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The extraction of parallel minor components algorithm in directional convergence in signal feature extraction was studied.By comparing the unweighted projection approximation subspace tracking (PAST) algorithm with the weighted PAST parallel minor component extraction algorithm,the evolution method of the minor component extraction algorithm was analyzed.Theorical analysis illustrated that the weighted rule was able to guide the angle evolution between the vectors of the state matrix and minor components.Finally,Matlab simulation verifies the validity of the proposed theory.

In the present work, based on the generalized principal component analysis, we propose a new approach to decompose the subspace of fault deviations, which is used for reconstruction-based fault diagnosis through principal component analysis (PCA) monitoring system. The proposed method is advanced since it lightens the computational burden by eliminating the irrelavant information and simplifying the fault subspace. The fault effects are extracted through analyzing the generalized principal components of the normal operating data and the fault data. The significant fault deviations that cause the alarming monitoring statistic are calculated. This is achieved by designing a two-part feature decomposition procedure. In the first part, the normal operating subspace is extracted through analyzing the generalized principal components of both the historical normal data and fault data. The fault-free part of the data is eliminated by projecting the data into the normal operating subspace. In the second part, principal component analysis is performed on the remaining part of the data, where the largest fault deviation directions are decomposed in order. By the two-part decomposition, an integrated fault subspace for all monitoring statistic indices is obtained, which separates the measurement data into two different parts for fault reconstruction. One part is related to the normal operating subspace, which is deemed to follow normal rules, and thus insignificant to remove alarming monitoring statistics. The other is related to the fault subspace, which contributes to the out-of-control signals. Theoretical support is constructed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with the data from the Tennessee Eastman (TE) benchmark process.

Aiming at the lack of information criterion functions of sub subspace,a novel criterion function was proposed by adding a penalty term to the Rayleigh quotient.Through analyzing the properties of all the stable points,it was proven that the criterion function exhibited the global maximum attained if and only if the weight matrices span the minor subspace.A minor subspace tracking algorithm was derived by gradient ascending method and its global convergence analysis was also accomplished.Numerical simulations and real application verifies the correctness of the criterion function and derived algorithm.

SRAM-based field programmable gate arrays (FPGAs) are widely used in mission-critical applications, such as aerospace and avionics. Due to the increasing working frequency and technology scaling of ultra-nanometer technology, single event transients (SETs) are becoming a major source of errors for these devices. In this paper, we propose a workflow for evaluating the behavior of SETs in SRAM-based FPGAs. The method is able to compute the propagation-induced pulse broadening (PIPB) effect introduced by the logic resources traversed by transient pulses. Besides, we developed an accurate look-up table (LUT) layout model able to effectively predict the kinds of the SETs induced by radiation-particle and to accurately mimic the phenomena of the SET generation and propagation. The proposed methodology is applicable to any recent technology to provide the SET analysis, necessary for an efficient mitigation technology. The experimental results achieved from a set of benchmark circuits mapped on a 28-nm SRAM-based FPGA and compared with the fault injection experiments demonstrate the effectiveness of our technique.

Generalized minor component analysis (GMCA) is of great use in modern signal processing. The GMCA algorithms can be simplified to extract the minor generalized eigenvector of the autocorrelation input matrices pencil. In contrast to batching methods, the Hebbian-rule-based algorithm can extract the minor generalized eigenvector online. Few Hebbian-rule-based GMCA algorithms have been reported in the literature, and most of them are not self-stabilizing. Thus, a novel algorithm for GMCA, which is advantageous in terms of good convergence speed, self-stabilizing property, and multiple generalized minor component extraction in sequence, is proposed in this paper. A theoretical analysis verifies these properties via matrix theory and the deterministic discrete-time method. Numerical simulations are conducted to further demonstrate the advantages of the proposed algorithm.

The partial least squares (PLS) method has been widely used in quality-related industrial process monitoring because of its ability to extract quality-related information. Generally, online quality monitoring data cannot be obtained in real time, and in this case, updating the online monitoring model is a serious challenge. In this paper, an online monitoring dynamic PLS (OMD-PLS) model that uses the relation between time-delay process data and time-delay quality data is proposed. To accurately monitor the quality-related and process-related fault data, we also propose an online monitoring dynamic concurrent PLS (OMDC-PLS) model based on OMD-PLS, which has the ability to detect slight deviations. Furthermore, an alarm-parameter alarm method based on the OMDC-PLS model is proposed and effectively reduces the false alarm rate. Finally, numerical simulations and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed methods.

In this paper, through the application of 3D design technology in the construction of 220kV Miluoxi substation, the main aspects of the application of 3D design technology in the construction are summarized, including inter discipline verification, collision inspection, real scene modeling, equipment 3D installation details, 4D construction simulation, VR technology application and mobile application solutions. This paper also summarizes the economic, management and social benefits of three-dimensional application, which can be used as a reference for the following projects.