10,549 research outputs found
Instability of multistage compressor K1501
The K1501 compressor, driven by a steam turbine, is used to transport synthetic gas in fertilizer plants of 1000 tons daily production. The turbo-compressor set, which had been in operation since 1982, vibrated rather intensely, and its maximum load was only about 95 percent of the normal value. Damaging vibration to pads and gas-sealing labyrinths occurred three times from 1982 to 1983 and resulted in considerable economic loss. From the characteristics of the vibration, we suspected its cause to be rotor instability due to labyrinth-seal excitation. But, for lack of experience, the problem was not addressed for two years. Finally, we determined that the instability was indeed produced by labyrinth-seal excitation and corrected this problem by injecting gas into the middle-diaphragm labyrinths. This paper primarily discusses the failure and the remedy described above
Macroscopical Entangled Coherent State Generator in V configuration atom system
In this paper, we propose a scheme to produce pure and macroscopical
entangled coherent state. When a three-level ''V'' configuration atom interacts
with a doubly reasonant cavity, under the strong classical driven condition,
entangled coherent state can be generated from vacuum fields. An analytical
solution for this system under the presence of cavity losses is also given
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees
This paper investigates an important problem in stream mining, i.e.,
classification under streaming emerging new classes or SENC. The common
approach is to treat it as a classification problem and solve it using either a
supervised learner or a semi-supervised learner. We propose an alternative
approach by using unsupervised learning as the basis to solve this problem. The
SENC problem can be decomposed into three sub problems: detecting emerging new
classes, classifying for known classes, and updating models to enable
classification of instances of the new class and detection of more emerging new
classes. The proposed method employs completely random trees which have been
shown to work well in unsupervised learning and supervised learning
independently in the literature. This is the first time, as far as we know,
that completely random trees are used as a single common core to solve all
three sub problems: unsupervised learning, supervised learning and model update
in data streams. We show that the proposed unsupervised-learning-focused method
often achieves significantly better outcomes than existing
classification-focused methods
Joint Training for Neural Machine Translation Models with Monolingual Data
Monolingual data have been demonstrated to be helpful in improving
translation quality of both statistical machine translation (SMT) systems and
neural machine translation (NMT) systems, especially in resource-poor or domain
adaptation tasks where parallel data are not rich enough. In this paper, we
propose a novel approach to better leveraging monolingual data for neural
machine translation by jointly learning source-to-target and target-to-source
NMT models for a language pair with a joint EM optimization method. The
training process starts with two initial NMT models pre-trained on parallel
data for each direction, and these two models are iteratively updated by
incrementally decreasing translation losses on training data. In each iteration
step, both NMT models are first used to translate monolingual data from one
language to the other, forming pseudo-training data of the other NMT model.
Then two new NMT models are learnt from parallel data together with the pseudo
training data. Both NMT models are expected to be improved and better
pseudo-training data can be generated in next step. Experiment results on
Chinese-English and English-German translation tasks show that our approach can
simultaneously improve translation quality of source-to-target and
target-to-source models, significantly outperforming strong baseline systems
which are enhanced with monolingual data for model training including
back-translation.Comment: Accepted by AAAI 201
Order flow dynamics around extreme price changes on an emerging stock market
We study the dynamics of order flows around large intraday price changes
using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a
significant reversal of price for both intraday price decreases and increases
with a permanent price impact. The volatility, the volume of different types of
orders, the bid-ask spread, and the volume imbalance increase before the
extreme events and decay slowly as a power law, which forms a well-established
peak. The volume of buy market orders increases faster and the corresponding
peak appears earlier than for sell market orders around positive events, while
the volume peak of sell market orders leads buy market orders in the magnitude
and time around negative events. When orders are divided into four groups
according to their aggressiveness, we find that the behaviors of order volume
and order number are similar, except for buy limit orders and canceled orders
that the peak of order number postpones two minutes later after the peak of
order volume, implying that investors placing large orders are more informed
and play a central role in large price fluctuations. We also study the relative
rates of different types of orders and find differences in the dynamics of
relative rates between buy orders and sell orders and between individual
investors and institutional investors. There is evidence showing that
institutions behave very differently from individuals and that they have more
aggressive strategies. Combing these findings, we conclude that institutional
investors are more informed and play a more influential role in driving large
price fluctuations.Comment: 22 page
Output entanglement and squeezing of two-mode fields generated by a single atom
A single four-level atom interacting with two-mode cavities is investigated.
Under large detuning condition, we obtain the effective Hamiltonian which is
unitary squeezing operator of two-mode fields. Employing the input-output
theory, we find that the entanglement and squeezing of the output fields can be
achieved. By analyzing the squeezing spectrum, we show that asymmetric detuning
and asymmetric atomic initial state split the squeezing spectrum from one
valley into two minimum values, and appropriate leakage of the cavity is needed
for obtaining output entangled fields
- …