Dezert-Smarandache Theory Research Articles

Fusion of Identification Information from ESM Sensors and Radars Using Dezert–Smarandache Theory Rules

This paper presents a method of fusion of identification (attribute) information provided by two types of sensors: combined primary and secondary (IFF) surveillance radars and ESMs (electronic support measures). In the first section, the basic taxonomy of attribute identification is adopted in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft). These standards provide the following basic values of the attribute identifications: FRIEND; HOSTILE; NEUTRAL; UNKNOWN; and additional values, namely ASSUMED FRIEND and SUSPECT. The basis of theoretical considerations is Dezert–Smarandache theory (DSmT) of inference. This paper presents and uses in practice six information-fusion rules proposed by DSmT, i.e., the proportional conflict redistribution rules (PCR1, PCR2, PCR3, PCR4, PCR5, and PCR6), for combining identification information from different ESM sensors and radars. This paper demonstrates the rules of determining attribute information by an ESM sensor equipped with the database of radar emitters. It is proposed that each signal vector sent by the ESM sensor contains an extension specifying a randomized identification declaration (hypothesis)—a basic belief assignment (BBA). This paper also presents a model for determining the basic belief assignment for a combined primary and secondary radar. Results of the PCR rules of sensor information combining for different scenarios of a radio electronic situation (deterministic and Monte Carlo) are presented in the final part of this paper. They confirm the legitimacy of the use of Dezert–Smarandache theory in information fusion for primary radars, secondary radars, and ESM sensors.

Evidential data association based on Dezert–Smarandache Theory

Evidential data association based on Dezert–Smarandache Theory

THE NEW APPROACH TO APPLYING THE DEZERT-SMARANDACHE THEORY IN LAND-COVER CLASSIFICATION IN UAV-BASED REMOTE SENSING

Nowadays UAV-based Remote Sensing gives a new opportunities for conducting scientific research in a much more detail way. Classification is one of the most important procedures in Remote Sensing tasks. This procedure can be applied in solution of numerous ecological and practical tasks, such as: forest classification, determing of soil types, exploring of oil and gas. Classification of incomplete, imprecise and high conflicting data has always been and still remains the one of most important procedures of remote sensing. In this paper the new approach to applying Dezert-Smarandache Theory in UAV-based remote sensing problems is proposed. This theory can deal with imprecise and highly conflicting information. Dezert-Smarandache Theory and its main concepts are compared with Dempster-Shafer Theory and Probability Theory. Main advantages and disadvantages of these theories were described and analyzed in this paper. The development of Dezert-Smarandache Theory arises from the necessity to overcome the limitations of Dempster-Shafer Theory and Probability Theory. Dempster-Shafer Theory deals only with exhaustive and exclusive hypotheses, that can sometimes provide wrong results of classification. But exhaustive hypotheses can potentially overlap, they can not properly identified and defined. Dezert-Smarandache Theory deals with exhaustive and not mutually exclusive hypotheses. This theory deals with all hypotheses, all intersections and unions of these hypotheses. It is shown, that Dezert-Smarandache Theory can solve classification tasks more efficiently than Probability Theory and Dempster-Shafer Theory, when we combine imprecise and high conflicting data. It is described classical Dezert-Smarandache fusion rule in this paper. It is also considered numerical example with applying Dezert-Smarandache fusion rule for classification of multispectral aerospace images. The proposed approach to applying the Dezert-Smarandache Theory in land-cover classification can be applied in different agriculture and practical tasks, ecological monitoring and searching for minerals.

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system’s reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

An ECR-PCR rule for fusion of evidences defined on a non-exclusive framework of discernment

In the research of uncertain information processing, Dempster-Shafer Theory (DST) provides a framework for dealing with uncertain information, where evidence is defined on a Frame of Discernment (FOD) consisting of mutually exclusive elements. However, the requirement of exclusiveness on FOD sometimes is not satisfied, as shown in Dezert-Smarandache Theory(DSmT), a derivative of DST. In DSmT, the non-exclusiveness is expressed by propositions’ intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution (PCR) rule. In order to handle non-exclusive FODs, a new framework called D Number Theory (DNT) has been proposed recently, which quantifies the non-exclusive degree between propositions different from DSmT. In previous studies, an Exclusive Conflict Redistribution (ECR) rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD, but there are some deficiencies in the ECR rule. In this paper, a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a non-exclusive FOD. Within the proposed rule, the definition of conflict utilizes the idea of ECR’s exclusive conflict, and the disposal of conflict is following the idea of PCR’s proportional redistribution. Properties of the ECR-PCR rule are presented. The effectiveness of the proposed new rule is verified through numerical examples and applications, in comparison with other fusion methods.

