Output consensus analysis and design for high-order linear swarm systems: Partial stability method

Output consensus analysis and design problems for high-order linear time-invariant swarm systems with directed interaction topologies are investigated. Firstly, as foundations of our approaches, several conclusions about partial stability are given. Then, two subspaces of the output space of swarm systems, namely an output consensus subspace and a complement output consensus subspace, are introduced. Based on output projection onto the two subspaces and partial stability, necessary and/or sufficient conditions for output consensus and limited-control-energy consensus are proposed respectively, an explicit expression of the output consensus function is presented based on the different contributions of initial states of agents and protocols, and an approach independent of the number of agents is shown to determine the gain matrices of output consensus protocols. Finally, a numerical example is given to demonstrate the theoretical results.     

A two-stage algorithm integrating genetic algorithm and modified Newton method for neural network training in engineering systems

A two-stage algorithm combining the advantages of adaptive genetic algorithm and modified Newton method is developed for effective training in feedforward neural networks. The genetic algorithm with adaptive reproduction, crossover, and mutation operators is to search for initial weight and bias of the neural network, while the modified Newton method, similar to BFGS algorithm, is to increase network training performance. The benchmark tests show that the two-stage algorithm is superior to many conventional ones: steepest descent, steepest descent with adaptive learning rate, conjugate gradient, and Newton-based methods and is suitable to small network in engineering applications. In addition to numerical simulation, the effectiveness of the two-stage algorithm is validated by experiments of system identification and vibration suppression.     

On the dynamic evidential reasoning algorithm for fault prediction

In this paper, a new fault prediction model is presented to deal with the fault prediction problems in the presence of both quantitative and qualitative data based on the dynamic evidential reasoning (DER) approach. In engineering practice, system performance is constantly changed with time. As such, there is a need to develop a supporting mechanism that can be used to conduct dynamic fusion with time, and establish a prediction model to trace and predict system performance. In this paper, a DER approach is first developed to realize dynamic fusion. The new approach takes account of time effect by introducing belief decaying factor, which reflects the nature that evidence credibility is decreasing over time. Theoretically, it is show that the new DER aggregation schemes also satisfy the synthesis theorems. Then a fault prediction model based on the DER approach is established and several optimization models are developed for locally training the DER prediction model. The main feature of these optimization models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune the DER prediction model whose initial parameters are decided by expert’s knowledge or common sense. Finally, two numerical examples are provided to illustrate the detailed implementation procedures of the proposed approach and demonstrate its potential applications in fault prediction.     

Construction of a new BRB based model for time series forecasting

It is important to predict the future behavior of complex systems. Currently there are no effective methods to solve time series forecasting problem by using the quantitative and qualitative information. Therefore, based on belief rule base (BRB), this paper focuses on developing a new model that can deal with the problem. Although it is difficult to obtain accurately and completely quantitative information, some qualitative information can be collected and represented by a BRB. As such, a new BRB based forecasting model is proposed when the quantitative and qualitative information exist simultaneously. The performance of the proposed model depends on the structure and belief degrees of BRB simultaneously. Moreover, the structure is determined by the delay step. In order to obtain the appropriate delay step using the available information, a model selection criterion is defined according to Akaike's information criterion (AIC). Based on the proposed model selection criterion and the optimal algorithm for training the belief degrees, an algorithm for constructing the BRB based forecasting model is developed. Experimental results show that the constructed BRB based forecasting model can not only predict the time series accurately, but also has the appropriate structure.     

Scaled cluster consensus of discrete-time multi-agent systems with general directed topologies

This note proposes a notion of scaled cluster consensus, wherein the final consensus states within different clusters converge to prescribed ratios. Unlike most results in existing literature on cluster consensus, no constraints are imposed on the system topologies under the designed protocol, i.e. the agents are not required to possess any cluster affiliation information of others. For the delay-free case, an explicit scaled cluster consensus function is provided by exploring the characteristics of stochastic matrices. Diverse input delays and asymmetric communication delays are both considered, and sufficient condition for scaled cluster consensus is derived based on frequency domain analysis. Finally, numerical examples are given to illustrate the effectiveness of the presented results.     

