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Globecom Workshops

作者： Abualhaol, Ibrahim Y. Matalgah, Mustafa M. Khalifa Univ Sci Technol & Res Sharjah U Arab Emirates Univ Mississippi University MS 38677 USA

ISBN: (纸本)9781424488650

The performance analysis of multi-carrier relay-based Unmanned Airborne Vehicles (UAVs) network is studied analytically over fading channels. A relay-based topology with one ground control unit (GCU) is assumed, where the cooperative UAVs can communicate with the GCU directly or through relay. The outage probability of the system is defined as the probability that either the transmission rate over any of the links drops below a predefined minimum threshold for that link or the Relay-GCU link is not able to transmit the aggregate data from all relayed UAVs. Finite Mixture with expectation-maximization algorithm is utilized to derive a simple approximate expression for the probability density function (pdf) of the achievable bit rate assuming adaptive M-ary quadrature amplitude modulation (M-QAM). The outage probability and the average achievable bit rate of a cooperative multi-carrier system are derived over generalized Gaussian-Finite-Mixture fading channels.

关键词： GCU Gaussian channels Gaussian-finite-mixture fading channels M-QAM M-ary quadrature amplitude modulation cooperative communication cooperative multi-carrier system expectation-maximization algorithm fading channels ground control unit mobile radio multic

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AN EM-algorithm APPROACH FOR THE DESIGN OF ORTHONORMAL BASES ADAPTED TO SPARSE REPRESENTATIONS

AN EM-ALGORITHM APPROACH FOR THE DESIGN OF ORTHONORMAL BASES...

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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Dremeau, A. Herzet, C. INRIA Ctr Rennes Bretagne Atlantique F-35000 Rennes France

ISBN: (纸本)9781424442966

In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] optimizes a dictionary made up of the union of orthonormal bases. In this paper, we propose a probabilistic interpretation of Sezer's algorithm and suggest a novel optimization procedure based on the EM algorithm. Comparisons of the performance in terms of missed detection rate show a clear superiority of the proposed approach.

关键词： Sparse representations dictionary learning expectation-maximization algorithm

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Incremental Learning of Multivariate Gaussian Mixture Models

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Joint Conference of 20th Brazilian Symposium on Artificial Intelligence (SBIA) /SBRN/JRI

作者： Engel, Paulo Martins Heinen, Milton Roberto UFRGS Informat Inst BR-91501970 Porto Alegre RS Brazil

ISBN: (纸本)9783642161377

This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile robotics and animats. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. We present some preliminary results obtained using synthetic data and also consider practical issues as convergence properties discuss future developments.

关键词： Incremental Learning Unsupervised Learning Bayesian Methods expectation-maximization algorithm Finite Mixtures Clustering

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Condition prognosis of mechanical drives based on nonlinear dynamical models

Condition prognosis of mechanical drives based on nonlinear ...

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Conference on Control and Fault-Tolerant Systems (SysTol)

作者： Gasperin, Matej Juricic, Dani Boskoski, Pavle Jozef Stefan Inst Dept Syst & Control Jamova 39 Ljubljana Slovenia

ISBN: (纸本)9781424481545

Forecasting the condition of the equipment is becoming an important ingredient of the advanced maintenance and asset management systems. In this paper a probabilistic approach to the prognosis of damage progression in gearboxes is presented. It is based on a stochastic nonlinear grey-box model of the underlying wear phenomena. Model parameters are estimated from the available vibration records by using an iterative Maximum Likelihood procedure. The procedure relies on the expectation-maximization algorithm and the Unscented Kalman filter for estimation of hidden system states. The algorithm has been used to predict the normal operating horizon of a single-stage gearbox system. Several test runs of the system have been preformed to validate the algorithm.

关键词： asset management systems condition monitoring condition monitoring condition prognosis damage progression drives expectation-maximisation algorithm expectation-maximization algorithm gearboxes gears maximum likelihood estimation maximum likelihood procedure mechanical drives nonlinear dynamical models stochastic nonlinear model unscented Kalman filter vibration vibrations wear wear

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Modeling Multi-source Remote Sensing Image Classifier Based on the MDL Principle: Experimental Studies

Modeling Multi-source Remote Sensing Image Classifier Based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xia, Huaiying Hu, Rukun Xu, Bingxin Guo, Ping Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781424465880

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

关键词： minimum description length remote sensing image classification technique mixture model expectation-maximization algorithm

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Greedy Maximal Scheduling in Wireless Networks

Greedy Maximal Scheduling in Wireless Networks

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Li, Qiao Negi, Rohit Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781424456383

In this paper we consider greedy scheduling algorithms in wireless networks, i.e., the schedules are computed by adding links greedily based on some priority vector. Two special cases are considered: 1) Longest Queue First (LQF) scheduling, where the priorities are computed using queue lengths, and 2) Static Priority (SP) scheduling, where the priorities are preassigned. We first propose a closed-form lower bound stability region for LQF scheduling, and discuss the tightness result in some scenarios. We then propose an lower bound stability region for SP scheduling with multiple priority vectors, as well as a heuristic priority assignment algorithm, which is related to the well-known expectation-maximization (EM) algorithm. The performance gain of the proposed heuristic algorithm is finally confirmed by simulations.

