(CoFe)Se2@NC超级电容器电极材料的制备及其电化学性能

金属-有机框架(MOF)衍生的过渡金属硒化物和多孔碳纳米复合材料具有巨大的储能优势,是应用于电化学储能的优良电极材料。采用共沉淀法制备CoFe类普鲁士蓝(CoFe-PBA)纳米立方,并通过静电组装在CoFe-PBA上包覆聚吡咯(PPy)得到CoFe-PBA@PPy;通过在400℃氮气中退火并硒化成功制备了氮掺杂的碳(NC)包覆(CoFe)Se₂的(CoFe)Se₂@NC纳米复合材料,并对其结构和形貌进行了表征。以(CoFe)Se₂@NC为电极制备了超级电容器,测试了其电化学性能,结果表明,在电流密度1 A/g时超级电容器的比电容达到1047.9 F/g,在电流密度5 A/g下1000次循环后具有良好的循环稳定性和96.55%的比电容保持率。由于其性能优越、无毒、成本低和易于制备,未来(CoFe)Se₂@NC纳米复合材料在超级电容器中具有非常大的应用潜力。     

A prediction approach of SLM based on the ensemble of metamodels considering material efficiency,energy consumption,and tensile strength

Journal of Intelligent Manufacturing - As a rapid developing additive manufacturing (AM) technology, selective laser melting (SLM) provides a promising way for intelligent manufacturing. The SLM...     

Confidence regions of two-stage stochastic linear complementarity problems

Two-stage stochastic linear complementarity problems (TSLCP) model a large class of equilibrium problems subject to data uncertainty, and are closely related to two-stage stochastic optimization problems. The sample average approximation (SAA) method is one of the basic approaches for solving TSLCP and the consistency of the SAA solutions has been well studied. This paper focuses on building confidence regions of the solution to TSLCP when SAA is implemented. We first establish the error-bound condition of TSLCP and then build the asymptotic and nonasymptotic confidence regions of the solutions to TSLCP by error-bound approach, which is to combine the error-bound condition with central limit theory, empirical likelihood theory, and large deviation theory.     

A multi-agent model for decision making on environmental regulation in urban agglomeration

The Journal of Supercomputing - The incomplete enforcement of environmental regulation by local governments will lead to environmental degradation. While the strategy selection for local...     

Sparse online kernelized actor-critic Learning in reproducing kernel Hilbert space

In this paper, we develop a novel non-parametric online actor-critic reinforcement learning (RL) algorithm to solve optimal regulation problems for a class of continuous-time affine nonlinear dynamical systems. To deal with the value function approximation (VFA) with inherent nonlinear and unknown structure, a reproducing kernel Hilbert space (RKHS)-based kernelized method is designed through online sparsification, where the dictionary size is fixed and consists of updated elements. In addition, the linear independence check condition, i.e., an online criteria, is designed to determine whether the online data should be inserted into the dictionary. The RHKS-based kernelized VFA has a variable structure in accordance with the online data collection, which is different from classical parametric VFA methods with a fixed structure. Furthermore, we develop a sparse online kernelized actor-critic learning RL method to learn the unknown optimal value function and the optimal control policy in an adaptive fashion. The convergence of the presented kernelized actor-critic learning method to the optimum is provided. The boundedness of the closed-loop signals during the online learning phase can be guaranteed. Finally, a simulation example is conducted to demonstrate the effectiveness of the presented kernelized actor-critic learning algorithm.

     

卫星USB测控副载波同步算法研究

吉林一号卫星测控采用USB测控体制,遥控接收采用FFT+DPLL1+DPLL2的架构,FFT主要实现对载波的粗捕,DPLL1在FFT的基础上实现对载波相位的精跟踪,DPLL2实现副载波的跟踪。对遥控副载波跟踪所用到的数字PLL技术进行了研究,首先给出了载波多普勒折算到副载波上的方法,然后对模拟PLL数字化进行了详细的论述,并设计了遥控副载波同步用的PLL2,对其稳定性、快捕带及快捕时间、捕获带和捕获时间、同步速率等进行了分析和仿真。结果表明,该方法具有捕获时间短、跟踪精度高、稳定性好等特点,易于数字化实现,已成功在吉林一号系列卫星中得到应用。     

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

International Journal of Computer Vision - Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become...     

Occluded Video Instance Segmentation: A Benchmark

International Journal of Computer Vision - Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset...     

Low-rank tensor completion via combined Tucker and Tensor Train for color image recovery

Applied Intelligence - In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good...