Many consumer products, e.g., printer’s cartridges, milk’s glass bottles, etc., are sold with reusable parts, which can be formally collected under a used-part-collection (UPC) program. In today’s markets with high volatility, risk is present for UPC operations. In this article, building via standard consumer utility-based models, we uncover the value of the UPC program in a production system. In the basic model, we consider the case when the manufacturer is risk averse. For both the cases with and without UPC, the optimal product pricing and quality decisions are determined and the respective manufacturer’s utility and consumer surplus are derived. The values of UPC to the manufacturer and consumers are found. We find that: 1) both quality improvement and UPC can be beneficial to consumers under some situations and 2) the optimal quality level, the optimal utility, and the consumer surplus are independent of the return fee. In the extended models, we explore two cases, namely, the case when consumers are risk averse, and the case with a collection platform, which can enhance the collection rate. We find that no matter what the level of consumers’ risk averse is, it reduces consumers’ benefits. Moreover, a higher platform’s power is beneficial to the manufacturer and consumers but may reduce the platform’s profit.

A blind identification method for non‐linear Wiener models is investigated. When the input signal of the system does not adopt a Gaussian random signal, the identification process with the input signal is changed into the one without input signal by using the first‐order statistical properties of the cyclostationary input signal and the inverse mapping of the non‐linear part of the model initially, moreover, all internal variables are recovered only based on the output signal. Then, the estimates of the order and parameters of the model are obtained by using the support vector machine regression theory and the higher‐order cumulants principle. Finally, compared with other methods, the simulation results show the effectiveness of the proposed method for identifying non‐linear Wiener models.

As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc . The network malware will spread in the SDN when the mobility rate q>qc . The malware will survive when q>qc and perish when q

The network distance service obtains the network latency among large-scale nodes. With increasing numbers of participating nodes, the network distance service has to balance the accuracy and the scalability. The network-coordinate methods scale well by embedding the pairwise latency into a low-dimensional coordinate system. The prediction errors are iteratively optimized by adjusting the coordinates with respect to neighbors. Unfortunately, the optimization process is vulnerable to the inaccurate coordinates, leading to destabilized positions. In this paper, we propose RMF, a relative coordinate-based distributed sparse-preserving matrix-factorization method to provide guaranteed stability for the coordinate system. In RMF, each node maintains a low-rank square matrix that is incrementally adjusted with respect to its neighbors' relative coordinates. The optimization is self-stabilizing, guaranteeing to converge and not interfered by inaccurate coordinates, since the relative coordinates do not have computational errors. By exploiting the sparse structure of the square matrix, the optimization enforces the $L_{1}$ -norm regularization to preserve the sparseness of the square matrix. Simulation results and a PlanetLab-based experiment confirm that RMF converges to stable positions within 10 to 15 rounds, and decreases the prediction errors by 10% to 20%.

The dynamic behaviour of many systems can be approximated by a static non‐linearity in series with a linear dynamic part. Systems with static input or output non‐linearities are very common in many engineering applications. Such models are known as block‐oriented models in the existing literature. The Hammerstein model is a special kind of block‐oriented model, where a non‐linear block is followed by a linear system. This study investigates the identification of Hammerstein systems with asymmetric two‐segment piecewise‐linear non‐linearities. The basic idea is to employ a key‐term separation technique and a corresponding auxiliary model initially. Then, the identification problem of non‐linear system is changed into a non‐linear function optimisation problem over parameter space. Further, the estimates of all the parameters of the non‐linear block, the linear subsystem and the noise part are obtained based on an improved particle swarm optimisation algorithm. Finally, simulation examples are included to demonstrate the effectiveness and robustness of the proposed identification scheme.

Information filtration is a kind of classical application of large-scale network content processing. It carries out filtration operation to successively coming information, remaining those information that meets users' demands and deleting those information that falls short of users' demands. Vector space model is one of its common classical models. This thesis made research and improvement on Vector Space Model (VSM), and on this basis put forward to introducing a kind of center space vector to solve the problem of thesaurus. This method has relatively high precision rate and recall rate, and improves the integral capability of the model.