废润滑油絮凝—吸附再生工艺的研究

以两种表面活性剂作絮凝剂、活性白土为吸附剂 ,经絮凝、吸附再生处理了废润滑油。考察了影响絮凝、吸附的各种因素。确定最佳絮凝条件 :温度 85°C、1 5 0 g废润滑油所需两种絮凝剂用量分别为 3.7g和 1 .3g;最佳吸附条件 :活性白土 1 2 .3%、温度 1 1 0°C、时间 90 min。经元素分析 ,净化精制后油品的 Zn、Pb、Ca、P等元素的含量显著降低。精制油理化指标符合国家标准。     

Effective naive Bayes nearest neighbor based image classification on GPU

Non-parametric classifier, Naive Bayes nearest neighbor, is designed with no training phase, and its performance outperforms many well-trained learning-based image classifiers. Unfortunately, despite its high accuracy, it suffers from great computational pressure from distance computations in space of local feature. This paper explores accelerating strategies from perspectives of both algorithm design and software development. Our approach integrates space decomposition capability of Product quantization (PQ) and parallel accelerating capability of underlying computational platform, Graphics processing unit (GPU). PQ is exploited to compress the indexed local features and prune the search space. GPU is used to ease most of computational pressure by processing the tasks in parallel. To achieve good parallel efficiency, a new sequential classification process is first designed and decomposed into independent components with high parallelism. Effective parallelization techniques are then presented to make use of computational resources. Parallel heap array is built to accelerate the process of feature quantization. Distance table lookup is built to speed up the process of feature search. Comparative experiments on UIUC-Sport dataset demonstrate that our integrated solution outperforms other implementations significantly on Core-quad Intel Core i7 950 CPU and GPU of NVIDIA Geforce GTX460. Scalability experiment on 80 million tiny images database shows that our approach still performs well when large-scale image database is explored.     

Point-based online value iteration algorithm in large POMDP

Partially observable Markov decision process (POMDP) is an ideal framework for sequential decision-making under uncertainty in stochastic domains. However, it is notoriously computationally intractable to solving POMDP in real-time system. In order to address this problem, this paper proposes a point-based online value iteration (PBOVI) algorithm which involves performing value backup at specific reachable belief points, rather than over the entire belief simplex, to speed up computation processes, exploits branch-and-bound pruning approach to prune the AND/OR tree of belief states online, and proposes a novel idea to reuse the belief states that have been searched to avoid repeated computation. The experiment and simulation results show that the proposed algorithm can simultaneously satisfy the requirement of low errors and high timeliness in real-time system.     

Leave-one-out cross-validation-based model selection for multi-input multi-output support vector machine

As an effective approach for multi-input multi-output regression estimation problems, a multi-dimensional support vector regression (SVR), named M-SVR, is generally capable of obtaining better predictions than applying a conventional support vector machine (SVM) independently for each output dimension. However, although there are many generalization error bounds for conventional SVMs, all of them cannot be directly applied to M-SVR. In this paper, a new leave-one-out (LOO) error estimate for M-SVR is derived firstly through a virtual LOO cross-validation procedure. This LOO error estimate can be straightway calculated once a training process ended with less computational complexity than traditional LOO method. Based on this LOO estimate, a new model selection methods for M-SVR based on multi-objective optimization strategy is further proposed in this paper. Experiments on toy noisy function regression and practical engineering data set, that is, dynamic load identification on cylinder vibration system, are both conducted, demonstrating comparable results of the proposed method in terms of generalization performance and computational cost.     

硫代糠酸乙酯和硫代糠酸糠酯的合成

以糠酰氯、乙硫醇、糠硫醇为原料，合成了硫代糠酸乙酯和硫代糠酸糠酯，产率分别为７７％和７４％。测定了硫代糠酸乙酯的沸点、折光率及硫代糠酸糠酯的熔点、沸点。对合成的硫代糠酸乙酯进行了评香鉴定     

钛系聚酯催化剂的研究进展

介绍了聚酯催化剂中锑、锗、钛三个系列的各自特点，综述了复合型钛系和超细TiO2型聚酯催化剂的最新进展，认为新型钛系催化剂催化活性更高，更环保，而且用它合成的聚酯产品色相好，热稳定性得到了改善。     

掺钇铌酸锌铋陶瓷的介电性能

通过X射线衍射、扫描电镜、精密阻抗分析仪等对A位Y3 取代的Bi(1.5-x)YxZnNb1.5O7介质陶瓷样品的结构和介电性能进行了研究.结果表明:当Y3 取代量较少时(x≤0.2),样品保持立方焦绿石结构;当Y3 取代较多时(x≥0.4),样品出现第二相.随着取代量的增加,样品的致密化温度升高,表观密度减小,晶粒尺寸减小,介电常数减小,介电损耗增加.Y3 取代后(x≤0.8),样品的温度系数得到优化,且具有良好的高频稳定性.低温的介电弛豫现象随着Y3 取代的增加发生有规律的变化,表现为介电常数下降,介电常数峰宽化和平坦.     

基于计算机测控技术的转矩流变仪的研制

介绍了基于计算机测控技术的聚合物转矩流变仪的研制，该仪器具有优良的性能指标，强大的数据文件处理能力、测试功能扩展能力，是高分子材料研究和开发的理想工具。     

煤中吡啶型氮热解机理的量子化学研究

用密度泛函 ( DFT)方法 ,在 UB3LYP/6- 31 G( d)水平上研究了煤中吡啶型氮的热解机理 ,对热解过程中由于官能团周围环境的不同而形成的三类吡啶自由基进行了量子化学计算 ,通过对键的 Mulliken布居数等计算结果的分析 ,分别得到了这三类自由基的热解途径 .并优化得到了反应物、中间体和产物的几何构型 .计算结果表明 ,吡啶型氮主要是以 HCN的形式释放出来的 .NH3 是 HCN二次反应的产物 .