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The dynamic brain network can reflect time-varying changes of the BLOD signal fluctuations, and has been widely used in the research of brain diseases identification. This network consists of a set of connection matrices, where each connection matrix represents the relationship between brain regions under a certain period. Researchers often convert these matrices into vectors, and then use the K-means clustering method to divide these matrices into different brain states according to their vector-based distances. Through analyzing these states, they can identify some brain abnormalities. However, simply using the vector-based distances may lead to two problems: 1) it ignores the topological properties and underlying mechanisms of brain networks, and 2) it never considers individual differences between subjects. Hence, to solve these two problems, we propose a novel method, called GK-BSC, for constructing brain states with dynamic brain networks. Specifically, we first use the graph kernel rather than the vector-based distances to measure the similarities between connection matrices of the dynamic brain network. Then, we aggregate these matrices to generate brain states based on these calculated similarities. This aggregation operation is sustained several times on one subject, whereby each subject is represented by a set of hierarchical brain states. Finally, we extract features from these states, and feed them into the multi-instance support vector machine (MI-SVM) for identifying patients. Experiments on a real schizophrenia dataset suggest that our method not only improves the performance of schizophrenia identification, but also accurately locates the brain abnormalities.

In the era of paper-based contract, a lot of time, human labor and expenses are required to handle the process of contract drafting, contract signing, contract execution, and payment settlement. The emerging of electronic contract enhances the tedious signing process of paper-based contract and improves the efficiency of contract management. However, due to the centralized system architecture and the database-based storage schema, the stored contract data is at high risk of information leaking, data tampering and hacker attacks. In this study, we introduce Blockchain technology to the contract management, and develop a process-oriented contract management system (BEcontractor) for a Hangzhou-located power grid enterprise X, aiming to solve a series of security issues existing in the traditional electronic contract system. By deploying BEcontractor, procurement activities could be resumed online among X and its nation-widely commodity suppliers during COVID-19 epidemic. Up to September 2020, 6336 electronic contracts have been signed, with an accumulated amount of 6.5 billion. It is showed that the cost for accomplishing the contract signing process was significantly reduced, and the payment period was shortened from three months to around one month.

The vehicle information in used-car transactions is always asymmetric and disputes always happen in China. In order to reduce such disputes caused by the lack of transparency in a transaction, Blockchain technology is adopted to construct a trust mechanism for vehicle information storage and sharing in a transparent manner. In this work, a Blockchain-based vehicle history storing and tracking service, named BCVehis, is proposed. BCVehis allows vehicle owners, vehicle authority, mechanic workshops, insurance brokers and other related individuals/organizations to upload vehicle historical records via simple manners (i.e. mobile app or application programming interfaces). The system provides trustworthy vehicle history to used-car dealers, potential buyers, and other business-related parties. The design rationale and functional implementation of the BCVehis are introduced, and the increased deal volume in a local used-car dealer X is presented, which integrated BCVehis to its online dealing system.

Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high-computation cost. To address this problem, obtaining sparse coefficients in KPCA is of paramount importance, particularly for real-time process monitoring and large-scale processes. In this paper, a new sparse kernel principal component analysis via sequential approach, named SSKPCA is proposed for nonlinear process monitoring. We first incorporate elastic net regularization into the framework of KPCA to establish a modified optimization problem. Then, a sequential approach is employed to derive the solution. Different from the existing sparse KPCA method based on elastic net regularization, the extra computations associated with the kernel matrix, such as matrix inversion and matrix square root are avoided in the optimizing procedure for solving the modified optimization problem. Therefore, the proposed SSKPCA method is more efficient in numerical implementation. The SSKPCA-based T2 and squared prediction error (Q) statistics are constructed for fault detection. Furthermore, the sensitivity analysis principle is adopted for fault identification. A comparative study of Tennessee Eastman Process (TEP) is carried out to illustrate the ability and efficiency of the proposed SSKPCA-based nonlinear process monitoring method.

Kernel principal component analysis (KPCA) has been widely used in nonlinear process monitoring since it can capture the nonlinear process characteristics. However, it suffers from high computational complexity and poor scalability while dealing with real-time process monitoring and large-scale process monitoring. In this paper, a novel dimension reduction technique, local and global randomized principal component analysis (LGRPCA), is proposed for nonlinear process monitoring. The proposed LGRPCA method first maps the input space onto a feature space to reveal nonlinear patterns through random Fourier features. With the aid of random Fourier features, the proposed LGRPCA method is scalable and with much lower computational and storage costs. To exploit the underlying local and global structure information in the feature space, local structure analysis is integrated into the framework of global variance information extraction. The resulting LGRPCA can provide an improved representation of input data than the traditional KPCA. Thus, the proposed LGRPCA method is quite suitable for real-time process monitoring and large-scale process monitoring.T2 and squared prediction error (SPE) statistic control charts are built for fault detection using the proposed LGRPCA method. Furthermore, contribution plots to LGRPCA-based T2 and SPE (Q) statistics are established to identify the root cause variables through a sensitivity analysis principle. The superior performance of the proposed LGRPCA-based nonlinear process monitoring method is demonstrated through a numerical example and the comparative study of the Tennessee Eastman benchmark process.