1,199 research outputs found
Comment on "Topological Nodal-Net Semimetal in a Graphene Network Structure"
Recently, a distinct topological semimetal, nodal-net semimetal, has been
identified by Wang et al. through ab initio calculations [Phys. Rev. Lett. 120,
026402 (2018)]. The authors claimed that a new body-centered tetragonal carbon
allotrope with I4/mmm symmetry, termed bct-C40, can host this novel state
exhibiting boxed-astrisk shaped nodal nets. In this Comment, we demonstrate
that bct-C40 is in fact a nodal surface semimetal, the concept of which has
been proposed as early as 2016 [Phys. Rev. B 93, 085427 (2016)]
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
Traffic prediction plays an important role in evaluating the performance of
telecommunication networks and attracts intense research interests. A
significant number of algorithms and models have been put forward to analyse
traffic data and make prediction. In the recent big data era, deep learning has
been exploited to mine the profound information hidden in the data. In
particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network
(RNN) schemes, has attracted a lot of attentions due to its capability of
processing the long-range dependency embedded in the sequential traffic data.
However, LSTM has considerable computational cost, which can not be tolerated
in tasks with stringent latency requirement. In this paper, we propose a deep
learning model based on LSTM, called Random Connectivity LSTM (RCLSTM).
Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the
formation of neural network, which is that the neurons are connected in a
stochastic manner rather than full connected. So, the RCLSTM, with certain
intrinsic sparsity, have many neural connections absent (distinguished from the
full connectivity) and which leads to the reduction of the parameters to be
trained and the computational cost. We apply the RCLSTM to predict traffic and
validate that the RCLSTM with even 35% neural connectivity still shows a
satisfactory performance. When we gradually add training samples, the
performance of RCLSTM becomes increasingly closer to the baseline LSTM.
Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits
even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure
Deep Learning with Long Short-Term Memory for Time Series Prediction
Time series prediction can be generalized as a process that extracts useful
information from historical records and then determines future values. Learning
long-range dependencies that are embedded in time series is often an obstacle
for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a
specific kind of scheme in deep learning, promise to effectively overcome the
problem. In this article, we first give a brief introduction to the structure
and forward propagation mechanism of the LSTM model. Then, aiming at reducing
the considerable computing cost of LSTM, we put forward the Random Connectivity
LSTM (RCLSTM) model and test it by predicting traffic and user mobility in
telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic
connectivity between neurons, which achieves a significant breakthrough in the
architecture formation of neural networks. In this way, the RCLSTM model
exhibits a certain level of sparsity, which leads to an appealing decrease in
the computational complexity and makes the RCLSTM model become more applicable
in latency-stringent application scenarios. In the field of telecommunication
networks, the prediction of traffic series and mobility traces could directly
benefit from this improvement as we further demonstrate that the prediction
accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how
we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference
Whirl frequency of a high speed spindle subjected to different pre-load mechanisms
Pre-loading, both in terms of application method and actual applied force, significantly affects the stiffness and natural frequency of a high speed spindle system. The gyroscopic moment at high speed leads to whirling of the spindle, and the whirl frequency is not equal to the system’s natural frequency. To discover the relationship between pre-load and whirl frequency, theoretical and experimental research was undertaken. Two numerical models of the angular contact ball bearings, based on rigid and constant pre-load mechanisms, were established. The shaft is considered as a set of Timoshenko beam elements, and gyroscopic moment and centrifugal force are both considered. Adding bearing stiffness in the form of springs to this finite element system produced a spindle-bearing coupled model. Iteration was used to deduce the interactions among bearing groups. The exact whirl frequency of a spindle subjected to different pre-load mechanisms has been calculated. To validate the proposed theory, frequency analysis was carried out on a Siemens CAT40 spindle. Experimental results agreed with theoretical calculations. The result shows that speed had a great influence on bearing stiffness and spindle whirl frequency. Adopting a reasonable pre-load method and pre-load force improved the spindle critical frequency
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