Quan Pan

Northwestern Polytechnical University, Xi’an, Liaoning, China

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Publications (335)208.15 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents the decentralized state estimation problem of discrete-time nonlinear systems with randomly delayed measurements in sensor networks. In this problem, measurement data from the sensor network is sent to a remote processing network via data transmission network, with random measurement delays obeying a Markov chain. Here, we present the Gaussian-consensus filter (GCF) to pursue a tradeoff between estimate accuracy and computing time. It includes a novel Gaussian approximated filter with estimated delay probability (GEDPF) online in the sense of minimizing the estimate error covariance in each local processing unit (PU), and a consensus strategy among PUs in processing network to give a fast decentralized fusion. A numerical example with different measurement delays is simulated to validate the proposed method.
    No preview · Article · Jul 2016
  • Hang Geng · Yan Liang · Feng Yang · Linfeng Xu · Quan Pan
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    ABSTRACT: In multi-sensor fusion, it is hard to guarantee that all sensors have an identical sampling rate, especially in the distributive and/or heterogeneous case. Meanwhile, stochastic noise, unknown inputs (UIs), and faults may coexist in complex environment. To this end, we propose the problem of joint optimal filtering and fault detection (FD) for multi-rate sensor fusion subject to UIs, stochastic noise with known covariance, and faults imposed on the actuator and sensors. Furthermore, the new scheme of optimal multi-rate observer (MRO) is presented and applied to detect faults. The observer parameters are determined optimally in pursuit of the UI decoupling and maximizing noise attenuation under the causality constraint due to multi-rate nature. Finally, the output estimation error of the MRO is used as a residual signal for FD via a hypothesis test in which the threshold is adaptively designed according to the MRO parameters. One numerical example is given to show the effectiveness of our proposed method.
    No preview · Article · May 2016 · Information Fusion
  • Xiaoxu Wang · Yan Liang · Quan Pan · Yonggang Wang