An Efficient Multistage Fusion Approach for Smartphone Security Analysis

Android smartphone ecosystem is inundated with innumerable applications mainly developed by third party contenders leading to high vulnerability of these devices. In addition, proliferation of smartphone usage along with their potential applications in diverse field entice malware community to develop new malwares to attack these devices. In order to overcome these issues, an android malware detection framework is proposed wherein an efficient multistage fusion approach is introduced. For this, a robust unified feature vector is created by fusion of transformed feature matrices corresponding to multi-cue using non-linear graph based cross-diffusion. Unified feature is further subjected to multiple classifiers to obtain their classification scores. Classifier scores are further optimally fused employing Dezert-Smarandache Theory (DSmT). Strength of suggested model is assessed both qualitatively and quantitatively by ten-fold cross-validation on the benchmarked datasets. On an average of outcome, we achieved detection accuracy of 98.97% and F-measure of 0.9936.

A Study on Human Learning Ability during Classification of Motion and Colour Visual Cues and Their Combination

Abstract The paper presents a study on the human learning process during the classification of stimuli, defined by motion and color visual cues and their combination. Because the classification dimension and the features that define each category are uncertain, we model the learning curves using Bayesian inference and more precisely the Normalized Conjunctive Consensus rule, and also on the base of the more efficient probabilistic Proportional Conflict Redistribution rule No 5 (pPCR5) defined within Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. Our goal is to study how these rules succeed to model consistently both: human individual and group behaviour during the learning of the associations between the stimuli and the responses in categorization tasks varying by the amount of relevant stimulus information. The effect of age on this process is also evaluated.

DSmT-based three-layer method using multi-classifier to detect faults in hydraulic systems

Fault identification in hydraulic valves is essential in maintaining the reliability and security of hydraulic systems. Due to the nonlinear characteristics of hydraulic systems under noisy working conditions, it is difficult to extract fault features from vibration signals collected from the surface of the valve body. Therefore, a DSmT-based three-layer method using multi-classifier is proposed to detect multiple faults occurred in hydraulic valves. Firstly, the raw signals are personalized to construct the training samples and the unknown testing samples. Secondly, a three-layer structure of the hybrid model called the layered hybrid model is constructed, which is suitable for hydraulic valves to detect the faults of different fault groups (including coil fatigue in the actuator and the abrasion inside the valve) and improve the diagnosis accuracy obviously. Finally, classification methods are selected to classify fault groups in the first two layers, and then the fault types are identified in the third layer by the fusion results using the Dezert-Smarandache Theory (DSmT). Experimental investigations are performed to validate the performance of the present method using a hydraulic valve (solenoid controlled pilot operated directional valve) controlled the hydraulic test rig. The results show that the average accuracy of detecting twelve types of faults is about 98.1%, which are better than those using other methods. It is expected that the present DSmT-based three-layer method using multi-classifier can be applied to more complex hydraulic systems.

Dezert-Smarandache Theory-Based Fusion for Human Activity Recognition in Body Sensor Networks

Multisensor fusion strategies have been widely applied in human activity recognition (HAR) in body sensor networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this article, Dezert–Smarandache theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the kernel density estimation (KDE)-based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of basic belief assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to deal with the conflict between the obtained BBAs, proportional conflict redistribution-6 (PCR6) is applied to fuse the acquired BBAs. Moreover, the missing data of the involved sensors are addressed as ignorance in the framework of the DSmT without manual interpolation or intervention. Experimental studies on two real-world activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) are conducted, and the results shows the superiority of our proposed method over some state-of-the-art approaches proposed in the literature.

A hierarchical parallel fusion framework for egocentric ADL recognition based on discernment frame partitioning and belief coarsening

Recently, egocentric activity recognition has become a major research area in pattern recognition and artificial intelligence due to its high significance in potential applications in medical care, rehabilitation, smart home/office, etc. In this study, we develop a hierarchical parallel multimodal fusion framework for the recognition of egocentric activities in daily living (ADL). This framework uses the Dezert–Smarandache theory and is constructed around three modalities: location, motion and vision data from a wearable hybrid sensor system. The reciprocal distance and a trained support vector machine classifier are used to form the basic belief assignments (BBA) of location and motion. For vision data composed of egocentric photo streams, a well-trained convolutional neural network is utilized to produce a set of textual tags and the entropy-based statistics for these tags are used to construct the vision BBA. Discernment partitioning and belief coarsening theory are adopted for the hierarchical fusion of the three BBA functions from different ADL levels. Experimental results show that the recognition accuracy of the proposed fusion method was significantly higher than that of the methods based on single modality or modality combinations when our method was applied to real-life multimodal egocentric activity datasets. In addition, our method also achieved higher adaptability and scalability.

