Antioxidant High-Conductivity Copper Pastes Based on Core–Shell Copper Nanoparticles for Flexible Printed Electronics

As a nontoxic and cost-effective material, copper pastes have attracted great attention in both academia and industry. However, achieving the long-term stability of copper pastes remains challenging due to their susceptibility to oxidation. Therefore, stable copper nanoparticles with a Cu(0)–Cu(I) core–shell structure containing a surface passivation layer of formate ions-involved Cu(I) coordination polymers are developed. Based on the self-reducing nature of the passivation layer, the nanoparticle-based copper pastes can be sintered in <1 min, showing high electrical conductivity (220 000 S cm⁻¹), mechanical flexibility, and long-term stability after sintering. The excellent properties of the developed copper pastes are even comparable with the ones of silver pastes. These stable copper pastes have broad applications in printed electronics (e.g., glucose sensors, RFID tags, and electromagnetic shielding films), showing great potential in the fabrication of flexible printed electronics.     

Nonlinear cascade control of an electro-hydraulic actuator with large payload variation

Electro-hydraulic actuators have been widely used in industrial production, but the unknown variable payload seriously affects its position control accuracy. Therefore, a radial basis function neural network disturbance observer is designed to estimate the lumped disturbance force through strong online learning ability in the absence of force sensor. Besides, a nonlinear cascade controller with double loop structure is proposed in this paper. A global fast terminal sliding mode control method is firstly applied in the outer loop position system, which can eliminate chattering and improve convergence speed comparing to traditional sliding mode control. The inner loop force system adopts a backstepping control method to calculate the actual input of the whole system. Theoretical analysis indicates that the proposed controller is stable even if existing time-variant disturbance. Moreover, three comparative controllers are designed and tested in both simulations and experiments. Comparative results show that the developed method has absolute average errors of 1.14 and 0.49 mm in different position tracking, which means more satisfactory tracking performance compared to the contrast controllers.     

Implicit discrete-time fast terminal sliding mode control with disturbance compensation

In this paper, an implicit discrete-time fast terminal sliding mode (DFTSM) control with disturbance compensation is designed and analyzed for uncertain high-order systems. First, a recursive discrete sliding surface is constructed based on implicit Euler technique, which can completely eliminate discretization chattering so as to significantly reduce the boundary layer of sliding mode motion. With the help of a high-order disturbance compensator, the accuracy limitation of implicit DFTSM control systems is overcome by increasing the order of sliding mode. Then the finite-time convergence of implicit DFTSM is proved for the first time, and the influence relationship of control parameters on the convergence speed and control accuracy of the algorithm is established. Finally, two numerical examples are provided to demonstrate the effectiveness and superiority of the proposed design approach.     

具有执行器故障的四旋翼无人机有限时间容错控制

本文主要研究了四旋翼无人机在外部干扰、执行器存在部分失效和偏置故障并发情况下有限时间轨迹跟踪的控制问题. 通过分析四旋翼无人机动力学特性, 构建了带有外部干扰、执行器机构故障的动力学模型. 基于鲁棒全局快速终端滑模控制算法, 设计了一种有限时间容错控制器, 提高了系统对故障的响应速度. 其次, 针对常值/时变故障和干扰,在控制器设计中采用改进的连续函数进行补偿, 减少了由切换函数引起的系统抖振, 并基于Lyapunov函数对控制器的稳定性进行了分析. 最后, 通过仿真实验验证了所设计控制器的有效性和可靠性, 同时存在执行器故障和外部干扰的情况下, 无人机能够实现较好的轨迹跟踪性能.     

光伏快速充电站配电变压器端电能质量综合控制研究

针对光伏快速充电站将光伏发电与电动汽车快速充电站有机结合,在充分发挥清洁能源优势和大量提升充电效率的同时所带来的电能质量扰动类型复杂、变化快速等问题,提出了一种基于串并联组合配电网柔性交流输电(distribution flexible AC transmission system, DFACTS)的新型电能质量补偿器的电能质量综合控制方法。深入研究了新型电能质量补偿器主电路拓扑,建立起了动态工作模型。详细分析了新型电能质量补偿器线性自抗扰控制方法的工作原理,设计出线性自抗扰控制器。并针对各DFACTS控制器间的交互影响,提出多DFACTS控制器的协调控制策略。最后搭建仿真模型,进行仿真验证。仿真结果表明,所提出的新型电能质量补偿器采用线性自抗扰控制方法相比传统PI控制方法,提高了电能质量扰动快速变化时的综合补偿能力和动态响应能力。并且验证了该协调控制能有效提高系统电压稳定能力,具有良好的可行性和有效性。     

快/慢烤试验中复合推进剂内部温度场的分布

为了研究不同烤燃条件下复合推进剂(PBT/HMX/Al/AP/BU)的响应特性,采用DSC研究了复合推进剂及单组分的热分解特性。复合推进剂的初始分解温度为187.27℃,单组分中BU初始分解温度最低,为192.95℃,表明复合推进剂的热分解过程是从BU开始;分别测量了在快/慢烤试验中,复合推进剂内部温度的变化。结果表明:1)快烤试验中,样品内部温度分布极不均匀。点火90 s后,样品发生反应,此时样品中心的温度为85℃,钢管端盖破裂,样品发生燃烧反应。2)慢烤试验中,样品内部温度分布均匀,几乎无温度差,样品发生反应时,样品内部温度与环境温度均为133℃,试验后样品钢管完全破碎,样品发生了爆轰。由此可见,慢烤试验中,由于样品内部温度分布均匀,发生反应时,大部分样品都处于临界反应温度,一旦激发,破坏效应比快烤试验更严重。     

AuPt合金的两种典型生长机理研究

分别采用晶种生长法和快速共还原法制备得到了AuPt合金。采用晶种生长法制备AuPt合金时,Au晶种的尺寸对Au-Pt双金属结构的形成有关键性影响,当用小尺寸的Au纳米颗粒(3nm)作为晶种时才会形成AuPt合金结构,当Au晶种尺寸较小时,由于颗粒内纳米尺度的原子快速扩散导致合金结构的形成。高温下油胺对Au和Pt前体的快速共还原会使Au和Pt在极短的时间内同时得到还原,避免了两相的分离,得到了AuPt合金结构。     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

基于单片机的高精度快速积分型AD转换器

针对现有积分型AD转换器存在转换速率较低的问题,提出了一种基于单片机的高精度快速积分型AD转换器的设计方案,详细介绍了AD转换的实现过程。该转换器采用单片机产生各种控制信号,保证了测量过程的精确同步,提高了AD测量结果的稳定性,最快转换速度可达500次/s。实际应用表明,该转换器转换速度快,测量精度高,达到了预期效果。