Correction to: A smart intuitionistic fuzzy‑based framework for round‑robin short‑term scheduler

The Journal of Supercomputing -     

Control engineering of continuous‑mode single‑photon states: a review

In this survey, we present single-photon states of electromagnetic fields, discuss discrete measurements of a single-photon field, show how a linear quantum system responds to a single-photon input, investigate how a coherent feedback network can be used to manipulate the temporal pulse shape of a single-photon state, present single-photon filter and master equations, and finally discuss the generation of Schr?dinger cat states by means of photon addition and subtraction.     

A phase‑locked loop using ESO‑based loop flter for grid‑connected converter: performance analysis

An extended state observer (ESO)-based loop flter is designed for the phase-locked loop (PLL) involved in a disturbed gridconnected converter (GcC). This ESO-based design enhances the performances and robustness of the PLL, and, therefore, improves control performances of the disturbed GcCs. Besides, the ESO-based LF can be applied to PLLs with extra flters for abnormal grid conditions. The unbalanced grid is particularly taken into account for the performance analysis. A tuning approach based on the well-designed PI controller is discussed, which results in a fair comparison with conventional PItype PLLs. The frequency domain properties are quantitatively analysed with respect to the control stability and the noises rejection. The frequency domain analysis and simulation results suggest that the performances of the generated ESO-based controllers are comparable to those of the PI control at low frequency, while have better ability to attenuate high-frequency measurement noises. The phase margin decreases slightly, but remains acceptable. Finally, experimental tests are conducted with a hybrid power hardware-in-the-loop benchmark, in which balanced/unbalanced cases are both explored. The obtained results prove the efectiveness of ESO-based PLLs when applied to the disturbed GcC.     

Data‑driven disturbance observer‑based control: an active disturbance rejection approach

In this paper, a data-driven method for disturbance estimation and rejection is presented. The proposed approach is divided into two stages: an inner stabilization loop, to set the desired reference model, together with an outer loop for disturbance estimation and compensation. Inspired by the active disturbance rejection control framework, the exogenous and endogenous disturbances are lumped into a total disturbance signal. This signal is estimated using an on-line algorithm based on a datadriven predictor scheme, whose parameters are chosen to satisfy high robustness-performance criteria. The above process is presented as a novel enhancement to design a disturbance observer, which constitutes the main contribution of the paper. In addition, the control strategy is completely presented in discrete time, avoiding the use of discretization methods for its digital implementation. As a case study, the voltage control of a DC-DC synchronous buck converter afected by disturbances in the input voltage and the load is considered. Finally, experimental results that validate the proposed strategy and some comparisons with the classical disturbance observer-based control are presented.     

Correction to: Risk‑aware business process management using multi‑view modeling: method and tool

Requirements Engineering - A correction to this paper has been published: https://doi.org/10.1007/s00766-021-00356-2     

Adaptive output consensus for heterogeneous nonlinear multi‑agent systems with multi‑type input constraints under switching‑directed topologies

This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies, in which all followers are subjected to multi-type input constraints such as unknown asymmetric saturation, unknown dead-zone and their integration. A unified representation is presented to overcome the difficulties originating from multi-agent input constraints. Moreover, the uncertain system functions in a non-lower triangular form and the interaction terms among agents are dealt with by exploiting the fuzzy logic systems and their special property. Furthermore, by introducing a nonlinear filter to alleviate the problem of “explosion of complexity” during the backstepping design, a distributed common adaptive control protocol is proposed to ensure that the synchronization errors converge to a small neighborhood of the origin despite the existence of multiple input constraints and arbitrary switching communication topologies. Both stability analysis and simulation results are conducted to show the effectiveness and performance of the proposed control methodology.     

The dorsolateral pre‑frontal cortex bi‑polar error‑related potential in a locked‑in patient implanted with a daily use brain–computer interface

While brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome (LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.     

