H_∞ couple-group consensus of stochastic multi-agent systems with fixed and Markovian switching communication topologies

The paper addresses the issue of H_∞ couple-group consensus for a class of discrete-time stochastic multi-agent systems via output-feedback control. Both fixed and Markovian switching communication topologies are considered. By employing linear transformations, the closed-loop systems are converted into reduced-order systems and the H_∞ couplegroup consensus issue under consideration is changed into a stochastic H_∞ control problem. New conditions for the mean-square asymptotic stability and H_∞ performance of the reduced-order systems are proposed. On the basis of these conditions, constructive approaches for the design of the output-feedback control protocols are developed for the fixed communication topology and the Markovian switching communication topologies, respectively. Finally, two numerical examples are given to illustrate the applicability of the present design approaches.

Turbidity analysis using visible and near-infrared light images

The conventional photoelectric detection system requires complex circuitry and spectroscopic systems as well as specialized personnel for its operation.To replace such a system,a method of measuring turbidity using a camera is proposed by combining the imaging characteristics of a digital camera and the high-speed information processing capability of a computer.Two turbidity measurement devices based on visible and near-infrared(NIR)light cameras and a light source driving circuit with constant light intensity were designed.The RGB data in the turbidity images were acquired using a self-developed image processing software and converted to the CIE Lab color space.Based on the relationship between the luminance,chromatic aberration,and turbidity,the turbidity detection models for luminance and chromatic aberration of visible and NIR light devices exhibiting values from 0-1000 NTU,less than 100 NTU,and more than 100 NTU were established.By comparing and analyzing the proposed models,the two measurement models with the best all-around performance were selected and fused to generate new measurement models.The experimental results prove that the correlation between the three models and the commercial turbidity meter measurements exhibite a significance value higher than 0.999.The error of the fusion model is within 1.05%,with a mean square error of 1.14.The visible light device has less error at low turbidity measurements and is less influenced by the color of the image.The NIR light device is more stable and accurate at full range and high turbidity measurements and is therefore more suitable for such measurements.

A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

It is still a huge challenge for traditional Pareto-dominatedmany-objective optimization algorithms to solve manyobjective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front,resulting in poor performance of those algorithms.For this reason,we propose a reference vector-assisted algorithmwith an adaptive niche dominance relation,for short MaOEA-AR.The new dominance relation forms a niche based on the angle between candidate solutions.By comparing these solutions,the solutionwith the best convergence is found to be the non-dominated solution to improve the selection pressure.In reproduction,a mutation strategy of k-bit crossover and hybrid mutation is used to generate high-quality offspring.On 23 test problems with up to 15-objective,we compared the proposed algorithm with five state-of-the-art algorithms.The experimental results verified that the proposed algorithm is competitive.

一种基于遗传算法的特征选择和权重确定方法

在模式识别中,特征选择是其中非常重要的步骤,特征集的选择直接影响分类器的精度.该文提出了一种基于遗传算法的特征权重确定方法,首先使用传统遗传算法进行特征的初步选择,得到一个粗选的特征集;然后使用实数编码的遗传算法在第一步的基础上进一步精选特征,并确定每个入选特征的权重.通过实验和一些传统特征选择方法进行对比,结果显示,该文提出的算法取得了较好的效果.

Influencing Factors and Clustering Characteristics of COVID-19:A Global Analysis

The unprecedented coronavirus disease 2019(COVID-19)pandemic is still raging(in year 2021)in many countries worldwide.Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and control of this epidemic.Descriptive statistics and regression analysis on COVID-19 data from different countries were conducted in this study to compare and evaluate various regression models.Results showed that the extreme random forest regression(ERFR)model had the best performance,and factors such as population density,ozone,median age,life expectancy,and Human Development Index(HDI)were relatively influential on the spread and diffusion of COVID-19 in the ERFR model.In addition,the epidemic clustering characteristics were analyzed through the spectral clustering algorithm.The visualization results of spectral clustering showed that the geographical distribution of global COVID-19 pandemic spread formation was highly clustered,and its clustering characteristics and influencing factors also exhibited some consistency in distribution.This study aims to deepen the understanding of the international community regarding the global COVID-19 pandemic to develop measures for countries worldwide to mitigate potential large-scale outbreaks and improve the ability to respond to such public health emergencies.

Turbidity measurement based on a multi-wavelength spectral sensor

A method of measuring turbidity based on a multi-wavelength spectral sensor is proposed by using SFH4737 broad-band infrared LED,a multi-wavelength spectral sensor and independently developed data processing software.Combining multiple wavelength data from the sensor,the unitary and multivariate fitting models were constructed to investigate the relationship among light intensity information,absorbance and turbidity,respectively.The turbidity of the actual water bodies was measured separately by using proposed method and a commercially visible spectrophotometer.The independent-samples T test(p>0.05)showed that there was no significant difference between the method in this paper and the standard assay method.The method is simple and inexpensive,and can be applied to the rapid detection of water turbidity,providing a new way of industrial online measurement.

