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A large number of experiments have proved that the ring structure is a common phenomenon in neural networks. Nevertheless, a few works have been devoted to studying the neurodynamics of networks with only one ring. Little is known about the dynamics of neural networks with multiple rings. Consequently, the study of neural networks with multiring structure is of more practical significance. In this article, a class of high-dimensional neural networks with three rings and multiple delays is proposed. Such network has an asymmetric structure, which entails that each ring has a different number of neurons. Simultaneously, three rings share a common node. Selecting the time delay as the bifurcation parameter, the stability switches are ascertained and the sufficient condition of Hopf bifurcation is derived. It is further revealed that both the number of neurons in the ring and the total number of neurons have obvious influences on the stability and bifurcation of the neural network. Ultimately, some numerical simulations are given to illustrate our qualitative results and to underpin the discussion.

The problem of fault-tolerant adaptive fuzzy tracking control against actuator faults is investigated in this article for a type of uncertain nonaffine fractional-order nonlinear full-state-constrained multi-input-single-output (MISO) system. By means of the existence theorem of the implicit function and the intermediate value theorem, the design difficulty arising from nonaffine nonlinear terms is surmounted. Then, the unknown ideal control inputs are approximated by using some suitable fuzzy-logic systems. An adaptive fuzzy fault-tolerant control (FTC) approach is developed by employing the barrier Lyapunov functions and estimating the compounded disturbances. Moreover, under the drive of the reference signals, a sufficient condition ensuring semiglobal uniform ultimate boundedness is obtained for all the signals in the closed-loop system, and it is proved that all the states of nonaffine nonlinear fractional-order systems are guaranteed to remain inside the predetermined compact set. Finally, two numerical examples are provided to exhibit the validity of the designed adaptive fuzzy FTC approach.

In this article, the problem of cooperative global robust practical output regulation is examined for uncertain nonlinear multiagent systems in a lower triangular form via event-triggered control. The problem is dealt with in three steps. At the first step, a decentralized internal model is constructed based on the lower triangular form such that the regulation issue is translated to a stabilization one. Next, a nonlinear decentralized state-feedback controller is designed to achieve input-to-state stabilization with the sampling error as the input. In the third step, a simple event-triggering mechanism is embedded in the controller to achieve an event-based controller redesign. An illustrative example is presented to verify the theoretical results.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

This article is concerned with the design of a super-twisting algorithm (STA) based sliding mode controller for permanent magnet synchronous motor (PMSM) speed regulation system under the self-triggered mechanism. By using the strict Lyapunov function approach, it is shown that the tracking error converges to an ultimate domain within the finite-time sense under the proposed self-triggered STA. A feasible self-triggered strategy is designed for both cases with and without external perturbation. Moreover, a nonlinear optimization problem is formulated in terms of the tradeoff between the ultimate domain and the communication burden. The optimized STA gains are obtained by solving the above-formulated optimization problem via a particle swarm optimization algorithm. Finally, the applicability of the proposed self-triggered STA for PMSM is verified by simulation and experiment results.

This article focuses on the issue of scaled tracking consensus for discrete-time second-order multiagent systems under random packet dropouts, where the cases with a static leader and a dynamic leader are considered, respectively. The scaled tracking consensus means that all agents reach a consensus value determined by the leader but with different scales, and the phenomenon of packet dropout on each communication link is described as a Bernoulli variable independent of other communication links. By virtue of random environment-based scaled consensus algorithms, it is shown how to reconstruct the original system into augmented error systems with random coefficient matrices. With the kind assistance of substochastic matrix and super-stochastic matrix, sufficient conditions for the cases with a static leader and a dynamic leader are derived, respectively. Moreover, computer simulations are performed to demonstrate the dynamics of network agents under random packet dropouts.

Energy harnessing from hydrokinetic systems has been explored over several centuries. With advancements in the technology in last decade, and the intermittent nature of other technologies for energy harvesting, interest in harnessing energy from water-based hydrokinetic systems has amplified. This paper reviews and studies the state-of-the-art of these systems in sea- and river-based applications. The history of development, working principles, different turbines classifications, and research prospects and opportunities are reviewed and discussed. We also conduct a survey of currently available commercial technologies. Elements of the design that need to be enhanced are presented in detail, along with further research prospects in areas related to the technology.

This article addresses the bipartite tracking consensus for a set of mobile autonomous agents over directed cooperation-competition networks. Here, cooperative and competitive interactions among the agents are described by positive and negative edges of the directed network topology, respectively. Both fixed and switching network topologies are considered. For the case with fixed network topology, the matrix product technique is utilized to derive the convergence result. For the case with switching network topologies, some key results related to the composition of binary relations are the main technical tools of analyzing the error system. In addition, the upper bound for the spectral radius of the system matrix is given to ensure the convergence of the system even if the single uncoupled system is strictly unstable. The applicability of the derived results is verified through two simulation experiments.

