Turbo spin-echo sequences in magnetic resonance imaging of the brain: Physics and applications

Fast SE imaging provides considerable measure time reduction, high signal-to-noise ratios as well as similar contrast behavior compared to conventional SE sequences. Besides TR and TE_eff, echo train length (ETL), interecho time , and-space trajectory determine image contrast and image quality in fast SE sequences. True proton density contrast (CSF hypointense) and not too strong T2 contrast are essential requirements in routine brain MRI. A Turbo SE sequence with very short echo train length (ETL=3), short TE_eff and short interecho time (17 ms), and TR=2000 ms was selected for proton density contrast; a Turbo SE sequence with ETL=7, TE_eff=90 ms, =22 ms, and TR=3250 ms was selected for T₂-weighted images. Using both single-echo Turbo SE sequences yielded 50% measure time reduction compared to the conventional SE technique. Conventional SE and optimized Turbo SE sequences were compared in 150 patients resulting in very similar signal and contrast behavior. Furthermore, reduced flow artifacts in proton density—and especially in T₂-weighted Turbo SE images—and better contrast of high-intensity lesions in proton density-weighted Turbo SE images were found. Slightly reduced edge sharpness—mainly in T₂-weighted Turbo SE images—did not reduce diagnostic reliability. Differences between conventional and Turbo SE images concerning image contrast and quality are explained regarding special features of fast SE technique.Address for correspondence: Institut für Röntgendiagnostik, Klinikum der Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93042 Regensburg, Germany. Additional reprints of this chapter may be obtained from the Reprints Department, Chapman & Hall, One Venn Plaza, New York, NY 10119.     

适用于直流断路器的大电流快速开关分闸特性

快速开关分闸稳定性是影响直流断路器开断性能的关键要素。文中对大电流快速开关的双弹簧永磁操动和电磁斥力双动机构的分闸过程,用有限元方法进行电磁、热和位移等多物理场耦合计算,分析了永磁操动机构驱动线圈是否有必要投入以及不同驱动线圈电流对双动机构分闸特性的影响。结果表明：在永磁操动机构驱动线圈投入的情况下,可提前将永磁吸力抵消,进而避免电磁斥力因做功时间较短而引起分闸回弹现象;由于分闸初期电磁斥力非常大,永磁操动机构驱动线圈的投入对分闸初期的速度影响较小;在电磁斥力消失后,永磁操动机构驱动线圈电流在一定范围内越大,到达额定开距的速度越大,为避免其造成分闸反弹,应合理选择驱动线圈电流值。将仿真结果与实际样机分闸特性曲线进行对比,二者具有较好的一致性,验证了仿真方法的正确性。     

一种永磁同步电机无模型超螺旋快速终端滑模控制方法

针对内置式永磁同步电机(interior permanent magnet synchronous motor, IPMSM)由于内部参数变化、外部扰动等各种不确定性因素导致控制性能不佳的问题,提出一种无模型超螺旋快速终端滑模控制方法。首先,建立考虑IPMSM不确定性的新型超局部模型,结合超螺旋算法和快速终端切换函数设计无模型超螺旋快速终端滑模控制器,确保系统状态有限时间收敛,并有效减小抖振。其次,设计扩展滑模扰动观测器精准估计超局部模型中的未知部分,并前馈补偿给设计的控制器,进一步提升系统的抗干扰能力和跟踪性能。最后,通过与PI控制和传统无模型滑模控制进行仿真实验对比,验证了该方法具有更快的收敛速度和更强的鲁棒性。     

Bio-Inspired Artificial Fast-Adaptive and Slow-Adaptive Mechanoreceptors With Synapse-Like Functions

Development of artificial mechanoreceptors capable of sensing and pre-processing external mechanical stimuli is a crucial step toward constructing neuromorphic perception systems that can learn and store information. Here, bio-inspired artificial fast-adaptive (FA) and slow-adaptive (SA) mechanoreceptors with synapse-like functions are demonstrated for tactile perception. These mechanoreceptors integrate self-powered piezoelectric pressure sensors with synaptic electrolyte-gated field-effect transistors (EGFETs) featuring a reduced graphene oxide channel. The FA pressure sensor is based on a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) thin film, while the SA pressure sensor is enabled by a piezoelectric ionogel with the piezoelectric-ionic coupling effect based on P(VDF-TrFE) and an ionic liquid. Changes in post-synaptic current are achieved through the synaptic effect of the EGFET by regulating the amplitude, number, duration, and frequency of tactile stimuli (pre-synaptic pulses). These devices have great potential to serve as artificial biological mechanoreceptors for future artificial neuromorphic perception systems.     

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.     

A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

During the last three decades, evolutionary algorithms (EAs) have shown superiority in solving complex optimization problems, especially those with multiple objectives and non-differentiable landscapes. However, due to the stochastic search strategies, the performance of most EAs deteriorates drastically when handling a large number of decision variables. To tackle the curse of dimensionality, this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions. The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution, and provides a fast clustering method to highly reduce the dimensionality of the search space. More importantly, all the operations related to the decision variables only contain several matrix calculations, which can be directly accelerated by GPUs. While existing EAs are capable of handling fewer than 10 000 real variables, the proposed algorithm is verified to be effective in handling 1 000 000 real variables. Furthermore, since the proposed algorithm handles the large number of variables via accelerated matrix calculations, its runtime can be reduced to less than 10% of the runtime of existing EAs.      

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.     

改进果蝇算法求解混合流水车间调度问题

针对混合流水车间调度问题(HFSP),本文提出了一种新的基于果蝇算法和变邻域搜索的混合优化方法.首先,将关键块内的工序与同阶段其他机器上的工序进行交换,提出了一种基于关键路径的HFSP新邻域结构.其次,针对HFSP的阶段式解码特性,提出了一种邻域解的快速评估方法,并验证了快速评估方法的高效性.然后,基于提出的新邻域结构,并将N7和K-insertion邻域结构引入HFSP,设计了基于上述3种邻域结构的变邻域搜索方法,以此为基础提出了一种针对HFSP的混合优化方法.最后,通过对Carlier和Liao等经典测试集进行测试,验证了所提新邻域结构的可行性和有效性,并将该方法与其他文献的方法进行了对比,验证了所提方法的优越性.