Quantum‑enhanced reinforcement learning for control: a preliminary study

Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained great achievements in biomedicine, Internet of Things (IoT), logistics, robotic control, etc. However, there are still many challenges for engineering applications, such as how to speed up the learning process, how to balance the trade-off between exploration and exploitation. Quantum technology, which can solve complex problems faster than classical methods, especially in supercomputers, provides us a new paradigm to overcome these challenges in reinforcement learning. In this paper, a quantum-enhanced reinforcement learning is pictured for optimal control. In this algorithm, the states and actions of reinforcement learning are quantized by quantum technology. And then, a probability amplification method, which can effectively avoid the trade-off between exploration and exploitation via quantized technology, is presented. Finally, the optimal control policy is learnt during the process of reinforcement learning. The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym. The preliminary study results validate that, compared with Q-learning, this quantized reinforcement learning method has better control performance without considering the trade-off between exploration and exploitation. The learning performance of this new algorithm is stable with different learning rates from 0.01 to 0.10, which means it is promising to be employed in unknown dynamics systems.     

Correction to: User‑dependent interactive light field video streaming system

Multimedia Tools and Applications -     

A neuro‑observer‑based optimal control for nonaffine nonlinear systems with control input saturations

In this study, an adaptive neuro-observer-based optimal control (ANOPC) policy is introduced for unknown nonaffine nonlinear systems with control input constraints. Hamilton–Jacobi–Bellman (HJB) framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input. ANOPC consists of both analytical and algebraic parts. In the analytical part, first, an observer-based neural network (NN) approximates uncertain system dynamics, and then another NN structure solves the HJB equation. In the algebraic part, the optimal control input that does not exceed the saturation bounds is generated. The weights of two NNs associated with observer and controller are simultaneously updated in an online manner. The ultimately uniformly boundedness (UUB) of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method. Finally, two numerical examples are provided to confirm the effectiveness of the proposed control strategy.     

Introduction to the special issue on self‑managing and hardware‑optimized database systems 2020

Distributed and Parallel Databases -     

UFMC‑MIMO波束成形传输方案设计

为了适应未来无线通信系统中的设备多样性、高速率、低时延和低功率消耗的需要,新型波形研究成为第5代无线通信系统的关键技术之一。通用滤波多载波（Universal?filtered multicarrier, UFMC）是一种广泛研究的5G侯选波形。然而现有的UFMC研究只涉及UFMC-SISO的场景,UFMC-MIMO的可行性和性能分析仍然空缺,而MIMO波束成形必然是5G通信系统的重要场景,因此对UFMC-MIMO进行评估和研究具有重要价值。本文提出了一个UFMC-MIMO系统可行方案,包括发射机、接收机和波束成形实现算法。通过数学推导证明该方案能正确恢复发送端数据并通过仿真验证其性能。仿真结果表明,不论在加性高斯白噪声（Additive white Gaussian noise,AWGN)信道还是在多径信道下,UFMC-MIMO都具有比OFDM-MIMO更优越的性能,尤其在抗频偏鲁棒性上表现出很大的优越性,进而验证了UFMC-MIMO传输方案具备应用5G通信的能力。     

Correction to: Novel dynamic load balancing algorithm for cloud‑based big data analytics

The Journal of Supercomputing -     

神经正切核K‑Means聚类

针对K-Means聚类算法利用均值更新聚类中心,导致聚类结果受样本分布影响的问题,提出了神经正切核K-Means聚类算法（NTKKM）。首先通过神经正切核（NTK）将输入空间的数据映射到高维特征空间,然后在高维特征空间中进行K-Means聚类,并采用兼顾簇间与簇内距离的方法更新聚类中心,最后得到聚类结果。在car和breast-tissue数据集上,对NTKKM聚类算法的准确率、调整兰德系数（ARI）及FM指数这3个评价指标进行统计。实验结果表明,NTKKM聚类算法的聚类效果以及稳定性均优于K?Means聚类算法和高斯核K-Means聚类算法。NTKKM聚类算法与传统的K-Means聚类算法相比,准确率分别提升了14.9%和9.4%,ARI分别提升了9.7%和18.0%,FM指数分别提升了12.0%和12.0%,验证了NTKKM聚类算法良好的聚类性能。     

