For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert-Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

Cell proliferation was inhibited following forced over-expression of miR-30a in the ovary cancer cell line A2780DX5 and the gastric cancer cell line SGC7901R. Interestingly, miR-30a targets the DNA replication protein RPA1, hinders the replication of DNA and induces DNA fragmentation. Furthermore, ataxia telangiectasia mutated (ATM) and checkpoint kinase 2 (CHK2) were phosphorylated after DNA damage, which induced p53 expression, thus triggering the S-phase checkpoint, arresting cell cycle progression and ultimately initiating cancer cell apoptosis. Therefore, forced miR-30a over-expression in cancer cells can be a potential way to inhibit tumour development.

In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.

No abstract

There are many prior studies for more natural Human-Computer Interaction. Until now, the efforts is continued in order to recognize motions in various directions. In this paper, we suggest a user-specific recognition by hit detection method using Kinect camera and human proportion. This algorithm extracts the user-specific valid recognition rage after recognizing the user's body initially. And it corrects the difference in horizontal position between the user and Kinect, so that we can estimate a action of user by matching cursor to target using only one frame. Ensure that efficient hand recognition in the game to take advantage of this method of suggestion.