal elements of conflict. Experiments show that the new combination rule improves the reliability and rationality of the combination results. Although evidences conflict another one highly, good combination results are also obtained.

A new evidential trust model for open communities

Open communities allow anonymous members to join and leave anytime, in which interactions commonly take place between parties who have insufficient information about each other. The openness brings risk to both the service provider and consumer. Trust is an important tool in human life, as it enables people to get along with strangers. It is accepted that trust management is a potential way to solve the above problem in computational systems. A trust management mechanism can assist members in the open community to evaluate each other and decide which and whether to interact. It provides an incentive to a good behavior and gives a punishment to a malicious behavior. The representation of trust in current computational trust and reputation models can be classified into four categories. We present a new evidential trust model based on the Dezert–Smarandache theory, which has a higher expressiveness than the trust models based on the Dempster–Shafer theory of evidence. We propose a smooth and effective approach of trust acquisition. The general rule of combination of the Dezert–Smarandache theory is used for trust aggregation. We consider that trust is transitive and present a rule of trust transitivity for our model. Lastly, we evaluate our model experimentally.

An improved partial Haar dual adaptive filter for rapid identification of a sparse echo channel

Recently, a coupled echo canceller was proposed that uses two short adaptive filters for sparse echo cancellation. The first filter operates in the partial Haar domain and is used to locate the channel's dispersive region; the second filter is then centered around this location to cancel the echo in the time domain. In this paper, we propose feasible solutions to improve the performance of this partial Haar dual adaptive filter (PHDAF) in practical applications. These include: (1) alleviating the dependence of the PHDAFs performance on the echo-path impulse response's bulk delay; (2) improving the tracking performance of the PHDAF in response to abrupt changes in the echo path; and (3) extending the original PHDAF structure to support the cancellation of multiple echoes. The proposed algorithmic solutions exploit the Haar transform's polyphase representation and make use of a novel peak tendency estimator (PTE) based on Dezert–Smarandache theory (DSmT). The improved PHDAF is evaluated in terms of its mean-square error (MSE) curves and its mean time to properly locate a dispersive region for different SNRs. Results show that enhanced performance can be obtained using the proposed solutions at a minimal increase in computational cost.

Aggregation and Desaggregation of Information as an Adaptive Task

(Received January 19, 2007; In final form January 28, 2008) Purpose - The paper aims at supporting innovation into Philosophy of Science approach on the humankind long-termtask of aggregation and desaggregation of information. This is a systemic task, involving human ideas and ITC by boosting synergy, non-entropy, and ephemeral efficiency properties at multiple echelons. The paper is dedicated to a deeper understanding on the Information Fusion – as part of a dual universal task; into this context the paper proposes a Philosophy of Science approach on Dempster Sheffer Theory (DST ), Dezert-Smarandache Theory (DSmT ), Interactive Modeling (IM), Structural-Phenomenological Modeling (S-PM ). Basic sub-goals: a) Shrinking the gap between the humanist perspective and the technologist one. b) Promoting an equilibrated anthropocentric & technocentric perspective by starting with stimuli fromthe real world expressed by (old and new) ideas regarding the (re)construction of a World/e-World balance. c) Illustrating a positive e-World response by sketching innovative, synergy-based, interactive models, considering metaphors linked to those ideas. Design/methodology/approach - A general framework is suggested and an instance model is presented, that of Interactive Modeling approach. To maximise synergy, to minimise entropy, and to prolong the ephemeral efficiency - all their sources are considered. The approach is based on real-world “stimulus-ideas” (here, the shift from algorithmic towards sub-symbolic reasoning) and “metaphor-prompted e-world (IT-)responses”. Findings - A) Regarding the framework : a) Based on semantic transfer, metaphors are an encouraging path to get closer humanists and technologists, i.e., real world and e-world. b) Corollary: heavily metaphor-based research domains – as artificial life and intelligence – are valuable interdisciplinary “[John von Neumann & Nicholas Georgescu Roegen & John K. Galbraith, and Mihailo Mesarovic & Herbert Simon]’s niches”. c) Even if the approach proposed does not ensure, transdisciplinarity, it provides a workable framework and pays tribute to “systems thinking”. B) Regarding the model: the 2005 research stage of DSmT is described in (Smarandache, and Dezert, 2005); while this paper type of approach in (Bulz, 2005) – but, here, co-referring DST , DSmT , IM , S-PM.. Research limitations/implications - Since no undertaking – particularly when it involves a rather general framework – can be commented upon in an apodictic manner after its first application, the assertions stated above have a variable degree of plausibility and should be amended after more applications. Keywords : (e-)World, Metaphor, Human-machine systems, Synergy, Non-entropy, Ephemeral efficiency

Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory

Existing algorithms that fuse level-2 and level-3 fingerprint match scores perform well when the number of features are adequate and the quality of images are acceptable. In practice, fingerprints collected under unconstrained environment neither guarantee the requisite image quality nor the minimum number of features required. This paper presents a novel fusion algorithm that combines fingerprint match scores to provide high accuracy under non-ideal conditions. The match scores obtained from level-2 and level-3 classifiers are first augmented with a quality score that is quantitatively determined by applying redundant discrete wavelet transform to a fingerprint image. We next apply the generalized belief functions of Dezert–Smarandache theory to effectively fuse the quality-augmented match scores obtained from level-2 and level-3 classifiers. Unlike statistical and learning based fusion techniques, the proposed plausible and paradoxical reasoning approach effectively mitigates conflicting decisions obtained from classifiers especially when the evidences are imprecise due to poor image quality or limited fingerprint features. The proposed quality-augmented fusion algorithm is validated using a comprehensive database which comprises of rolled and partial fingerprint images of varying quality with arbitrary number of features. The performance is compared with existing fusion approaches for different challenging realistic scenarios.

Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition

This paper presents an integrated image fusion and match score fusion of multispectral face images. The fusion of visible and long wave infrared face images is performed using 2 ν -granular SVM which uses multiple SVMs to learn both the local and global properties of the multispectral face images at different granularity levels and resolution. The 2 ν -GSVM performs accurate classification which is subsequently used to dynamically compute the weights of visible and infrared images for generating a fused face image. 2D log polar Gabor transform and local binary pattern feature extraction algorithms are applied to the fused face image to extract global and local facial features, respectively. The corresponding match scores are fused using Dezert Smarandache theory of fusion which is based on plausible and paradoxical reasoning. The efficacy of the proposed algorithm is validated using the Notre Dame and Equinox databases and is compared with existing statistical, learning, and evidence theory based fusion algorithms.

Analysis of information fusion combining rules under the DSm theory using ESM inputs

In the context of electronic support measures, the use of the Dempster-Shafer theory is not flexible enough to obtain a clear evaluation of the state of allegiance for a detected target. With the new theory of plausible, paradoxical, and neutrosophic reasoning, the Dezert-Smarandache theory, we are able to get a clearer assessment. The current paper presents our research for these cases.