Time series are widely used in many classification and regression tasks. However, numerous time series contain unavoidable missing data, making it challenging to model the temporal dynamics of sequential data. Various data imputation methods have been proposed to infer missing values in time series. Although sequences recorded at fixed time intervals are presented in discrete form, they possess an inherent temporal continuity, which is ignored in most existing approaches. In this paper, we propose an end-to-end Attentive Continuous-Time Generative Adversarial Network (ACGANet) to estimate unobserved values in irregular sequences. ACGANet captures the temporal dynamics by transforming the discrete sequence into the continuous-time flow, thereby modeling the underlying distribution of the real data. Furthermore, ACGANet employs an adversarial learning strategy to alleviate the error introduced by imputed values, with the discriminator distinguishing between real and generated samples. Additionally, ACGANet introduces the log-density of hidden temporal states as an auxiliary loss to further optimize the generator. This allows the model to simultaneously focus on the overall temporal dynamics of the time series and the underlying distribution of the missing data. Extensive experiments on three publicly available real-world datasets demonstrate that ACGANet achieves state-of-the-art performance in imputing incomplete time series. Moreover, both qualitative and quantitative analyses validate the effectiveness of the proposed model.

Summary Cluster ensemble combines multiple base clustering results in a suitable way to improve the accuracy of the clustering result. In the conventional cluster ensemble frameworks, pairwise constraints and constraint projections have not been used together, and spectral clustering algorithm is rarely adopted to serve as the consensus function. In this paper, we design a constraint projections for semi‐supervised spectral clustering ensemble (CPSSSCE) model. It takes advantages of spectral clustering algorithm and executes semi‐supervised learning twice. Compared to traditional cluster ensemble approaches, CPSSSCE is characterized by several properties. First, the original data are transformed to lower‐dimensional representations by constraint projection before base clustering. Second, a similarity matrix is constructed using the base clustering results and modified using pairwise constraints. Third, the spectral clustering algorithm is applied to process the similarity matrix to obtain a consensus cluster result. Extensive experiments on standard University of California Irvine Machine Learning Repository (UCI) and Microsoft datasets demonstrated that the CPSSSCE is superior to other cluster ensemble algorithms including a semi‐supervised spectral clustering ensemble.

SUMMARY On the basis of CSC (Cluster Supply Chain) networks with two four‐echelon single supply chains, chain‐to‐chain inventory transshipment between two retailers is taken into account in the context of the two single chains cooperation. The uncertain robust switched control system model is built. Meanwhile, the methodology of robust optimization is introduced to minimize the bullwhip effect, and the solution of optimal decision serials is explored utilizing H‐infinity switched control algorithm. The online decision‐making simulation framework structure is developed to demonstrate the switching procedure. The simulation example proves that chain‐to‐chain inventory transshipment in CSC networks and robust switch control method can effectively weaken the bullwhip effect, reduce stock, dwindle fluctuation of order, and improve response to cluster market. Copyright © 2011 John Wiley & Sons, Ltd.

A novel adaptive mutation particle swarm optimization (AMPSO) algorithm based on Fuzzy matter-element analysis for generalized dynamic constraints satisfaction (GDCS) was presented to resolve the coupling domain level and knowledge level constraints introduced by collaborative simulation results. Firstly, the Fuzzy relation-element optimization method (FREOM) was used to change the solution space into the optimization space by establishing the formalized model of fuzzy relation-element for GDCS, and the regulated correlation function was regarded as the fitness function judging the stand and fall of particle; Then, in the implementation process of PSO algorithm, the mutation mechanics was introduced to mutate the inactive particle and the particle with the smallest fitness according to mutation probability, which is intended to make the algorithm converge faster and respond better to changes in dynamic optimization problems; Finally, a design example is illustrated to show effectiveness of this proposed method.

Based on the research of knowledge modeling and the practice of engineering design, the knowledge expression system for knowledge-based cooperative design system was put forward, which includes the engineering language knowledge, the engineering data table knowledge, the engineering instance knowledge and the engineering graphic knowledge. According to the requirement of the collaborative development of enterprise group, using the resources of Chengdu-Deyang-Mianyang networked manufacturing and ASP platform, a knowledge-based cooperative design system has been developed and integrated with ASP platform. A reasoning system based on knowledge expression system has been developed and applied in the cooperative design system. The collaboration among enterprises and knowledge-based design have been implemented. The system has been put into practice in many enterprises.