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.
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This fileset consists of 13 data files, 1 code file and 2 ReadMe files.The dataset data.mat is in .mat file format and therefore not openly-accessible. The following datasets, are an openly-accessible version of the .mat file: Fig2_1.txt in .txt file formatFig2_2.txt in .txt file formatFig2_3.txt in .txt file formatFig2_4.txt in .txt file formatFig2_5.txt in .txt file formatFig2_6.txt in .txt file formatraw_COVID.txt in .txt file formatraw_Helthy.txt in .txt file formatraw_Suspected.txt in .txt file formatraw_Tube.txt in .txt file formattable2_data.txt in .txt file formatwave_number.txt in .txt file formatThe code file is the following: code.m in .m file formatThe two ReadMe files are the following: readme.txt in .txt file format and readme.m in .m file format. Data in Fig2_1.txt, Fig2_2.txt, Fig2_3.txt, Fig2_4.txt, Fig2_5.txt and Fig2_6.txt were used to plot Figure 2 in the related manuscript.raw_COVID.txt contains the raw Raman spectroscopy data from the serum samples obtained from the 53 confirmed COVID-19 patients.raw_Helthy.txt contains the raw Raman spectroscopy data from the serum samples obtained from healthy individuals.raw_Suspected.txt contains the raw Raman spectroscopy data from the serum samples obtained from suspected cases (individuals suspected of COVID-19 infection)raw_Tube.txt contains the raw spectra data from cryopreservation tubes with saline solution inside.wave_number.txt contains data of the Raman Spectrum shift.table2_data.txt was used to generate Table 2 in the related manuscript.The code code.m was used for data processing. Software needed to access data: data.mat can only be accessed using the Matlab software. Running the code code.m also requires Matlab. Study aims and methodology: The recommended diagnosis method for the coronavirus disease (COVID-19 is a qPCR-based technique, however, it is a time consuming, expensive, and a sample dependent procedure with relative high false negative ratio. The aim of this study was to develop a widely available, cheap and quick method to diagnose COVID-19 disease based on Raman spectroscopy.A total of 157 serum samples were collected from 53 confirmed patients, 54 suspected cases (fever but not COVID-19) and 50 healthy controls. Raman spectroscopy was used to analyse these samples and the machine learning support vector machine (SVM) method were applied to the spectral dataset to build a diagnostic algorithm. The experimental set up consisted of a Volume Phase Holographic (VPH) spectrograph, deep-cooled CCD camera, and a Raman probe and laser. A total of 2355 spectra from 157 individuals were imported to MATLAB (R2013a) software (Math-200 works, Inc.).For more details on the methodology, please read the related article.
Abstract Liquid crystal polymer (LCP) has been employed as a major type of substrate in flexible printed circuit manufacturing due to its excellent electrical and mechanical properties. In order to improve the peel strength of LCP to copper, a three‐step process was proposed in this study. In the process, the surface of LCP was treated with oxygen plasma to create holes and increase the number of hydrophilic groups. The surface of copper was then oxidized and coated with silane coupling agent. Above surface treatment aimed to introduce amine group to facilitate the interconnection between copper and LCP and improve the surface roughness of the copper surface. The final samples were laminated by the above treated copper and LCP. In this study, different oxidation conditions were explored to analyze the morphology and composition of the copper surface after oxidation. The maximum peel strength of the sample prepared by this process was 8.92 N cm −1 . This developed process can effectively reduce product thickness compared to current methods in industry and can reduce lamination temperature compared to previous studies.
In recent years, p-GaN high electron mobility transistors (HEMTs) have become the most promising devices of the third-generation semiconductors for its excellent performance. However, the reliability problems of p-GaN HEMTs under repetitive short circuit stresses are significant issues for both researchers and engineers working on electronic science and technology. Due to the lack of sufficient knowledges, it is difficult to build physic-of-failure (PoF) solutions to predict the degradation processes. Nevertheless, existed measurement procedures can collect enough information of the degradation process of the devices. Hence, data-driven (DD) techniques are promising tools to monitor the degradation states of p-GaN HEMTs. However, state-of-art DD methods cannot depict the relationship of high-dimension features under the degradation processes. To overcome the weakness, we proposed a prediction model called PSO-based Volterra tensor network (PSO- VTN). The proposed model builds tensor network respect to a Volterra system and take PSO algorithms to optimize the corresponding parameters. Experiments show that PSO-VTN can successfully predict the output performance and transfer performance of the target devices with the historical data and provide support information for reliability decisions.
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.
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.
Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semisupervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-ofthe-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.
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.
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