As demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded at regular time intervals, thereby restricting the range of data collection tools to loop detectors or other equipment that generate regular data. The study reported here represents an attempt to expand on several existing time series forecasting methods to accommodate data recorded at irregular time intervals, thus ensuring these methods can be used to obtain predicted traffic speeds through intermittent data sources such as the GPS. The study tested several methods using the GPS data from 480 Hong Kong taxis. The results show that the best performance is obtained using a neural network model with acceleration information predicted by ARIMA model.
The Chinese alligator, Alligator sinensis, is a critically endangered species. A conservation project of gene resources for an endangered species first involves the preservation of organs, tissues, gametes, genomic DNA libraries and cell lines. The present study is the first to establish and cryopreserve cell lines of liver, heart and muscle tissues from the Chinese alligator. The study revealed that there was a large discrepancy in cell migration time in primary cultures among liver (11-12 d), heart (13-14 d) and muscle (17-18 d) tissue pieces. The differences in time in primary cell culture suggested that it was relatively easy to build visceral-derived cell lines for reptiles. Biological analysis showed that the population doubling time for thawed cells was approximately 36 h. Karyotyping revealed that the frequency of Chinese alligator cells showing chromosome number as 2n=32 was 88.6%-93.4%. Chinese alligator cell lines established here provide a vital resource for research and are likely to be useful for protection of this rare and critically endangered species. Furthermore, the establishment of these methods may supply technical and theoretical support for preserving genetic resources at the cellular level for other reptile species.Chinese alligator, conservation, tissue culture, cell line
Citation:Zeng C J, Ye Q, Fang S G. Establishment and cryopreservation of liver, heart and muscle cell lines derived from the Chinese alligator (Alligator sinensis).
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.
This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F
1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.
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