Analysis of Software Trustworthiness Based on FAHP-CRITIC Method

Software trustworthiness includes many attributes.Reasonable weight allocation of trustworthy at-tributes plays a key role in the software trustworthiness measurement.In practical application,attribute weight usually comes from experts'evaluation to attributes and hidden information derived from attributes.Therefore,when the weight of attributes is researched,it is necessary to consider weight from subjective and objective as-pects.First,a novel weight allocation method is proposed by combining the fuzzy analytical hierarchy process(FAHP)method and the criteria importance though intercrieria correlation(CRITIC)method.Second,based on the weight allocation method,the trustworthiness measurement models of component-based software are estab-lished according to the seven combination structures of components.Third,the model reasonability is verified via proving some metric criteria.Finally,a case is carried out.According to the comparison with other models,the result shows that the model has the advantage of utilizing hidden information fully and analyzing the com-bination of components effectively.It is an important guide for measuring the trustworthiness measurement of component-based software.

Two-thirds simulation indexes and modal logic characterization

Two-thirds simulation provides a kind of abstract description of an implementation with respect to a specification. In order to characterize the approximate two-thirds simulation, we propose the definition of a two- thirds simulation index which expresses the degree to which a binary relation between processes is two-thirds simulation. 2-two-thirds simulation and its substitutivity laws are given in this paper. And, based on 2-two-thirds simulation, we present a measure model for describing the degree of approximation between processes. In particular, we give the modal logical characterization of 2-two-thirds simulation.

Correlation Functions of Perfect Binary Sequences of Same Lengths

The sequences with good correlation properties are widely used in engineering applications, especially in the area of communications. In this paper, the relationships among crosscorrelation functions of arbitrary four binary sequences of period N are presented. Based on them, for a sequences set, the relationships between cross-correlation functions and autocorrelation functions are studied, by which we prove that they cannot keep optimal at the same time.

The Infinite Evolution Mechanism of ∈-Bisimilarity

In this paper, we focus on the convergence mechanism of ε-bisimulation under probabilistic processes to discuss the dynamic correctness of the software. Firstly, ε-limit bisimulation is defined for reflecting the dynamic relation between software specification and implementation. Some special ε-limit bisimulations are showed. Secondly, ε-bisimulation limit is proposed, which states the specification is the limit of implementation under ε-bisimulation. The uniqueness of ε-bisimulation limit and consistence with ε-bisimulation are presented. Finally, the substitutivity laws of ε-bisimulation limit with various combinators are proved.

Probabilistic Model of Software Approximate Correctness

Correctness is a key attribute of software. Parameterized bisimulation provides a kind of abstract description to verify the correctness of software under environment. In real world situations, many softwares contain some probabilistic information. In this paper, we focus on the probabilistic construction of parameterized bisimulation. Firstly, we extend parameterized bisimulation to probabilistic parameterized bisimulation. Secondly, a quantitative model of approximate correctness of software is proposed. Finally, the subsititutivity laws of quantitative model with various combinators are proved.

Lattice-based sequential aggregate signatures with lazy verification

This paper proposes the first lattice-based sequential aggregate signature （SAS） scheme with lazy verification that is provably secure in the random oracle model. As opposed to large integer factoring and discrete logarithm based systems, the security of the construction relies on worst-case lattice problem, namely, under the small integer solution （SIS） assumption. Generally speaking, SAS schemes enable any group of signers ordered in a chain to sequentially combine their signatures such that the size of the aggregate signature is much smaller than the total size of all individual signatures. Unlike prior such proposals, the new scheme does not require a signer to retrieve the keys of other signers and verify the aggregate-so-far before adding its own signature, and the signer can add its own signature to an unverified aggregate and forward it along immediately, postponing verification until load permits or the necessary public keys are obtained. Indeed, the new scheme does not even require a signer to know the public keys of other signers.

An Attribute-Based Signature Scheme from Lattice Assumption

Inspired by the framework of Boyen, in this paper, an attribute-based signature（ABS） scheme from lattice assumption is proposed. In this attribute-based signature scheme, an entity＇s attributes set corresponds to the concatenation of a lattice matrix with the sum of some random matrices, and the signature vector is generated by using the Preimage Sampling algorithm. Compared with current attribute-based signature schemes, this scheme can resist quantum attacks and enjoy shorter public-key, smaller signature size and higher efficiency.