    No preview · Article · Jan 2016 · IEEE Transactions on Automatic Control
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    ABSTRACT: Many target tracking problems can actually be cast as joint tracking problems where the underlying target state may only be observed via the relationship with a latent variable. In the presence of uncertainties in both observations and latent variable, which encapsulates the target tracking into a variational problem, the expectation-maximization (EM) method provides an iterative procedure under Bayesian inference framework to estimate the state of target in the process which minimizes the latent variable uncertainty. In this paper, we treat the joint tracking problem using a united framework under the EM method and provide a comprehensive overview of various EM approaches in joint tracking context from their necessity, benefits, and challenging viewpoints. Some examples on the EM application idea are presented. In addition, future research directions and open issues for using EM method in the joint tracking are given.
    Full-text · Article · Dec 2015
  • Yong Liu · Yan Liang · Zhun‐Ga Liu · Quan Pan
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    ABSTRACT: An Interacting Multiple Model (IMM) filter suppresses outliers by integrating model-conditioned estimation and model mode recognition through adaptive mode switches based on a Bayesian probability update. However, it may face significant outlier-caused peak errors, which are undesirable or even impermissible in many applications, for example, target tracking. This paper considers that, from the view of the Dempster-Shafer Theory, the IMM filter actually fuses the Bayesian beliefs of the model mode with Dempster's Rule of Combination, which can deal with uncertainties instead of the evidence conflicts that may exist as outliers appear. Therefore, we propose the adaptive robust multiple model (RMM) filter through introducing practical expert knowledge in the belief function framework, which runs online to reduce outlier-caused peak errors. In RMM, the Likelihood Temporal Ratio (LTR) is incorporated to provide extra information on the tendency of mode switching. Moreover, expert rules are introduced to construct adaptive belief functions using discount techniques and to select the combination rule that better deals with evidence conflicts when outliers appear. The simulations in target tracking scenarios show that the proposed RMM filter significantly reduces outlier-caused peak errors and hence obtains a lower rate of track loss in the cluttered environment.
    No preview · Article · Nov 2015 · Asian Journal of Control
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    Kuang Zhou · Arnaud Martin · Quan Pan
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    ABSTRACT: Communities are of great importance for understanding graph structures in social networks. Some existing community detection algorithms use a single prototype to represent each group. In real applications, this may not adequately model the different types of communities and hence limits the clustering performance on social networks. To address this problem, a Similarity-based Multi-Prototype (SMP) community detection approach is proposed in this paper. In SMP, vertices in each community carry various weights to describe their degree of representativeness. This mechanism enables each community to be represented by more than one node. The centrality of nodes is used to calculate prototype weights, while similarity is utilized to guide us to partitioning the graph. Experimental results on computer generated and real-world networks clearly show that SMP performs well for detecting communities. Moreover, the method could provide richer information for the inner structure of the detected communities with the help of prototype weights compared with the existing community detection models.
    Full-text · Article · Oct 2015 · Physica A: Statistical Mechanics and its Applications
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    ABSTRACT: In classification of incomplete pattern, the missing values can either play a crucial role in the class determination, or have only little influence (or eventually none) on the classification results according to the context. We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory. At first, we try to classify the object (incomplete pattern) based only on the available attribute values. As underlying principle, we assume that the missing information is not crucial for the classification if a specific class for the object can be found using only the available information. In this case, the object is committed to this particular class. However, if the object cannot be classified without ambiguity, it means that the missing values play a main role for achieving 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 classified according to each training class, and the classification results represented by basic belief assignments 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 (which are particular disjunctions of several single classes). The 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.
    No preview · Article · Oct 2015
  • H.-Z. Li · J.-N. Zhang · Q. Pan · C.-G. Wang · L. Deng
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    ABSTRACT: Based on the characteristics of laser gyro's high bandwidth and digital output, two basic problems in laser gyro digital signal processing are put forward, i. e. rational sampling frequency and anti-aliasing filter. The laser gyro spectrum characteristics in domestic high-precision laser strapdown inertial navigation are analyzed, and it is found that there are some fixed frequency jamming signals in the laser gyro output at 2000 Hz sampling frequency. A noise restraint method is put forward by gradually increasing sampling frequency to locate spectrum peak position, and the frequency calculation formulas are given. It is verified that there are 3 times and 5 times of dithering frequency interference signals in laser gyro output signal by using these formulas. The domestic high-precision laser gyro's reasonable minimum sampling frequency is given, which should be 4500 Hz at least. In order to solve the problem of anti-aliasing filter, an oversampling technology should be used to reduce the power density of the laser gyro in frequency field. Meanwhile the low-pass filter should be employed to restrain noises and improve the laser gyro's accuracy. © 2015, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
    No preview · Article · Oct 2015
  • H.-Z. Li · Q. Pan · N. Yang · J.-N. Zhang · L. Deng
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    ABSTRACT: The power spectrum characteristics of ring laser gyros in laser inertial navigation system (LINS) are analyzed, which suggests that three laser gyros' dithering frequencies will interfere with each other in LINS. A novel low-pass finite impulse response (FIR) filter with three-notch is proposed to filter the dither frequencies of the ring laser gyros. A multi-notch FIR filter is designed with frequency-domain design method by specifying the amplitude of three dithering frequencies to be a small quantity. Three notches are produced naturally in the FIR amplitude frequency characteristic curve. The filter adopts a standard FIR algorithm. Meanwhile, the low-pass filter for the three laser gyros' signal in LINS and notching for the dithered signal are realized. A 24-order multi-notch FIR filter is given as an example to process the data of a high-precision LINS. The amplitude-frequency characteristic analysis shows that the new filter provides extra attenuation by at least 80 dB in the possible dithering frequency band due to three notch points concentrated. The extra decay can effectively prevent the filter performance degradation due to the laser gyro's dithering frequencies drift. Experiments suggest that the data oscillation of the output from the new filter is decreased by 5~8 times compared with that of the old filter in the LINS. Furthermore, the three-axis wobbling test results show that the navigation precision can also be improved. © 2015, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
    No preview · Article · Aug 2015
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    ABSTRACT: In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.
    Full-text · Article · Jul 2015
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    ABSTRACT: Among the computational intelligence techniques employed to solve classification problems, the fuzzy rule-based classification system (FRBCS) is a popular tool capable of building a linguistic model interpretable to users. However, it may face lack of accuracy in some complex applications, by the fact that the inflexibility of the concept of the linguistic variable imposes hard restrictions on the fuzzy rule structure. In this paper, we extend the fuzzy rule in FRBCS with a belief rule structure and develop a belief rule-based classification system (BRBCS) to address imprecise or incomplete information in complex classification problems. The two components of the proposed BRBCS, i.e., the belief rule base (BRB) and the belief reasoning method (BRM), are designed specifically by taking into account the pattern noise that existes in many real-world data sets. Four experiments based on benchmark data sets are carried out to evaluate the classification accuracy, robustness, interpretability and time complexity of the proposed method.
    Full-text · Article · Jul 2015 · Information Sciences
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    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.
    Full-text · Conference Paper · Jul 2015
  • Zhun-ga Liu · Yong Liu · Jean Dezert · Quan Pan
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    ABSTRACT: It can be quite difficult to correctly and precisely classify the incomplete data with missing values, since the missing information usually causes ambiguities (uncertainty) in the classification result. Belief function theory can well model such uncertain and imprecise information, and a new belief-based method for credal classification of incomplete data (CCI) is proposed using the K nearest neighbors (KNNs) strategy. In CCI, the KNNs of object (incomplete data) are respectively used to estimate the missing values, and one can obtain K versions of edited pattern with estimated values from the KNNs. The K edited patterns are classified by any classical method to get K pieces of classification results with different discounting (weighting) factors depending on the distances between the object and its KNNs, and global fusion of the K classification results represented by the basic belief assignments (bba’s) is used for credal classification of the object. The conflicting beliefs produced in the fusion process can well capture the imprecision degree of classification, and it will be transferred to the selected meta-class defined by the disjunction of several classes (i.e. the set of several classes) according to the current context. Thus, the incomplete data that is hard to correctly classify because of the missing values will be reasonably committed to proper meta-class, which is able to characterize the imprecision of classification and reduce the errors as well. Three experiments are given to illustrate the potential and interest of CCI approach.
    No preview · Article · Jul 2015 · Knowledge-Based Systems
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    ABSTRACT: In urban canyons, non-line-of-sight (NLOS) multipath interferences affect position estimation based on global navigation satellite systems (GNSS). This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test (MLRT) is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen׳s inequality allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment.
    No preview · Article · Jul 2015 · Signal Processing
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    Kuang Zhou · Arnaud Martin · Quan Pan

