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Summary: The asynchronous filtering problem for networked fuzzy non‐homogeneous Markov jump systems is investigated, with the consideration of packet dropout and quantization. An event‐triggered dynamic quantization scheme is proposed, and a stability criterion is given to ensure the stochastic stability of the filtering error systems with desired extended dissipative performance. This performance provides a unified framework in the sense that it can degenerate to H∞$$ {H}_{\infty } $$, l2−l∞$$ {l}_2-{l}_{\infty } $$, dissipativity and passivity filter, respectively, under certain parameter sets. Furthermore, an asynchronous filter design method with extended dissipative performance is given based on the proposed stability criterion. In this method, the existence of the quantizer is ensured by dynamic quantization levels and the solution scope of the filter is enlarged by a free‐connection weighting matrices method. The theoretical design and analysis is finally evaluated using a practical example. [ABSTRACT FROM AUTHOR]

Cluster consensus is investigated for multiple coupled harmonic oscillators under a weighted cooperative-competitive network. Consensus protocols for three categories of communication networks are constructed by employing a weighted gain, and sufficient conditions for guaranteeing cluster consensus are obtained. It is found that under the proposed protocols, the states of all oscillators can be guaranteed to reach periodic orbits that are the same in frequency no matter which cluster the oscillators belong to. In particular, cluster partitions here are not given a prior, but are determined by the communication topology among oscillators. Numerical examples are given to validate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the ${\boldsymbol{\beta}}$ 1, ${\boldsymbol{\beta}}$ 2, and $\ {\boldsymbol{\gamma}}$ 2 bands, among which, it was highest in the ${\boldsymbol{\beta}}$ 1 band, and reached its maximum at the 22–30 scale. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls’ right hand in the ${\boldsymbol{\beta}}$ 1 and ${\boldsymbol{\beta}}$ 2 bands and the stroke-unaffected side in the ${\boldsymbol{\beta}}$ 1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of ${\boldsymbol{\gamma}}$ 2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction. [ABSTRACT FROM AUTHOR]

In this paper, a predictive control-based approach is proposed for a Hammerstein-type system which is closed through some form of network. The approach uses a two-step predictive controller to deal with the static input nonlinearity of the Hammerstein system and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented. Index Terms--Delay and dropout compensation scheme (DDCS), Hammerstein system, networked control systems (NCSs), predictive control, two-step approach.

The scheduling and control of wireless cloud control systems involving multiple independent control systems and a centralized cloud computing platform are investigated. For such systems, the scheduling of the data transmission as well as some particular design of the controller can be equally important. From this observation, we propose a dual channel-aware scheduling strategy under the packet-based model predictive control framework, which integrates a decentralized channel-aware access strategy for each sensor, a centralized access strategy for the controllers, and a packet-based predictive controller to stabilize each control system. First, the decentralized scheduling strategy for each sensor is set in a noncooperative game framework and is then designed with asymptotical convergence. Then, the central scheduler for the controllers takes advantage of a prioritized threshold strategy, which outperforms a random one neglecting the information of the channel gains. Finally, we prove the stability for each system by constructing a new Lyapunov function, and further reveal the dependence of the control system stability on the prediction horizon and successful access probabilities of each sensor and controller. These theoretical results are successfully verified by numerical simulation. [ABSTRACT FROM AUTHOR]

This article investigates the predictive control scheme and related stability issue for a class of discrete-time perturbed nonlinear system with state and input constraints. First, we propose a novel control framework, i.e., networked dual-mode adaptive horizon model predictive control (MPC), which consists of a local controller, a remote controller that is subject to packet losses, and a judger coordinating the switchings between them. The optimization procedure of MPC with variable prediction horizon is implemented in the remote controller while a simple state-feedback control law is in the local one. Second, to establish the stability condition, we propose a new Lyapunov function. By specifying the relation between the Lyapunov function and the optimal MPC value function, the input-to-state practical stability is established. Finally, simulation results show the effectiveness of our proposed control scheme. [ABSTRACT FROM AUTHOR]

