Search

In case of a metro disruption, the collaborative optimization for train rescheduling combined with passenger flow demand is quite essential since passenger flow is not adequately considered. To handle this gap, this study first establishes two separated optimization models, i.e. train rescheduling model and passenger flow model. On train side, short-turnings, fully cancellation or partial cancellation are taken into account to construct the disrupted timetable with the aims of minimizing train delays and timetable deviations from the original timetable. On passenger side, time-dependent OD passenger demand is incorporated into passenger’s arrival, passenger’s loading and alighting process. Then, an iterative framework is proposed to interact these two models. In each iteration loop, random short-turnings and cancellations are applied to calculate new departure and arrival for the services. Next, the corresponding departure time and arrival time is transferred to passenger flow model to evaluate the impacts on passenger’s travel time and the numbers of stranded passengers. The iterative results illustrates that less short-turning can effectively mitigate the numbers of stranded passengers at platforms but it comes at the cost of increasing train delays and passenger travel time. Cancelling trains can help to reduce passenger travel time as well but it should avoid causing heavier train delays to other non-cancelled services. Therefore, reasonable numbers of short-turnings and cancellations during disruption is trade-off between train delays and passenger delays during disruptions.

Influence maximization for opinion formation (IMOF) in social networks is an important problem, which is used to determine some initial nodes and propagate the most ideal opinions to the whole network. The existing researches focus on improving the opinion formation models to compute the opinion of each node. However, little work has been done to describe the IMOF process mathematically, and the current researches cannot provide an effective mechanism to deal with the IMOF. In this paper, the IMOF is formulated mathematically and solved by an iterative framework. At first, we describe the IMOF as a constrained optimization problem. Then, based on node influence and neighbor coordination, the weighted coordination model is proposed to compute the opinions of network nodes with the change of iterations. In particular, in order to determine top- $k$ influential nodes (i.e., seed nodes), an iterative framework for the IMOF, called IIMOF is presented. Based on the framework, the score and rank of each node by Iterative 2-hop algorithm, i.e., SRI2 is proposed to compute the influence score of each node. Based on small in-degree and high out-degree, one-hop measure is proposed to better reflect the rank of all initial nodes. We also prove that IIMOF converges to a stable order set within the finite iterations. The simulation results show that IIMOF has superior average opinions than the comparison algorithms.

We propose a model reduction procedure for approximating large-scale linear systems with quadratic output by means of lower dimensional systems with the same structure. The framework is based on an iteration that, at each step, computes left and right projections matrices (hence two-sided). This is done by means of solving two linear Sylvester equations.

Nowadays, more and more people have their own accounts in different social networks, and they might use the different email addresses or phone numbers in different networks, so how to identify the same person among different social networks become a vital problem, called network alignment. Users with different accounts are called anchor users, researches showed that using some known anchor users to predict the potential anchor links for the full network is an effective way. To predict more accurate anchor links, the paper proposes a new prediction framework ISS, based on a reality of partially aligned social networks, it applies supervised learning based on social feature extraction and strict stable matching, which improve the accuracy of the prediction result, what is more, we apply an iterative framework to refine known information and maximize the prediction results. Experiments have conducted in two realworld heterogeneous social networks, Foursquare and Twitter, and it demonstrates that ISS can predict anchor links among heterogeneous social networks very well and outperform other similar prediction methods.

To deal with the rigid template matching problem in real-world scenarios, we propose a novel iterative feature-pair updating framework which is also robust to high levels of outliers, such as background changing, complex nonrigid deformation and partial occlusion. Given a pair of template image and target image, we first extract a set of corresponding feature-pairs as candidates. Then, we propose a robust objective function under the iterative framework for discriminatively updating these candidates, where the space distance, appearance distance, and the overlapping percentage of feature pairs are integrated simultaneously. Finally, a hierarchical matching strategy is provided with the parameter discussion. Experimental results compared with the-state-of-art methods on public data sets demonstrate the effectiveness of the proposed method.

In this paper, the autofocus compressed sensing (ACS) imaging method is proposed to obtain the imagery with some unknown parameters in the forward model. The proposed method updates the unknown parameters of the measurement matrix and constructs the imagery alternately within an iterative framework. The unknown parameters, denoted as hyperparameters, are estimated using the nonlinear conjugate gradient method. The proposed ACS imaging method is validated using through-the-wall imaging radar data.

In this paper, the auto-focusing compressed sensing (ACS) algorithm is proposed to retrieve behind-the-wall image with unknown wall parameters. The ACS algorithm proposed updates the measurement matrix and constructs the behind-the-wall image alternately within an iterative framework. The nonlinear conjugate gradient method is used to estimate the wall parameters, where the gradient functions in analytical form are derived based on the generalized electromagnetic wave penetrating propagation model for through-the-wall radars. Real data are used to validate the proposed method. It is shown that the proposed method can yield finer resolution with less data as compared with the conventional through-the-wall imaging method, e.g. back-projection penetrating imaging method.

In this paper, we consider a speech reconstruction problem, which is efficiently solved via sparse representation. Though a variety of speech reconstruction methods based on the sparse representation are developed, they seldom take into account the intrinsic attributes of speech, e.g., harmonic structures. To address this issue, a harmonic-based sparse representation algorithm is proposed to emphasize harmonic correlations between the adjacent speech frames. Sequently, a corresponding sparse optimal model is presented with a harmonic regularization term, which can be efficiently solved in an iterative framework. Simulation results demonstrate that the proposed method can achieve a better performance to improve the quality of the reconstructed speech than other traditional sparse representation methods.

This paper presents a novel approach for detection and estimation of fundamental parameters of linear frequency modulation (LFM) signals, i.e., the initial frequency and Chirp rate. The proposed approach is based on sparse representation of noisy input signals over two specific dictionaries, each designed for finding a parameter of LFM signal. Moreover, an iterative framework is proposed for simultaneous sparse representation over the two dictionaries. Experimental results demonstrate that the presented method is comparable with the optimum transform for LFM signal estimation, Wigner-Hough, and furthermore, has significantly higher speed.

This paper presents a synthesis method and its experimental validation for microfluidic concentration gradient generators that use multi-stream laminar flow. The method is based on an iterative framework in which designs are evaluated with an analytical convection-diffusion model. Comparing to the expensive trial-and-error experimentation or the time consuming finite element simulation, this design scheme enables the efficient design of concentration gradient generators capable of generating complex concentration profiles. We describe the synthesis method, and validate it by experiments with devices that are designed based on the method. The concentration profiles obtained from the experiments agree well with the prescribed ones.