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