基于1D CNN与XGBoost的恶意代码纹理检测

恶意代码数量已经呈现爆炸式增长,对于恶意代码的检测防护显得尤为重要.近几年,基于深度学习的恶意代码检测方法开始出现,基于此,提出一种新的检测方法,将恶意代码二进制文件转化为十进制数组,并利用一维卷积神经网络(1 Dimention Convolutional Neural Networks,1D CNN)对数组进行分类和识别.针对代码家族之间数量不平衡的现象,该算法选择在分类预测上表现良好的XGBoost,并对Vision Research Lab中的25个不同恶意软件家族的9458个恶意软件样本进行了实验.实验结果表明,所提的方法分类预测精度达到了97％.     

“电工学”国家级一流课程建设与实践

根据新时代新工科人才需求特点和电工学课程特点,分析非电专业学生的电类实践需求,提出构建电工学课程为核心的电工电子实践创新能力培养新体系。通过优化教学内容、开展教学模式和考核模式改革、构建教学资源体系和进行教师队伍建设,使我校电工学课程教学质量提升、学生工程实践和创新能力提高、学生满意度增加、一流课程建设成效显著。     

Influence of surfactants on rheological behaviors of polyacrylonitrile/dimethyl sulfoxide/silicon blending polymer solutions

KH550, KH560, CTAB, and F127 were adopted to modify silicon (Si) to improve the dispersity and stability of Si in the polyacrylonitrile/dimethyl sulfoxide (PAN/DMSO) polymer solutions. The influence of surfactants on rheological behaviors of PAN/DMSO/Si blending polymer solutions was investigated by an advanced solution and melt rotation rheometer. The homogeneity and stability were also studied. The results showed that the surfactants could change the viscosity dependence of blending polymer solutions on shear rate, temperature and storage time by increase the steric hindrance of Si. Among the four solutions, PAN/DMSO/Si blending polymer solution with F127 exhibited the lowest viscosity, activation energy and the smallest structural viscosity index and exhibited the trend close to the Newtonian fluids. Moreover, PAN/DMSO/Si blending polymer solution with F127 exhibited the best dispersity and stability, indicating its best physical properties and machinability.     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

Electrospun Zn-doped Ga2O3 nanofibers and their application in photodegrading rhodamine B dye

Ultrawide band gap semiconductor materials have attracted considerable attention in recent years owing to their great potential in the photocatalytic field. In this study, Zn-doped Ga₂O₃ nanofibers with various concentrations were synthesized via electrospinning; they exhibited a superior photocatalytic degradation performance of rhodamine B dye compared to that of undoped Ga₂O₃ nanofibers. The Zn dopant replaced Ga sites via replacement doping, which could increase the concentration of oxygen vacancies and lead to enhanced photocatalytic properties. When the Zn concentration increased, a Ga₂O₃/ZnGa₂O₄ hybrid structure formed, which could further enhance the photocatalytic performance. The separation of photogenerated carriers due to Zn doping and heterojunctions were the primary causes of the enhanced photocatalytic performance. This study provides experimental data for the fabrication of high-performance photocatalysts based on Ga₂O₃ nanomaterials.     

土壤中有机污染物的检测技术研究进展

土壤污染防治法的实施促进了国内土壤监测业务的发展.对比分析了目前土壤中有机污染物的采样、前处理技术.挥发性有机污染物(VOCs)对采样环节的要求较高,需使用采样器和保存剂来减少挥发损失,进样前提取过程宜选用吹扫捕集等具有封闭系统的高效自动化方式.半挥发性有机污染物(SVOCs)的回收率主要受萃取、净化方式的影响,二氯甲烷和加速溶剂萃取的组合方式既快速又节约溶剂;净化材料宜选择弗罗里硅土、氧化铝类物质.选择合适的采样、前处理方法才能保证回收率、质量控制的要求.     

Thermal transport and chemical conversion in single reacting sorbent particles

The effects of particle size and carbon dioxide concentration on chemical conversion in engineered spherical particles undergoing calcium oxide looping are investigated. Particles are thermochemically cycled in a furnace under different carbon dioxide concentrations. Changes in composition due to chemical reactions are measured using thermogravimetric analysis. Gas composition at the furnace exit is evaluated with mass spectroscopy. A numerical model of thermal transport phenomena developed previously is adapted to match the physical system investigated in the present study. The model is used to elucidate effects of reacting medium characteristics on particle temperature and reaction extent. Experimental and numerical results show that (1) an increase in particle size results in a decrease in carbonation extent, and (2) the carbonation step consists of fast and slow reaction regimes. The reaction rates in the fast and slow carbonation regimes increase with increasing carbon dioxide concentration. The effect of carbon dioxide concentration and the distinction between the fast and slow regimes become more pronounced with increasing particle size.