基于进化信度规则库的故障预测

在假设信度规则库(BRB)的输入为均匀分布的情况下,已有文献提出了一种序贯自适应的学习算法以实现BRB的参数在线辨识和结构的自适应调整.然而在实际问题中,信度规则库的输入一般是未知的、难以得到的,这在一定程度上限制了序贯自适应学习算法的实用性,因此就需要研究一种改进的BRB学习算法以实现参数和结构的同时辨识.本文在序贯自适应方法的基础上,通过定义BRB的完整性准则,提出了改进的BRB进化策略.与现有方法相比,该方法可以实现信度规则的自动增减,且无需输入样本的概率密度函数.此外,该方法继承了BRB的特点,仅需要部分的输入输出信息.基于改进的进化策略,提出了一种新的故障预测算法,最后通过陀螺仪故障预测实验验证了本文方法的有效性.     

Kinetics and equilibria of tea infusion: Part 7—The effects of salts and of pH on the rate of extraction of caffeine from Kapchorua Pekoe Fannings

The rates of extraction of caffeine from sieved Kapchorua PF (600–710 μm) have been measured at 80°C with a range of aqueous salt and buffer solutions of ionic strength 0·11 mol dm⁻³. The first-order rate constants and the half-times of infusion showed no trend with pH when buffers from pH 3·0 to pH 8·3 were employed. The rate constants decreased on the addition of common salts like NaCl, KCl and CaCl₂ but increased in the presence of electrolytes such as Bu₄NCl that contain large ions. The results cannot be interpreted by changes in osmotic pressure although Donnan effects may be involved. Close parallels were found between the rate constants and the solubilities of caffeine in electrolyte solutions at 25°C. In particular, the values of both properties rise appreciably in the presence of species containing aromatic or other organic rings with which caffeine molecules associate.     

2D-Antimonene-assisted hetero-epitaxial growth of perovskite films for efficient solar cells

There are many grain boundaries and defects in polycrystalline perovskite films, resulting in sacrificed efficiency and instability for perovskite solar cells (PSCs). By regulating the growth of perovskite grains along the vertical direction through epitaxial growth, one may expect fewer grain-boundaries, effective charge transport, improved crystalline quality, and reduced defect density. However, there is still no suitable epitaxial growth substrate for perovskite. Here, we developed an electrochemical lithiation intercalation and ultrasonication method to prepare high-quality antimonene nanosheets (ANs). It is found that the perovskite film grows preferentially along the (012) planes of the ANs that have perfect lattice match with the (001) planes of the perovskite, leading to a high-quality perovskite film with a preferential orientation along the [001] direction and greatly enlarged grain size. Consequently, the oriented perovskite-based PSC achieves a remarkable PCE of 24.54% and shows an enhanced stability under ambient conditions, thermal annealing or light illumination. This work opens an effective avenue to effectively control the oriented growth of perovskite film for high-performance perovskite optoelectrical devices.     

An Algorithmic Framework of Generalized Primal–Dual Hybrid Gradient Methods for Saddle Point Problems

The primal–dual hybrid gradient method (PDHG) originates from the Arrow–Hurwicz method, and it has been widely used to solve saddle point problems, particularly in image processing areas. With the introduction of a combination parameter, Chambolle and Pock proposed a generalized PDHG scheme with both theoretical and numerical advantages. It has been analyzed that except for the special case where the combination parameter is 1, the PDHG cannot be casted to the proximal point algorithm framework due to the lack of symmetry in the matrix associated with the proximal regularization terms. The PDHG scheme is nonsymmetric also in the sense that one variable is updated twice while the other is only updated once at each iteration. These nonsymmetry features also explain why more theoretical issues remain challenging for generalized PDHG schemes; for example, the worst-case convergence rate of PDHG measured by the iteration complexity in a nonergodic sense is still missing. In this paper, we further consider how to generalize the PDHG and propose an algorithmic framework of generalized PDHG schemes for saddle point problems. This algorithmic framework allows the output of the PDHG subroutine to be further updated by correction steps with constant step sizes. We investigate the restriction onto these step sizes and conduct the convergence analysis for the algorithmic framework. The algorithmic framework turns out to include some existing PDHG schemes as special cases, and it immediately yields a class of new generalized PDHG schemes by choosing different step sizes for the correction steps. In particular, a completely symmetric PDHG scheme with the golden-ratio step sizes is included. Theoretically, an advantage of the algorithmic framework is that the worst-case convergence rate measured by the iteration complexity in both the ergodic and nonergodic senses can be established.