Pre-Activation of CO2 at Cobalt Phthalocyanine-Mg(OH)2 Interface for Enhanced Turnover Rate

Cobalt phthalocyanine (CoPc) anchored on heterogeneous scaffold has drawn great attention as promising electrocatalyst for carbon dioxide reduction reaction (CO₂RR), but the molecule/substrate interaction is still pending for clarification and optimization to maximize the reaction kinetics. Herein, a CO₂RR catalyst is fabricated by affixing CoPc onto the Mg(OH)₂ substrate primed with conductive carbon, demonstrating an ultra-low overpotential of 0.31 ± 0.03 V at 100 mA cm⁻² and high faradaic efficiency of >95% at a wide current density range for CO production, as well as a heavy-duty operation at 100 mA cm⁻² for more than 50 h in a membrane electrode assembly. Mechanistic investigations employing in situ Raman and attenuated total reflection surface-enhanced infrared absorption spectroscopy unravel that Mg(OH)₂ plays a pivotal role to enhance the CO₂RR kinetics by facilitating the first-step electron transfer to form anionic *CO₂⁻ intermediates. DFT calculations further elucidate that introducing Lewis acid sites help to polarize CO₂ molecules absorbed at the metal centers of CoPc and consequently lower the activation barrier. This work signifies the tailoring of catalyst-support interface at molecular level for enhancing the turnover rate of CO₂RR.     

Event-triggered control for stochastic systems with multiple delays

This paper investigates the stabilization problem for continuous-time stochastic systems with multiple delays under continuous event-triggered mechanisms, of which both static case and dynamic case are considered individually. In order to avoid zeno phenomenon in every sample path, a suspension time after each successful execution is forced for our event-triggered mechanisms, resulting in intermittent detection of system states. Under such control strategy, we deduce mean square exponential stability of stochastic systems with multiple delays by means of Hanalay-type inequality and obtain a delay-dependent-based and less-conservative stabilization criterion without involving the upper bound of time delays. Besides, a co-design procedure is proposed for linear controller and event-triggered mechanisms. In the end, an illustrative example is presented to show effectiveness of the proposed co-design procedure and contrasts the system performance under static and dynamic event-triggered mechanisms.     

Simulation and analysis of edge ghosting for microcapsule e-paper based on particle dynamics and light scattering model

Electronic paper (e-paper) is a reflective display technology with unique advantages, such as bistability, low-power consumption, and high ambient contrast ratio. These features make e-paper a promising candidate for future Internet of Things applications. Among different technologies of e-paper, electrophoretic display (EPD) is the most successful one for commercialization. However, the edge ghosting (also known as the fringing effect) still limits the performance of EPD. Herein, we established a model of particle dynamics of electrophoresis, simulated the edge ghosting of microcapsule EPD, analyzed the edge ghosting effect, and revealed the relationship between thicknesses, dielectric constants of the back binder layer, and the edge ghosting. Two EPD panels with different thicknesses of back binder layer were demonstrated, which verifies the accuracy of this simulation model. With the proposed model, many device mechanisms and product issues can be analyzed and illustrated, which is supposed to guide the researchers in optimizing the device structure design of EPD.     

Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties

For networked sensor systems (NSSs) with hard and soft sensors including five uncertainties, two universal approaches of solving the robust fusion estimation problems are presented. It includes an integrated sequential covariance intersection (SCI) fusion minimax robust Kalman filtering approach with cross-covariance information and a generalized Lyapunov equation approach with four pairs of Lyapunov equations. Applying them, the robust local and SCI fused time-varying and steady-state Kalman filters are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The equivalent batch SCI fusers are also presented. Their robustness and accuracy relations are proved, and the sensitivity of the SCI fuser with respect to the fused orders of sensors is analyzed. Applying the dynamic error system analysis method and the dynamic variance error system analysis method, a new convergence and absolute asymptotic stability theory of robust fusion Kalman filtering is presented. The classical Kalman filtering convergence and stability theory is developed. Compared with the original covariance intersection fuser, they significantly reduced the computational complexity and burden. Compared with the optimal and conservative SCI fusers, they significantly improved the robust accuracies. They are suitable to deal with asynchronous or random delayed data and are suitable for real-time applications. A simulation applied to the two-mass spring damper mechanical system shows their effectiveness.     

