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USB was designed to standardize the connection of computer peripherals (including keyboards, pointing devices, digital cameras, printers, portable media players, disk drives and network adapters) to personal computers, both to communicate and to supply electric power. It has become commonplace on other devices, such as smartphones, PDAs and video game consoles. USB has effectively replaced a variety of earlier interfaces, such as parallel ports, as well as separate power chargers for portable devices. Since USB is hot pluggable, the connectors would be used more frequently. In this context, its reliability and life span is vital. Generally, an exhaustive series of circular insertion/extraction testing can be carried out to evaluate the USB life span. However, this method is time consuming and costly. This paper proposes a novel testing method based on BPNN for USB connector life span, which can estimate the useful life by predicting the residual life of the connector. This method allows the entire test process to stop before the specimen fails, and predicts the specimen’s life in advance based on pre-test data. Modeling process is described in detail in this paper and the test results show that the model can realize accurate prediction within a certain range.

Focusing on a sharp decline in the performance of endpoint detection algorithm in a complicated noise environment, a new speech endpoint detection method based on BPNN (back propagation neural network) and multiple features is presented. Firstly, maximum of short-time autocorrelation function and spectrum variance of speech signals are extracted respectively. Secondly, these feature vectors as the input of BP neural network are trained and modeled and then the Genetic Algorithm is used to optimize the BP Neural Network. Finally, the signal's type is determined according to the output of Neural Network. The experiments show that the correct rate of this proposed algorithm is improved, because this method has better robustness and adaptability than algorithm based on maximum of short-time autocorrelation function or spectrum variance.