关键词： closed-form lower bound stability region expectation-maximisation algorithm expectation-maximization algorithm greedy algorithms greedy maximal scheduling algorithm heuristic priority assignment algorithm longest queue first scheduling priority vector rad

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Applying Training Hidden Features to Joint Curve Evolution for Brain MRI Segmentation

Applying Training Hidden Features to Joint Curve Evolution f...

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11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Farzinfar, Mahshid Teoh, Eam Khwang Xue, Zhong Nanyang Technol Univ Sch Elect Elect Engn Singapore 639798 Singapore Cornell Univ Methodist Hosp Res Inst Weill Med Coll Houston TX USA

ISBN: (纸本)9781424478132

According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. The second group of techniques applies active contour models, in which the objective is to find the optimal partition of the image domain using a closed or open curve by using prior constraints on the shape variation. However, estimating optimal curve is intractable due to the incomplete observation data. This paper extends a previously reported joint active contour model for medical image segmentation in a new expectation-maximization (EM) framework, wherein the evolution curve is constrained not only by a shape-based statistical model but also by applying a hidden variable model from the image observation. In this approach, the hidden variable model is defined by the local voxel labeling computed from its likelihood function, depended on the image functions and the prior anatomical knowledge. Comparative results on segmenting putamen and caudate shapes in MR brain images confirmed both robustness and accuracy of the proposed curve evolution algorithm.

关键词： Statistical shape model active contours level set methods expectation-maximization algorithm

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System Identification of Nonlinear Dynamical Models: Application to Wastewater Treatment Plant

System Identification of Nonlinear Dynamical Models: Applica...

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IEEE International Conference on Control Applications Part of 2010 IEEE Multi-Conference on Systems and Control

作者： Gasperin, Matej Vrecko, Darko Juricic, Dani Jozef Stefan Inst SI-1000 Ljubljana Slovenia

ISBN: (纸本)9781424453634

The paper presents a novel approach to identification of stochastic nonlinear dynamic systems using efficient approximation methods. The motivation behind this work is to develop a computationally efficient and robust algorithm for estimation of wastewater treatment plant model parameters. The mathematical model of the plant is required for the application of advanced predictive control algorithms and condition monitoring. The presented algorithm employs the expectation-maximization algorithm to compute the Maximum likelihood estimates of the unknown model parameters. The algorithm uses the Unscented Transformation (UT) to approximate the posterior distribution of the random variable that undergoes a nonlinear transformations. The advantage of this approach lies in efficient approximation methods that greatly reduce the computational load of the algorithm and is therefore suitable for on-line implementation.

关键词： approximation method condition monitoring condition monitoring expectation-maximisation algorithm expectation-maximization algorithm industrial plants mathematical model maximum likelihood estimate model parameter nonlinear dynamical model nonlinear dynamical systems nonlinear transformation posterior distribution predictive control predictive control random processes random variable stochastic nonlinear dynamic system stochastic systems system identification unscented transformation wastewater treatment wastewater treatment plant

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Mixing Linear SVMs for Nonlinear Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2010年第12期21卷 1963-1975页

作者： Fu, Zhouyu Robles-Kelly, Antonio Zhou, Jun Australian Natl Univ Coll Engn & Comp Sci Canberra ACT 2601 Australia Univ New S Wales Australian Def Force Acad Canberra ACT 2600 Australia

In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of large-scale nonlinear datasets. The motivation is to exploit both the efficiency of linear SVMs (LSVMs) in learning and prediction and the power of nonlinear SVMs in classification. To this end, we develop a LSVM mixture model that exploits a divide-and-conquer strategy by partitioning the feature space into subregions of linearly separable datapoints and learning a LSVM for each of these regions. We do this implicitly by deriving a generative model over the joint data and label distributions. Consequently, we can impose priors on the mixing coefficients and do implicit model selection in a top-down manner during the parameter estimation process. This guarantees the sparsity of the learned model. Experimental results show that the proposed method can achieve the efficiency of LSVMs in the prediction phase while still providing a classification performance comparable to nonlinear SVMs.

关键词： Classification expectation-maximization algorithm mixture of experts model selection support vector machines

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RETRACTED: Development of censored data-based robust design for pharmaceutical quality by design (Retracted Article)

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第9-12期49卷 839-851页

作者： Cho, Byung Rae Choi, Yongsun Shin, Sangmun Clemson Univ Dept Ind Engn Clemson SC 29634 USA Inje Univ Dept Syst Management & Engn Gimhae 621749 Gyeong Nam South Korea

Robust design techniques, which are based on the concept of building quality into products or processes, are increasingly popular in many manufacturing industries. In this paper, we propose a new robust design model in the context of pharmaceutical production research and development. Traditional robust design principles have often been applied to situations in which the quality characteristics of interest are typically time insensitive. In pharmaceutical manufacturing processes, time-oriented quality characteristics, such as the degradation of a drug, are often of interest. As a result, current robust design models for quality improvement which have been studied in the literature may not be effective in finding robust design solutions. To address such practical needs, this paper develops a robust design model using censored data, which is perhaps the first attempt in the robust design field. We then study estimation methods, such as the expectation-maximization algorithm and the maximum likelihood method, in the robust design context. Finally, comparative studies are discussed for model verification via a numerical example.

关键词： Robust design Mixture experiments Pharmaceutical formulations Censored data expectation-maximization algorithm Maximum likelihood estimation

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