ADAPTIVE VISUAL TRACKING METHOD OF FUSING MULTIPLE INFORMATION

The objective is to present an adaptive tracking approach of visual objects in the framework of particle filters (PF) and conflict redistribution strategy based on Dezert-Smarandache theory (DSmT). A combination rule of conflict redistribution was introduced into the researches on visual objects tracking, position and color information of moving objects were combined by modified combination strategy information, and the objects tracking model of fusing multiple information on the condition of occluded objects was established effectively. On the basis of building the execution panel of objects tracking simulation platform, many tracking experiments in complicated scene were carried out to validate the correctness and availability of the introduced method by comparing with other tracking method. Results showed that the introduced approach had excellent adaptive ability for dealing with high conflict among evidences under different occlusion condition, and could solve partly high-level vision tracking problems efficiently.

DSmT-Based Ultrasonic Detection Model for Estimating Indoor Environment Contour

As for uncertainties of ranging and direction angle in the ultrasonic sensor measurement, an ultrasonic distance measurement model is first proposed for representing these uncertainties through analyzing the working principle of the ultrasonic sensor, which can be adopted to detect the contour of wall plan and cylinder. Moreover, the Dezert–Smarandache Theory (DSmT) method is employed to fuse the uncertainty data measured by using the ultrasonic sensor. Next, extended Hough transform (EHT) and least-squares method (LSM) are combined to identify the environmental contour. Then, the measurement uncertainty of the ultrasonic sensor is analyzed for setting the threshold Th used for distinguishing line and cylinder, and the detection range of the cylinder radius is estimated. Finally, we build an indoor environment and design the ultrasonic sensor hardware system to detect the indoor environment for experimental verification. The indoor environment contour obtained in the experimental results is consistent with the real environment, which illustrates the feasibility and effectiveness of the proposed method. The proposed method has certain reference value for research of simultaneous localization and mapping (SLAM) of the mobile robot.

Method of classification of global machine conditions based on spectral features of infrared images and classifiers fusion

ABSTRACTThis paper describes an original method of global machine condition assessment for infrared condition monitoring and diagnostics systems. This method integrates two approaches: the first is processing and analysis of infrared images in the frequency domain by the use of 2D Fourier transform and a set of F-image features, the second uses fusion of classification results obtained independently for F-image features. To find the best condition assessment solution, the two different types of classifiers, k-nearest neighbours and support vector machine, as well as data fusion method based on Dezert–Smarandache theory have been investigated. This method has been verified using infrared images recorded during experiments performed on the laboratory model of rotating machinery. The results obtained during the research confirm that the method could be successfully used for the identification of operational conditions that are difficult to be recognised.

Iris recognition system based on a new combined feature extraction method

Recent science studies are interested in automatic systems without human intervention. The security field is in a great need of automatic identification system based on biometric properties. The uniqueness of the texture present in the human iris is a natural password. In this paper, we propose a novel iris recognition approach based on a combination of two systems. The first system extracts the local variation of the mean and the variance. The second system is based on the rotation invariant neighbourhoodbased binary pattern method (Hamouchene and Aouat, 2014a). Two sets of support vector machines (SVM) are used to train each system. Dempster-Shafer theory is used to distribute unitary mass over the two output sets of SVMs. Finally, the combined belief measures are transformed to a probability by applying the Dezert-Smarandache theory. In the experiments, the proposed system is compared to famous iris recognition systems and obtained better recognition rates.

Oral Health Diagnosis by Using Combination of Evidence in Dezert-Smarandache Theory

Based on World Health Organization (WHO) children and adults have a problem with their oral health, such as Dental cavities and periodontal disease. It is not easy to obtain the high convince level of result of the dental and periodontal diseases. Because each of them have different degrees of uncertainty and there have several discounting factors (error rates) in different of survey. To solve this problem we propose the Dezert-Smarandache Theory (DSmT) for efficient combination of uncertain, imprecise and highly conflicting sources of information. Moreover, we apply the SEFP as a context reasoning. Finally, we make the simulation by using 12 surveys and compare Propotional Conflict Redistribution 5 (PCR5) and Dempster-Shafer Theory (DST) to show the belief or probability for the low, a heavy, high and ultra-high risk situation.

