17714396
Reliability Analysis of the Retrial Queue with Server Breakdowns and Repairs
Retrial queues have been widely used to model many problems arising in telephone switching systems, telecommunication networks, computer networks and computer systems, etc. It is of basic importance to study reliability of retrial queues with server breakdowns and repairs because of limited ability of repairs and heavy influence of the breakdowns on the performance measure of the system. However, so far the repairable retrial queues are analyzed only by queueing theory. In this paper we give a detailed analysis for reliability of retrial queues. By using the supplementary variables method, we obtain the explicit expressions of some main reliability indexes such as the availability, failure frequency and reliability function of the server. In addition, some special queues, for instance, the repairable M/G/1 queue and repairable retrial queue can be derived from our results. These results may be generalized to the repairable multi-server retrial models.
16882491
A MAP/G/1 Queue with Negative Customers
In this paper, we consider a MAP/G/1 queue with MAP arrivals of negative customers, where there are two types of service times and two classes of removal rules: the RCA and RCH, as introduced in section 2. We provide an approach for analyzing the system. This approach is based on the classical supplementary variable method, combined with the matrix-analytic method and the censoring technique. By using this approach, we are able to relate the boundary conditions of the system of differential equations to a Markov chain of GI/G/1 type or a Markov renewal process of GI/G/1 type. This leads to a solution of the boundary equations, which is crucial for solving the system of differential equations. We also provide expressions for the distributions of stationary queue length and virtual sojourn time, and the Laplace transform of the busy period. Moreover, we provide an analysis for the asymptotics of the stationary queue length of the MAP/G/1 queues with and without negative customers.
36836516
Visualization of geostress tensor along oil well trajectory
A simple and efficient method is proposed to visualize a symmetric second order tensor along 3D curves. It is also applied to an oil well model in which, along the well trajectory, the spatial distribution of geostress is visualized to predict the stability of the oil well bore. To give visual representations several forms are used: (1) we visually compare the magnitude of stresses to determine the mud weight; (2) based on a set of ellipse cross-sections along the well trajectory and the ellipticity of the ellipse we predict the shear failure direction and the stability of the oil well bore; (3) from a hinged curved surface, we emphasize the direction of principal stresses; (4) to give useful information we apply other skills such as magnified details, animation and an interactive interface. Our method avoids two defects in the references, that is, the visual cluster caused by discrete point tensor glyphs, and the integration problem of the hyperstreamline.
14077243
Tail asymptotics for the queue length in an M/G/1 retrial queue
In this paper, we study the tail behavior of the stationary queue length of an M/G/1 retrial queue. We show that the subexponential tail of the stationary queue length of an M/G/1 retrial queue is determined by that of the corresponding M/G/1 queue, and hence the stationary queue length in an M/G/1 retrial queue is subexponential if the stationary queue length in the corresponding M/G/1 queue is subexponential. Our results for subexponential tails also apply to regularly varying tails, and we provide the regularly varying tail asymptotics for the stationary queue length of the M/G/1 retrial queue.
3052468
Performance Modeling and Analysis for Resource Scheduling in Data Grids
Data Grids normally deal with large data-intensive problems on geographically distributed resources; yet, most current research on performance evaluation of resource scheduling in Data Grids is based on simulation techniques, which can only consider a limited range of scenarios. In this paper, we propose a formal framework via Stochastic Petri Nets to deal with this problem. Within this framework, we model and analyze the performance of resource scheduling in Data Grids, allowing for a wide variety of job and data scheduling algorithms. As a result of our research, we can investigate more scenarios with multiple input parameters. Moreover, we can evaluate the combined effectiveness of job and data scheduling algorithms, rather than study them separately.
8816096
Distributed User Access Control in Sensor Networks
User access control in sensor networks defines a process of granting user the access right to the information and resources. It is essential for the future real sensor network deployment in which sensors may provide users with different services in terms of data and resource access. A centralized access control mechanism requires base station to be involved whenever a user requests to get authenticated and access the information stored in the sensor node, which is inefficient, not scalable, and is exposed to many potential attacks along the long communication path. In this paper, we propose a distributed user access control under a realistic adversary model in which sensors can be compromised and user may collude. We split the access control into local authentication conducted by the sensors physically close to the user, and a light remote authentication based on the endorsement of the local sensors. Elliptic Curve Cryptography (ECC), a public key cryptography scheme, is used for local authentication. We implement the access control protocols on a testbed of TelosB motes. Our analysis and experimental results show that our scheme is feasible for real access control requirement.
663178
Efficient Kernel Discriminant Analysis via QR Decomposition
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and space. However, LDA/QR is based on linear projection, which may not be suitable for data with nonlinear structure. This paper first proposes an algorithm called KDA/QR, which extends the LDA/QR algorithm to deal with nonlinear data by using the kernel operator. Then an efficient approximation of KDA/QR called AKDA/QR is proposed. Experiments on face image data show that the classification accuracy of both KDA/QR and AKDA/QR are competitive with Generalized Discriminant Analysis (GDA), a general kernel discriminant analysis algorithm, while AKDA/QR has much lower time and space costs.
7169369
A two-stage linear discriminant analysis via QR-decomposition
Linear discriminant analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as image and text classification. An intrinsic limitation of classical LDA is the so-called singularity problems; that is, it fails when all scatter matrices are singular. Many LDA extensions were proposed in the past to overcome the singularity problems. Among these extensions, PCA+LDA, a two-stage method, received relatively more attention. In PCA+LDA, the LDA stage is preceded by an intermediate dimension reduction stage using principal component analysis (PCA). Most previous LDA extensions are computationally expensive, and not scalable, due to the use of singular value decomposition or generalized singular value decomposition. In this paper, we propose a two-stage LDA method, namely LDA/QR, which aims to overcome the singularity problems of classical LDA, while achieving efficiency and scalability simultaneously. The key difference between LDA/QR and PCA+LDA lies in the first stage, where LDA/QR applies QR decomposition to a small matrix involving the class centroids, while PCA+LDA applies PCA to the total scatter matrix involving all training data points. We further justify the proposed algorithm by showing the relationship among LDA/QR and previous LDA methods. Extensive experiments on face images and text documents are presented to show the effectiveness of the proposed algorithm.
654543
A survey on wavelet applications in data mining
Recently there has been significant development in the use of wavelet methods in various data mining processes. However, there has been written no comprehensive survey available on the topic. The goal of this is paper to fill the void. First, the paper presents a high-level data-mining framework that reduces the overall process into smaller components. Then applications of wavelets for each component are reviewd. The paper concludes by discussing the impact of wavelets on data mining research and outlining potential future research directions and applications.
37613840
Improving medical/biological data classification performance by wavelet preprocessing
Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.