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This paper presents a hybrid control scheme based on a super-twisting sliding mode control and radial basis function (RBF) neural network control method for micro gyroscopes with unknown model uncertainties and external disturbances. In consideration of unknown model of micro gyroscope system, this paper utilizes the RBF neural network to realize the adaptive approximation of the unknown part of the model and ensure that the controller does not rely on the precise mathematical model of the controlled system. The neural network adaptive law is obtained by using the Lyapunov method, and the stability and convergence of the closed-loop system are guaranteed by adjusting the adaptive weight. The super-twisting sliding mode control method is adopted in order to improve the convergence speed and weaken the chattering. Finally, the simulation results verify the superiority and validity of the proposed method and compare it with the super-twisting sliding mode control without RBF neural network. Simulation results indicate that the control method proposed in this paper is very effective and feasible, achieving the required dynamic and static performance. It not only effectively weakens the chattering of the control system but also ensures the convergence of the system in limited time and improves the property of the control system.

Mass localization is important in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, we present an automated classification system for the detection of masses in mammographic images. Suspicious regions are located with an adaptive region growing firstly. Then, the initial regions are further refined with narrow band based active contour, which can improve the segmentation accuracy of masses. CLBP (Complete Local Binary Pattern) texture features are extracted from the ROIs (regions of interest) containing the segmented suspicious regions. Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist's diagnosis. The method was evaluated on a dataset with 231 images, containing 245 masses. Among them, 125 images containing 133 masses are used to optimize the parameters and are used to train SVM. The remaining 106 images are used to test the performance. It obtained 1.36 FPsI at the sensitivity 76.8%. It shows that the proposed method is a promising approach to achieve low FPsI while maintain a high sensitivity.

Efficient T-cell detector maturation algorithm is one of the most important problems of anomaly detection. Aiming at solving problems of current algorithms, such as low TP value, fixed matching threshold value and large detector set size. A novel T-detector maturation algorithm based on cooperative co-evolution genetic algorithm is put forward in this paper. According to morphologic analysis of intrusion detection system and covering problem algorithm. And then each population evolves according to each self partition and their best population would be combined as the final matured detector set, which decreases redundancy of detectors and maintains diversity of detectors. The efficiency is proved by theoretical foundation and tests.

A secure cross-platform instant messaging software based on de-centralized structure model is designed and implemented. This software establishes a p2p overlay network without any central node, each node in this overlay network is called a peer, identified with an UUID, every connection between two peers in the overlay network is completely peer to peer. Two peers who trust each other utilize asymmetrical encryption scheme to create a secure pipe, then use this pipe to transmit a key, this key is used to transfer data between peers using symmetrical encryption scheme. To traverse Nat, data between two peers behind different Nat are bridged by a third peer who can be accessed by both sides, this bridge peer simply transmit data between peers but has not access to the encrypted data content. All peers keep route map for all peers link to it directly and use flooding technique to update the route map.

Classification of mammographic masses as malignant or benign may assist radiologists in reducing the biopsy rate without increasing false negatives. In this paper, we investigated the classification of masses with level set segmentation and multiple kernel learning. Based on the initial contour guided by radiologist, level set segmentation is used to deform the contour and achieves the final segmentation. Morphological features are extracted from the boundary of segmented regions. Linear discriminant analysis, support vector machine and multiple kernel learning are investigated for the final classification. Mammography images from DDSM were used for experiment. The method based on the level set segmentation and the morphological features achieved an accuracy of 76%. The experimental result shows that level set based segmentation can improve the characterization of masses compared with manually rough segmentation.

Workflow management systems (WFMS) facilitate the routine operation of business processes and gain popularity in recent years. To ensure the correctness of business process specification and execution, model verification (especially structure verification) must be conducted so that we can identify any violations and consequently take proper action to remove them in time. However, little progress has been made on the perfect business process model syntactic and semantic structure verification in the workflow management environment, especially simple efficient algorithm is lack. In this paper, we present a biologically-inspired algorithm: Wolf-pack algorithm (WPA) for workflow syntactic and semantic structure verification. Our algorithm responds to distinguishing syntactic structure inconsistencies (such as deadlock, lack of synchronization, dead loop) and semantic structure inconsistencies (such as data source conflict) from workflow specification. Furthermore, we apply the algorithm to JTangFlow workflow management system and achieved good results.