    Full-text · Article · Jun 2015
  • Xiaoxu Wang · Quan Pan · Yan Liang · Hanzhou Li
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    ABSTRACT: This paper focuses on designing Gaussian sum approximation filter for both accurately and rapidly estimating the state of a class of nonlinear dynamic time-delay systems. Firstly, a novel nonaugmented Gaussian filter (GF) is derived, whose superiority in computation efficiency is theoretically analyzed as compared to the standard augmented GF. Secondly, a nonaugmented Gaussian sum filter (GSF) is proposed to accurately capture the state estimates by a weight sum of the above-proposed GF. In GSF, each GF component is independent from the others and can be performed in a parallel manner so that GSF is conducive to high-performance computing across many compute nodes. Finally, the performance of the proposed GSF is demonstrated by a vehicle suspension system with time delay, where the GSF achieves higher accuracy than the single GF and is computationally much more efficient than the particle filter with the almost same accuracy.
    No preview · Article · Jun 2015 · Nonlinear Dynamics
  • Hua Lan · Zengfu Wang · Feng Yang · Quan Pan

    No preview · Conference Paper · Jun 2015
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    Lianmeng Jiao · Quan Pan · Xiaoxue Feng
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    ABSTRACT: The performance of nearest-neighbor (NN) classifiers is known to be very sensitive to the distance metric used in classifying a query pattern, especially in scarce-prototype cases. In this paper, a class-conditional weighted (CCW) distance metric related to both the class labels of the prototypes and the query patterns is proposed. Compared with the existing distance metrics, the proposed metric provides more flexibility to design the feature weights so that the local specifics in feature space can be well characterized. Based on the proposed CCW distance metric, a multi-hypothesis nearest-neighbor (MHNN) classifier is developed. The scheme of the proposed MHNN classifier is to classify the query pattern under multiple hypotheses in which the nearest-neighbor sub-classifiers can be implemented based on the CCW distance metric. Then the classification results of multiple sub-classifiers are combined to get the final result. Under this general scheme, a specific realization of the MHNN classifier is developed within the framework of Dempster-Shafer theory due to its good capability of representing and combining uncertain information. Two experiments based on synthetic and real data sets were carried out to show the effectiveness of the proposed technique.
    Preview · Article · Mar 2015 · Neurocomputing
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    Kuang Zhou · Arnaud Martin · Quan Pan
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    ABSTRACT: Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure.
    Full-text · Article · Jan 2015 · Communications in Computer and Information Science
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    Kuang Zhou · Arnaud Martin · Quan Pan
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    ABSTRACT: Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incom-plete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior infor-mation with the current imprecise knowledge conveyed by the observed data.
    Full-text · Article · Jan 2015 · Communications in Computer and Information Science

Publication Stats

2k Citations
208.15 Total Impact Points

Institutions

  • 2003-2015
    • Northwestern Polytechnical University
      • School of Automation
      Xi’an, Liaoning, China
  • 2013
    • University of Melbourne
      • Department of Electrical and Electronic Engineering
      Melbourne, Victoria, Australia
  • 2010
    • University of Alberta
      • Department of Electrical and Computer Engineering
      Edmonton, Alberta, Canada
  • 1999-2010
    • Northwestern Polytechnic University
      China, Maine, United States
  • 2008
    • Shanghai Jiao Tong University
      • State Key Laboratory of Medical Genomics
      Shanghai, Shanghai Shi, China
    • Xi'an Technological University
      Ch’ang-an, Shaanxi, China
    • Ruijin Hospital North
      Shanghai, Shanghai Shi, China
  • 2007
    • Nanjing Institute of Technology
      Nan-ching-hsü, Jiangxi Sheng, China
  • 2005
    • Robert-Bosch Krankenhaus
      Stuttgart, Baden-Württemberg, Germany
  • 2001
    • University of Illinois, Urbana-Champaign
      • Department of Electrical and Computer Engineering
      Urbana, Illinois, United States