芳香族硝基化合物还原制芳胺生产工艺评析

本文简要叙述了芳胺用途、常用合成工艺,重点对各生产工艺进行了技术、经济评析,并对芳香族硝基化合物还原方法的选择提出了自己的看法。     

颜色和形状特征分离和局部整合的研究

特征绑定是特征分离和整合的过程。为探究视觉系统处理图像颜色和形状特征的动态绑定过程,提出基于任务的独立成分分析(T_ICA)和最短距离聚类相结合的方法,并将其应用于任务态下采集的脑功能(fMRI)数据,实现颜色和形状处理功能的分离和局部整合。结果表明,T_ICA将f MRI数据分离成彼此独立的特征成分,其中包含有目标特征成分和其他成分;聚类方法实现了颜色功能和形状功能的局部整合,形成了两个分别处理颜色和形状的基本感知系统。通过研究人脑视觉特征绑定机制为计算机视觉捆绑的研究与应用提供一定的参考价值。     

模数对无机富锌涂料和涂层性能的影响

硅酸钾富锌涂料是目前广泛使用的钢铁防腐蚀涂料。作为涂料粘结剂的高模数硅酸钾溶液对涂料和涂层的性能有重要影响。本研究考察了硅酸钾粘结剂的模数变化对涂料及涂层性能产生的影响。实验结果表明：粘结剂模数的提高可以提高固化后涂层的耐水性能，当粘结剂模数在5.0左右时，所得富锌涂层具有最佳的耐磨性能，但当粘结剂模数超过5.5时会造成涂料施工性能和贮存稳定性下降，涂层柔韧性降低，从而降低涂层的耐磨性能。     

磷酸铁锂锂离子电池正极材料的研究

锂离子电池是绿色高能可充电池，具有工作电压高、比能量大、自放电小、循环寿命长、无记忆效应、无环境污染等突出优点。本文从磷酸铁锂的结构与性能、材料的制备方法、改性、粒径控制等几方面综述了近年来对橄榄石型磷酸铁锂（LiFePO4）锂离子电池正极材料的研究进展。材料的粒度大小及其分布、离子和电子的传导能力对产品的电化学性能影响很大。在制备时，采用惰性气氛、掺杂导电材料和控制晶粒生长制备粉体是获得性能优良的LiFePO4的有效方法。     

防止过拟合的属性约简

近年来,机器学习的过拟合问题备受关注,尤其在属性约简中。为解决这一难题,提出一种融合集成策略和去除操作的算法。首先将训练模型数据平分为◢M份;然后将其中M◣-1份采用集成策略进行潜在约简计算;最后将剩余的一份进行提前测试,一旦发生过拟合则将刚加入的属性从潜在约简集中去除。利用提前测试潜在属性约简的方法来防止过拟合现象的发生,几组UCI数据的实验结果说明了新算法的有效性,同时为丰富和发展属性约简提供了一种新的方向。     

S＆W超选择性裂解炉分区裂解探讨

介绍了S＆W超选择性裂解炉分区裂解改造的情况，包括分区裂解炉的运行情况、对后系统的影响、对产品收率的影响、投用过程存在的问题及处理。指出分区裂解能提高装置的生产能力，而产品的收率基本不变。     

草酸镁分解法制备纳米氧化镁

以草酸和氯化镁为原料，采用固液法制备了纳米级氧化镁粉体。优化了试验条件，并用TEM和XRD对纳米氧化镁的粒子结构和形貌进行了研究。结果表明，在600℃焙烧，得到的纳米氧化镁粒度分布均匀，平均粒径为15nm。并对固液法生成纳米氧化镁的机理进行了初步探讨。     

分流叶片对离心泵内部固液两相流的影响

为了研究分流叶片对离心泵内部固液两相流的影响,建立固液两相流模型,对无分流叶片离心泵和3种带分流叶片的离心泵进行全流道非定常数值模拟。重点分析了添加分流叶片对离心泵外特性、叶片表面压力脉动、叶片表面颗粒体积分数分布、叶片表面颗粒滑移速度的影响。分析结果表明:添加分流叶片以后,离心泵的扬程和效率均有显著的提高,叶片表面的颗粒分布更加均匀,叶片表面颗粒的滑移速度有不同程度的下降,使颗粒对叶片表面的磨损程度得以降低;当分流叶片的进口直径为0.65D2时,叶片出水边处的压力脉动幅值最小,减小了离心泵在运行过程中的振动和噪声。     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts

The Yellow River Estuary area of China is under great pressure from both human intervention and natural processes. For analysis of the changes in this area, this article presents a novel change-detection method based on a local fit-search model and kernel-induced graph cuts in multitemporal synthetic aperture radar images. Change detection involves assigning a label to every pixel. This task is naturally formulated in terms of energy minimization, which can be effectively solved by graph cuts. The difference image is transformed implicitly by a kernel function so that an alternative to complex modelling of the original data makes the piecewise constant model become applicable for graph cuts formulation. An issue is that graph cuts are sensitive to the initial estimate. The local fit-search model is proposed to approximate to the local histogram while selecting an optimal threshold for the initial labelling, which leads to an effective constraint for graph cuts and computational benefits as well. Visual and quantitative analyses obtained on the Yellow River Estuary data set confirm the effectiveness of the proposed method and that it outperforms the other state-of-the-art methods of change detection.