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This article investigates the problem of event‐based resilient and robust H∞ control for semi‐Markov jump systems (S‐MJSs) under stochastic cyber attacks. It is assumed that the uncertain S‐MJS is threaten by stochastic cyber attacks characterized by two different functions. To save the resource of bandwidth limited network, the dynamic event‐triggered (DET) scheme is adopted to reduce the total number of released data through the transmission channel. In addition, the actuator fault, the signal quantization, and the asynchronous phenomenon of the controller are taken into consideration simultaneously, which makes the analysis and synthesis more practical. Based on the linear matrix inequality (LMI) approach, the sufficient conditions are derived to ensure the stochastic stability of the system with a predefined H∞ performance. Then the co‐design of resilient controller gains and weighting matrices of the DET scheme is presented in terms of a group of feasible LMIs. Finally, simulation examples are given to validate the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Incremental extreme learning machine has been proved to be an efficient and simple universal approximator. However, the network architecture may be very large due to the inefficient nodes which have a tiny effect on reducing the residual error. More to the point, the output weights are not the least square solution. To reduce such inefficient nodes, a method called bidirectional ELM (B-ELM), which analytically calculates the input weights of even nodes, was proposed. By analyzing, B-ELM can be further improved to achieve better performance on compacting structure. This paper proposes the modified B-ELM (MB-ELM), in which the orthogonalization method is involved in B-ELM to orthogonalize the output vectors of hidden nodes and the resulting vectors are taken as the output vectors. MB-ELM can greatly diminish inefficient nodes and obtain a preferable output weight vector which is the least square solution, so that it has better convergence rate and a more compact network architecture. Specifically, it has been proved that in theory, MB-ELM can reduce residual error to zero by adding only two nodes into network. Simulation results verify these conclusions and show that MB-ELM can reach smaller low limit of residual error than other I-ELM methods. [ABSTRACT FROM AUTHOR]

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure. [ABSTRACT FROM AUTHOR]

In aquaculture, fish feeding behavior under culture conditions holds important information for the aquaculturist. In this study, near-infrared imaging was used to observe feeding processes of fish as a novel method for quantifying variations in fish feeding behavior. First, images of the fish feeding activity were collected using a near-infrared industrial camera installed at the top of the tank. A binary image of the fish was obtained following a series of steps such as image enhancement, background subtraction, and target extraction. Moreover, to eliminate the effects of splash and reflection on the result, a reflective frame classification and removal method based on the Support Vector Machine and Gray-Level Gradient Co-occurrence Matrix was proposed. Second, the centroid of the fish was calculated by the order moment, and then, the centroids were used as a vertex in Delaunay Triangulation. Finally, the flocking index of fish feeding behavior (FIFFB) was calculated to quantify the feeding behavior of a fish shoal according to the results of the Delaunay Triangulation, and the FIFFB values of the removed reflective frames were fitted by the Least Squares Polynomial Fitting method. The results show that variations in fish feeding behaviors can be accurately quantified and analyzed using the FIFFB values, for which the linear correlation coefficient versus expert manual scoring reached 0.945. This method provides an effective method to quantify fish behavior, which can be used to guide practice. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have gained worldwide attention in recent years. Since WSNs can be conveniently deployed to monitor a given field of interest, they have been considered as a great long-term economic potential for military, environmental, and scientific applications and so forth. One of the most active areas of research in WSNs is the coverage which is one of the most essential functions to guarantee quality of service (QoS) in WSNs. However, less attention is paid on the heterogeneity of the node and the energy balance of the whole network during the redeployment process. In this work, the energy balanced problems in mobile heterogeneous WSNs redeployment have been analyzed. The virtual force algorithm with extended virtual force model is used to improve the QoS of the deployment. Furthermore energy model is added to enhance or limit the movement of the nodes so that the energy of nodes in the whole WSNs can be balanced and the lifetime of the networks can be prolonged. The simulation results verify the effectiveness of this proposed algorithm. [ABSTRACT FROM AUTHOR]

It is challenging to generate optimal trajectories for nonlinear dynamic systems under external disturbances. In this brief, we present a novel approach for planning safe trajectories of the chance-constrained trajectory optimization problems with nonconvex constraints. First, the chance constraints are handled by deterministic ones which show its availability. We derive an iterative convex optimization method to solve the optimal control problem. Then the chance-constrained optimal control (CCOCP) problem is reformed to be a nonlinear programming problem (NLP) through the hp -adaptive pseudospectral method. An iterative successive linearization algorithm is detailed to convex the NLP to be a convex optimization one which described as a second-order cone programming problem. We demonstrate the proposed approach on a 3-DoF of unmanned aerial vehicle system under chance-constrained. The simulation results show reliable solutions for the UAV chance-constrained trajectory optimization problem. [ABSTRACT FROM AUTHOR]

