Network management integrating SNMP/CMIP protocol implementations

In this paper, a new approach to integratingSNMP andCMIP protocols in a network management system is introduced. It is based on the use of proxy systems allowing to integrate SNMP network management agents in a general network management framework based onCMIP. The system architecture for marrying the protocols is first presented. Then the key features of a new protocol gateway implementing the proxy function are described, with emphasis on the explanation of theSNMP/CMIP mapping algorithm and the threshold/event reporting functions.     

In Situ Topochemical Transformation of ZnIn2S4 for Efficient Photocatalytic Oxidation of 5-Hydroxymethylfurfural to 2,5-Diformylfuran

Photocatalytic selective oxidation of 5-hydroxymethylfurfural (HMF) coupled H₂ production offers a promising approach to producing valuable chemicals. Herein, an efficient in situ topological transformation tactic is developed for producing porous O-doped ZnIn₂S₄ nanosheets for HMF oxidation cooperative with H₂ evolution. Aberration-corrected high-angle annular dark-field scanning TEM images show that the hierarchical porous O-ZIS-120 possesses abundant atomic scale edge steps and lattice defects, which is beneficial for electron accumulation and molecule adsorption. The optimal catalyst (O-ZIS-120) exhibits remarkable performance with 2,5-diformylfuran (DFF) yields of 1624 µmol h⁻¹ g⁻¹ and the selectivity of >97%, simultaneously with the H₂ evolution rate of 1522 µmol h⁻¹ g⁻¹. Mechanistic investigations through theoretical calculations show that O in the O-ZIS-120 lattice can reduce the oxidation energy barrier of hydroxyl groups of HMF. In situ attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) results reveal that DFF^* (C₄H₂(CHO)₂O^*) intermediate has a weak interaction with O-ZIS-120 and desorb as the final product. This study elucidates the topotactic structural transitions of 2D materials simultaneously with electronic structure modulation for efficient photocatalytic DFF production.     

Regulating the Water Molecular in the Solvation Structure for Stable Zinc Metal Batteries

Rechargeable aqueous zinc batteries are promising energy storage devices because of their low cost, high safety, and high energy density. However, their performance is plagued by the unsatisfied cyclability due to the dendrite growth and hydrogen evolution reaction (HER) at the Zn anode. Herein, it is demonstrated that the concentrated hybrid aqueous/non-aqueous ZnCl₂ electrolytes constitute a peculiar chemical environment for not only the Zn-ions but also water molecules. The high concentration of chloride ions substitutes the H₂O molecular in the solvation structure of Zn²⁺, while the acetonitrile further interacts with H₂O to decrease its activity. The hybrid electrolytes both inhibit the dendrite formation and HER, enabling an ultrahigh average Coulombic efficiency of 99.9% in the Zn||Cu half-cell and a highly reversible Zn plating/stripping with a low overpotential of 21 mV. Using this hybrid electrolyte, the Zn||polytriphenylamine (PTPAn) full cell deliveres a high discharge capacity of 110 mAh g⁻¹, a high power density of 9200 W kg⁻¹ at 100 °C and maintains 85% of the capacity for over 6000 cycles at 10 °C. This study provides a deep understanding between the solvation structure and columbic efficiency of Zn anode, thus inspiring the development for stable Zn batteries.     

Teachers cooperation: team-knowledge distillation for multiple cross-domain few-shot learning

Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target set are from different domains, which is also known as cross-domain few-shot learning (CD-FSL). Utilizing more source domain data is an effective way to improve the performance of CD-FSL. However, knowledge from different source domains may entangle and confuse with each other, which hurts the performance on the target domain. Therefore, we propose team-knowledge distillation networks (TKD-Net) to tackle this problem, which explores a strategy to help the cooperation of multiple teachers. Specifically, we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning framework. It incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen tasks. Moreover, our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective knowledge. Extensive experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.     

考虑风险管理的电力市场多时段均衡分析方法

均衡分析方法是电力市场运行效率分析及机制设计论证的有力工具,而如何计及差价合同和市场主体风险偏好的影响,以及高效求解多时段均衡模型,已成为使电力市场均衡分析方法实用化亟待解决的问题。从差价合同的确定、考虑风险偏好的电力市场均衡建模、基于多智能体深度强化学习的求解等方面,提出了考虑风险管理的电力市场多时段均衡分析方法。在模型框架方面,分别针对市场化差价合同和政府授权差价合同,提出了基于市场均衡结果合理确定合同价格及其曲线的方法;采用条件风险价值评估市场风险,并建立了发电商报价决策的随机优化模型;结合前瞻性安全约束机组组合和经济调度模型建立了现货市场出清模型,以保证结果的合理性。在求解算法方面,通过改进深度强化学习方法,提出了基于风险管理的多智能体深度强化学习算法,并对模型进行迭代求解。最后,通过算例验证了均衡分析方法的合理性和有效性,并剖析了不同比例市场化差价合同或政府授权差价合同,以及不同风险偏好对市场均衡的影响。     

光学经纬仪一测回水平方向标准偏差的测量不确定度评定

本文采用多齿分度台方法对DJ2级光学经纬仪一测回水平方向标准偏差进行测量,分析了测量结果不确定度的主要来源,并对测量值的不确定度进行评定,最后提出了用多齿分度台方法进行检定时应注意的问题。     

熔融沉积成型桌面3D打印机的基础几何误差分析

为了探讨熔融成型桌面打印机成型的基础几何尺寸精度,并为熔融成型桌面打印机的市场定位提供设计参考依据,设计了线纹、步距、间隙、孔径、台阶等典型几何特征的三维模型,并采用选用PLA材料对这些基础几何特征进行熔融沉积成型。实验分别在自组装FDM 3D打印机、厦门螺壳电子科技有限公司、浙江闪铸三维科技有限公司的标准打印机上实施。最后基于对FDM成型的标准版基础几何特征尺寸精度能力的测试,初步提出一些适用于FDM打印品的质量评价方法以及标准测试样板的设计规则,对3D打印机的市场定位以及建立相关标准提供第三方检验测试手段。     

Genetic interacting multiple model algorithm based on <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> filter for maneuvering target tracking

In order to have a good performance for maneuvering target tracking, a genetic interacting multiple model (GIMM) algorithm based on the H _∞ filter is proposed in this paper. It introduces the H _∞ filter as model-conditional filter, which keeps its robustness by constantly adjusting parameters, to improve the performance and the precision. Meanwhile, it optimizes model probabilities using the genetic algorithm (GA), chooses sub-models which are close to true models from a set of models, adjusts the number of models and parameters in real-time, reduces excessive competition, and improves the performance of the algorithm. The simulation results indicate that, the algorithm has higher tracking accuracy and stronger robustness than the standard IMM algorithm.     

新型军械装备损坏程度评估方法研究

根据新型军械装备损坏程度具有不确定性的特点,提出了基于D-S证据理论的新型军械装备损坏程度评估方法,并求出了新型军械装备损坏程度区分.通过对典型新型军械装备损坏程度的评估,验证了该方法的有效性和实用性.