Dynamic Hypergraph Modeling and Robustness Analysis for SIoT

The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.

Robustness analysis for a class of nonlinear descriptor systems

The robustness analysis problem of a class of nonlinear descriptor systems is studied. Nonlinear matrix inequality which has the good computation property of convex feasibility is employed to derive some sufficient conditions to guarantee that the nonlinear descriptor systems have robust disturbance attenuation performance, which (avoids) the computational difficulties in conversing nonlinear matrix and Hamilton-Jacobi inequality. The computation property of convex feasibility of nonlinear matrix inequality makes it possible to apply the results of nonlinear robust control to practice.

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

The flight dynamics model of air-breathing hypersonic vehicles （AHVs） is highly nonlinear and multivariable cou- pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator （LQR） design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela- tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given prohabilily levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control Systems:A Case Study in Level Control Process

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this work.Interval uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world plants.Also,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)controller.To the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time delay.The three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise reduction.Finally,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

An Adaptive Design for Six Sigma(ADFSS): a Simulated Annealing and Regression Analysis Embedded Approach

Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into four major phases viz. identify, design, optimize, and validate （IDOV）. And an adaptive design for six sigma （ADFSS） incorporating the traits of artifidai intelligence and statistical techniques is presented. In the identify phase of the ADFSS, fuzzy relation measures between customer attributes （CAs） and engineering characteristics （ECs） as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers （FLCs）. These two measures are then used to establish the cumulative impact factor for ECs. In the next phase （ i. e. design phase）, a transfer function is developed with the aid of robust multiple nonlinear regression analysis. Furthermore, 1this transfer function is optimized with the simulated annealing （ SA ） algorithm in the optimize phase. In the validate phase, t-test is conducted for the validation of the design resulted in earlier phase. Finally, a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.

Identification of Heterogeneity of Social and Economic Environment of Land Uses in China

The robust principal component analysis （RPCA） is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses （SEELU）. RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU.

Design and Robust Performance Evaluation of a Fractional Order PID Controller Applied to a DC Motor

This paper proposes a methodology for the quantitative robustness evaluation of PID controllers employed in a DC motor. The robustness analysis is performed employing a 2~3 factorial experimental design for a fractional order proportional integral and derivative controller(FOPID), integer order proportional integral and derivative controller(IOPID)and the Skogestad internal model control controller(SIMC). The factors assumed in experiment are the presence of random noise,external disturbances in the system input and variable load. As output variables, the experimental design employs the system step response and the controller action. Practical implementation of FOPID and IOPID controllers uses the MATLAB stateflow toolbox and a NI data acquisition system. Results of the robustness analysis show that the FOPID controller has a better performance and robust stability against the experiment factors.

A Fuzzy-based Sliding Mode Control Approach for Acceleration Slip Regulation of Battery Electric Vehicle

Due to quick response and large quantity of electric motor torque,the traction wheels of battery electric vehicle are easy to slip during the initial phase of starting.In this paper,a sliding mode control approach of acceleration slip regulation is designed to prevent the slip of the traction wheels.The wheel slip ratio is used as the state variable for the formulation of system dynamics model.The fuzzy algorithm is utilized to adjust the switch function of sliding mode controller.After stability and robustness analysis,the sliding mode control law is transferred into C code and downloaded into vehicle control unit,which is validated under wet and dry road conditions.The experimental results with a small overshoot and a quick response during starting indicate that the sliding mode controller has good control efect on the slip ratio regulation.This article proposes an acceleration slip regulation method that improves the safety during acceleration for battery electric vehicle.

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

Percolation transitions in edge-coupled interdependent networks with directed dependency links

We propose a model of edge-coupled interdependent networks with directed dependency links(EINDDLs)and develop the theoretical analysis framework of this model based on the self-consistent probabilities method.The phase transition behaviors and parameter thresholds of this model under random attacks are analyzed theoretically on both random regular(RR)networks and Erd¨os-Renyi(ER)networks,and computer simulations are performed to verify the results.In this EINDDL model,a fractionβof connectivity links within network B depends on network A and a fraction(1-β)of connectivity links within network A depends on network B.It is found that randomly removing a fraction(1-p)of connectivity links in network A at the initial state,network A exhibits different types of phase transitions(first order,second order and hybrid).Network B is rarely affected by cascading failure whenβis small,and network B will gradually converge from the first-order to the second-order phase transition asβincreases.We present the critical values ofβfor the phase change process of networks A and B,and give the critical values of p andβfor network B at the critical point of collapse.Furthermore,a cascading prevention strategy is proposed.The findings are of great significance for understanding the robustness of EINDDLs.

Accelerated Matrix Recovery via Random Projection Based on Inexact Augmented Lagrange Multiplier Method

In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

Robust Design Optimization Method for Centrifugal Impellers under Surface Roughness Uncertainties Due to Blade Fouling

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression（SVR） metamodel is combined with the Monte Carlo simulation（MCS） method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.