Logistics management is concerned with the planning, implementation and control of the movement of physical objects and the associated information. This associated information is the domain of logistics management information system (MIS). In recent years, the ability to realize the profit and strategic potential of efficient and effective logistics of garment commodity has increasingly depended on logistics MIS. Because of lacking a unified standard and solution of basic architecture and interface, traditional logistics MIS has many shortcomings, such as poor reusability and poor extensibility. In order to solve the problems, the logistics MIS of Garment Commodity based on WebGIS is proposed and analyzed. The model can build relatively independent Web Service on Intranet and Internet based on WebGIS by constructing core application program. It is easy to develop, integrate and maintain the model. The paper applies the model to the logistics MIS of Garment Commodity, which includes the management subsystem of order, electromechanical product information, bill information, and customer relations.

According to the mixed smart sensor model of IEEE1451.4, the improved mixed mode transducer node is realized. Through making good use of transducer electronic data sheet (TEDS) and network capable application processor (NCAP), this kind of node is equipped with configuring driver because of the different IEEE model, and can realize adding standardized functions of limited self-description to traditional sensor. Also this mixed-mode transducer node has the APIs of supporting continuous development and capability of connected to Internet.

Network and host Intrusion Detection Systems (IDS) have become a standard component in security infrastructures. As the action of intrusion represents variable, complicated, and uncertainty characteristic, they face so many problems to resolve for intrusion detection. Each approach has its strengths and weaknesses. We propose a hybrid IDS, which combines network and host IDS, with anomaly and misuse detection mode, utilizes auditing programs to extract an extensive set of features that describe each network connection or host session, and applies data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. We use an association rule to track all relevant data dependency rule sets for different access roles using a hierarchical structure. We identify malicious transactions from the transaction logs in the database using the data dependency rule sets. These rule sets are continuously updated and stored in a repository. The optimized algorithm actually improves the performance of IDS. Our approach is shown to reduce data access bottlenecks, and ensures minimal manual intervention for maintaining a secure database.

Network and host Intrusion Detection Systems (IDS) have become a standard component in security infrastructures. As the action of intrusion represents variable, complicated, and uncertainty characteristic, they face so many problems to resolve for intrusion detection. Each approach has its strengths and weaknesses. A truly effective intrusion detection system will employ both technologies. We discusses the differences in host- and network-based intrusion detection techniques to demonstrate how the two can work together to provide additionally effective intrusion detection and protection. We propose a hybrid IDS, which combines network and host IDS, with anomaly and misuse detection mode, utilizes auditing programs to extract an extensive set of features that describe each network connection or host session, and applies data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities.

With the demand of cooperative work between the various enterprise applications growing, communications between systems becomes the focus of attention. A prerequisite of joining an enterprise system is the ability to cope with the rigorous demands experienced within such systems. One of the most fundamental of these demands is the requirement for enterprise-level systems to have guaranteed reliable messaging between the participants of the system. The use of Java Message Service (JMS) for enterprise applications communication and integration is increasing very quickly. This paper introduces a new JMS transport protocol, Selector Based Transport Protocol (SBTP) for enterprise applications. The new protocol uses JMS to deliver messages according to the Queue and Publish/Subscribe principle. This paper provides a brief overview of the design of SBTP, evaluates its performance, and examines the benefits of the SBTP in comparison to the other available protocols for JMS; it then concludes and highlights plans for the development of the message transport protocols.

Mammography is currently regarded as one of the best ways to detect breast cancer in the early stage. However, due to the limitation in imaging condition and the subtleness of the difference between normal and abnormal features, it is generally difficult to interpret the mammograms. Thus, image enhancement techniques have been widely used in screening mammograms. In this paper, a multiscale contrast enhancement algorithm based on Laplacian Pyramid is developed to enhance the contrast of the mammograms and improve the discernibility of the abnormal features. In the proposed algorithm, an image is first decomposed into a multi-level Laplacian Pyramid and then the enhancement is performed in the reconstruction stage. A multiscale contrast measure is used to modify the coefficients iteratively level by level and the enhanced image is obtained at the lowest level. Experiments proved the effectiveness of the proposed algorithm.