Incremental extreme learning machine has been verified that it has the universal approximation capability. However, there are two major issues lowering its efficiency: one is that some "random" hidden nodes are inefficient which decrease the convergence rate and increase the structural complexity, the other is that the final output weight vector is not the minimum norm least-squares solution which decreases the generalization capability. To settle these issues, this paper proposes a simple and efficient algorithm in which the parameters of even hidden nodes are calculated by fitting the residual error vector in the previous phase, and then, all existing output weights are recursively updated based on inverse partitioned matrix. The algorithm can reduce the inefficient hidden nodes and obtain a preferable output weight vector which is always the minimum norm least-squares solution. Theoretical analyses and experimental results show that the proposed algorithm has better performance on convergence rate, generalization capability and structural complexity than other incremental extreme learning machine algorithms. [ABSTRACT FROM AUTHOR]

This paper explores a dynamic output predictive control issue for networked systems with random delays and uncertainties. Based on the networked predictive control systems, an adaptive event-triggered dynamic output predictive control strategy is presented. And a networked delay compensator method is employed to compensate the communication delay actively. Furthermore, the problem we addressed can be transformed into the delay-dependent stability and stabilisation problem of networked control systems. Finally, the Lyapunov–Krasovskii functional approach and linear matrix inequality method are introduced to solve the expected solution. Both the results of numerical simulations and experiment are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the relay tracking problem for networked nonlinear multi-agent systems with communication delays. By setting up monitoring agents in a certain area, the object of this paper is to track the targets which cross over the monitoring area through the relay cooperative tracking strategy. Based on an improved data-driven control method, a data-driven relay tracking control strategy is proposed. It is shown that under the presented relay tracking strategy, all pursued agents asymptotically achieve tracking the target agents. Moreover, with the introduced delay compensation mechanism in the control strategy, the communication delays are compensated actively. Finally, some simulations are provided to illustrate the performance of the proposed strategies. [ABSTRACT FROM AUTHOR]

Liao et al. (Neurocomputing 128:81-87, 2014) proposed a meta-learning approach to extreme learning machine (Meta-ELM), which can obtain good generalization performance by training multiple ELMs. However, one of its open problems is overfitting when minimizing training error. In this paper, we propose an improved meta-learning model of ELM (improved Meta-ELM) to handle the problem. The improved Meta-ELM architecture is composed of some base ELMs which are error feedback incremental extreme learning machine (EFI-ELM) and the top ELM. The improved Meta-ELM includes two stages. First, each base ELM with EFI-ELM is trained on a subset of training data. Then, the top ELM learns with the base ELMs as hidden nodes. Simulation results on some artificial and benchmark datasets show that the proposed improved Meta-ELM model is more feasible and effective than Meta-ELM. [ABSTRACT FROM AUTHOR]

The node localization is a crucial technology that affects practicality, accuracy and effectiveness of the wireless sensor networks (WSNs). Sensor nodes are often deployed non-uniformly in anisotropic WSNs with holes in various applications such as monitoring area terrain. The existence of holes will invariably affect the Euclidean distances between nodes and result in low accuracy of node localization. In this paper, a Heuristic Multidimensional Scaling (HMDS) algorithm is proposed to improve accuracy of node localization in anisotropic WSNs with holes. By exploring the virtual node and constructing the shortest paths between nodes, the Euclidean distances between nodes are obtained via employing the heuristic approach such that they can be used to calculate more accurate locations of the nodes. The HMDS algorithm greatly reduces the communication complexity and computational complexity compared with the MDS-MAP algorithm. Simulation results demonstrate that the HMDS algorithm requires fewer anchors to obtain the node locations. The HMDS algorithm is suitable for four different topologies, including the semi-C-shape topology, the O-shape topology, the multiple O-shape topology and the concave-shape topology and is exceedingly accurate and efficient comparing with state-of-the-art methods in anisotropic WSNs with holes. [ABSTRACT FROM AUTHOR]

Predictions regarding the solar greenhouse temperature and humidity are important because they play a critical role in greenhouse cultivation. On account of this, it is important to set up a predictive model of temperature and humidity that would precisely predict the temperature and humidity, reducing potential financial losses. This paper presents a novel temperature and humidity prediction model based on convex bidirectional extreme learning machine (CB-ELM). Simulation results show that the convergence rate of the bidirectional extreme learning machine (B-ELM) can further be improved while retaining the same simplicity, by simply recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. The performance of the CB-ELM model is compared with other modeling approaches by applying it to predict solar greenhouse temperature and humidity. The experiment results show that the CB-ELM model predictions are more accurate than those of the B-ELM, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Radial Basis Function (RBF). Therefore, it can be considered as a suitable and effective method for predicting the solar greenhouse temperature and humidity. [ABSTRACT FROM AUTHOR]

This article investigates the resilient and event‐triggered control problem of stochastic jump systems subject to randomly occurring denial of service (DoS) attacks and deception attacks. First, a novel resilient and memory event‐triggered scheme (RMETS) is proposed. The influence caused by DoS attacks is characterized as an uncertainty of the triggering condition. Under the RMETS, the desired security performance of the system and limited network resources can be well balanced while stochastic deception and DoS attacks occur. Second, by using the Lyapunov theory and the linear matrix inequality method, the resilient controller and the proposed RMETS are co‐designed. Sufficient conditions are established to ensure the security performance under the two types of attacks. Finally, numerical and practical examples are rendered to illustrate the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

