First results of negative ion extraction with Cs for CRAFT prototype negative beam source

In order to understand the physics and pre-study the engineering issues for radio frequency(RF)negative beam source, a prototype source with a single driver and three-electrode accelerator was developed. Recently, the beam source was tested on the RF source test facility with RF plasma generation, negative ion production and extraction. A magnetic filter system and a Cs injection system were employed to enhance the negative ion production. As a result, a long pulse of 105 s negative ion beam with current density of 153 A m-2 was repeatedly extracted successfully. The source pressure is 0.6 Pa and the ratio of co-extracted electron and negative ion current is around0.3. The details of design and experimental results of beam source were shown in this letter.     

气动弹片对垂直轴风力机性能影响研究

片对垂直轴风力机气动性能的影响并分析了其流动机理。结果表明：气动弹片在高尖速比下可显著抑制流动分离并改善其动态失速特性，功率系数得到极大提升；切向力和转矩系数在气动弹片作用相位角范围内，均有一定提高，而在其它相位角内，风力机性能不受影响。     

催化裂化柴油加氢转化装置长周期生产运行分析

中国石化安庆分公司（简称安庆分公司）为优化企业产品结构，提高经济效益，采用中国石化石油化工科学研究院研发的催化裂化柴油加氢裂化生产高辛烷值汽油的RLG技术及其专用的加氢精制催化剂和加氢裂化催化剂，新建了一套1.0 Mt/a催化裂化柴油加氢转化装置（简称RLG装置）。该装置已平稳运行18个月，装置长周期生产运行及工业技术标定结果表明，RLG装置以100%劣质催化裂化柴油为原料，高辛烷值汽油调合组分收率为45%～60%、RON为90~95、硫质量分数小于2 μg/g，柴油产品十六烷指数提高12~14个单位、硫质量分数小于5 μg/g，实现了催化裂化柴油高效转化为高辛烷值汽油，汽油和柴油产品性质好，气体产率低。RLG装置投产后，安庆分公司的柴汽比由1.03下降至0.74，经济效益显著提高。     

基于并行多通道卷积长短时记忆网络的轴承寿命预测方法

在预测轴承剩余使用寿命时，数据间的时序特性是一个可以利用的重要隐藏信息。为了更好地提取具有时序信息的特征用于预测，提出了一种基于并行多通道卷积长短时记忆网络（PMCCNN-LSTM）的剩余使用寿命预测模型。该模型主要由两部分组成：前端为并行多通道卷积网络（PMCCNN），提取信号特征，挖掘数据的时序特性，并采用逐层训练和微调的方式提升参数的收敛性；后端为长短时记忆（LSTM）网络，基于特征进行剩余使用寿命预测，并采用加权平均的方法对预测结果进行平滑处理。在一个轴承加速寿命实验的公开数据集上使用留一法验证了该模型的准确性，实验结果表明：所提模型的平均误差与最大误差分别比传统的卷积神经网络（CNN）低23.38%和15.84%，比传统的LSTM低24.14%和19.01%，比卷积长短时记忆网络（CNN-LSTM）低30.32%和23.09%。     

Responses of dairy cows with divergent residual feed intake as calves to metabolic challenges during midlactation and the nonlactating period

Residual feed intake (RFI) is defined as the difference between the actual and expected feed intake required to support animal maintenance and growth. Thus, a cow with a low RFI can obtain nutrients for maintenance and growth from a reduced amount of feed compared with a cow with a high RFI. Variation in RFI is underpinned by a combination of factors, including genetics, metabolism, thermoregulation and body composition; hypothalamic-pituitary-adrenal (HPA) axis responsiveness is also a possible contributor. Responses to 3 metabolic challenges were measured in lactating and nonlactating dairy cattle. Sixteen Holstein Friesian cows with phenotypic RFI measurements that were obtained during the growth period (188–220 d old) were grouped as either low-calfhood RFI (n = 8) or high-calfhood RFI (n = 8). An ACTH (2 µg/kg of body weight), insulin (0.12 U/kg), and epinephrine (a low dose of 0.1 µg/kg and a high dose of 1.6 µg/kg of epinephrine) challenge were each conducted during both midlactation (122 ± 23.4 d in milk) and the nonlactating period (dry period; approximately 38 d after cessation of milking). Cows were housed in metabolism stalls for the challenges and were fed a diet of alfalfa cubes ad libitum for at least 10 d before the experiment (lactating cows also were offered a total of 6 kg of dry matter/d of crushed wheat grain plus minerals fed as 3 kg of dry matter at each milking) and were fasted for 12 h before the challenges. The efficiency of conversion of feed into milk (the ratio of feed consumed to milk produced over the 7 d before the experiment) during midlactation was better (lower) in low-calfhood RFI cows, although dry matter intake did not differ between RFI groups. Low-calfhood RFI cows exhibited a lower plasma cortisol response to the ACTH challenge than high-calfhood RFI cows, particularly in midlactation (?15%). The low-calfhood RFI cows had a greater plasma insulin-like growth factor-1 response to the insulin challenge and plasma fatty acid response to epinephrine compared with the high-calfhood RFI cows. These data suggest that high-calfhood RFI cows exhibit a more responsive HPA axis. As divergence in RFI measured during growth is retained (although reduced) during lactation, it is possible that energy is used to respond to HPA axis activation at the expense of production in high-calfhood RFI dairy cattle during lactation and contributes to a decrease in overall feed use efficiency.     

