Zhao Lin

Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach.

Lithium sulfide is an attractive cathode material with a high theoretical specific capacity. However, the poor cycle life and rate capability have remained significant challenges, preventing its practical application. Here, Li2S spheres with size control have been synthesized for the first time, and a CVD method for converting them into stable carbon-coated Li2S core-shell particles has been successfully employed. These Li2S@C particles with protective and conductive carbon shells show promising specific capacities and cycling performance with a high initial discharge capacity of 972 mAh g -1 Li2S at the 0.2C rate. Even with no added carbon, a very high Li2S content electrode composed of 98 wt % 1 μm Li2S@C spheres and 2 wt % binder shows rather stable cycling performance, and little morphology change after 400 cycles at the 0.5C rate. © 2014 American Chemical Society.

This research investigated how interpersonal communication with a large audience can influence communicators’ attitudes. Research on the saying-is-believing effect has shown that when an individual’s attitude is perceived in advance by a communicator, the communicator tunes the message to the person, which biases the communicator’s attitude toward the person’s attitude. In this study, we examined the conditions under which audience tuning and attitude bias can occur with audiences containing more than one individual. We manipulated communicators’ perceived group entity for a large audience and the audience’s prior attitudinal valence and measured the audience’s epistemic trust. The results showed that communicators tuned their messages to the audience’s attitude when they perceived group entitativity and epistemic trust. Furthermore, tuning the message to the audience was found to bias communicators’ subsequent impressions of the topic in a direction closer to the audience’s attitude. These results suggest that perceiving a large audience as a group influences the subsequent impressions of electronic word-of-mouth product or service communicators.