Local singularity and S–A methods for analyzing ore-producing anomalies in the Jianbiannongchang area of Heilongjiang,China

The Heilongjiang Jianbiannongchang area is located at the confluence of the Great and Lesser Xing’an Ranges.This area has a complex magmatic and tectonic evolutionary history that has resulted in a complex and diverse geological background for mineralization.In this study,isometric logarithmic ratio(ILR)transformations of Au,Cu,Pb,Zn,and Sb contents were performed in the1:50,000 soil geochemical data of the Jianbiannongchang area.Robust principal component analysis(RPCA)was conducted based on ILR transformation.The local singularity and spectrum-area(S-A)methods were used to extract information on mineralogic anomalies.The results showed that:(1)the transformed data eliminated the influence of the original data closure effect,and the PC1and PC2 information obtained by applying RPCA reflected ore-producing element anomalies dominated by Au and Cu.(2)The local singularity method can enhance the information of the local strong and weak slow anomalies.After performing local singularity analysis on PC1 and PC2,the obtained local anomalies reflected the local singularity spatial anomaly patterns related to Cu and Au mineralization in this area,which is an effective method for trapping ore-producing anomalies.(3)Furthermore,the composite anomaly decomposition of PC1 and PC2 was performed using the S-A method,and the screened anomalous and background fields reflect the ore-producing anomalies related to Cu and Au mineralization.This information is in agreement with known Cu and Au mineralization.(4)The geochemical anomalies with mineralization potential were obtained outside the known mineralization sites by integrating the information of oreproducing anomalies extracted by the local singularity and S-A methods,providing the theoretical basis and exploration direction for future exploration in the study area.

Mainlobe jamming suppression via improved BSS method for rotated array radar

This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio(SNR)of pulse compression.In this paper,we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal(RAMS)model firstly.Then the corresponding algorithm improved blind source separation(BSS)using the frequency domain of robust principal component analysis(FDRPCA-BSS)is proposed based on the established rotating model.It can eliminate the influence of the rotating parts and address the problem of loss of SNR.Finally,the measured peakto-average power ratio(PAPR)of each separated channel is performed to identify the target echo channel among the separated channels.Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.

Robust D-stability LMI conditions of matrix polytopes via affine parameter-dependent Lyapunov functions

The problem of the robust D-stability analysis for linear systems with parametric uncertainties is addressed. For matrix polytopes, new conditions via the affine parameter-dependent Lyapunov function of uncertain systems are developed with the benefit of the scalar multi-convex function. To be convenient for applications, such conditions are simplified into new linear matrix inequality （LMI） conditions, which can be solved by the powerful LMI toolbox. Numerical examples are provided to indicate that this new approach is less conservative than previous results for Hurwitz stability, Schur stability and D-stability of uncertain systems under certain circumstances.

A NEW STATISTICAL APPROACH FOR THE ANALYSIS OF UNCERTAIN SYSTEMS

This paper addresses the issues of conservativeness and computational complexity of probabilistie robustness analysis. The authors solve both issues by defining a new sampling strategy and robustness measure. The new measure is shown to be much less conservative than the existing one. The new sampling strategy enables the definition of efficient hierarchical sample reuse algorithms that reduce significantly the computational complexity and make it independent of the dimension of the uncertainty space. Moreover, the authors show that there exists a one to one correspondence between the new and the existing robustness measures and provide a computationally simple algorithm to derive one from the other.

Comparative analysis of metabolic network of pathogens

BACKGROUND： Metabolic networks are complex and system of highly connected chemical reactions and hence it needs a system level computational approach to identify the genotype- phenotype relationship. The study of essential genes and reactions and synthetic lethality of genes and reactions plays a crucial role in explaining functional links between genes and gene function predictions. METHODS： Flux balance analysis （FBA） has been developed as a powerful method for the in silico analyses of metabolic networks. In this study, we present the comparative analysis of the genomic scale metabolic networks of the four microorganisms i.e. Salmonella typhimurium, Mycobacterium tuberculosis, Staphylococcus aureus, and Helicobacter pylori. The fluxes of all reaction were obtained and the growth rate of the organism was calculated by setting the biomass reaction as the objective function. RESULTS ＆ CONCLUSIONS： The average lethality fraction of all the four organisms studied ranged from 0.2 to 0.6. It was also observed that there are very few metabolites which are highly connected. Those metabolites that are highly connected are supposed to be the ‘global players＇ similar to the hub protein in the protein - protein interaction network.

Multivariate Statistical Process Monitoring Using Robust Nonlinear Principal Component Analysis

The principal component analysis （PCA） algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the nonlinear PCA （NLPCA） using autoassociative bottle-neck neural networks is so sensitive that the obtained model differs significantly from the underlying system. In this paper, a robust version of NLPCA is introduced by replacing the generally used error criterion mean squared error with a mean log squared error. This is followed by a concise analysis of the corresponding training method. A novel multivariate statistical process monitoring （MSPM） scheme incorporating the proposed robust NLPCA technique is then investigated and its efficiency is assessed through application to an industrial fluidized catalytic cracking plant. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms and is, hence, expected to better monitor real-world processes.