结合句法依存信息的方面级情感分类

引入句法依存信息到原方面术语,提出一种新的方面术语表示方法,利用Glove词向量表示单词以及单词与单词之间的依存关系,构造出包含句法依存信息的依存关系邻接矩阵和依存关系表示矩阵,利用图卷积神经网络和多头注意力机制将句法依存信息融入到方面术语中,使得方面术语表达与上下文结构高度相关。将改进后的方面词术语表示替换到现有模型后,模型泛化能力得到有效提升。对比试验和分析结果表明:该方法具有有效性和泛化性。     

一类分数阶微积分的中值定理及应用

Riemann-Liouville分数阶微积分算子是一类带有一个函数的分数阶微积分算子的特殊情形，以Riemann-Liouville分数阶微积分算子的积分中值定理和微分中值定理为基础，我们得到了一类带有一个函数的分数阶微积分算子的积分中值定理和微分中值定理，并给出其在计算方面的一些应用。     

切顶卸压技术在双鸭山矿区沿空留巷中的应用

为了使双鸭山矿区煤炭资源最大程度地被采掘,减少资源浪费,提高矿井效益,延长矿井服务年限及促进矿井安全生产,以双鸭山矿区3个缓倾斜中厚煤层综采工作面为工程背景,对切顶卸压无煤柱开采技术进行深入探索,经过实际的检验证明,在双鸭山矿区缓倾斜中厚煤层中,采用切顶卸压技术进行沿空留巷,技术可行,经济合理,工艺简单,成巷率高。     

Flash Lamps as Ignition and Initiation Sources of the VS-2 Pyrotechnic Composition

In the present work it is found that the pyrotechnic composition VS-2 can be initiated with flash lamps IFC-500 and EVIS. VS-2 pyrotechnic composition contains 90% of mercury（Ⅱ） 5-hydrazinotetrazolate perchlorate and 10% of optically transparent copolymer of 2-methyl-5-vinyltetrazole and methacrylic acid （PVMT）. We have found that the flash lamps make it possible to initiate combustion of VS-2 composition with its transition to detonation both in cylindrical charges placed in brass caps of 5 mm diameter and 2 mm high， and film charges with 10 mm×80 mm in size and surface weights of 60 mg·cm^-2 and 90 mg·cm^-2， showing ignition delay times 10 μs and 3 μs， respectively. We also measured detonation velocities for VS-2 composition film charges， which were 4375-4505 m·s^-1 （of the charge being surface mass 60 mg·cm^-2） and 4221-4281 m·s^-1 （of the charge being surface mass 90 mg·cm^-2） and their blasting action on the aluminum plate. The depths of the normal shock wave imprints at the charge-barrier interface were 0.6-0.7 mm （for surface mass of the film charges 60 mg·cm^-2） and 1.2-1.3 mm （for surface mass of the film charges 90 mg·cm^-2）.     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

Economic competitiveness of compact steam methane reforming technology for on-site hydrogen supply: A Foshan case study

On-site hydrogen production through steam-methane reforming (SMR) from city gas or natural gas is believed to be a cost-effective way for hydrogen-based infrastructure due to high cost of hydrogen transportation. In recent years, there have been a lot of on-site hydrogen fueling stations under design or construction in China. This study introduces current developments and technology prospects of skid-mounted SMR hydrogen generator. Also, technical solutions and economic analysis are discussed based on China's first on-site hydrogen fueling station project in Foshan. The cost of hydrogen product from skid-mounted SMR hydrogen generator is about 23 CNY/kg with 3.24 CNY/Nm³ natural gas. If hydrogen price is 60 CNY/kg, IRR of on-site hydrogen fueling station project reaches to 10.8%. While natural gas price fall to 2.3 CNY/Nm³, the hydrogen cost can be reduced to 18 CNY/kg, and IRR can be raised to 13.1%. The conclusion is that skid-mounted SMR technology has matured and is developing towards more compact and intelligent design, and will be a promising way for hydrogen fueling infrastructures in near future.     

用于电弧检测的电力专用SoC设计

针对传统的电弧电路故障检测结果不准确的问题,设计用于电弧检测的SoC系统,并且在55nm工艺下进行流片验证。采用包含两种结构的模数转换器的片上电压源,设计了锁相环以及复位电路,精度最高可达8.67 bit。验证结果表明,本设计可提高电弧检测的准确性。     

Nonlinearity in regulating the metal to insulator transition of ReNiO3 towards low temperature range

Among the existing material family of the correlated oxides, the rare earth nickelates (ReNiO₃) exhibit broadly adjustable metal to insulator transition (MIT) properties that enables correlated electronic applications, such as thermistors, thermochromics, and logical devices. Nevertheless, how to accurately control the critical temperature (T_MIT) of ReNiO₃ via the co-occupation of the rare-earth elements is yet worthy to be further explored. Herein, we demonstrate the non-linearity in adjusting the T_MIT of ReNiO₃ towards lower temperatures via introducing Pr co-occupation within ReNiO₃ (e.g., Pr_xNd_1-xNiO₃ and Pr_xSm_1-xNiO₃) as synthesized by KCl molten-salt assisted high oxygen pressure reaction approach. Although the T_MIT is effectively reduced via Pr substitution, it does not strictly follow a linear relationship, in particular, when there is large difference in the ionic radius of the co-occupation rare-earth elements. Furthermore, the most significant deviation in T_MIT from the expected linear relationship appears at an equal co-occupation ratio of the two different rare-earth elements, while the abruption in the variation of resistivity across T_MIT is also reduced. The present work highlights the importance to use adjacent rare-earth elements with co-occupation ratio away from 1:1 for achieving more linear adjustment in designing the metal to insulator transition properties for ReNiO₃.