镀锌层与氟碳漆涂层的相容性研究

在部分产品中,钢表面镀锌后表面再涂一层漆,其目的是为了增加耐腐蚀性能。但是锌可能会与漆层中的一些组份发生反应,降低漆膜性能。本文通过开展氟碳漆涂层和镀锌层湿热试验后性能测试研究,表明两者之间具有较好的相容性,可提高产品的防护性能。     

涂料用稀土催干剂中稀土金属含量分析研究

在一定条件下，以ＥＤＴＡ为络合剂，以二甲酚橙为指示剂，用络合滴定法测定了涂料用稀土催干剂中稀土金属的含量。     

Generalized Transfer Subspace Learning Through Low-Rank Constraint

It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.     

合成氢化松香乙酯的新工艺研究

以DRC为催化剂,在无溶剂条件下,通过氢化松香与乙醇的直接酯化反应合成氢化松香乙酯。探索了反应温度、催化剂用量、反应时间、物料配比等因素对反应酯化率的影响,确定最佳反应条件为:反应温度180℃,催化剂用量为原料氢化松香质量的6%,反应时间6h,醇与酸的摩尔比为3∶1。在最佳条件下酯化率达92.2%。还探讨了用阴离子交换树脂柱层析分离纯化氢化松香乙酯粗产物的方法。此外,利用红外光谱对氢化松香乙酯精制产物进行了表征;用GC-MS分别对氢化松香乙酯粗产物及其精制产物进行了定性和定量分析,比较了柱层析前后化学组成的变化。     

一种新型二肽甜味剂的合成及表征

以3-环己基-L-丙氨酸、Boc-L-天冬氨酸-4-苄酯为原料合成二肽L-天冬氨酰-3-环己基-L-丙氨酸-1-甲酯(Ⅰ),以3-羟基-4-甲氧基苯甲醛为原料经过Witting反应、H2还原和DIBAL-H还原三步反应合成3-羟基-4-甲氧基苯丙醛(Ⅱ),然后Ⅰ与Ⅱ在氢气、Pd/C催化作用下进行还原氨化反应,得到目标产物N-[3-(3-羟基-4-甲氧基苯基)丙基]-α-L-天冬氨酰-3-环己基-L-丙氨酸-1-甲酯(Ⅲ),总产率为47%。产物的结构采用IR、~1HNMR、~(13)CNMR和HRMS进行表征,并验证其甜度约为蔗糖的25000倍。合成终产物的最佳工艺条件为:n(Ⅱ)∶n(Ⅰ)=1∶1,Pd/C催化剂用量为反应物总重的10%,体积分数80%的甲醇水溶液为溶剂,反应温度35℃,反应时间20 h。     

Tactile display with tangential and normal skin displacement for robot-assisted surgery

This paper proposes a tactile display providing both shear and normal feedback to the fingertip for generating three-axis tactile feedback during teleoperation of a surgical robot. The display is composed of five balloons actuated by controlling the pneumatic pressure. The implemented display is 18?mm?×?18?mm?×?15?mm. This size is suitable for mounting the display onto the master controls of a surgical robot. The maximum normal and shear displacements are 2 and 1.3?mm, respectively. The proposed tactile display may provide perceivable stimuli to a human finger pad in all five directions: normal, distal, proximal, radial, and ulnar. This paper also reports on the results of psychophysical measurement of the minimum perceivable movement of the developed tactile display for each of the five directions.     

Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking

An interval type-2 fuzzy weighted support vector machine (IT2FW-SVM) is proposed to address the problem of high energy consumption for biped walking robots. Different from the traditional machine learning method of ‘copy learning’, the proposed IT2FW-SVM obtains lower energy cost and larger zero moment point (ZMP) stability margin using a novel strategy of ‘selective learning’, which is similar to human selections based on experience. To handle the uncertainty of the experience, the learning weights in the IT2FW-SVM are deduced using an interval type-2 fuzzy logic system (IT2FLS), which is an extension of the previous weighted SVM. Simulation studies show that the existing biped walking which generates the original walking samples is improved remarkably in terms of both energy efficiency and biped dynamic balance using the proposed IT2FW-SVM.     

Mining top-k frequent patterns with combination reducing techniques

Top-k frequent pattern mining finds interesting patterns from the highest support to the k-th support. The approach can be effectively applied in numerous fields such as marketing, finance, bio-data analysis, and so on since it does not need constraints by a minimum support threshold. Top-k mining methods use the support of the k-th pattern, not a user-specified minimum support. Thus, the methods conduct mining operations based on very low supports until the k-th pattern is detected. When a low support is used in the mining process, single-paths with numerous items are generated, where the top-k mining algorithm extracts valid patterns by combining the items for each single-path. Therefore, the bigger the number of combinations is, the larger the increase in time and memory consumption is. In this paper, in order to mine top-k frequent patterns more efficiently, we consider converting patterns obtained from single-paths into composite patterns during the mining process and recovering them as the original patterns when the top-k frequent patterns are extracted. For this, we define a new concept, the composite pattern, and propose novel techniques for reducing pattern combinations in the single-path. Two algorithms are introduced in this paper, where the former is CRM (Combination Reducing method), applying our reduction manner, and the latter is CRMN (Combination Reducing method for N-itemset), considering N-itemset, i.e., patterns’ lengths. A performance evaluation shows that CRM and CRMN algorithms can efficiently reduce pattern combinations in single-paths compared to state-of-the-art algorithms. The experimental results also illustrate that our approaches have outstanding performance in terms of runtime, memory, and scalability.     

Study of collective user behaviour in Twitter: a fuzzy approach

The study of collective user behaviours in social networking sites has become an increasing important topic in social media mining. Understanding such behaviours has its potential to extract actionable patterns that can be beneficial to develop effective marketing strategies, optimise user experiences and maximise website revenues. With the rapid development of micro-blogging, Twitter has become a richer source of intelligence that can be used to study collective user behaviour, due to its efficient and meaningful user-to-user interactions. However, the classical statistical methods have some drawbacks in bridging the gap between user-generated data and human analysts who mostly use linguistic terms to analyse data and model/summarise knowledge learned. To address this gap, this work proposes a new approach, which employs the mass assignment theory-based fuzzy association rules algorithm (MASS-FARM), for the first time, to extract useful interaction behaviour of Twitter users. The influential factors (including activity time, number of friends/followers and the number of tweets) are represented as fuzzy granules, and the associations amongst are studied by employing MASS-FARM. The collective user behaviours are analysed in the Reply category and the Non-Reply category, respectively. The applicability and usefulness of the proposed method are demonstrated via an empirical study on a collected Twitter data set. The derived results are also discussed and compared with existing works.     

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 .