Jacquard image denoising is an active research field in jacquard image processing and has attracted much attention in the past years. In this paper, we present a novel denoising algorithm within the framework of variational schemes and Beltrami manifold technique. Variational schemes for image denoising consist of minimizing a functional which incorporates both the data and a Beltrami flow term. The main advantage of the proposed algorithm is that it has both good noise reduction and edge protection capabilities. Experimental results show that the proposed method offers effective noise removal in real noisy jacquard images while maintaining fine structure of patterns.

The correct segmentation of textural pattern into different meaningful regions is one of the most important problems in automatic texture image recognition. In this paper, we presented a variational integration of shape prior statistics into a phase-field based segmentation process. By derivating the new phase field functionals with gradient shape policy, we obtain the interface evolution process for texture pattern. The interface evolving uses information from shape templates to guide its behavior to desired features. Experimental results on noisy texture images demonstrated the efficacy of the proposed algorithm.

Local Feature Extraction (LFE) algorithm is an effective feature extraction method developed in recent years. One of the shortcomings of the current LFE algorithm is that it can only process labeled data, and does not work well when the amount of the labeled data is limited. However, it is usually easy to obtain large amount of unlabeled data but only a few labeled data. In this paper, we propose a new feature extraction algorithm, called Semi-Supervised LFE (SSLFE), which can handle both labeled and unlabeled data to perform feature extraction. In the proposed algorithm, the labeled data are used to maximize the margin and the unlabeled data are used as regulations with respect to the intrinsic geometric structure of the data. The final projection matrix can be obtained by eigenvalue decomposition. Experiments on several datasets demonstrate that SSLFE achieves much higher classification accuracy than LFE.

Recently, Web Service technology has found its application in workflow management for supporting loosely coupled E-business processes. However, current Web services languages do not permit to describe distributed E-business services by lack of expressive power and formal semantics. This paper presents a novel method for modeling distributed E-business service composition with pi-calculus technology. Firstly, the syntactic structure of WS-BPEL is analyzed, and an approach to modeling inter-organizational e-business processes with pi-calculus is given. Then, based on the execution semantics of the pi-calculus, the composite behavior of distributed e-business system are defined precisely. Finally, a case study is carried and the effective of composition model is verified.

Jacquard image inpainting is an interesting new research topic in pattern preprocessing for jacquard CAD. Phase field model has been well acknowledged as an important method for image inpainting. This paper discussed the problem of jacquard image inpainting by approaching the phase field paradigm from a numerical approximation perspective. The basic idea is to represent the damaged pattern of interest in implicit form, and fill-in the damaged parts with a system of geometric partial differential equations derived from phase-field model. The novelty of our approach lies primarily in exploiting explicitly the constraint enforced by the numerical solving of the sequential evolving of phase-field model. Extensive experiments are carried out in order to validate and compare the algorithm both quantitatively and qualitatively. They show the advantages of our algorithm and its readily application to jacquard texture.

Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. The target optimization problem involved can be solved efficiently with eigenvalue decomposition. Experimental results on several datasets demonstrate the effectiveness of our method.

Locality sensitive discriminant analysis (LSDA) algorithm is a new data analysis tool for studying the class relationship between data points, which can utilize local geometry structure of the data manifold and discriminant information at the same time. A major disadvantage of LSDA is it that can only deal with vector data, and thus is often confronted with singularity problem. In this paper, an extension of LSDA is proposed, called two-dimensional locality sensitive discriminant analysis (2DLSDA), which is directly based on 2D image matrices for face recognition, can overcome the singularity problem and utilize the spatial information among pixels more effectively. Besides, based on the Schur decomposition, the projection matrices can be obtained efficiently with high numerical stability, and orthogonality of projection matrix is guaranteed. Experiments on both ORL and Yale datasets demonstrate that the proposed method can achieve better performance than PCA, LDA and LSDA methods.

Dimension reduction is critical in many areas of pattern classification and machine learning and many discriminant analysis algorithms have been proposed. In this paper, a Pairwise Covariance-preserving Projection Method (PCPM) is proposed for dimension reduction. PCPM maximizes the class discrimination and also preserves approximately the pairwise class covariances. The optimization involved in PCPM can be solved directly by eigenvalues decomposition. Our theoretical and empirical analysis reveals the relationship between PCPM and Linear Discriminant Analysis (LDA), Sliced Average Variance Estimator (SAVE), Heteroscedastic Discriminant Analysis (HDA) and Covariance preserving Projection Method (CPM). PCPM can utilize class mean and class covariance information at the same time. Furthermore, pairwise weight scheme can be incorporated naturally with the pairwise summarization form. The proposed methods are evaluated by both synthetic and real-world datasets.

