数字电子技术四重混合式教学的探索与实践

针对数字电子技术课程教学过程中存在传统的教学模式不适应电子技术的飞速发展、学时少内容多、理论与实践难以兼顾等问题,提出“线上+线下、理论+实践、仿真+实物、校内+企业”的四重混合式教学。以数字电子技术课程中“计数器及其应用”为例介绍具体实施过程。教学实践表明,四重混合式教学较好地激发了学生学习的积极性,提升了学生的专业素质,提高了学生的工程能力和创新能力,增强了学生的社会责任感。

Roles of furfural during the thermal treatment of bio-oil at low temperatures

The reactive O-containing species in bio-oil could induce the polymerization of bio-oil during its thermal treatment, which affects the relevant utilization of bio-oil significantly. Furans, as the highly reactive Ocontaining species in bio-oil, play important roles during the thermal treatment of bio-oil. In this study,furfural was chosen as the representative of the furans in bio-oil to investigate its roles during the thermal treatment of bio-oil. The raw bio-oil with and without the addition of extra furfural(10 wt% of bio-oil) and pure furfural were pyrolyzed in a fixed-bed reactor at 200–500 ℃. The results show that the interactions among furfural and bio-oil components can take place prior to the evaporation of furfural(<140 ℃) to form the intermediates, then these intermediates could be further polymerized to form large molecular compounds, and coke can be formed via the interactions at temperatures ≥ 300 ℃. At temperatures ≤ 300 ℃, furfural mainly interacts with anhydrosugars. As the temperature further increases, the aromatics are involved in the interactions to form coke. The increased percentage of the coke formed via the interactions is in a linear relation with the conversion of furfural during the pyrolysis at 300–500 ℃(no coke formed at 200 ℃). Meanwhile, more non-aromatic light components(≤ C6) and less aromatics in the tars could be formed due to the interactions.

3D打印技术在生物医药方面的应用研究进展

3D打印技术的迅速发展,使其在生物医药领域具有广泛应用。由于基于生物医药的3D打印技术在个性化定制及复杂结构调控制造上具有独特优势,通过对生物材料或活细胞进行3D打印,可构建复杂生物三维结构如个性化植入体、可再生人工骨、人工器官和人造血管等。导致3D生物医药打印的最大进步之一是生物材料、细胞和用于制造功能性活组织的支持组件的开发。本文主要从骨科治疗、皮肤组织、人造血管和个性化药物研发等方面描述3D打印技术在生物医药领域的应用研究现状,并对其未来发展提出了展望。

Startup Speed with Dead Band in Wind Farms with Low-Medium Wind Speed Profile—Case Study of Hong Kong

Wind turbines are usually designed and operated with fixed startup speed. It could perform startup and shutdown operations repeatedly when the wind fluctuates around the startup speed. The excessive stress induced by frequent startup and shutdown could enhance the likelihood of component failure and hence negatively impact the availability of a wind turbine. Startup speed with dead band is proposed in this article to prevent the wind turbine from frequent startup. 22 years wind data from the Cheung Chau wind station in Hong Kong are analyzed to evaluate the reduction in the number of startup and potential loss of wind power production using the proposed approach. Numerical simulation suggests that the number of startup could be reduced by half with trivial reduction in potential wind power generation in most of investigated sites once an appropriate dead band is adopted.

Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.

Effects of ambient temperature on regulated gaseous and particulate emissions from gasoline-, E10- and M15-fueled vehicles

Ambient temperature has substantial impacts on vehicle emissions,but the impacts may differ between traditional and alcohol gasolines.The objective of this study was to investigate the effects of temperature on gaseous and particulate emissions with both traditional and alcohol gasoline.Regulated gaseous,particle mass(PM),particle number(PN)and black carbon(BC)emissions from typical passenger vehicles were separately quantified with gasoline,E10(10%ethanol and 90%gasoline by volume)and M15(15%methanol and 85%gasoline by volume)at both 30°C and-7°C.The particulate emissions with all fuels increased significantly with decreased temperature.The PM emissions with E10 were only 48.0%–50.7%of those with gasoline at 30°C but increased to 59.2%-79.4%at-7°C.The PM emissions with M15 were comparable to those with gasoline at 30°C,but at-7°C,the average PM emissions were higher than those with gasoline.The variation trend of PN emissions was similar to that of PM emissions with changes in the fuel and temperature.At 30°C,the BC emissions were lower with E10 and M15 than with gasoline in most cases,but E10 and M15 might emit more BC than gasoline at-7°C,especially M15.The results of the transient PN and BC emission rates show that particulate emissions were dominated mainly by those emitted during the cold-start moment.Overall,the particulate emissions with E10 and M15 were more easily affected by ambient temperature,and the advantages of E10 and M15 in controlling particulate emissions declined as the ambient temperature decreased.

Key problems and solutions in arch dam heightening

The dam heightening,which is an effective way to increase reservoir volume,has been paid close attention by engineers.Three problems should be dealt with when an arch dam needs to be heightened:stress state getting worse at dam heel,cracking on new added concrete dam surface,and weak bonding between new added concrete and old dam.Taking Geba arch dam as an example,these problems are examined in details through simulation analysis by the finite element method.The tensile stresses on dam’s surface and joint face that have certain relations to the dam heightening can be controlled by some measures.

Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D

The charging load of electric vehicles(EVs)has a strong spatiotemporal randomness.Predicting the dynamic spatiotemporal distribution of the charging load of EVs is of great significance for the grid to cope with the access of large-scale EVs.Existing studies lack a prediction model that can accurately describe the dual dynamic changes of EVs charging the load time and space.Therefore,a spatial-temporal dynamic load forecasting model,dilated causal convolution-2D neural network(DCC-2D),is proposed.First,a hole factor is added to the time dimension of the three-dimensional convolutional convolution kernel to form a two-dimensional hole convolution layer so that the model can learn the spatial dimension information.The entire network is then formed by stacking the layers,ensuring that the network can accept long-term historical input,enabling the model to learn time dimension information.The model is simulated with the actual data of the charging pile load in a certain area and compared with the ConvLSTM model.The results prove the validity of the proposed prediction model.