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1.
Most existing consensus control in multi-agent systems (MASs) require agents to update their state synchronously, which means that some agents need to wait for all individuals to complete the iteration before starting the next iteration. To overcome this bottleneck, this paper studied asynchronous consensus problems of second-order MASs (SOMASs) with aperiodic communication. An asynchronous pulse-modulated intermittent control (APIMC) with heterogeneous pulse-modulated function and time-varying control period, which can unify impulsive control and sampled-data control, is proposed for the consensus of SOMASs. A time-varying discrete system is constructed to describe the evolution of the sample values of position and velocity of the SOMAS. Then, by the analysis tools from the stochastic matrix and the properties of the Laplace matrix of graph, some effective conditions are obtained to show the relationship between the convergence of the controlled SOMASs and the control parameters. Finally, a 300-node SOMAS whose topology is a random geographic network is included to verify the feasibility of the proposed control and the correctness of the theoretical analysis.  相似文献   

2.
Mathematical results are derived, which enable one to find a vector of parameters k0 such that (P1(s,k0)?H)∩(P2(k0)=0), where P1(s,k) is a polynomial in s and in the components of k,P2(k) is a polynomial in the components of k, and H is the set of Hurwitz polynomials. The algorithm is based on an extension of the root locus technique to the multiparameter case. The design problem of coupling networks between a resistive generator and a passive load, under prescribed power gain characteristics, is translated into the above formulation. A numerical example is provided.  相似文献   

3.
For singular systems, i.e. for systems of the form E[xdot] = Ax + Bu, with E singular, the problem of computing the transfer function matrix has been studied. An algorithm is developed which is similar to the corresponding algorithm proposed by Faddeev or Leverrier for regular systems. The present results involve the Drazin inverse and yield an expression for the transfer function matrix suitable for computer use.  相似文献   

4.
Techniques developed in the Sturm—Liouville problem and its Inverse problem are well known in solving the analysis and synthesis problems of non-uniform distributed networks (or NUDN) (1)-(6), (15). However, very few practical results have been obtained from the theory, especially as regards the synthesis part of the problem. In this paper, we show that the chain matrix of an inhomogeneous ladder network (or IHLN) of N sections has undergone exactly the limit process of first-order difference equation approximation of the corresponding differential equation converges to the chain matrix of the corresponding NUDN uniformly on every compact subset of p = z(s)y(s) plane. Therefore an optimal NUDN is proven to be either symmetrical or antimetrical (7). Specifically, a class of optimal NUDN which is optimal on every subinterval of [O,L] has closed-form solutions, and is proven to be both symmetrical and antimetrical.  相似文献   

5.
Due to the harmful impact of fabricated information on social media, many rumor verification techniques have been introduced in recent years. Advanced techniques like multi-task learning (MTL), shared-private models suffer from many strategic limitations that restrict their capability of veracity identification on social media. These models are often reliant on multiple tasks for the primary targeted objective. Even the most recent deep neural network (DNN) models like VRoC, Hierarchical-PSV, StA-HiTPLAN etc. based on VAE, GCN, Transformer respectively with improved modification are able to perform good on veracity identification task but with the help of additional auxiliary information, mostly. However, their rise is still not substantial with respect to the proposed model even though the proposed model is not using any additional information. To come up with an improved DNN model architecture, we introduce globally Discrete Attention Representations from Transformers (gDART). Discrete-Attention mechanism in gDART is capable of capturing multifarious correlations veiled among the sequence of words which existing DNN models including Transformer often overlook. Our proposed framework uses a Branch-CoRR Attention Network to extract highly informative features in branches, and employs Feature Fusion Network Component to identify deep embedded features and use them to make enhanced identification of veracity of an unverified claim. Moreover, to achieve its goal, gDART is not dependent on any costly auxiliary resource but on an unsupervised learning process. Extensive experiments reveal that gDART marks a considerable performance gain in veracity identification task over state-of-the-art models on two real world rumor datasets. gDART reports a gain of 36.76%, 40.85% on standard benchmark metrics.  相似文献   

6.
It is shown that a theorem on essential gyrators presented by Rosenberg (1) and used in (2) claims too much and that the internal structure of the multiport elements of the system must be studied in order to be able to decide whether a gyrator is essentially contained in the system or not. Bond graph terminology is used (3)—(6) and a new theorem is formulated, which provides an algorithm to decide on the essentiality of a gyrator by immediate inspection of the bond graph.As a side-result of this approach some new methods for junction structure simplification can be formulated. The significance of junction 3-ports for the concept of the essential gyrator is elaborated by providing equivalence rules for all kinds of junction 3-ports and introducing a unit essential junction 3-port (ES) and a unit non-essential junction 3-port (NES). Finally the hydraulic junction is treated as an example of a physical non-potential junction, i.e. a junction congruent with an ES.  相似文献   

7.
Dynamic problems of axially moving materials as exemplified by strings in textile industry and band saws, belts and chains in mechanical machinery have recently received some attention (1–15). In the present study, the parametric resonance of an axially accelerated beam is investigated. The beam which has encastré ends is subjected to a periodic root force as shown in Fig. 1. The object of the investigation is to identify regions of instability of this system for various combinations of the excitation frequency and amplitude of the axial oscillations.  相似文献   