Potassium-selenium (K-Se) batteries have attracted significant attention as one of the most promising alternatives of lithium-ion storage systems owing to high energy density and low cost. In the design of Se-based cathode materials, however, the low utilization rate of active Se and the rapid dissolution of polyselenides seriously weaken the capacity and cycle stability. Therefore, how to make full use of Se species without loss during the charge and discharge process is the key to design high-performance Se-based cathode. In this paper, a 3D “water cube”-like Se/C hybrid (denoted as Se−O−PCS) is constructed with the assistance of Na2CO3 templates. Thanks to the abundant carbonate groups (CO32−) originated from the Na2CO3 templates, the molten Se species are firmly anchored into the pore of carbon skeleton by strong C−O−Se bonding. Thus, this unique Se−O−PCS model not only improves the utilization of active Se species, but also can reduce the contact with the electrolyte to inhibit the shuttle effect of polyselenides. Moreover, flexible carbon skeleton gives Se-O-PCS hybrid a good electrical conductivity and excellent structural robustness. Consequently, the resultant Se−O−PCS hybrid is endowed with an obviously enhanced K-ions storage property.

In this article, we investigate leader–follower flocking for the Cucker–Smale model with lossy links and general weight functions. Here, the loss phenomenon of the control packages from the controller to the actuator is characterized by a Bernoulli stochastic variable and the edge weights of the interaction network are determined by a general update rule based on the distance between agents. A method based on the products of substochastic matrices is developed to analyze the flocking behavior with lossy links. By means of this method, a sufficient condition which depends on the agents’ initial states, the topology structure, the successful information transmission rate and the weight function is established. Compared with the previous Cucker–Smale flocking results, which are only applicable to positive and decreasing weight functions with specific forms, our result is more general and can be applied to arbitrary positive and decreasing weight functions with nonzero lower bounds. Finally, our result is illustrated through a simulation example.

Deep convolutional neural network (CNN) is difficult to deploy to mobile and portable devices due to its large number of parameters and floating-point operations (FLOPs). To tackle this problem, we propose a novel channel pruning method. We use the modified squeeze-and-excitation blocks (MSEB) to measure the importance of the channels in the convolutional layers. The unimportant channels, including convolutional kernels related to them, are pruned directly, which greatly reduces the storage cost and the number of calculations. For ResNet with basic blocks, we propose an approach to consistently prune all residual blocks in the same stage to ensure that the compact network structure is dimensionally correct. After pruning we retrain the compact network from scratch to restore the accuracy. Finally, we verify our method on CIFAR-10, CIFAR-100 and ILSVRC-2012. The results indicate that the performance of the compact network is better than the original network when the pruning rate is small. Even when the pruning amplitude is large, the accuracy can also be maintained or decreased slightly. On the CIFAR-100, when reducing the parameters and FLOPs up to 82% and 62% respectively, the accuracy of VGG-19 even improve by 0.54% after retraining. The source code is available at https://github.com/JingfeiChang/UCP.

Challenges in multi-fidelity modelling relate to accuracy, uncertainty estimation and high-dimensionality. A novel additive structure is introduced in which the highest fidelity solution is written as a sum of the lowest fidelity solution and residuals between the solutions at successive fidelity levels, with Gaussian process priors placed over the low fidelity solution and each of the residuals. The resulting model is equipped with a closed-form solution for the predictive posterior, making it applicable to advanced, high-dimensional tasks that require uncertainty estimation. Its advantages are demonstrated on univariate benchmarks and on three challenging multivariate problems. It is shown how active learning can be used to enhance the model, especially with a limited computational budget. Furthermore, error bounds are derived for the mean prediction in the univariate case.

Enhancing pseudocapacitance of Ti3C2Tx MXene-based electrode materials induced by sufficient surface terminating groups (such as Ti–O bonding) becomes one promising way for fabricating high energy-density supercapacitors. However, most of terminating group activity cannot be excited well in the multilayer Ti3C2Tx due to the layer stacking feature, which limits its further performance improvement. In this paper, a CQDs@Ti3C2Tx hybrid material is synthesized by modifying few-layer Ti3C2Tx MXene matrix with highly dispersed carbon quantum dots (CQDs). In this unique hybrid model, the Ti3C2Tx MXene matrix with high conductivity and high specific surface area is just like the “paddy field”, which not only promotes the charge rapid transfer, but also provides the high double layer capacitance by ion adsorption. More importantly, the CQDs act as “rice seedlings” firmly anchored to the Ti3C2Tx matrix, and greatly stimulate the activity of the termination groups of Ti3C2Tx. As expected, this hybrid achieves a harmonious coexistence of high conductivity and high pseudocapacitance. As a result, the CQDs@Ti3C2Tx electrode delivers high reversible capacitances of 441.3 F g−1 at 1 A g−1 and 310.1 F g−1 at 20 A g−1, and a relatively stable cyclability with almost 100% capacitance retention after 10 000 cycles at 10 A g−1.