热处理对挤压态Mg-6Gd-5Y-1Zn合金组织与性能的影响

通过对Mg-6Gd-5Y-1Zn(质量分数,%)合金在固溶和时效处理状态下显微组织和力学性能的研究发现,α-Mg基体、沿挤压方向分布的条状18R-LPSO相、少量的Mg₂₄(GdYZn)₅ 相以及细层片状的14H-LPSO相构成了挤压态合金的组成相。挤压态合金经固溶(T4)处理后,一部分18R-LPSO相溶入基体,并且基体中的14H-LPSO相伸长同时粗化。挤压态合金经过固溶加时效(T6)处理后,大量β′相从α-Mg基体中析出。T6态合金的室温力学性能最好,其屈服强度、抗拉强度及伸长率分别为272 MPa、406 MPa和6.1%。β′相沉淀也发生在挤压态合金的直接人工时效(T5)处理过程,但相比于T6处理,14H-LPSO相和β′相在基体中的体积分数均偏低。     

垂直轴风力机两种翼型气动性能比较研究

叶片是风力机最重要的组成部分,在不同的风能资源情况下,翼型的选择对垂直轴风力机气动特性有着重要的影响。文章分别以NACA0018翼型(对称翼型)和NACA4418翼型(非对称翼型)建立3叶片H型垂直轴风力机二维仿真模型。应用数值模拟的研究方法,从功率系数、单个叶片切向力系数等方面比较两种风力机模型在不同叶尖速比下的气动特性,并采用风洞实验数据验证了流场计算的准确性。CFD计算结果表明:在低叶尖速比下,NACA4418翼型风力机气动特性优于NACA0018翼型风力机,适用于低风速区域;在高叶尖速比下,NACA0018翼型风力机气动特性较好,适用于高风速地区。而且在高叶尖速比时,NACA0018翼型在上风区时,切向力系数平均值要高于NACA4418翼型,在下风区时,NACA418翼型切向力系数平均值高。该研究可为小型垂直轴风力机翼型的选择提供参考。     

Study on estimating electric potential in space between parallel electrodes

We have been studying on estimating distribution of permittivity between measurement electrodes using capacitance and electric potential. Two arc electrodes were separated by long distance and there electrodes were surrounded by additional electrodes respectively. In past research work, we carried out numerical electric analysis for calculating the capacitance and electric potential using Finite Element Method (FEM) and compared with experimental and numerical results. The capacitance values were almost agreed with experimental and numerical results. However, the electric potential values were different between experimental and numerical results in conventional studies. In this paper, we proposed an equivalent circuit including the stray capacity and measurement method for capacitance, the electric potential in space between long distance electrodes was estimated.     

基于深度学习的彩色以及近红外图像去马赛克

单传感器捕获的彩色-近红外（RGB-NIR）图像存在光谱干扰，从而导致重建出的标准彩色图像（RGB）图像与近红外（NIR）图像存在色彩失真以及细节信息模糊。针对这个问题提出一种基于深度学习的去马赛克方法，通过引入跳远连接与稠密连接解决了梯度消失和梯度弥散问题，使得网络更容易训练，并且提升了网络的拟合能力。首先，用浅层特征提取层提取了马赛克图像的像素相关性以及通道相关性等低级特征；然后，将得到的浅层特征图输入到连续多个的残差稠密块以提取专门针对去马赛克的高级语义特征；其次，为充分利用低级特征与高级特征，将多个残差稠密块提取到的特征进行组合；最后，通过全局跳远连接恢复最终的RGB-NIR图像。在深度学习框架Tensorflow上使用公共的图像与视觉表示组（IVRG）数据集、有植被的户外多光谱图像（OMSIV）数据集和森林（Forest）三个公开数据集进行实验。实验结果表明，所提方法优于基于多级自适应残差插值、基于卷积卷积和神经神经网络以及基于深度残差U型网络的主流的RGB-NIR图像去马赛克方法。