A CS-AdaBoost-BP model for product quality inspection

Quality inspection is essential in preventing defective products from entering the market. Due to the typically low percentage of defective products, it is generally challenging to detect them using algorithms that aim for the overall classification accuracy. To help solve this problem, we propose an ensemble learning classification model, where we employ adaptive boosting (AdaBoost) to cascade multiple backpropagation (BP) neural networks. Furthermore, cost-sensitive (CS) learning is introduced to adjust the loss function of the basic classifier of the BP neural network. For clarity, this model is called a CS-AdaBoost-BP model. To empirically verify its effectiveness, we use data from home appliance production lines from Bosch. We carry out tenfold cross-validation to evaluate and compare the performance between the CS-AdaBoost-BP model and three existing models: BP neural network, BP neural network based on sampling, and AdaBoost-BP. The results show that our proposed model not only performs better than the other models but also significantly improves the ability to identify defective products. Furthermore, based on the mean value of the Youden index, our proposed model has the highest stability.

     

Quantifying Degradation Parameters of Single-Crystalline Ni-Rich Cathodes in Lithium-Ion Batteries

Single-crystal LiNi_xCo_yMn_zO₂ (SC-NCM, x+y+z=1) cathodes are renowned for their high structural stability and reduced accumulation of adverse side products during long-term cycling. While advances have been made using SC-NCM cathode materials, careful studies of cathode degradation mechanisms are scarce. Herein, we employed quasi single-crystalline LiNi_0.65Co_0.15Mn_0.20O₂ (SC-NCM65) to test the relationship between cycling performance and material degradation for different charge cutoff potentials. The Li/SC-NCM65 cells showed >77 % capacity retention below 4.6 V vs. Li⁺/Li after 400 cycles and revealed a significant decay to 56 % for 4.7 V cutoff. We demonstrate that the SC-NCM65 degradation is due to accumulation of rock-salt (NiO) species at the particle surface rather than intragranular cracking or side reactions with the electrolyte. The NiO-type layer formation is also responsible for the strongly increased impedance and transition-metal dissolution. Notably, the capacity loss is found to have a linear relationship with the thickness of the rock-salt surface layer. Density functional theory and COMSOL Multiphysics modeling analysis further indicate that the charge-transfer kinetics is decisive, as the lower lithium diffusivity of the NiO phase hinders charge transport from the surface to the bulk.     

Controllable Electrodeposition of Platinum Nanoparticles on Graphene Nanosheet for Methanol Oxidation Reaction

A simple and cost-effective electrochemical method synthesized platinum nanoparticles on graphene nanosheet (PtNPs@GNS) is reported, and the Pt loading of the PtNPs@GNS can be controlled by electrodeposition. The structure and element analysis of the PtNPs@GNS have been investigated by scanning electron microscopy (SEM), Raman spectrum, X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS). The electrochemical measurement including electrochemical active surface area, current density, mass activity, oxidation peak potential,shows the PtNPs@GNS have more performance electrocatalytic properties for methanol oxidation reaction (MOR) compared to Vulcan XC-72 carbon (XC-72) supported PtNPs electrocatalysts. Probably, the cause which may be attributes to no aggregation of PtNPs and the well-dispersion on surface of GNS, so PtNPs@GNS show large electrochemically active surface area, highly electrocatalytic activity and stability in direct methanol fuel cells.     

香豆素羟胺在糠醛类化合物荧光定量分析中的应用

糠醛类化合物在食品工业中是一种独特的风味物质,但在人体积蓄过多时具有一定的致癌作用,因此在食品工业和环境检测中糠醛类化合物的定量分析十分重要。本实验以香豆素羟胺作为荧光标记物,通过硝酮化反应对糠醛类化合物进行荧光衍生,并结合液相色谱-荧光检测法准确测定了奶粉中糠醛、5-甲基糠醛和5-羟甲基糠醛的含量,具有选择性高、线性范围宽、检测限低等优点。本实验将最新的科学研究成果转换为应用化学专业“综合化学实验”课程中的一个研究设计性实验,力图通过全方位的实验操作和训练,使学生充分了解荧光与分子结构的关系、荧光衍生试剂的设计与选择、现代分析仪器的检测原理及应用范围;通过对仪器测试条件和衍生反应条件进行优化,学会实验设计的优化方法,了解科学研究的基本流程,从而激发学生对科学研究的兴趣,提高学生的动手操作能力,培养其勤于思考的科学精神,全面提升其创新能力和综合素质。     

基于BP神经网络的卫星故障诊断方法

为了有效地利用卫星下传的海量遥测数据,在测试过程中对卫星进行实时的故障诊断,提出了一种基于BP神经网络的卫星故障诊断方法;该方法包括离线自主学习和实时在线故障诊断两部分;离线自主学习部分基于历史数据库和更新样本进行自主学习,学习获得神经网络模型存储于知识库;实时在线故障诊断部分依据相应的神经网络模型,对遥测数据进行实时在线的诊断;为了验证基于BP神经网络的卫星故障诊断方法的有效性和优越性,以现有型号三轴稳定近地卫星控制分系统为实验对象,利用该方法对具有代表性的红外地球敏感器和动量轮的相关遥测数据进行分析;通过将该方法的实验结果与基于Kalman滤波的方法的实验结果进行对比分析,表明该方法能够有效地对卫星的故障进行诊断。     

毛细管气相色谱法同时测定油漆溶剂中二甲苯和乙二醇乙醚乙酸酯

基于乙二醇乙醚乙酸酯的极性、沸点与二甲苯的几种同分异构体相近而难以分离的特性,选择合适的二甲基聚硅氧烷柱毛细管分离柱(AT.SE-30),以氮气为载气,流量为0.6 mL/m in,内标法定量,建立了同时测定油漆溶剂中二甲苯和乙二醇乙醚乙酸酯含量的毛细管气相色谱法。用该方法对油漆样品进行测定,乙二醇乙醚乙酸酯和二甲苯的回收率为96.5%~102.8%,相对标准偏差不大于2.0%。     

具有恒定温度环境的反应热量计的研制

恒温套热量计是用来测定各类反应热,溶解热的基本常用仪器。国内现用的此类热量计多为金属制成,而且容量较大。我们参照英国伦敦大学皇家霍洛威和贝特福德学院的芬奇教授所提供的仪器,加以改进,建立了一套能测定固-液、液-液系统,具有恒定温度环境的反应热量计。