This article is devoted to study the cluster synchronization for a kind of complex dynamical networks consisting of nonidentical nonlinear Lur’e systems. Different from general time delays, a proportional delay is taken into consideration in this article, which is a kind of unbounded time-varying delays and thus largely increases the difficulty in network synchronization. In consideration of the topology structures of the Lur’e networks, an effective impulsive pinning controller is proposed, which will be placed on the Lur’e systems having directed paths with those in the other clusters. Considering different functional roles that the impulsive effects play, sufficient criteria for the cluster synchronization of the nonidentically coupled Lur’e dynamical networks are obtained by applying the proportionally delayed impulsive comparison principle, the concept of average impulsive interval, and the extended parameters variation formula. Simultaneously, the exponential convergence rates are successfully estimated with respect to different functions of the impulsive effects. In the end of this article, three numerical simulations are proposed to denote the effectiveness of the control protocols and the theoretical results.

Bifurcation control remains largely unresolved for fractional-order dynamical systems. This article addresses the optimal control issue of bifurcation for a delayed complex networks model with Caputo derivative, where the time delay is selected as the variable parameter. We first devise a fractional-order proportional-integral-derivative (PID) feedback synthesis for regulation of the Hopf bifurcation embedded in a delayed small-world network model with Caputo derivative. Dynamic stability criterion and Hopf bifurcation condition are obtained by carrying out the eigenvalue analysis of the controlled network. The stability range of the parameters of the PID control is evaluated completely for the small-world network. We can optimize the dynamics of stability and bifurcation of small-world networks by manipulating the gain parameters. Finally, we implement some simulations to show the performance of the presented PID scheme. The numerical simulations verify the advantage of the fractional PID controller in bifurcation control.

Finding an alternative way to replace the magnetic compass to determine the robot heading angle indoor is always a challenge in the robotics society. This brief proposes a structurally simple yet efficient nonmagnetic heading determination system, which can be used in the planar indoor environment with abundant ferromagnetic and electromagnetic interferences, by the combination of gyroscope and vision. The gyroscope is utilized to perceive the yaw rate, whereas a downward-looking camera is used to capture the prelaid auxiliary strips to acquire the absolute angle of the robot heading. Due to the existence of pseudomeasurement, varying noise statistical characteristics, and asynchronization between state propagation and measurement, the existing Kalman filters cannot be applied to fuse the gyroscopic and visual data. Therefore, a novel fusion algorithm named pseudomeasurement-resistant adaptive asynchronous Kalman filter is proposed, which is experimentally verified to be efficient in the environment with various interferences.

PurposeThis study investigates factors that motivate social media users to retransmit rumors. We focus on everyday rumors rather than catastrophic rumors and develop a model of everyday rumor retransmission based on the uses and gratification theory, the rumor retransmission model, and the basic law of rumor.Design/methodology/approachAn Internet survey is conducted to collect data and test the proposed model. This study’s hypotheses are tested through partial least squares regression analysis.FindingsThe results show that socializing, information seeking and status seeking increase the intention to retransmit rumors. Perceived rumor credibility has a moderating effect on the impacts of socializing and status seeking on retransmission intention.Originality/valueOur research model provides a theoretical foundation for future studies that want to explore motivations or values that determine rumor-sharing intention on social media. The findings can help government agencies and businesses to manage rumor retransmission on social media.

Due to the robustness, the matrix inversion methods based on matrix factorization are often adopted in engineering while the triangular matrix inversion is one key part of those methods. This brief reviews the adjoint matrix and presents a novel scheme for field-programmable gate arrays (FPGAs) calculating the triangular matrix inversion. Employing the more characteristics of both the triangular matrix and its inversion, the proposed diagonal-wise algorithm for the triangular matrix inversion has the high parallelism and extensibility in the hardware implementation and is suitable for the different matrix triangular factorization (QR, LDL, Cholesky and LU). Meanwhile, the recursive diagonal-wise algorithm is designed for the large scale triangular matrices. Compared with the traditional row-/column-wise methods, our algorithm has a good performance at the low computation load. Finally, the experiments are conducted on one Xilinx Virtex-7 FPGA to illustrate the performances of the four different methods.

In recent years, deep learning has been widely used in the field of image visualization, and has attracted the attention of researchers. The main component of iron tailings is SiO2, In the process of quenching and tempering slag from iron tailings, melted SiO2 particles will randomly walk in the crucible, therefore, how to intelligently identify and track targets (SiO2 particles) in sequence image to promote the low consumption and efficient production of high value-added slag cotton has become an urgent problem. Aims at the temperature requirements of slag cotton preparation process, using intelligent algorithm with deep learning and hierarchical clustering, in-depth analysis of the visual information of the high temperature melting process of SiO2 particles. Through centroid tracking and positioning technology, intelligent edge feature extraction technology and fitting method of experimental data through high temperature melting process of SiO2 particles quantitative characterization functions of visual characteristics of SiO2 particles melting process under high temperature environment were obtained in this article. Approximation of the melting process of iron tailings in high temperature environments and embed this function into the static mathematical model of process thermal compensation an accurate thermal dynamic intelligent compensation model for slag cotton preparation process is realized.

