A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.      

Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism.First,a Petri net for the deadlock control of such systems is defined.Its admissible markings and first-met inadmissible markings(FIMs)are introduced.Next,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable transitions.ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition.In addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs.The condition under which the supervisor is maximally permissive in behavior is given.Finally,experimental results with the proposed method and existing ones are given to show its effectiveness.     

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.      

Foldings and grazings of tori in current controlled interleaved boost converters

Interleaved boost converters (IBCs) are used when energy conversion is required at high current levels. Such converter systems may undergo various nonlinear phenomena which can affect their performance adversely. In this paper, we study an IBC and demonstrate the first instability through a Neimark–Sacker bifurcation, resulting in a torus. An analysis based on the calculation of the monodromy matrix reveals that the torus has a rather strange form as the complex Floquet multipliers that became unstable have a real value close to ?1. We show that further variation in a parameter can result in novel nonlinear phenomena where the torus itself folds and grazes a switching manifold, resulting in a ‘wobbling’ of the closed loop that represents the torus in discrete time. Numerical and analytical results validate our work. Copyright © 2013 John Wiley & Sons, Ltd.     

QoS Prediction Model of Cloud Services Based on Deep Learning

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud services.Specifically,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual evaluations.The multi-metric features include global,local,and individual ones.Experimental results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.     

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

Recently, multimodal multiobjective optimization problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solutions diversity. In this paper, we propose a new multiobjective fireworks algorithm for them, which is able to balance exploitation and exploration in the decision space. We first extend a latest single-objective fireworks algorithm to handle MMOPs. Then we make improvements by incorporating an adaptive strategy and special archive guidance into it, where special archives are established for each firework, and two strategies (i.e., explosion and random strategies) are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives. Finally, we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems. Experimental results show that the proposed algorithm is superior to compared algorithms in solving them. Also, its runtime is less than its peers’.      

Fast‐scale stability limits of a two‐stage boost power converter

There are many applications in power electronics that demand high step‐up conversion ratio between the source and the load. A simple way of achieving such a high voltage ratio is by cascading DC–DC boost converters in a few stages. The individual converters in such a cascaded system are usually designed separately applying classical design criteria. This paper investigates the stability of the overall system of a cascade connection of two boost converters under current mode control. We first demonstrate the bifurcation behavior of the system, and it is shown that the desired periodic orbit can undergo fast‐scale period doubling bifurcation leading to subharmonic oscillations and chaotic regimes under parameter variation. The value of the intermediate capacitor is taken as a design parameter, and we determine the minimum ramp slope in the first stage required to maintain stability. It is shown that smaller capacitance values give rise to wider stability range. We explain the bifurcation phenomena using a full‐order model. Then, in order to simplify the analysis and to obtain a closed‐form expression to explain the previous observation, we develop a reduced‐order model by treating the second stage as a current sink. This allows us to obtain design‐oriented stability boundaries in the parameter space by taking into account slope interactions between the state variables in the two stages. Copyright © 2015 John Wiley & Sons, Ltd.     

Artificial neural networks for water quality soft-sensing in wastewater treatment: a review

This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic analysis and modeling principle of water quality soft-sensing. The common soft-sensing models mainly include a back-propagation neural network, radial basis function neural network, fuzzy neural network (FNN), echo state network (ESN), growing deep belief network and deep belief network with event-triggered learning (DBN-EL). They are compared in terms of accuracy, efficiency and computational complexity with partial-least-square-regression DBN (PLSR-DBN), growing ESN, sparse deep belief FNN, self-organizing DBN, wavelet-ANN and self-organizing cascade neural network (SCNN). In addition, this paper generally discusses and explains what factors affect the accuracy of the ANNs-based soft-sensing models. Finally, this paper points out several challenges in soft-sensing models of WWTP, which may be helpful for researchers and practitioner to explore the future solutions for their particular applications.