This paper investigates the design problem of sliding mode observer using quantized measurements for a class of Markovian jump systems against actuator faults. Such a problem arises in modern networked-based digital systems, where data has to be transmitted and exchanged over a digital communication channel. In this paper, a new descriptor sliding mode observer approach using quantized signals is presented, in which a discontinuous input is synthesized to reject actuator faults by an off-line static compensation of quantization effects. It is revealed that the lower bound on the density of a logarithmic quantizer is 1/3, under which the quantization effects could be compensated completely by using the sliding mode observer approach. Based on the proposed observer method, the asymptotical estimations of sate vector and quantization errors can be obtained simultaneously. Finally, an example of linearized model of an F-404 aircraft engine system is included to show the effectiveness of the presented observer design method.
We focus on essay generation, which is a challenging task that generates a paragraph-level text with multiple topics.Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training and evaluation resources. To address this, we develop a multi-topic aware long short-term memory (MTA-LSTM) network.In this model, we maintain a novel multi-topic coverage vector, which learns the weight of each topic and is sequentially updated during the decoding process.Afterwards this vector is fed to an attention model to guide the generator.Moreover, we automatically construct two paragraph-level Chinese essay corpora, 305,000 essay paragraphs and 55,000 question-and-answer pairs.Empirical results show that our approach obtains much better BLEU score compared to various baselines.Furthermore, human judgment shows that MTA-LSTM has the ability to generate essays that are not only coherent but also closely related to the input topics.
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