The resilient cooperative source seeking problem of double-integrator multi-robot systems under $\mathbf {d}$ -local deception attacks is investigated. In this article, we first propose a resilient cooperative control algorithm and present a necessary and sufficient condition for its achievement of resilient consensus. Then, through applying the particle swarm optimization algorithm to the resilient cooperative control algorithm, a resilient cooperative source seeking algorithm is proposed. We show that this algorithm can solve the source seeking problems under $\mathbf {d}$ -local deception attacks with an additional potential of addressing the multiple local extrema case. Finally, the feasibility of the proposed algorithms is illustrated by simulation and experimental results.

This paper develops a new estimate-based dynamic event-triggered output feedback controller for networked control systems subject to nonlinear uncertainties. Specifically, based on the sampled-data, a discrete-time output feedback controller and a discrete-time dynamic event-triggering condition are proposed by the virtue of feedback domination technique. The proposed event-triggered control method is easy to implement in digital computers due to the form of discrete time. Under the proposed dynamic event-triggered control method, the selection regions of the sampling period and the scaling gain are explicitly given to guarantee the global practical/asymptotic stability of the closed-loop system. Finally, two examples are employed to verify the efficiency of the proposed dynamic event-triggered control approach.

Considering the fact that in the real world, a certain agent may have some sort of advantage to act before others, a novel hierarchical optimal synchronization problem for linear systems, composed of one major agent and multiple minor agents, is formulated and studied in this article from a Stackelberg-Nash game perspective. The major agent herein makes its decision prior to others, and then, all the minor agents determine their actions simultaneously. To seek the optimal controllers, the Hamilton-Jacobi-Bellman (HJB) equations in coupled forms are established, whose solutions are further proven to be stable and constitute the Stackelberg-Nash equilibrium. Due to the introduction of the asymmetric roles for agents, the established HJB equations are more strongly coupled and more difficult to solve than that given in most existing works. Therefore, we propose a new reinforcement learning (RL) algorithm, i.e., a two-level value iteration (VI) algorithm, which does not rely on complete system matrices. Furthermore, the proposed algorithm is shown to be convergent, and the converged values are exactly the optimal ones. To implement this VI algorithm, neural networks (NNs) are employed to approximate the value functions, and the gradient descent method is used to update the weights of NNs. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

In this article, we present two algorithms for identification of continuous-time linear time-invariant systems in frequency domain. These algorithms are constructed in terms of continuous rational orthogonal basis functions. First, a two-stage algorithm is developed through one-by-one selection of poles for the basis functions, and such consecutive selection is easy to realize. Next, a direct algorithm is proposed to select poles for the rational orthogonal basis functions. Different poles result in different bases and these selections guarantee that better approximations can be reached. For different systems, there are different sequences of poles selected for basis functions, which shows the adaptivity of the proposed algorithms. A numerical example is given to show that the proposed algorithms are useful. Also, in this example, comparison is made with the method that uses rational orthogonal basis functions in which all poles of the basis functions are true poles of the original system.

In this article, the problem of disturbance rejection control is studied for discrete-time systems within an event-driven control framework. Through a predefined event-driven scheduler, both full- and reduced-order extended state observer (ESO)-based output feedback controllers are designed. With the proposed ESOs, both the disturbance and the system states are estimated, and the controllers are constructed with the estimated disturbance and states. Then, the stability and disturbance rejection analyses are conducted. It can be found that with the established event-driven control approaches, the updating frequency of the controller can be prominently reduced, and the disturbance can also be compensated in the output channels of the systems. Finally, the validity of the established control approaches is illustrated by the numerical simulations.

This paper is concerned with the robust constrained control problem for a class of fault-tolerant time-varying systems against actuator faults and input amplitude saturation. An adaptive technique is proposed to compensate for the impacts of actuator bias faults and partial loss of control effectiveness, as well as to eliminate the effects of unknown time-varying parameters of the systems. An auxiliary system is developed to ensure that the actuator behaves within the limitation of the actuator amplitude. Based on the compensation strategies and auxiliary signals, a novel adaptive fault-tolerant constrained controller is constructed to guarantee the convergence of the system states into a small stability domain in the presence of actuator faults, actuator amplitude limitations, and unknown system parameters. An example of F-18 flight control systems is given to illustrate the proposed procedures and its effectiveness.

This article investigates the convex optimization problem with general local constraints including $N$ equality constraints, $N$ inequality constraints, and $N$ closed convex set constraints. Especially, the objective function of the considered problem is the sum of $N$ convex functions, which are seen as local objective functions. First, for the distributed setting, a weight-unbalanced digraph with $N$ nodes is introduced to describe the communication topology. Clearly, in the distributed setting, each node is responsible for a local objective function accompanied with an equality constraint, an inequality constraint, and a closed convex set constraint, which are viewed as local constraints. The aim of this article is to solve the considered convex optimization problem in a distributed manner, i.e., to lead the states of all nodes to a common optimal solution of the considered problem under the condition that each node only knows its own local objective function, and local constraints, and the information of its neighbors’ states. Towards this end, by resorting to the exact penalty function method and the gradient descent-like method, two distinct distributed discrete-time algorithms are developed for two different cases, respectively. Furthermore, the convergence properties of the designed algorithms are rigorously analyzed under some common and standard assumptions, and the convergence rates are described in detail. Finally, simulation results are provided to verify the theoretical results.