Fusion of multispectral imagery and DSMs for building change detection using belief functions and reliabilities

ABSTRACTThe extraction of building changes from very high resolution satellite images is an important but challenging task in remote sensing. Digital Surface Models (DSMs) generated from stereo imagery have proved to be valuable additional data sources for this task. In order to efficiently use the change information from the DSMs and spectral images, belief functions have .been introduced. In this article, two-step building change detection fusion models based on both Dempster-Shafer Theory (DST) and Dezert-Smarandache Theory (DSmT) frameworks are proposed. In the first step, basic belief assignments (BBAs) of the change indicators from images and DSMs are calculated by using a refined sigmoidal BBA model. Then these BBAs are employed for the new proposed building change detection decision fusion approach. In order to cover the miss-detections introduced by the wrong height values of the DSMs and incomplete information from images, disparity maps from the DSM generation procedure and shadow maps from the multispectral channels are adopted to generate reliability maps, which are further integrated to the fusion models. In the last step, building change masks are generated based on four decision-making criteria. In the experimental part of this work, we evaluate the performance of this new building change detection method on real satellite images thanks to a building change reference mask representing the ground truth. Substantial accuracy improvements are achieved when comparing the new results with those obtained from classical 3D change detection approaches.

Online Object Tracking via Novel Adaptive Multicue Based Particle Filter Framework for Video Surveillance

Multicue based object tracking frameworks have been extensively explored due to their numerous applications in the field of computer vision. However, the online adaptive fusion of multicue under scale and illumination variations, partial or full occlusion, background clutters and object deformation remains an open challenge problem. In order to address this, we propose an online visual tracking algorithm using adaptive integration of multicue in a particle filter framework. The particle level fusion process is modelled as Shafer’s model with a power set defined over two focal elements. Partial conflicting masses and conjunctive consensus among three cues are estimated for each evaluated particle. Partial conflicts among cues are redistributed using Dezert-Smarandache Theory (DSmT) based proportional conflict redistribution rules (PCR-6). Additionally, context sensitive transductive cues reliabilities are used for discounting the particle likelihoods for quick adaptation of tracker. In the proposed model, automatic boosting of good particles and suppression of low performing particles not only improves resampling process but also enhances tracker accuracy. Experimental validation over benchmarked video sequences reveals that the proposed multicue tracking framework outperforms state-of-the-art trackers under various dynamic environmental challenges.

Characterization of the Earth's Surface State by Unsupervised Classification: Case of Vegetated, Aquatic and Mineral Surfaces

In this study, we propose an unsupervised classification scheme based on the Dempster-Shafer Theory (TDS) and the Dezert-Smarandache Theory (DSmT) to characterize vegetated, aquatic and mineral surfaces. From pre-processed ASTER satellite images (georeferencing, geometric correction and 15 m re-sampling), neo-channels were produced by determining the spectral indices NDVI, MNDWI and NDBaI, considered as sources of information for classification of a given pixel. NDVI is a contrast function to highlight vegetation. By account, the MNDWI makes it possible to characterize the water and the NDBaI makes it possible to recognize the mineral resources. Then, we modeled respectively the formalisms of the DST and the DSmT, these formalisms are modeling tools close to advanced probabilities based on the notions of belief and fusion functions to take into account certain imperfections (uncertainty, ignorance, etc.) encountered in the acquisition of images. In addition, the DST manages a formalism of disjunction between the sources during the DSmT simultaneously manages a disjunction and a conjunction between the sources. Next we realized the algorithms and related codes that we implemented in the MATLAB environment. Our contribution lies in taking into account the imperfections (inaccuracies and uncertainties) linked to source information through the use of mass functions based on a simple Gaussian distribution support model in order to model each focal element independently of the others and to evaluate the belonging of a pixel to a class with respect to the majority of elements representing said class. The resulting results show that the DST approach is relatively satisfactory for the unsupervised classification of mineral surfaces and aquatic surfaces while it is not satisfactory for vegetated surfaces according to all proposed models. As for the DSmT, it presents satisfactory results for all the models proposed. The model with the exclusion integrity constraint E∩V ∩ M = φ was selected as the best model because having, in addition to an average rate of well-graded pixels of 93.34%, a compliance rate (96, 37%) with the terrain higher than those of the other models implemented.

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment.

To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability.

DSmT Decision-Making Algorithms for Finding Grasping Configurations of Robot Dexterous Hands

In this paper, we present a deciding technique for robotic dexterous hand configurations. This algorithm can be used to decide on how to configure a robotic hand so it can grasp objects in different scenarios. Receiving as input, several sensor signals that provide information on the object’s shape, the DSmT decision-making algorithm passes the information through several steps before deciding what hand configuration should be used for a certain object and task. The proposed decision-making method for real time control will decrease the feedback time between the command and grasped object, and can be successfully applied on robot dexterous hands. For this, we have used the Dezert–Smarandache theory which can provide information even on contradictory or uncertain systems.