Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partitions. Specifically, we align each partition to form a consensus cluster indicator matrix through a distinct rotation matrix. Moreover, a weight is assigned for each view to account for the clustering capacity differences of views. Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework. We demonstrate the effectiveness of our approach on several real datasets, where significant improvement is found over other state-of-the-art multi-view clustering methods.

Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. Among all PHM designs, testability design and maintainability design face critical difficulties. First, testability design requires much labor and knowledge preparation, and wastes the sensor recording information. Second, maintainability design suffers bad influences by improper testability design. We proposed a test strategy optimization based on soft-sensing and ensemble belief measurements to overcome these problems. Instead of serial PHM design, the proposed method constructs a closed loop between testability and maintenance to generate an adaptive fault diagnostic tree with soft-sensor nodes. The diagnostic tree generated ensures high efficiency and flexibility, taking advantage of extreme learning machine (ELM) and affinity propagation (AP). The experiment results show that our method receives the highest performance with state-of-art methods. Additionally, the proposed method enlarges the diagnostic flexibility and saves much human labor on testability design.

Recently, p-GaN gate high electron mobility transistors (HEMTs) have emerged as competitive participants for next-generation high-performance power supply applications. However, the threshold voltage ( V _th ) instability caused by short-circuit events jeopardizes the overall reliability of p-GaN gate HEMT and the power system in real applications. Hence, a noninvasive condition-based monitoring technique for critical device parameters is urgently required to enhance system safety without affecting the features of power electronic systems. In this paper, the threshold voltage instability dynamics of p-GaN gate HEMT under repetitive short stress were investigated to achieve high estimation accuracy and good monitoring efficiency. A double-phase adaptive neural network to predict the V _th degradation kinetics based on the historical degradation recordings of the target device is developed. The degradation process contains a monotonous increasing process and an oscillation process divided by random changing point subject to Weibull distribution. Based on such characteristics, the extreme learning machines were combined with classic activation functions and periodic activation functions to predict the threshold voltage tendencies of p-GaN HEMTs under repetitive SC stress. The experiment results validate that the developed model based on static investigations can provide degradation predictions with high accuracies. Besides, the proposed method endows substantial benefits for the conditional-based monitoring problem of other newly emerging semiconductor devices that feature multiple scenarios during the dynamic process and differences between individual units.

The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment.

In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

With large usage of mechanical and electric systems, condition monitoring and prognosis have urgently required improvements to meet increasing complexity and variable operating conditions. However, existed methods can hardly obtain high information usage and keep robustness considering the non-identity of objects. Considering both similarity and non-identity of degradation observations, we propose the correntropy-based aligned predictor (CAP) to estimate the degradation condition of different devices with the same type. We use the subspace clustering to provide an aligned matrix to describe the relationships between objectives and develop the ensemble learning technique with correntropy-based extreme predictor. The degradation prediction of IGBTs’ accelerated aging testing proves that CAP enhances the information usage during degradation prediction and achieve high robustness.