In this letter, considering the lack of core and drilling cuttings, an interpretable semisupervised classification method (ISSCM) under multiple smoothness assumptions is proposed and applied to lithology identification. The contribution is threefold. First, the novel semisupervised learning algorithm is developed based on the decision tree, the interpretability of which is highly beneficial to solve risk-aware problems. Second, both smoothness in the feature space and depth is utilized to generate pseudo-labels for the unlabelled data by using label propagation. Third, an algorithm to approximate the optimal affinity matrix is added to avoid degradation rendered by inappropriate manual settings under multiple smoothness assumptions. All these contributions could yield a classification model that is interpretable, accurate, and insusceptible to imprecise empirical settings. In the experiment, the proposed method is applied to lithology identification and verified by real-world data.

Lithology identification plays an essential role in geologic exploration and reservoir evaluation. In recent years, machine-learning-based logging lithology identification has received considerable attention due to its ability to fit complex models. Existing work develops machine-learning models under the assumption that the data gathered from different wells are from the same probability distribution, so that the model trained on data from old wells can be directly applied to predict the lithologies of a new well without losing accuracy. In fact, due to variations in sedimentary environment and well-logging technique, the data from different wells may not have the same probability distribution. Therefore, such a direct application is unreliable. To prevent the accuracy from being reduced by the distribution difference, we integrate the unsupervised domain adaptation method into lithology identification, under the assumption that no lithology labels are available on a new well. Specifically, we have developed a two-flow multilayer neural network. We train our network with a maximum mean discrepancy optimization, and the training process is interrupted by an early stopping criterion. These methods ensure that the feature representations learned by our network are domain invariant and discriminative. Our method is evaluated from multiple perspectives on a total of 21 wells located in the Jiyang depression, Bohai Bay Basin. The experimental results demonstrate that our method effectively mitigates the performance degradation caused by data distribution differences and outperforms the baselines by approximately 10%.

In this article, the sliding mode control (SMC) design is studied for a class of stochastic switching systems subject to semi-Markov process via an adaptive event-triggered mechanism. Network-induced communication constraints, semi-Markov switching parameters, and uncertain parameters are considered in a unified framework for the SMC design. Due to the constraint of measuring transducers, the system states always appear with unmeasurable characteristic. Compared with the traditional event-triggered mechanism, the adaptive event-triggered mechanism can effectively reduce the number of triggering than the static event-triggered mechanism. During the data transmission of network communication systems, network-induced delays are characterized from the event trigger to the zero-order holder. The aim of this work is to design an appropriate SMC law based on an adaptive event-triggered communication scheme such that the resulting closed-loop system could realize stochastic stability and reduce communication burden. By introducing the stochastic semi-Markov Lyapunov functional, sojourn-time-dependent sufficient conditions are established for stochastic stability. Then, a suitable SMC law is designed such that the system state can be driven onto the specified sliding surface in a finite-time region. Finally, the simulation study on boost converter circuit model (BCCM) illustrates the effectiveness of the theoretical findings.

To investigate the mechanisms of electroacupuncture (EA) at Zusanli (ST 36), Liangmen (ST 21) and Sanyinjiao (SP 6) in intervening diabetic gastroparesis (DGP) based on calcium-activated chloride channel. Forty Sprague-Dawley rats were randomly divided into four groups, including a normal control group (group A), a model group (group B), an EA group (group C) and a metoclopramide group (group D), with 10 rats in each group. A single intraperitoneal injection of 2% streptozotocin (STZ) combined with 8-week high-glucose high-fat diet was used to establish a DGP rat model. After intervention, gastrointestinal propulsive rate was observed; the expression level of transmembrane protein 16A (TMEM16A) was examined by immunohistochemistry; the Ca2+ concentration in interstitial cells of Cajal (ICCs) was detected by immunofluorescence; and whole-cell patch-clamp technique was applied to detect the current intensity of calcium-activated chloride channel (ICaCC) in ICCs in gastric antrum. After modeling, the blood glucose levels in group B, group C and group D were significantly increased compared with group A (all P

In this study, phosphorus doped graphene supported PtNiP nanocluster electrocatalyst (PtNiP/P-graphene) was successfully prepared via a simple hypophosphite-assisted co-reduction method. The improved anchoring force and increased anchoring sites of graphene support result from phosphorus doping as well as size-confined growth effect of NaH2PO2 leads to uniform dispersion of ultrafine PtNiP nanoclusters. Doped P also promotes the removal of CO-like intermediate by adjusting Pt electronic structure combining with alloyed Ni via electronic effects. As a result, the as-prepared PtNiP/P-graphene catalyst with more exposed active sites and optimized electronic structure of Pt alloy shows excellent electrocatalytic performances for methanol oxidation reaction (MOR) both in activity and durability in an acidic medium.