Simultaneous and continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. A surface electromyography (sEMG) state-space model is proposed to estimate simultaneous and continuous lower-limb-joint movements from sEMG signals in this article. The model combines the forward dynamics with Hill-based muscle model (HMM), making the extended model capable of estimating the lower-limb-joint motion directly. sEMG features including root-mean-square and wavelet coefficients are then extracted to construct a measurement equation used to reduce system error and external disturbances. With the proposed model, unscented Kalman filter is used to estimate joint angle from sEMG signals. In the experiments, sEMG signals were recorded from ten subjects during muscle contraction involving three lower-limb-joint motions (knee-joint motion, ankle-joint motion, and simultaneous knee-ankle-joint motion). Comprehensive experiments are conducted on three motions and the results show that the mean root–mean–square error for knee-joint motion, ankle-joint motion, and simultaneous motion of the proposed model are 5.1143°, 5.2647°, and 6.3941°, respectively, and significant improvements are demonstrated compared with the traditional methods. [ABSTRACT FROM AUTHOR]

Schisandrin B has been proved to possess anti‐inflammatory and anti‐endoplasmic effects, could improve cardiac function, inhibit apoptosis, and reduce inflammation after ischemic injury. However, the detailed metabolic mechanism and potential pathways of Schisandrin B effects on myocardial injury are unclear. Metabolomics could yield in‐depth mechanistic insights and explore the potential therapeutic effect of natural products. In this study, the preparation of doxorubicin‐induced myocardial injury rat model for evaluation of Schisandrin B on viral myocarditis sequelae related pathological changes and its mechanism. The metabolite profiling of myocardial injury rats was performed through ultra‐high performance liquid chromatography combined with mass spectrometry combined with pattern recognition approaches and pathway analysis. A total of 15 metabolites (nine in positive ion mode and six in negative ion mode) were considered as potential biomarkers of myocardial injury, and these metabolites may correlate with the regulation of Schisandrin B treatment. A total of six metabolic pathways are closely related to Schisandrin B treatment, including glycerophospholipid metabolism, sphingolipid metabolism, purine metabolism, etc. This study revealed the potential biomarkers and metabolic network pathways of myocardial injury, and illuminated the protective mechanism of Schisandrin B on myocardial injury. [ABSTRACT FROM AUTHOR]

Ultra‐performance liquid chromatography/mass spectrometry‐based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh‐pressure liquid chromatography with mass spectrometry‐based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide‐induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high‐resolution mass spectrometry‐based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN. [ABSTRACT FROM AUTHOR]

Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods. [ABSTRACT FROM AUTHOR]

Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re‐identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross‐modality variations caused by heterogeneous images in RGB and infrared, and the intra‐modality variations caused by the heterogeneous human poses, camera position, light brightness etc. To meet these challenges, a novel feature learning framework, hard pentaplet and identity loss network (HPILN), is proposed. In the framework existing single‐modality re‐identification models are modified to fit for the cross‐modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to increase the accuracy of the modified cross‐modality re‐identification models. Based on the benchmark of the SYSU‐MM01 dataset, extensive experiments have been conducted, showing that the authors' method outperforms all existing ones in terms of cumulative match characteristic curve and mean average precision. [ABSTRACT FROM AUTHOR]

A classification‐based control approach is proposed for wireless networked control systems with lossy multipacket transmission. This approach takes advantage of a state reconstruction process to deal with the distinct feature of partial failure of data transmission caused by multipacket transmission, and then classifies the difference of the latest received system states and reconstructed ones to design a classification‐based controller. The closed‐loop stability of the system is proven using the switched systems theory. By considering more communication characteristics of multipacket transmission, the proposed approach is shown to give rise to a better system performance by a numerical example. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The stochastic stabilisation of networked control systems is investigated with a special focus on the lossy multi‐packet transmission in the wireless communication context. The resulting partially available system states due to multi‐packet transmission are firstly reconstructed at the controller, and the sufficient conditions for stochastic stability are then given for the closed‐loop system, which finally leads to a controller design method with explicit consideration of multi‐packet transmission. The proposed theoretical results are verified by both numerical and TrueTime‐based examples. [ABSTRACT FROM AUTHOR]