Localisation is an essential and important part in wireless sensor networks (WSNs). Many applications require location information. So far, there are less researchers studying on mobile sensor networks (MSNs) than static sensor networks (SSNs). However, MSNs are required in more and more areas such that the number of anchor nodes can be reduced and the location accuracy can be improved. In this paper, we firstly propose a range-free Voronoi-based Monte Carlo localisation algorithm (VMCL) for MSNs. We improve the localisation accuracy by making better use of the information that a sensor node gathers. Then, we propose an optimal region selection strategy of Voronoi diagram based on VMCL, called ORSS-VMCL, to increase the efficiency and accuracy for VMCL by adapting the size of Voronoi area during the filtering process. Simulation results show that the accuracy of these two algorithms, especially ORSS-VMCL, outperforms traditional MCL. [ABSTRACT FROM AUTHOR]

We propose an iterative learning control (ILC) tracking strategy to solve the tracking problem of multi-agent systems with nonlinear dynamics and time-varying communication delays. The distributed tracking strategy, in which each tracking agent only utilises its own and neighbours’ information, enables the tracking agents successfully track a maneuvering target in a finite time interval although with presence of time delays. Compared with the existing related work, the quantitative relationship between the boundary of tracking errors and the estimation of time delays is derived. Furthermore, in many practical control problems, identical initialisation condition may not be satisfied, which is called initial-shift problem. Hence, a forgetting factor is introduced to deal with that problem. It is proved that the presented results are effective via conducting numerical examples. [ABSTRACT FROM AUTHOR]

It is becoming increasingly attractive to execute workflows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing resources on demand. However, despite being a key to efficiently managing application execution in the cloud, traditional workflow scheduling algorithms face significant challenges in the cloud environment. The gradient-based optimizer (GBO) is a newly proposed evolutionary algorithm with a search engine based on the Newton's method. It employs a set of vectors to search in the solution space. This study designs a gradient-based scheduler by using GBO for workflow scheduling to minimize the usage costs of workflows under given deadline constraints. Extensive experiments are conducted on well-known scientific workflows of different sizes and types using WorkflowSim. The experimental results show that the proposed scheduling algorithm outperforms five other state-of-the-art algorithms in terms of both the constraint satisfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling problems. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking problem of a class of multi-agent systems under uncertain communication environments which has been modelled by a finite number of constant Laplacian matrices together with their corresponding scheduling functions. Sliding mode control method is applied to solve this nonlinear tracking problem under a time-varying topology. The controller of each tracking agent has been designed by using only its own and neighbours’ information. Sufficient conditions for the existence of a sliding mode control tracking strategy have been provided by the solvability of linear matrix inequalities. At the end of this work, numerical simulations are employed to demonstrate the effectiveness of the proposed sliding mode control tracking strategy. [ABSTRACT FROM AUTHOR]

In this paper, tracking problem for high-order agents under uncertain communication topology and time varying delays is investigated. Each tracking agent is assumed only its own and neighbors׳ information is used in their tracking strategy design. The uncertain communication topology is modelled by a finite number of Laplacian matrices with their corresponding scheduling functions. Sufficient conditions for the existence of a tracking strategy have been expressed in terms of the solvability of linear matrix inequalities. Finally, numerical examples are given to verify the effectiveness and advantages of proposed tracking strategy. Through a set of simulations with various control parameters γ , the relationship between γ and the maximum allowable communications delays is tabulated. [ABSTRACT FROM AUTHOR]

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy. [ABSTRACT FROM AUTHOR]

This paper studies denial-of-services (DoS) attacks against industrial cyber-physical systems (ICPSs) for which we built a proper ICPS model and attack model. According to the impact of different attack rates on systems, instead of directly studying the time delay caused by the attacks some security zones are identified, which display how a DoS attack destroys the stable status of the ICPS. Research on security zone division is consistent with the fact that ICPSs' communication devices actually have some capacity for large network traffic. The research on DoS attacks' impacts on ICPSs by studying their operation conditions in different security zones is simplified further. Then, a detection method and a mimicry security switch strategy are proposed to defend against malicious DoS attacks and bring the ICPS under attack back to normal. Lastly, practical implementation experiments have been carried out to illustrate the effectiveness and efficiency of the method we propose. [ABSTRACT FROM AUTHOR]

This study is centred around the economic dispatch (ED) problem of thermal generating units over a multi‐time interval, to have generators collectively meet the load demand and network losses while minimising the system fuel costs. The underlying optimisation problem displays non‐convexity due to the consideration of the valve point effect and ramping rates limit of generators. Additionally, the system operator by itself cannot determine the behaviours of generators since they are not willing to share their own cost functions and wish to make decisions by themselves. In view of these reasons, the authors provide a decentralised method based on gradient projection and piecewise approximation for the ED problem. It is shown that the proposed method converges to the global optimum when applying appropriate step‐size parameters. The performance of the proposed method is demonstrated on the IEEE standard 5 and 10 units test systems, and via comparisons with the existed methods in the literature. [ABSTRACT FROM AUTHOR]