基于雨雾分离处理和多尺度网络的图像去雨方法

雨带来的雨条纹和雨雾会降低户外拍摄图像的质量,为了去除雨雾对图像的影响,提出了一种基于雨雾分离处理和多尺度卷积神经网络的图像去雨方法。首先利用导向滤波将雨线和图像细节信息提取到高频层,雨雾和背景信息则分离到低频层;然后构建多尺度卷积神经网络来去除高频层中的雨线,网络中融入多个稠密连接模块以提升特征提取的准确性;其次构建多层特征融合的轻量级去雾网络来去除低频层中的雨雾,采用参数一体化结构避免了估计多个大气散射模型参数导致的次优解;最后再结合处理后的高低频结果还原出清晰图像。在多个合成的雨雾数据集以及真实自然场景图像上进行测试,定性和定量结果表明,提出的方法在去除雨雾影响的同时较好地保留了色彩信息,和近年的算法相比,图像结构相似性约提升了0.02~0.08,图像峰值信噪比约提升了0.2~3.5 dB。     

基于ETAP的海上风电场接入对电能质量的评估分析

由于海上风资源的多变性,以及风力发电机中存在较多的电力电子元器件,海上风电场并网后会产生电能质量问题。本文选取某海上风电场作为研究对象,基于风电场实际运行数据,参考国标规定,对该风电场的电能质量（电压偏差、电压闪变、电压波动、谐波）进行求解。基于风机的无功特性,本文对某海上风电场接入电网公用变电站的无功补偿容量进行研究。在电能质量计算与分析方面,参考相关规程规定,完成电压偏差、电压闪变、电压波动部分指标的限值与实际值计算,并进行对比;通过ETAP软件中的谐波分析模块,进行谐波模拟仿真,得到谐波指标的实际值,并与理论计算的限值对比。当谐波电流超出国家标准的时候,通过在母线接入单调谐滤波器的方法,进行谐波治理,达到电能质量相关要求。     

An adaptive reaching law of chattering-free discrete-time sliding mode control for systems with external disturbance

An adaptive reaching law of chattering-free discrete-time sliding mode control is proposed for the systems with external disturbance. An adaptive function is proposed in the reaching law which can increase reaching speed as sliding variable is far away from zero and avoid undesired chattering when sliding variable is close to origin. Moreover, a third-order backward difference of disturbance is added to obtain a narrower quasi-sliding mode band (QSMB). Both the closed-loop system performance and finite reaching steps to achieve the desired QSMB are theoretically analyzed. The simulation results verify the effectiveness of the proposed control strategy.     

Neural network robust control of a 3-DOF hydraulic manipulator with asymptotic tracking

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high-performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three-axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high-gain feedback issue in RISE feedback control will be avoided. The proposed RISE-based neural network robust controller theoretically guarantees an excellent semi-global asymptotic stability. Comparative simulation is performed on a 3-DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.     

基于分段电容阵列的改进型逐次逼近型ADC

为缩短高速模数转换器(ADC)中高位(MSB)电容建立时间以及减小功耗,提出了一种基于分段式电容阵列的改进型逐次逼近型(SAR)ADC结构,通过翻转小电容阵列代替翻转大电容阵列以产生高位数字码,并利用180 nm CMOS工艺实现和验证了此ADC结构。该结构一方面可以缩短产生高位数码字过程中的转换时间,提高量化速度;另一方面其可以延长大电容的稳定时间,减小参考电压的负载。通过缩小比较器输入对管的面积以减小寄生电容带来的误差,提升高位数字码的准确度。同时,利用一次性校准技术减小比较器的失配电压。最终,采用180 nm CMOS工艺实现该10 bit SAR ADC,以验证该改进型结构。结果表明,在1.8 V电源电压、780μW功耗、有电路噪声和电容失配情况下,该改进型SAR ADC得到了58.0 dB的信噪失真比(SNDR)。     

Optimizing Financial Engineering Time Indicator Using Bionics Computation Algorithm and Neural Network Deep Learning

Computational Economics - The present work aims to optimize the time index of financial engineering to improve the efficiency of financial decision-making. A Back Propagation Neural Network (BPNN)...