A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

Different durations of whole raw soybean supplementation during the prepartum period: Measures of cellular immune function in transition cows

The objective of this study was to evaluate different durations of whole raw soybean (WS) supplementation (diet rich in n-6 fatty acid) during the prepartum period on cellular immune function of dairy cows in the transition period and early lactation. Thirty-one Holstein cows were used in a completely randomized design and assigned to 4 experimental groups (G) [G90, G60, G30, and G0 (control)] supplemented with a diet containing 12% of WS from 90, 60, 30 and 0 d relative to the calving date, respectively. Cows were dried off 60 d before the expected calving date. After parturition, all cows were fed a diet containing 12% of WS until 84 DIM. Blood samples were collected before the morning feeding (d ?56 ± 2, ?28 ± 2, ?14 ± 2, ?7 ± 2, at the day of partum, 7 ± 2, 14 ± 2, 28 ± 2, and 56 ± 2 relative to parturition). Cell phenotyping and phagocytosis assays were carried out using monoclonal antibodies and flow cytometry technique. Duration of WS supplementation linearly increased the percentage of blood CD3⁺ cells, as well as increased the percentage of blood CD8⁺ cells in the postpartum period, notably in G30, whereas the lowest values were observed in G0. Further, the duration of WS supplementation linearly increased the reactive oxygen species median fluorescence intensity of CH138⁺ cells after phagocytizing Staphylococcus aureus in the postpartum period. Longer periods of WS supplementation linearly increased phagocytosis median fluorescence intensity of CH138⁺ cells in the prepartum period of cows. Duration of WS supplementation linearly increased the percentage of blood CD14⁺ cells producing reactive oxygen species when stimulated either by Staph. aureus or Escherichia coli in the postpartum period. In conclusion, longer periods of WS supplementation during late lactation and the dry period (beginning on d 90 of the expected calving date) alter the leukocyte population and improve neutrophil immune response in the postpartum period with no detrimental effects on cow performance.     

基于自适应阈值活动语音检测和最小均方误差对数谱幅度估计的低信噪比降噪算法

针对低信噪比(SNR)环境下传统方法对声信号降噪的局限性，提出了一种联合自适应阈值活动语音检测（VAD）算法和最小均方误差对数谱幅度估计（MMSE-LSA）的实时降噪算法。首先，在VAD算法中通过基于能量概率最大值的概率统计来对背景噪声进行估计，对得到的背景噪声进行实时更新并保存；然后，将实时更新的背景噪声作为MMSE-LSA的参考噪声，并对噪声幅度谱进行自适应更新，最后进行降噪处理。通过在真实场景中对四类声信号进行实验，结果表明，该算法在保证对低SNR声信号的实时处理的情况下，相较于传统MMSE-LSA算法，降噪信号的SNR能够提高10~15 dB，且不存在信号过减的情况，可应用于实际工程。     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.     

Large neighborhood search for the bike request scheduling problem

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.     

两相区配分时间对IQ&P钢组织与性能的影响

以含Cu低碳钢为研究对象,利用SEM、EPMA和拉伸试验研究了两相区配分时间对其组织演变、元素配分以及经IQ&P处理后力学性能的影响,并利用Dictra软件对元素配分行为进行了动力学计算。结果表明,IQ处理后试验钢中的块状马氏体形成于原铁素体区域;随两相区配分时间延长,粒状马氏体数量减少,板条状马氏体之间的间距减小。检测和计算结果的对比显示,C、Mn、Cu 3种元素的相对配分速率与计算结果一致,但实际配分速率低于各自的计算结果。随两相区配分时间延长,经IQ&P处理后试验钢的抗拉强度先增加后减小,而伸长率持续减小;在600 s时达到较好的强塑性匹配,强塑积为16 963.24 MPa·%。     

A stiffness parameter and truncation error criterion for adaptive path following in structural mechanics

We explore a truncation error criterion to steer adaptive step length refinement and coarsening in incremental-iterative path following procedures, applied to problems in large-deformation structural mechanics. Elaborating on ideas proposed by Bergan and collaborators in the 1970s, we first describe an easily computable scalar stiffness parameter whose sign and rate of change provide reliable information on the local behavior and complexity of the equilibrium path. We then derive a simple scaling law that adaptively adjusts the length of the next step based on the rate of change of the stiffness parameter at previous points on the path. We show that this scaling is equivalent to keeping a local truncation error constant in each step. We demonstrate with numerical examples that our adaptive method follows a path with a significantly reduced number of points compared to an analysis with uniform step length of the same fidelity level. A comparison with Abaqus illustrates that the truncation error criterion effectively concentrates points around the smallest-scale features of the path, which is generally not possible with automatic incrementation solely based on local convergence properties.     

Learning bipartite Bayesian networks under monotonicity restrictions

ABSTRACT

Learning parameters of a probabilistic model is a necessary step in machine learning tasks. We present a method to improve learning from small datasets by using monotonicity conditions. Monotonicity simplifies the learning and it is often required by users. We present an algorithm for Bayesian Networks parameter learning. The algorithm and monotonicity conditions are described, and it is shown that with the monotonicity conditions we can better fit underlying data. Our algorithm is tested on artificial and empiric datasets. We use different methods satisfying monotonicity conditions: the proposed gradient descent, isotonic regression EM, and non-linear optimization. We also provide results of unrestricted EM and gradient descent methods. Learned models are compared with respect to their ability to fit data in terms of log-likelihood and their fit of parameters of the generating model. Our proposed method outperforms other methods for small sets, and provides better or comparable results for larger sets.     

某贫磁赤铁矿石选矿工艺试验研究

为了使某矿山贫磁赤铁矿资源得到合理的开发利用，针对现开采部位矿石的性质和可选性问题，进行了工艺矿物学和可选性试验研究。试验结果表明：采用弱磁、强磁—阴离子反浮选工艺可得到精矿铁品位68.55%、精矿产率36.41%、金属回收率79.92%、尾矿品位9.86%的较好选别指标，说明矿石的可选性较好，为该矿石的合理开发利用提供了可靠的技术依据。