In this paper, we review the development of a phase theory for systems and networks in its first five years, represented by a trilogy: Matrix phases and their properties; The MIMO LTI system phase response, its physical interpretations, the small phase theorem, and the sectored real lemma; The synchronization of a multi-agent network using phase alignment. Towards the end, we also summarize a list of ongoing research on the phase theory and speculate what will happen in the next five years.

In this paper, we offer a review of type-3 fuzzy logic systems and their applications in control. The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems. In this case, we review their most important applications in control and other related topics with type-3 fuzzy systems. Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important. This paper reviews the main applications that make use of Intelligent Computing methods. Specifically, type-3 fuzzy logic systems. The aim of this research is to be able to appreciate, in detail, the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques. This is done with the construction and visualization of bibliometric networks, developed with VosViewer Software, which it is a free Java-based program, mainly intended to be used for analyzing and visualizing bibliometric networks. With this tool, we can create maps of publications, authors, or journals based on a co-citation network or construct maps of keywords, countries based on a co-occurrence networks, research groups, etc.

To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.

Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the prediction performance in complex industrial processes. To fully utilize data information in PLS residual subspaces, a deep residual PLS (DRPLS) framework is proposed for quality prediction in this paper. Inspired by deep learning, DRPLS is designed by stacking a number of PLSs successively, in which the input residuals of the previous PLS are used as the layer connection. To enhance representation, nonlinear function is applied to the input residuals before using them for stacking high-level PLS. For each PLS, the output parts are just the output residuals from its previous PLS. Finally, the output prediction is obtained by adding the results of each PLS. The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.

Sparse large-scale multi-objective optimization problems (SLMOPs) are common in science and engineering. However, the large-scale problem represents the high dimensionality of the decision space, requiring algorithms to traverse vast expanse with limited computational resources. Furthermore, in the context of sparse, most variables in Pareto optimal solutions are zero, making it difficult for algorithms to identify non-zero variables efficiently. This paper is dedicated to addressing the challenges posed by SLMOPs. To start, we introduce innovative objective functions customized to mine maximum and minimum candidate sets. This substantial enhancement dramatically improves the efficacy of frequent pattern mining. In this way, selecting candidate sets is no longer based on the quantity of non-zero variables they contain but on a higher proportion of non-zero variables within specific dimensions. Additionally, we unveil a novel approach to association rule mining, which delves into the intricate relationships between non-zero variables. This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value. We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs. The results demonstrate that our approach achieves competitive solutions across various challenges.

In the context of multiple-target tracking and surveillance applications, this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest. Each camera is dedicated to tracking a specific target or cluster of targets. The key innovation of this study, in comparison to existing approaches, lies in incorporating the zooming factor for the onboard cameras into the optimization problem. This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the on-board cameras, in exchange for varying real-world distances to the corresponding targets, thereby providing additional degrees of freedom to the optimization problem. The proposed optimization framework aims to strike a balance among various factors, including distance to the targets, verticality of viewpoints, and the required focal length for each camera. The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations. To this end, we develop an original convex approximation strategy. The paper also includes simulations of diverse scenarios, featuring varying numbers of onboard tracking cameras and target motion profiles, to validate the effectiveness of the proposed approach.

Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multitasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2 (IT2) fuzzy technique under a differential evolution algorithm. To provide a more reasonable utilization of the constrained communication channel, a novel adaptive memory event-triggered (AMET) mechanism is developed, where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data. Sufficient conditions with less conservative design of the fuzzy imperfect premise matching (IPM) controller are presented by introducing the Wirtinger-based integral inequality, the information of membership functions (MFs) and slack matrices. Subsequently, under the IPM policy, a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 Takagi-Sugeno (T-S) fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect. Finally, simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a unified approach for designing FxT gradient flows (FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is provided. A unified method for designing first-order FxTGFs is shown under Polyak-Łjasiewicz inequality assumption, a weaker condition than strong convexity. When there exist both bounded and vanishing disturbances in the gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance rejection is presented. Under the strict convexity assumption, Newton-based FxTGFs is given and further extended to solve time-varying optimization. Besides, the proposed FxTGFs are further used for solving equation-constrained optimization. Moreover, an FxT proximal gradient flow with a wide range of parameters is provided for solving nonsmooth composite optimization. To show the effectiveness of various FxTGFs, the static regret analyses for several typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are applied to solve two network problems, i.e., the network consensus problem and solving a system linear equations, respectively, from the perspective of optimization. Particularly, by choosing component-wisely sign-preserving functions, these problems can be solved in a distributed way, which extends the existing results. The accelerated convergence and robustness of the proposed FxTGFs are validated in several numerical examples stemming from practical applications.

This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation. For the distributed estimation structure under consideration, an estimation center is not necessary, and the estimator derives its information from itself and neighboring nodes, which fuses the state vector and the measurement vector. In an effort to cut down data conflicts in communication networks, the stochastic communication protocol (SCP) is employed so that the output signals from sensors can be selected. Additionally, a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data. On this basis, sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error. Finally, a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.