Local floating coordinate system is used to represent the deployment motion of each rigid and flexible body of multibody system dynamics. Normal substructure modes are employed to describe the flexibility of a flexible body. Constraint equations establish the linkage between different bodies, part of them to specify positions and the others to specify orientations. System's governing equations are then derived using generalized coordinates by Lagrange methods. The resulting differential-algebraic equations are transformed to algebraic equations using backward differential formula corrector method, thus highly coupled nonlinear equations are obtained. However, Jacobian matrix of the nonlinear equations is hard to calculate, and then a quasi-Newton method based on Broyden–Fletcher–Goldfarb–Shanno update approach for the solution of the nonlinear equations is proposed. And a suitable line search approach is combined with the Broyden–Fletcher–Goldfarb–Shanno method to improve its efficiency. Some numerical results are reported to show efficiency of the proposed method. Afterwards, the Broyden–Fletcher–Goldfarb–Shanno method is integrated into multibody dynamics method. A rigid multibody case and a rigid-flex multibody case are further studied to show the efficiency of the proposed multibody solver.

A novel hybrid particle swarm optimization variant is proposed, which combines particle swarm optimization with Gaussian mutation operation based on random strategy. It applies Gaussian mutation on the positions of some randomly selected particles to enhance the search accuracy and convergence speed of swarm. The proposed algorithm can retain the diversity of population and improve the ability of global search. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. The results have been compared with three state-of-the-art particle swarm optimization variants. Experimental results show that the Gaussian mutation and the random select strategy help the proposed algorithm to achieve faster convergence rate and provide better solutions in most of the problems. Further, the new algorithm has been tested on the high-dimensional problems. The results show that the proposed algorithm is not sensitive to high-dimensional problems and can even give a better performance. Moreover, the sensitivity analysis of the parameters was carried out and the setting of the parameters was given.

Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension. Because some subspace segmentation approaches can be better applied in the high-dimensional space, three new subspace clustering models for gene expression data sets are proposed in this work. The proposed projection subspace clustering models have projection sparse subspace clustering, projection low-rank representation subspace clustering and projection least-squares regression subspace clustering which combine projection technique with sparse subspace clustering, low-rank representation and least-square regression, respectively. In order to compute the inner product in the high-dimensional space, the kernel function is used to the projection subspace clustering models. The experimental results on six gene expression data sets show these models are effective.

This paper presents the design of network-coded TCP (NCTCP). NCTCP is a reliable TCP-like transport protocol that uses network coding to dramatically improve the overall performance in networks with lossy links. By sending datagrams that represent a linear combination of packets, we are able to receive data in an orderless fashion and still push data in byte-correct order to the receiver.s application layer. To eliminate roundoff errors, we generate consecutive-ones linear combinations of packets that form totally unimodular matrices. We then decode these datagrams using an efficient technique. In addition, NCTCP has an additive increase multiplicative decrease (AIMD) congestion control mechanism that uses round-trip times to increase the performance on wireless networks without hurting the performance on wired networks. Unlike TCP.s AIMD mechanism, NCTCP does not use a congestion window. Furthermore, NCTCP can be implemented in user space and hence does not need any changes to the kernel. We test our protocol using ns-2 simulator using several performance measurements, namely, throughput, goodput, efficiency and receiver efficiency. Our simulations show that NCTCP performs better than standard TCP implementations and previously proposed network coding protocols; TCP/NC and CTCP. We also demonstrate that NCTCP is TCP-friendly and maintains the fairness property essential for the health of the internet.

The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function κ is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

Regularization is a key element of variational models in image processing. To overcome the weakness of models based on total variation, various high order (typically second order) regularization models have been proposed and studied recently. Among these, Euler's elastica energy based regularizer is perhaps the most interesting in terms of both mathematical and physical justifications. More importantly its success has been proven in applications; however it has been a major challenge to develop fast and effective algorithms. In this paper we propose a new idea for deriving a primal dual algorithm, based on Legendre–Fenchel transformations, for representing the elastica regularizer. Combined with an augmented Lagrangian for-mulation, we are able to derive an equivalent unconstrained optimization that has fewer variables to work with than previous works based on splitting methods. We shall present our algorithms for both the image restoration problem and the image segmentation model. The idea applies to other models where the elastica regularizer is required. Numerical experiments show that the proposed method can produce highly competitive results with better efficiency.

Automatic segmentation in the variational framework is a challenging task within the field of imaging sciences. Achieving robustness is a major problem, particularly for images with high levels of intensity inhomogeneity. The two-phase piecewise-constant case of the Mumford-Shah formulation is most suitable for images with simple and homogeneous features where the intensity variation is limited. However, it has been applied to many different types of synthetic and real images after some adjustments to the formulation. Recent work has incorporated bias field estimation to allow for intensity inhomogeneity, with great success in terms of segmentation quality. However, the framework and assumptions involved lead to inconsistencies in the method that can adversely affect results. In this paper we address the task of generalising the piecewise-constant formulation, to approximate minimisers of the original Mumford-Shah formulation. We first review existing methods for treating inhomogeneity, and demonstrate the inconsistencies with the bias field estimation framework. We propose a modified variational model to account for these problems by introducing an additional constraint, and detail how the exact minimiser can be approximated in the context of this new formulation. We extend this concept to selective segmentation with the introduction of a distance selection term. These models are minimised with convex relaxation methods, where the global minimiser can be found for a fixed fitting term. Finally, we present numerical results that demonstrate an improvement to existing methods in terms of reliability and parameter dependence, and results for selective segmentation in the case of intensity inhomogeneity.

The new era of global cooperation and competition has created challenges for manufacturing enterprises in selecting optimal services and collaboration enterprises. Multi-criteria decision making (MCDM) and evaluation has been applied in the domain of manufacturing service to address these issues. This study proposes a novel method for MCDM and evaluation of manufacturing services using collaborative filtering and interval-valued intuitionistic fuzzy (IVIF) theory. Quality of service (QoS)-aware collaborative filtering predicts missing QoS values in an IVIF rating matrix that is normalized from initial matrices that employ mixed numerical terms. An interval-valued intuitionistic fuzzy weighted arithmetic (IIFWA) aggregation operator derived from IVIF theory is utilized to evaluate and select the optimal service(s) or supplier(s). An illustrative example of manufacturing service evaluation and comparison are provided to validate the effectiveness and practicability of the proposed method.

The geodesic active contour (GAC) model has been widely used due to the high precision of edge detection and the continuity of boundaries. However, compared to the gradient vector flow (GVF) field, it usually has poor performance in the object boundaries with concave shape. In this paper, the improvements that integrate the good performance of the GAC model and the GVF field are described in detail and the GAC_GVF&B model combining the GAC model with the GVF field and a balloon force is proposed. The experimental results demonstrate that the proposed method is insensitive to the positions of initial curves and can segment complex-shape or multi-objects images correctly with higher convergence.