Abstract: An analog circuit fault mode classification method comprises the following implementation steps: (1) collecting M groups of voltage signal sample vectors Vij to each of fault modes Fi of the analog circuit by using a data collection board; (2) sequentially extracting fault characteristic vectors VijF of the voltage signal sample vectors Vij by using subspace projection; (3) standardizing the extracted fault characteristic vectors VijF to obtain standardized fault characteristic vectors; (4) constructing a fault mode classifier based on a support vector machine, inputting the standardized fault characteristic vectors, performing learning and training on the classifier, and determining structure parameters of the classifier; and (5) completing determination of fault modes according to fault mode determination rules. The fault mode classifier of the present invention is simple in learning and training and reliable in mode classification accuracy.

Abstract: An analog circuit fault diagnosis method using a single testable node comprises the following steps: (1) obtaining prior sample data vectors under each fault mode; (2) computing a statistical average of the prior sample data vectors under each of the fault modes; (3) decomposing a signal by an orthogonal Haar wavelet filter set; (4) extracting the feature factor of the prior sample fault modes; (5) extracting a fault-mode-to-be-tested feature factor; (6) computing a correlation coefficient matrix and correlation metric parameters between the feature factor of the prior sample fault modes and the feature factor of the fault-mode-to-be-tested; and (7) determining a fault mode according to a maximal correlation principle by comparing the correlation metric parameters.

Abstract: A wireless sensor network sensor network charging method for multi-charge nodes, including the following steps: (1) establishing a WSNs model; (2) dividing field ranges of charging trolleys; and (3) charging the charging trolleys: (a) initializing: l=0 and j=0, wherein l is the total number of received alarm nodes, and j is the serial number of an alarmed node; (b) receiving an alarm signal, updating values of l and j, generating a shortest charging path sl, and computing an energy discriminate vector; (c) if a vector element satisfies Qlj?5% B and j=1, 2, . . .

Abstract: An analog circuit fault mode classification method comprises the following implementation steps: (1) collecting M groups of voltage signal sample vectors Vij to each of fault modes Fi of the analog circuit by using a data collection board; (2) sequentially extracting fault characteristic vectors VijF of the voltage signal sample vectors Vij by using subspace projection; (3) standardizing the extracted fault characteristic vectors VijF to obtain standardized fault characteristic vectors; (4) constructing a fault mode classifier based on a support vector machine, inputting the standardized fault characteristic vectors, performing learning and training on the classifier, and determining structure parameters of the classifier; and (5) completing determination of fault modes according to fault mode determination rules. The fault mode classifier of the present invention is simple in learning and training and reliable in mode classification accuracy.

Abstract: An analog circuit fault diagnosis method using a single testable node comprises the following steps: (1) obtaining prior sample data vectors under each fault mode; (2) computing a statistical average of the prior sample data vectors under each of the fault modes; (3) decomposing a signal by an orthogonal Haar wavelet filter set; (4) extracting the feature factor of the prior sample fault modes; (5) extracting a fault-mode-to-be-tested feature factor; (6) computing a correlation coefficient matrix and correlation metric parameters between the feature factor of the prior sample fault modes and the feature factor of the fault-mode-to-be-tested; and (7) detennining a fault mode according to a maximal correlation principle by comparing the correlation metric parameters.

Abstract: A wireless sensor network sensor network charging method for multi-charge nodes, including the following steps: (1) establishing a WSNs model; (2) dividing field ranges of charging trolleys; and (3) charging the charging trolleys: (a) initializing: l=0 and j=0, wherein l is the total number of received alarm nodes, and j is the serial number of an alarmed node; (b) receiving an alarm signal, updating values of l and j, generating a shortest charging path sl, and computing an energy discriminate vector; (c) if a vector element satisfies Qlj?5% B and j=1, 2, . . .

Abstract: The invention refers to a video recording device, having a timer comprising time input means for receiving a start time. The timer further comprises means for computing an end time based on the start time, start time entering conditions and a predetermined duration.