Highly active and durable electrocatalysts with minimal Pt usage are desired for commercial fuel cell applications. Herein, we present a highly dispersed L10-PtZn intermetallic catalyst for the oxygen reduction reaction (ORR), in which a Zn-rich metal-organic framework (MOF) is used as an in situ generated support to confine the growth of PtZn particles. Despite requiring high-temperature treatment, the intermetallic L10-PtZn particles exhibit a small mean size of 3.95 nm, which confers the catalysts with high electrochemical active surface area (81.9 m2 gPt−1) and atomic utilization. The Pt electron structure and binding strength between Pt and oxygen intermediates are optimized through ligand effect and compressive strain. These advantages result in ORR mass activity and specific activity of 0.926 A mgPt−1 and 1.13 mA cm−2, respectively, which are 5.4 and 4.0 times those of commercial Pt/C. The stable L10 structure provides the catalysts with superb durability; only a halfwave potential loss of 11 mV is observed after 30,000 cycles of accelerated stress tests, through which the structure evolves into a more stable PtZn-Pt core-shell structure. Therefore, the development of a Zn based MOF as a catalyst support is demonstrated, providing a synergy strategy to prepare highly dispersed intermetallic alloys with high activity and durability.

Most of the existing image style transfer algorithms transfer the whole image style as a whole. Style feature is a set of correlation matrix based on style image, namely Gram matrix. Each matrix is a global description of the style image. This kind of methods can perform well in the insensitive semantic scenes (such as the style transfer between landscape photos), but in the sensitive semantic scenes (such as the style transfer between portrait photos), the problem of semantic mismatch will be highlighted, such as transferring the background texture of the style image to the foreground of the target image. Although the existing research takes the manually annotated semantic image as an input of the algorithm, and then guides the style transfer based on the semantic information, and finally achieves good results in the style transfer between portraits. But there are still two problems: first, semantic images need to be manually annotated, which costs human resources. In practical applications, large-scale image style transfer is often needed. Second, the details of the synthesized image are fuzzy, and the definition is not enough. We propose an image style transfer algorithm based on semantic segmentation to resolve semantic mismatching in image style transfer. Our algorithm extracts the semantic information of style image and content image automatically through a semantic segmentation network and uses the semantic information to guide the style transfer. Our algorithm builds a semantic segmentation network based on mask R-CNN, introduces semantic information, and then makes style transfer on the patch level, realizes the style transfer between similar objects (consistent semantic information). Experiments on Celeba and Wikiart show that our method could automatically extract the semantic information of style image and content image. Compared with the state-of-art approaches in this field, our method can effectively avoid semantic mismatch in the process of image style transfer. That is, it can maintain semantic consistency in the process of style transfer.

With the in-depth application of 3D models in many fields, the display of models, especially the model display based on B / S architecture, has become an indispensable part of the application of 3D models. The development of 3D modeling technology has led to the emergence of a large number of complex three-dimensional models with more than one hundred thousand facets and hundred megabytes of file size. Coupled with the limit of B / S architecture by the server’s concurrency, network bandwidth, browser’s processing capabilities, and many other objective factors, how to achieve its real-time and high-realistic display on the web pages has become one of the hot spots research in the field of graphics, which has great research significance and challenge. In order to resolve the above problems, we propose a new model display method. The method pre-renders the model into a two-dimensional image by sampling the subdivide surface of the model’s circumscribed sphere and uses the view-dependent level-of-detail method to divide the pre-rendered image into different layers and tiles. Combined with the optimization, including adaptive and progressive network transmission, index-weighted user interaction prediction, and Hilbert-based image indexing, the method achieves a frame rate of more than 20 frames per second in the actual tests.

Metal-nitrogen-carbon materials (M-N-C) are non-noble-metal-based alternatives to platinum-based catalysts and have attracted tremendous attention due to their low-cost, high abundance, and efficient catalytic performance towards the oxygen reduction reaction (ORR). Among them, Fe-based materials show remarkable ORR activity, but they are limited by low selectivity and low stability. To address these issues, herein, we have synthesized FeCu-based M-N-C catalysts, inspired by the bimetal center of cytochrome c oxidase (CcO). In acidic media, the selectivity was notably improved compared with Fe-based materials, with peroxide yields less than 1.2% (

This brief deals with the bipartite output consensus problem for heterogeneous multi-agent systems (MASs) under antagonistic interactions. Two distributed controllers with state feedback control and output feedback control are designed, respectively. By virtue of the Lyapunov theory and the output regulation technique, it is proved that bipartite output consensus for heterogeneous MASs can be guaranteed under structurally balanced graph. Numerical examples are finally provided for demonstration of the validity of the derived results.