Executing business workflows on large-scale heterogeneous distributed computing systems is a challenging task. The grid workflow is aimed at enabling resource sharing and coordinated problem-solving between computers and people in a distributed and heterogeneous manner. In this paper, we present a formal specification methodology for grid workflow modeling which is founded upon the Pi-calculus and CTR technology. This method works well for characterizing the behaviors and interactions of the workflow processes that belong to grid environment in terms of the semantics of pi-calculus and CTR methodology. Experimental results show that the proposed method can be used for grid workflow execution and can effectively execution of grid processes automatically.

Workflow modeling technology has seen an explosion of interest and advances in recent years. Numerous technical challenges have been addressed to provide concurrent workflow management systems which are required by dynamic application domains. Concurrent transaction logic (CTR) has become one of the most exciting methods for concurrent business process modeling. This paper presents a novel modeling method for flexible workflow specification. The method extends sequential transaction logic with logical connectives for concurrent execution of processes. It presents the semantic specification for workflow structures which are based on the proposed extended semantic logic. An experimental case is put forward to show that the proposed method is appropriate for modeling concurrent workflow systems under dynamic environments.

Image retouching is an iterative technology for repairing damaged areas from images. Most existing retouching algorithms are local in nature and extrapolate information from neighboring pixels into the damaged regions. In this paper, a novel retouching algorithm based on the Mumford-Shah (MS) model was proposed. First, the Mumford-Shah model was improved by imposing some explicit smooth constrains on the formation of discontinuities. Then, a sequence of functionals Gamma-convergent to the retouching model was proposed. Finally, the gradient flow equation associated to the sequence of functional was defined, and the finite difference approximation for numerical solving of the gradient flow equation was also presented. Experimental results on noisy jacquard images demonstrate the effectiveness of the proposed retouching algorithm.

Automatic pattern segmentation of jacquard images plays an important role in jacquard pattern analysis. This paper deals with the problem of low accuracy in segmentation of jacquard images under noisy environment A novel iterative relaxation algorithm based on Mumford-Shah model was proposed. In this algorithm, the Mumford-Shah model was approximated in the sense of /spl Gamma/-convergence by a sequence of discrete models defined on finite element spaces of adaptive triangulation. During each iteration, an adjustment procedure for the triangulation was enforced to characterize the essential contour structure of a jacquard pattern. A quasi-Newton algorithm was applied to find the absolute minimum of the discrete model at the current iteration. Experimental results on synthetic and jacquard images have shown the effectiveness and robustness of the algorithm.

Electronic commerce on the Internet has become exceedingly dynamic and inter-organizational in recent times. Interorganizational workflow management system has become an important paradigm for business-to-business electronic commerce applications. The modeling of interorganizational business processes is a challenging task, due to the high degree of autonomy and heterogeneity of the cooperative organizations. A number of standardization initiatives are working on standards for modeling languages that can be employed under interorganizational environments. This paper presents a formal specification methodology for interorganizational workflow modeling which is founded upon the pi-calculus process algebra. This method works well for characterizing the behaviors and interactions of the workflow processes that belong to different organizations in terms of the semantics of pi-calculus. Experimental results show that the proposed method works well for the modeling of electronic commerce systems under interorganizational environments.

This paper presents an adaptive-weighted hidden Markov models (AWHMM) method for classification of 3D models into a set of pre-determinated model classes. Two new features are proposed to capture model surface orientation information. In the method, each model class is represented by four HMMs corresponding to four type features. During the training process, for each type of feature, the feature statistics of each model class and the spatial dynamics are learned by an HMM. During the classification process, characteristics of the test model are analyzed by the HMMs corresponding to each model class. The likelihood scores provided by the HMMs are calculated, and the highest weighted sum score provides the class identification of the test model. Furthermore, with unsupervised learning, each HMM and type weight are adapted with test models, which results in better modeling over time. Based on experiments, the proposed algorithm achieves much better performance than a baseline method and better performance than HMMs using only two features with fixed weights method.

Currently available workflow management systems lack abilities for supporting interorganizational processes. In this paper, we present an approach to model interorganizational business processes based on the pi-calculus technique. This approach provides a formal foundation for processes with interorganizational interaction structures, appropriate for modeling distributed cooperative workflow systems.