The control problem of vehicle platoons considering sensors with limited measurement range and actuator time delay is investigated in the face of constraints. A new delayed feedback model predictive control scheme is proposed to solve the problem while satisfying the constraints on measurement range and driving behaviors. A family of controllers is presented with free parameters which are then computed by online solving of a receding horizon optimal control problem. Terminal equality constraints are adopted to guarantee stability of the closed‐loop system. Some sufficient conditions with guaranteed string stability of the platoon and zero steady‐state error are established. The effectiveness and advantages of the presented method are demonstrated by simulating two classical road scenarios. [ABSTRACT FROM AUTHOR]

The output consensus of multi‐agent systems is investigated, where constant communication delay is present and the dynamics of the agents are heterogeneous. Based on the networked predictive control scheme, the distributed consensus protocol with dynamic output feedback controller is designed, and the sufficient conditions of the output consensus are obtained. Numerical examples illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The bipartite consensus problem is investigated for double‐integrator multi‐agent systems in the presence of measurement noise. A distributed protocol with time‐varying consensus gain is proposed. By using tools of state transition matrix and algebraic graph theory, necessary and sufficient conditions for the designed protocol to be a mean square bipartite linear χ‐consensus protocol are given. It is shown that the signed digraph being structurally balanced and having a spanning tree are not only sufficient, but also necessary for bipartite consensus. Furthermore, the protocol is proved to be a mean square bipartite average consensus protocol if the signed graph is weight balanced. [ABSTRACT FROM AUTHOR]

The bipartite consensus problem for integrator multi‐agent systems over signed fixed digraphs is investigated in the presence of measurement noise. A time‐varying consensus gain is introduced and then a stochastic type protocol is proposed, whose performance is analysed using the state transition matrix of the closed‐loop system. Necessary and sufficient conditions for ensuring a mean square bipartite consensus protocol are obtained in the presence of noise. Furthermore, in the absence of noise it is shown that these conditions are also necessary and sufficient for ensuring the bipartite consensus except for the quadratic integrability of the consensus gain. It is found that the signed digraph being structurally balanced and having a spanning tree are the weakest assumptions on connectivity for achieving bipartite consensus regardless of the measurement noise. In particular, if the signed digraph is structurally unbalanced, then under some mild conditions, the states of the closed‐loop system converge to zero in mean square, regardless of the initial states. [ABSTRACT FROM AUTHOR]

mRNA translation is a complex process involving the progression of ribosomes on the mRNA, resulting in the synthesis of proteins, and is subject to multiple layers of regulation. This process has been modelled using different formalisms, both stochastic and deterministic. Recently, we introduced a Probabilistic Boolean modelling framework for mRNA translation, which possesses the advantage of tools for numerically exact computation of steady state probability distribution, without requiring simulation. Here, we extend this model to incorporate both random sequential and parallel update rules, and demonstrate its effectiveness in various settings, including its flexibility in accommodating additional static and dynamic biological complexities and its role in parameter sensitivity analysis. In these applications, the results from the model analysis match those of TASEP model simulations. Importantly, the proposed modelling framework maintains the stochastic aspects of mRNA translation and provides a way to exactly calculate probability distributions, providing additional tools of analysis in this context. Finally, the proposed modelling methodology provides an alternative approach to the understanding of the mRNA translation process, by bridging the gap between existing approaches, providing new analysis tools, and contributing to a more robust platform for modelling and understanding translation. [ABSTRACT FROM PUBLISHER]

An extended asynchronous switching model is investigated for a class of switched stochastic nonlinear retarded systems in the presence of both detection delay and false alarm, where the extended asynchronous switching is described by two independent and exponentially distributed stochastic processes, and further simplified as Markovian. Based on the Razumikhin-type theorem incorporated with average dwell-time approach, the sufficient criteria for global asymptotic stability in probability and stochastic input-to-state stability are given, whose importance and effectiveness are finally verified by numerical examples. [ABSTRACT FROM PUBLISHER]