In this article, the output-based predictive control problem for networked control systems (NCSs) with communication delays is investigated by utilizing the event-triggered mechanism (ETM). First, the output-based ETM is introduced in order to save limited network resources effectively. Second, the system states are estimated from the received output measurements by the Luenberger observer. Third, the model-based networked predictive control (NPC) approach is developed to actively deal with the time delay and stabilize the system. Moreover, two different approaches including the augmented model and the piecewise linear model are adopted, and sufficient conditions that guarantee the closed-loop stability and performance are further provided, respectively. Finally, two simulation examples are given to demonstrate the effectiveness and applicability of the proposed methods for the addressed NCSs.

With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.

Robotic terrain classification refers to the ability that a field robot could identify the traversing terrains autonomously under as little human supervision as possible. Such a task could be achieved by semi-supervised learning which works in the premise of smoothness assumption in the feature space. However, we found that the feature smoothness assumption cannot be fully satisfied (i.e., there is no apparent low-density region in the feature space) in the robotic terrain classification, which motivates us to propose the feature-temporal semi-supervised extreme learning machine (FT-S2ELM). With introducing the feature-temporal similarity matrix, the accuracy of the classifier trained by semi-supervised learning increases significantly. Furthermore, considering the uncertainty in determining the smoothness degree (i.e., the free parameters of similarity matrix), we introduce an automatic approach to find the optimal graph Laplacian, thus increasing the safety. The proposed method is verified experimentally on the data gathered by a micro tracked robot.

The reachable set synthesis problem of singular Markovian jump systems (SMJSs) is investigated in this paper. Both state reachable set and output reachable set are considered for SMJSs without delay and with distributed delay. By utilizing proportional and derivative state feedback control technique, sufficient conditions for delay-free SMJSs are proposed to guarantee the reachable set of closed-loop systems to be mean-square bounded by a prescribed ellipsoid firstly. Then the result is extended to SMJSs with distributed delay. Numerical examples are given to demonstrate the effectiveness of the obtained result.

Existing style transfer methods have achieved great success in artwork generation by transferring artistic styles onto everyday photographs while keeping their contents unchanged. Despite this success, these methods have one inherent limitation: they cannot produce newly created image contents, lacking creativity and flexibility. On the other hand, generative adversarial networks (GANs) can synthesise images with new content, whereas cannot specify the artistic style of these images. The authors consider combining style transfer with convolutional GANs to generate more creative and diverse artworks. Instead of simply concatenating these two networks: the first for synthesising new content and the second for transferring artistic styles, which is inefficient and inconvenient, they design an end-to-end network called ArtistGAN to perform these two operations at the same time and achieve visually better results. Moreover, to generate images of higher quality, they propose the bi-discriminator GAN containing a pixel discriminator and a feature discriminator that constrain the generated image from pixel level and feature level, respectively. They conduct extensive experiments and comparisons to evaluate their methods quantitatively and qualitatively. The experimental results verify the effectiveness of their methods.

Recent studies using deep neural networks have shown remarkable success in style transfer, especially for artistic and photo-realistic images. However, these methods cannot solve more sophisticated problems. The approaches using global statistics fail to capture small, intricate textures and maintain correct texture scales of the artworks, and the others based on local patches are defective on global effect. To address these issues, this study presents a unified model [global and local style network (GLStyleNet)] to achieve exquisite style transfer with higher quality. Specifically, a simple yet effective perceptual loss is proposed to consider the information of global semantic-level structure, local patch-level style, and global channel-level effect at the same time. This could help transfer not just large-scale, obvious style cues but also subtle, exquisite ones, and dramatically improve the quality of style transfer. Besides, the authors introduce a novel deep pyramid feature fusion module to provide a more flexible style expression and a more efficient transfer process. This could help retain both high-frequency pixel information and low-frequency construct information. They demonstrate the effectiveness and superiority of their approach on numerous style transfer tasks, especially the Chinese ancient painting style transfer. Experimental results indicate that their unified approach improves image style transfer quality over previous state-of-the-art methods.

High-entropy alloys (HEAs) generally possess complex component combinations and abnormal properties. The traditional methods of investigating these alloys are becoming increasingly inefficient because of the unpredictable phase transformation and the combination of many constituents. The development of compositionally complex materials such as HEAs requires high-throughput experimental methods, which involves preparing many samples in a short time. Here we apply the high-throughput method to investigate the phase evolution and mechanical properties of novel HEA film with the compositional gradient of (Cr,Fe,V)-(Ta,W). First, we deposited the compositional gradient film by co-sputtering. Second, the mechanical properties and thermal stability of the (Cr0.33Fe0.33V0.33)x(Ta0.5W0.5)100−x (x = 13–82) multiple-based-elemental (MBE) alloys were investigated. After the deposited wafer was annealed at 600°C for 0.5 h, the initial amorphous phase was transformed into a body-centered cubic (bcc) structure phase when x = 33. Oxides were observed on the film surface when x was 72 and 82. Finally, the highest hardness of as-deposited films was found when x = 18, and the maximum hardness of annealed films was found when x = 33.

