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A major performance index for analyzing a stochastic transport network is the two-terminal capacity reliability Rd, defined as the probability that d units of goods can be successfully transported via stochastic arc capacities from the source to the destination. This paper presents an efficient d-minimal path method to calculate Rd based on some newly obtained results. The proposed method uses a simple method to check d-minimal path candidates and a more efficient approach to remove duplicate d-minimal paths that are the biggest obstacle in solving d-minimal paths, along with an indication of the advantage over the existing methods. Besides, sensitivity analysis is adopted to explore the most important arc whose reliability change affects the network reliability most significantly, which helps supervisor identify and enhance the critical arcs for improving the network reliability more effectively. Computational and application examples demonstrate the efficiency and utility of the method, respectively.

This paper presents a d -minimal cut based algorithm to evaluate the performance index R d +1 of a distribution network, defined as the probability that a specified demand d + 1 can be successfully distributed through stochastic arc capacities from the source to the destination. To improve the efficiency of solving d -minimal cuts, a novel technique is developed to determine the minimal capacities of arcs. Also, two new judging criteria are proposed to detect duplicate d -minimal cuts. Both theoretical and computational results indicate that our algorithm outperforms the existing methods. Furthermore, a real case study is provided to illustrate the application of the algorithm.

A number of real-world complex networks can be modeled as multistate networks for performance analysis. A multistate network consists of multistate components and possesses multiple different performance levels. For such a network, reliability is concerned with the probability of the network capacity level greater than or equal to a predetermined demand level d. One major method for multistate network reliability evaluation is using d-minimal paths. This paper proposes an efficient algorithm to find d-minimal paths. First, a new concept of qualified state vector is defined so as to fix a relatively smaller search space of d-minimal paths, and a sufficient and necessary condition for a qualified state vector to be d-minimal path is established. Then, the max-flow algorithm and the enumeration algorithm are integrated to search for d-minimal paths in the determined search space that is recursively divided into subspaces such that the searching efficiency can be increased as much as possible. Both analytical and numerical results show that the proposed algorithm is more efficient in finding all d-minimal paths. In addition, a case study related to power transmission network is performed to demonstrate the implication of network reliability.

This paper focuses on performance assessment of a freight network with stochastic capacities by using reliability analysis, in which a node denotes a supplier, a transfer center, a distribution center, or a market, while an arc denotes a logistics service provider offering the freight traffic service for a pair of nodes. Due to some uncertainties in real environment (for example, a proportion of vehicles owned by the provider may be reserved by other customers), the available capacity of the provider along each arc is stochastic. Thus, network reliability that at least d units of commodity demand can be successfully delivered from the source to the destination denotes the operational performance of such a freight network. A d-minimal path–based method is developed to evaluate the freight network reliability. To advance the efficiency of solving d-minimal paths, an improved model is established by redefining capacity bounds of arcs and minimal paths. Furthermore, a new concept of expected capacity grounded on network reliability is presented to measure the service performance of the freight network. A practical case related to the coal delivery network is studied to demonstrate the implications of both network reliability and expected capacity.

The cooperative output regulation problem for a class of nonlinear uncertain multi-agent systems is considered. Based on the distributed internal model, the problem is firstly transformed into a global stabilization problem of the augmented system. Then, using the backstepping design method, a distributed control law with its stability analysis is proposed to solve the global stabilization problem of the augmented system. Finally, a numerical simulation is made to show the efficacy of the analytic results.

This paper presents a (d, c)-minimal paths based algorithm to evaluate the reliability index R(d,c), defined as the probability that the source distributes a demand d successfully to the destination with the total distribution cost not exceeding budget constraint c. The proposed algorithm employs two schemes to reduce the search space of (d, c)-minimal paths: (1) by proposing some conditions, an improved method for solving (d, c)-minimal paths is developed; (2) an existing decomposition technique is applied to limit the search space. Computational results show a clear advantage of the proposed algorithm in seeking (d, c)-minimal paths.

Multi-state two-terminal reliability for level d (MS2TR d ) is one of the most important performance indices of a multi-state system. One common method for computing MS2TR d is using the multi-state minimal path (named d-MP). In this article, an efficient algorithm is presented to compute MS2TR d in terms of d-MPs. The main characteristics of the proposed algorithm include: (1) it requires neither minimal paths nor minimal cuts to be known; (2) it is a hybrid algorithm combining the max-flow method and the implicit enumeration method. Two examples are provided to illustrate the performance of the presented algorithm, and the computational results show that the hybrid algorithm is superior to the best-known method.

A more practical and desirable performance index of multi-state systems is the two-terminal reliability for level (d, c) (2TRd, c), defined as the probability that d units of flow can be transmitted from the source node to the sink node with the total cost less than or equal to c. In this article, a simple algorithm is developed to calculate 2TRd, c in terms of (d, c)-MPs. Two major advantages of the proposed algorithm include: (1) as of now, it is the only algorithm that searches for (d, c)-MPs without requiring all minimal paths (MPs) and the procedure of transforming feasible solutions; (2) it is more practical and efficient in solving (d, c)-MP problem in contrast to the best-known method. An example is provided to illustrate the generation of (d, c)-MPs by using the presented algorithm, and 2TRd, c is thus evaluated. Furthermore, the computational experiments are conducted to verify the performance of the presented algorithm.

Infrared and visible image fusion is an important precondition to realize target perception for unmanned aerial vehicles (UAV) based on which UAV can perform various missions. The details in visible images are abundant, while the target information is more outstanding in infrared images. However, the conventional fusion methods are mostly based on region segmentation, and then the fused image for target recognition can’t be actually acquired. In this paper, a novel fusion method of infrared and visible image based on target regions in discrete wavelet transform (DWT) domain is proposed, which can gain more target information and preserve the details. Experimental results show that our method can generate better fused image for target recognition.

Rough Set Theory is used to make uncertainty description of the real world, and attribute reduction is one of the core theories of Rough Set Theory. This article elaborates on the rough set theory, studies attribute reduction algorithm combined with the genetic algorithm, and finally demonstrate the feasibility and effectiveness by using the ROSETTA software.

The denoising of a noisy image using wavelet methods is very representative, however, the wavelet methods may smooth the edges while denoising and the optimal thresholds are hardly acquired. In this paper, an efficient algorithm of image denoising based on multi-objective optimization in the discrete curvelet transform (DCT) domain is proposed, which can achieve the Pareto optimal denoised image with the optimal curvelet thresholds. First, the second generation discrete curvelet transform (Fast DCT) is introduced, and the thresholding functions are analyzed; then the multiple criteria for image denoising are presented, and the relation between these criteria and the curvelet thresholds is analyzed; finally the algorithm of multi-objective constriction particle swarm optimization (MOCPSO) is designed to optimize the curvelet thresholds. In MOCPSO, a new crowding operator is used to maintain the population diversity; the adaptive mutation operator is introduced to avoid the earlier convergence; the uniform design is used to obtain the optimal combination of the algorithm parameters. Experiments indicate that the denoising method based on Pareto optimal curvelet thresholds is more effective than other methods, and can attain the Pareto optimal denoising results.