自适应步长的Alpha‑shape表面重建算法

三维物体表面重建在现代临床医学、场景建模和林业测量等方面有着重要应用价值。为了更好地理解三维物体表面形状,本文先介绍了三维空间离散点集的Alpha形状的相关概念。在分析表面重建的Alpha?shape算法的基础上,本文提出一种自适应步长的Alpha?shape算法。通过kd?tree和k近邻平均距离来动态更新α值,使得算法在处理点集密度较大的区域时也能以较少的遍历次数进行表面重建,从而改善了重建效果并提高了算法运行效率。大量随机数据和现实三维采样数据的实验结果表明,本文提出的改进算法与原始算法相比,能大幅度地提高运行效率。     

Output digitization of simple measure‑preserving linear systems

We examine three simple linear systems from the viewpoint of ergodic theory. We digitize the output and record only the sign of the output at integer times. We show that even with this minimal output we can recover important information about the systems. In particular, for a two-dimensional system viewed as a flow on the circle, we can determine the rate of rotation. We then use these results to determine the slope of the trajectories for constant irrational flow on the two-dimensional torus. To achieve this, we randomize the system by partitioning the state space and only recording which partition the state is in at each integer time. We show directly that these systems have entropy zero. Finally, we examine two four-dimensional systems and reduce them to the study of linear flows on the two-dimensional torus.     

Sequencing of multi‑robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.     

Asymptotic stability of port‑hamiltonian systems with constant inputs

In this paper, the asymptotic stability of Port-Hamiltonian (PH) systems with constant inputs is studied. Constant inputs are useful for stabilizing systems at their nonzero equilibria and can be realized by step signals. To achieve this goal, two methods based on integral action and comparison principle are presented in this paper. These methods change the convex Hamiltonian function and the restricted damping matrix of the previous results into a Hamiltonian function with a local minimum and a positive semidefinite matrix, respectively. Due to common conditions of Hamiltonian function and damping matrix, the proposed method asymptotically stabilizes more classes of PH systems with constant inputs than the existing methods. Finally, the validity and advantages of the presented methods are shown in an example.     

Simplifying ADRC design with error‑based framework: case study of a DC–DC buck power converter

In this work, the problem of designing a robust control algorithm for a DC-DC buck power converter is investigated. The applied solution is based on a recently proposed error-based version of the active disturbance rejection control (ADRC) scheme, in which the unknown higher-order terms of the reference signal are treated as additional components of the system “total disturbance”. The motivation here is to provide a practical following of a reference voltage trajectory for the buck converter in specifc cases where neither the analytical form of the desired signal nor its future values are known a’priori, hence cannot be directly used for control synthesis. In this work, the application of the error-based ADRC results in a practically appealing control technique, with compact structure, simplifed control rule, and intuitive tuning (inherited from the conventional output-based ADRC scheme). Theoretical, numerical, and experimental results are shown to validate the efcacy of the error-based ADRC in buck converter control, followed by a discussion about the revealed theoretical and practical limitations of this approach.     

基于1‑bit压缩感知的高效无线联邦学习算法

在无线联邦学习（FL）的架构中,用户端与服务器端之间需要持续交换模型参数数据来实现模型的更新,因此会对用户端造成较大的通信开销和功率消耗。目前已经有多种通过数据量化以及数据稀疏化来降低通信开销的方法。为了进一步降低通信开销,提出了一种基于1?bit压缩感知的无线FL算法。在无线FL架构的上行链路中,这种算法首先在用户端记录其本地模型数据的更新参数,包括更新幅值和趋势;接着对幅值和趋势信息进行稀疏化,并确定更新所需的阈值;最后对更新趋势信息进行1?bit压缩感知,从而压缩上行数据。在此基础上,通过设置动态阈值的方法进一步压缩数据大小。在MNIST数据集上的实验结果表明：引入动态阈值的1?bit压缩感知过程能够获得与无损传输过程相同的效果,在FL应用的上行通信过程中能将用户端需要传输的模型参数数据量降低至不采用该方法的标准FL过程的1/25;而在全局模型训练到相同水平时,能将用户上传数据总大小降低至原来的2/11,将传输能耗降低至原来的1/10。