The objective of this paper is to estimate the reachable set for a class of delayed neural networks (NNs) subject to Markovian jump parameters and bounded disturbance. First, by virtue of delay-partitioning method, the time-varying delay is divided into some delay components. With suitably constructing Lyapunov–Krasovskii functionals (LKFs), a less conservative delay-dependent condition of finding an ellipsoid-like set to contain all state trajectories that start from the origin is derived in terms of linear matrix inequalities (LMIs). Then the integrally free-matrix-based inequality approach together with the extended reciprocally convex technique is employed to further reduce the conservatism on characterizing bounds of some integral terms. Thanks to a group of free-connection weighting matrices, the proposed reachable set estimation approach is extended to the case that transition probabilities are partially known. Finally, numerical simulations indicate that the derived results are effective and less conservative.

A common feature of the traditional high-order sliding mode (HOSM) control method is that the matched uncertainty of HOSM dynamics is enlarged by taking the high-order derivatives on the pre-selected sliding variable directly, which could cause much burden to the controller design. This deficiency is removed in this paper via introducing some nonlinear terms in the mismatched channels. On this basis, a new arbitrary-order HOSM dynamics with lower-triangular nonlinearities will be obtained. Then, a novel finite-time HOSM control approach an be developed by the aid of the mathematical tool named as adding a power integrator (API). Under the proposed HOSM algorithm, the mismatched uncertainties existing in the new HOSM dynamics are not required to be sufficiently smooth, but are needed to comply with a few homogeneous growth conditions. Moreover, the common constant boundary for the matched uncertainty in the majority of the available HOSM methods can be extended to a time-varying one. The rigorous proof is given based on the Lyapunov theory to show the finite-time stability of the considered closed-loop system. At last, an example of the robotic tracking system is adopted to validate the effectiveness of the developed finite-time HOSM control method.

This paper investigates the circular motion of a group of n(n ≥ 2) nonholonomic robots over time-varying communication networks. We aim to achieve a balanced circular motion centered at a location determined by each robot under the assumption that the global ranking of any robot is unknown. A back-stepping based controller is firstly designed to make all the robots rotate around a common center, the position of which is obtained through executing a consensus algorithm by each robot. Then, the maximum and minimum consensus algorithm and a distributed modified ordinal ranking algorithm are applied to set the rotation radius, angular velocity, and orientation parameters in a distributed manner such that all the robots can uniformly space on the common circle. At last, the effectiveness of the proposed algorithms is illustrated through a simulation example.

This article is concerned with the periodic tracking control problem for nonlinear systems. First, the Takagi–Sugeno (T–S) fuzzy model is employed to describe the nonlinear control systems. Second, considering the partly unmeasurable states of the system, a novel fuzzy observer-based repetitive controller, which is the mixed controller of the fuzzy observer-based controller and the fuzzy repetitive controller, is designed to deal with the periodic tracking control problem. To reduce the conservatism and increase the feasible solution space of the stabilization conditions, a new fuzzy relaxed matrix technique is developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Then, the fuzzy Lyapunov functional with an additional separation parameter and the augmented fuzzy matrix technique (the interactions of fuzzy observer subsystems) are proposed such that the delay-dependent stability condition of the closed-loop system in the form of linear matrix inequality is obtained with less conservatism. It is worth noting that due to introducing an additional parameter in the fuzzy Lyapunov functional, the fuzzy controller and fuzzy observer can be separately designed, which largely enhances the flexibility of design with low computational complexity. Finally, three examples are provided to illustrate the effectiveness and less conservatism of the proposed method.

The development of an environmentally friendly binder can immensely benefit and promote the BF slag fiber industry. Accordingly, in this study, fiber insulation pipes were prepared using water glass, polyvinyl alcohol (PVA), and PVA–glutaraldehyde (GA); moreover, their bulk density, moisture absorption capacity, and thermal conductivity coefficient were measured and compared. The results demonstrated that the values of these three parameters depend on the binder concentration and dosage under certain fiber quality, pipe sizes, and manufacturing process conditions. The best binder composition was proven to be that of PVA–GA, prepared using 3 wt% PVA and 5 wt% GA herein, which facilitated reduction in the bulk density and improvement of the waterproof properties and insulation performance of the slag fiber pipes.

In this technical note, the tradeoff between the attack detectability and the performance degradation in stochastic cyber-physical systems is investigated. We consider a linear time-invariant system in which the attack detector performs a hypothesis test on the innovation of the Kalman filter to detect malicious tampering with the actuator signals. We adopt a notion of attack stealthiness to quantify the degree of stealth by limiting the maximum achievable exponents of both false alarm probability and detection probability below certain thresholds. And the conditions for any actuator attack to have a specific level of stealthiness are derived. Additionally, we characterize the upper bound of the performance degradation induced by attacks with a given extent of stealthiness that produces independent and identically distributed Gaussian innovations, and design the attack, which achieves the stated upper bound for right-invertible systems. Finally, our results are illustrated via numerical examples.