Stabilization of uncertain networked control systems with actuator saturation and probabilistic cyberattacks

This paper investigates the stabilization of uncertain networked control systems (NCSs) with actuator saturation and cyberattacks. The cyberattacks are governed by a set of independent random variables satisfying Bernoulli distribution. To relieve the network bandwidth load effectively, an event-triggered communication strategy is proposed. By employing Lyapunov stability theory and stochastic analysis techniques, a stability criterion is obtained for the system with actuator saturation and cyberattacks. Moreover, the desired controller gain is derived by solving some matrix inequalities. Finally, the validity and applicability of the criteria are verified through numerical examples.     

Syntax–Aware graph convolutional network for the recognition of chinese implicit inter-sentence relations

The Journal of Supercomputing - In the literature, most previous studies on English implicit inter-sentence relation recognition only focused on semantic interactions, which could not exploit the...     

浅谈汽油中醚醇类含氧化合物应用气相色谱柱切换-反吹技术

本文采用柱切换-反吹气相色谱技术分析检测汽油中含氧化合物(醇类和醚类)的含量。该方法利用强极性的微填充TCEP不锈钢柱作预切柱,放空挥发性轻烃,保留醇醚类含氧化合物,并将其反吹至石英毛细管WCOT柱进行详细分离。分析了阀切换时间可能影响定量准确性的原因,通过调节分流比和阻力阀阻力建立最佳实验条件。在25min内,用内标法定量可以完成汽油中可能存在的13种醇醚类含氧化合物的分析。准确度实验表明,含氧化合物的回收率在95.2%~100.7%之间,方法的相对标准偏差(RSD)≤0.55%。     

Bioinspired yeast microcapsules loaded with self-assembled nanotherapies for targeted treatment of cardiovascular disease

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Design of dynamic genetic memory

In electronic systems, dynamic random access memory (DRAM) is one of the core modules in the modern silicon computer. As for a bio‐computer, one would need a mechanism for storage of bio‐information named ‘data’, which, in binary logic, has two levels, logical high and logical low, or in the normalised form, ‘1’ and ‘0’. This study proposes a possible genetic DRAM based on the modified electronic configuration, which uses the biological reaction to fulfil an equivalent RC circuit constituting a memory cell. The authors implement fundamental functions of the genetic DRAM by incorporating a genetic toggle switch for data hold. The results of simulation verify that the basic function can be used on a bio‐storage module for the future bio‐computer.Inspec keywords: DRAM chips, genetic engineering, biocomputers, bioinformatics, equivalent circuits, RC circuitsOther keywords: dynamic genetic memory design, electronic systems, dynamic random access memory, modern silicon computer, biocomputer, bioinformation, binary logic, logical high level, logical low level, normalised form, genetic DRAM, modified electronic configuration, biological reaction, equivalent RC circuit, memory cell, fundamental functions, genetic toggle switch, data hold, biostorage module     

船舶舱室正压排烟方案优化及仿真分析

本文主要针对船舶模拟舱室的正压排烟进行研究,以期为现代船舶的消防建设提供参考,将烟控区抽离建模如下文图一,设计正交排烟方案,由实验结果的极差分析得到最优的正压排烟方案即:风速15m/s,出口开度1.2m,进口开度0.3m。针对最优方案,由Fluent仿真分析得到:烟气聚集的地方,温度要高于周围,旋涡的存在导致烟气的滞留,说明针对烟气流动路径的研究将有利于排烟效果的改善。     

Large capacitance and fast polarization response of thin electrolyte dielectrics by spin coating for two-dimensional MoS2 devices

Nano Research - A spin-coating method was applied to obtain thinner and smoother PEO/LiClO4 polymer electrolyte films (EFs) with a lower level of crystallization than those obtained using a...     

基于数字兆欧表自动切换档位的研究与实现

随着电力、电气设备的日益复杂化,对于其安全性能也有了更高的要求,数字兆欧表作为测量绝缘阻值的重要工具,已在工业领域中广泛应用;为了满足测量精度以及兆欧表的可靠性,通过基于S3C2440的嵌入式平台,提出了一种基于数字兆欧表的自动切换档位的实现方案,以达到更加精确的测量;此方案通过判断采集到的电压值选择相应的采样电阻来实现,并结合硬件平台和软件设计,分别介绍了驱动程序和应用程序的编写与测试,并很好地应用到了实际电路中;经测试,能够达到预期的效果,测量精度高,测量误差控制在±2%。     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.