Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

Heterogeneous Catalysis of Ozone Using Iron–Manganese Silicate for Degradation of Acrylic Acid

Iron–manganese silicate (IMS) was synthesized by chemical coprecipitation and used as a catalyst for ozonating acrylic acid (AA) in semicontinuous flow mode. The Fe-O-Mn bond, Fe-Si, and Mn-Si binary oxide were formed in IMS on the basis of the results of XRD, FTIR, and XPS analysis. The removal efficiency of AA was highest in the IMS catalytic ozonation processes (98.9% in 15 min) compared with ozonation alone (62.7%), iron silicate (IS) catalytic ozonation (95.6%), and manganese silicate catalytic ozonation (94.8%). Meanwhile, the removal efficiencies of total organic carbon (TOC) were also improved in the IMS catalytic ozonation processes. The IMS showed high stability and ozone utilization. Additionally, H₂O₂ was formed in the process of IMS catalytic ozonation. Electron paramagnetic resonance (EPR) analysis and radical scavenger experiments confirmed that hydroxyl radicals (•OH) were the dominant oxidants. Cl⁻, HCO₃⁻, PO₄³⁻, Ca²⁺, and Mg²⁺ in aqueous solution could adversely affect AA degradation. In the IMS catalytic ozonation of AA, the surface hydroxyl groups and Lewis acid sites played an important role.     

Darboux Transformation for Tzitzeica Equation

We present a Darboux transformation for Tzitzeica equation associated with 3 × 3 matrix spectral problem. The explicit solution of Tzitzeica equation is obtained.     

几种粉螨同工酶比较及亲缘关系初探

以5种粉螨为实验材料,以普通肉食螨和桔全爪螨为对照,采用垂直板型聚丙烯酰胺凝胶电泳法（PAGE）,对常见同工酶EST、MDH、ADH、CAT等进行比较研究。结果表明：不同种类粉螨间的酶带具有多样性,存在不同程度的相似相异性,为粉螨的分类鉴定提供了佐证,利用相似系数初步分析了几种粉螨间亲缘关系的远近。     

皮尔斯电子枪非迭代综合法的修正

 对Tiwary非迭代综合法进行了整理分析,根据阴极半锥角大小的不同范围对其计算公式做出了相应修正,使得计算结果与Vaughan迭代综合法的计算结果相吻合,二者之间相差可以实现小于1°;对阳极半径公式也做了合理的修正,使阳极与阴极之间的间隙及电子枪射程的计算精度也与迭代综合法相当。采用非迭代法的修正方法计算精度受阴极半锥角大小的影响大大减小,扩大了其应用范围。     

GlyNet: a multi-task neural network for predicting protein–glycan interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan–protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet''s output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein–glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.

GlyNet, a neural net model of glycan-protein binding strengths. Given a glycan it outputs binding to each of several protein samples. Reproducing glycan array data, it extrapolates the binding of untested glycans against the protein samples.     

液相色谱法测定酱油中的乙酰丙酸

建立了高效液相色谱测定配制酱油中乙酰丙酸的方法,并测定了市售30个酱油中的乙酰丙酸含量。确定了酱油样品的最佳前处理方法,有效去除了酱油中的大量干扰成分。采用DIAMONSIL C18柱分离,以0．1％磷酸溶液为流动相。用紫外检测器在268nm波长下检测,分离效果良好,结果准确。采用外标法定量,乙酰丙酸浓度与其峰面积在一定范围内有良好的线性关系,相关系数为0．9999。平均加标回收率为95．6％～98．0％（相对标准偏差为1．3％～5．4％）,检出限为0．0015mg／mL。在市售酱油中检出了乙酰丙酸,表明有部分厂家在酿造酱油中添加了酸水解植物蛋白液。     

LBO晶体超光滑表面抛光机理

胶体SiO2抛光LBO晶体获得无损伤的超光滑表面,结合前人对抛光机理的认识,探讨了超光滑表面抛光的材料去除机理,分析了化学机械抛光中的原子级材料去除机理.在此基础上,对胶体SiO2抛光LBO晶体表面材料去除机理和超光滑表面的形成进行了详细的描述,研究抛光液的pH值与材料去除率和表面粗糙度的关系.LBO晶体超光滑表面抛光的材料去除机理是抛光液与晶体表面的活泼原子层发生化学反应形成过渡的软质层,软质层在磨料和抛光盘的作用下很容易被无损伤的去除.酸性条件下,随抛光液pH值的减小抛光材料的去除率增大;抛光液pH值为4时,获得最好的表面粗糙度.     

碘化铅单晶体生长新方法探索

本文分析了用布里奇曼法生长碘化铅单晶体的结晶过程。发现坩埚下降法生长碘化铅单晶体过程中,碘的蒸发与凝结、铅的分凝与沉淀是影响晶体质量的两个重要因素。提出了采用U型安瓿提拉方式生长碘化铅单晶体的新方法,有效抑制了碘的蒸发并排除了分凝的铅,获得了桔红色半透明的碘化铅晶体。经测试,晶体的结晶性好,红外透过率达45%,电阻率为1.7×1012Ω.cm,为制备性能优异的碘化铅室温核辐射探测器奠定了基础。     

燃烧剂对低温燃烧合成Pr-CeO2粉体结构与性能的影响

以乙酸铵和柠檬酸为燃烧剂,Ce(NO3)3·6H2O和Pr6O11为主要原料,采用低温燃烧法(LCS)制备了Ce0.95Pr0.5O2纳米晶粉体.用DSC、XRD、SEM及色度测试等手段研究了Ce0.95Pr0.5O2纳米晶微粒前驱体的着火温度、产物晶体结构、晶体形貌及色度.结果表明:乙酸铵和柠檬酸作为燃烧剂的反应前驱体着火温度分别在250℃和300℃左右.两种燃烧产物均为单一的萤石型固溶体.与柠檬酸相比,乙酸铵作为燃烧剂得到的燃烧产物结晶程度更完善、Pr离子进入CeO2晶格的含量更多、呈色更好,且颗粒的团聚程度变小.根据Scherrer公式计算,用两种燃烧剂制备产物的平均晶粒尺寸分别为20～30 nm和10～15 nm,为纳米晶颗粒.最后得到Ce0.95Pr0.5O2粉体的颗粒尺寸则在200～300 nm之间.乙酸铵与硝酸铈的最佳摩尔配比为2:1,柠檬酸与硝酸铈的最佳摩尔配比为3:1.