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.     

掺锗硅基高非线性光纤超连续光源研究

利用非线性偏振旋转锁模掺铒光纤激光器和1100m长的掺锗硅基高非线性光纤制作了超连续光源,获得了从1150～1750nm的超宽带输出光谱,其中1150～1350nm波段光谱起伏小于3dB,1600～1700nm波段平坦度优于1dB,并有很好的向长波延展空间。光谱展宽的机理为孤子分裂与受激拉曼散射,而四波混频使光谱进一步展宽。     

HT-7电子加热实验的软X射线能谱诊断

介绍了HT-7装置上软X射线能谱诊断系统,用软X射线能谱对HT-7托卡马克上电子温度进行了测量。在离子伯恩斯坦波和低杂波协同实验中,观察到了对等离子体的较好的加热效果。     

A Hierarchy of Integrable Lattice Soliton Equations and New Integrable Symplectic Map

Starting from a discrete spectral problem, a hierarchy of integrable lattice soliton equations is derived. It is shown that the hierarchy is completely integrable in the Liouville sense and possesses discrete bi-Hamiltonian structure. A new integrable symplectic map and finite-dimensional integrable systems are given by nonlinearization method. The binary Bargmann constraint gives rise to a Bäcklund transformation for the resulting integrable lattice equations. At last, conservation laws of the hierarchy are presented.     

Dynamic Scaling of Ramified Clusters Formed on Liquid Surfaces

A comprehensive simulation model -deposition,diffusion, rotation, reaction and aggregation model is presented to simulate the formation processes of ramified clusters on liquid surfaces, where clusters can diffuse and rotate easily. The mobility (including diffusion and rotation) of clusters is related to its mass, which is given by Dm = Dos-γD and θm =′θos-γθ, respectively. The influence of the reaction probability on the kinetics and structure formation is included in the simulation model. We concentrate on revealing dynamic scaling during ramified cluster formation. For this purpose, the time evolution of the cluster density and the weight-average cluster size as well as the cluster-size distribution scaling function at different time are determined for various conditions. The dependence of the cluster density on the deposition flux and time-dependence of fractal dimension are also investigated. The obtained results are helpful in understanding the formation of clusters or thin film growth on liquid surfaces.     

DMF体系中Cu2O球晶的制备及形貌分析

以N,N-二甲基甲酰胺(DMF)为溶剂,通过改变铜源和表面活性剂,调控反应参数,溶剂热条件下制备了三维十字形、空心及实心的Cu₂O球晶。利用XRD、SEM等表征手段,分析探讨了工艺条件变化对Cu₂O球晶形貌的影响。研究表明,随着DMF浓度的增大,体系的还原能力增强,Cu⁺增多,溶液的过饱和度增大,Cu₂O晶体集合体形态由晶体结构控制的各向异性与对称性的球晶逐渐向各向同性球晶演变。十二烷基硫酸钠(SDS)、十六烷基三甲基溴化铵(CTAB)、聚乙烯吡咯烷酮(PVP)等表面活性剂有助于降低溶液的过饱和度,增加结晶质的表面扩散能力,有利于规则形态Cu₂O晶粒的形成。反应体系中,Cu(Ac)₂·H₂O水解生成的羧基与DMF中的甲酰基在高温下发生脱羧反应产生CO₂气体以及SDS发泡作用产生的气体是形成空心Cu₂O球晶的重要原因。     

金催化乙炔氢氯化的密度泛函理论研究进展

乙炔氢氯化(AH)是生产氯乙烯的主要途径之一,传统上使用高毒性的汞催化剂,因此开发无汞催化剂迫在眉睫。金(Au)催化剂是最有潜力的替代催化剂之一,然而其活性Au物种、反应物的活化过程或反应过渡态结构等催化机理仍不够清晰。密度泛函理论（DFT）在研究由Au催化AH的反应机理中发挥了极其重要的作用。本文综述了DFT对金催化剂活性位点、反应物在催化剂上的吸附性质及反应机理的研究进展。重点讨论了DFT对阳离子金和金簇催化AH反应过程的模拟计算,包括Au电子状态、其它原子掺杂及金簇尺寸和形状对催化AH反应影响的模拟。结果表明DFT模拟计算在微观分子尺度上研究反应物的吸附、反应中间体及过渡态等方面发挥了关键作用,对理解Au催化AH反应机理做出了重要贡献。     

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.     

Eu3+掺杂硼酸盐玻璃的光谱性质研究

测试了不同浓度掺杂下Eu^3＋离子在硼酸盐玻璃的吸收光谱、激发光谱与发射光谱,根据荧光光谱计算了各样品的强度参数力2与／24,分析了Eu^3＋离子掺杂浓度对其发光强度的影响．研究结果表明：在Eu^3＋离子高掺杂浓度时,会发生浓度猝灭效应,但由于Eu^3＋激活离子之间能量传递几率很小,使得Eu^3＋离子猝灭浓度较高。