8.
Document-level relation extraction (RE) aims to extract the relation of entities that may be across sentences. Existing methods mainly rely on two types of techniques: Pre-trained language models (PLMs) and reasoning skills. Although various reasoning methods have been proposed, how to elicit learnt factual knowledge from PLMs for better reasoning ability has not yet been explored. In this paper, we propose a novel Collective Prompt Tuning with Relation Inference (CPT-RI) for Document-level RE, that improves upon existing models from two aspects. First, considering the long input and various templates, we adopt a collective prompt tuning method, which is an update-and-reuse strategy. A generic prompt is first encoded and then updated with exact entity pairs for relation-specific prompts. Second, we introduce a relation inference module to conduct global reasoning overall relation prompts via constrained semantic segmentation. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed CPT-RI as compared to the baseline model (ATLOP (Zhou et al., 2021)), which improve the 0.57% on the DocRED dataset, 2.20% on the CDR dataset, and 2.30 on the GDA dataset in the F1 score. In addition, further ablation studies also verify the effects of the collective prompt tuning and relation inference.  相似文献   

9.
Semantic information in judgement documents has been an important source in Artificial Intelligence and Law. Sequential representation is the traditional structure for analyzing judgement documents and supporting the legal charge prediction task. The main problem is that it is not effective to represent the criminal semantic information. In this paper, to represent and verify the criminal semantic information such as multi-linked legal features, we propose a novel criminal semantic representation model, which constructs the Criminal Action Graph (CAG) by extracting criminal actions linked in two temporal relationships. Based on the CAG, a Graph Convolutional Network is also adopted as the predictor for legal charge prediction. We evaluate the validity of CAG on the confusing charges which composed of 32,000 judgement documents on five confusing charge sets. The CAG reaches about 88% accuracy averagely, more than 3% over the compared model. The experimental standard deviation also show the stability of our model, which is about 0.0032 on average, nearly 0. The results show the effectiveness of our model for representing and using the semantic information in judgement documents.  相似文献   

10.
This paper proposes a new wave digital filter derived from doubly terminated LC-ladder networks by replacing each series or shunt arm element of the ladder by its equivalent digital two-port. It is shown that such two-ports may be cascaded without the use of adapters defined by Fettweis (1). A number of realizations of the wave digital two-ports, which are canonic with respect to both multipliers and delays, have been obtained. Also a realization which is canonic with respect to multipliers only is given and an example considered using this realization. The sensitivity of this filter with respect to the multiplier coefficient changes due to finite word length is compared with the conventional cascaded digital filter and also the one proposed by Renner and Gupta. It is found that the proposed filter appears to be a more desirable form of implementation than the conventional cascade form and comparable to that of Renner and Gupta (2).  相似文献   

11.
In this paper, a novel decentralized adaptive neural control approach based on the backstepping technique is proposed to design a decentralized H∞ adaptive neural controller for a class of stochastic large-scale nonlinear systems with external disturbances and unknown nonlinear functions. RBF neural networks are utilized to approximate the packaged unknown nonlinearities. A novel concept with regard to bounded-H∞ performance is proposed. It can be applied to solve an H∞ control problem for a class of stochastic nonlinear systems. The constant terms appeared in stability analysis are dealt with by using Gronwall inequality, so that H∞ performance criterion is satisfied. The assumption that the approximation errors of neural networks must be square-integrable in some literature can be eliminated. The design process for decentralized bounded-H∞ controllers is given. The proposed control scheme guarantees that all the signals in the resulting closed-loop large-scale system are uniformly ultimately bounded in probability, and each subsystem possesses disturbance attenuation performance for external disturbances. Finally, the simulation results are provided to illustrate the effectiveness and feasibility of the proposed approach.  相似文献   

12.
A method of using orthogonal shifted Legendre polynomials for identifying the parameters of a process whose behaviour can be modelled by a linear differential equation with time-varying coefficients in the form of finite-order polynomials is presented. It is based on the repeated integration of the differential equation and the representations of ∫0ts(τ) dτ = Ps(t) and ts(t) = Rs(t), where P and R are constant matrices and s(t) is a shifted Legendre vector whose elements are shifted Legendre polynomials. The differential input-output equation is converted into a set of overdetermined linear algebraic equations for a least squares solution. The results of simulation studies are included to illustrate the applicability of the method.  相似文献   

13.
Having obtained anomalous results in an attempt to continue the simulation study of moth-wasp interaction in Auslander, Oster and Huffaker (J. Franklin Inst. 297, 345-375), attention was centered on the role of spatial heterogeneity in an environment with the moth (Anagasta kühniella) alone. Because of the probable presence of chaotic components in the population behavior (random appearing behavior that is actually caused by deterministic influences), a statistically-based parameter sensitivity and parameter identification method was used. By defining a binary performance criterion that measured the ability of a model with a specific set of parameters to maintain a stable population, the importance of spatial heterogeneity was confirmed. In addition, the use of Monte-Carlo type simulation studies, combined with a binary performance criterion, was demonstrated to be effective for parameter identification and/or parameter sensitivity determination of at least some systems with chaotic or nearly chaotic behavior.  相似文献   

14.
Let f(χ) together with its first two derivatives be continuous in the domain D and additionally let χM?D be an extremum (or turning point) of this function. Also, let χn+1 = T (χn,χn-1,χn-2) be Jarratt's Method for computing the extremum (or turning point) of a function. Criteria are demonstrated which insure that, for any triple of initial assumptions (χ1,χ0,χ-1)?D, Jarratt's Method, converges to the extremum of f(χ), and that from and after some n = N0, the rate of convergence of this method increases steadily, finally becoming unbounded when the solution χM is attained.  相似文献   

15.
Incorporating topic information can help response generation models to produce informative responses for chat-bots. Previous work only considers the individual semantic of each topic, ignoring its specific dialog context, which may result in inaccurate topic representation and hurt response coherence. Besides, as an important feature of multi-turn conversation, dynamic topic transitions have not been well-studied. We propose a Context-Controlled Topic-Aware neural response generation model, i.e., CCTA, which makes dialog context interact with the process of topic representing and transiting to achieve balanced improvements on response informativeness and contextual coherence. CCTA focuses on capturing the semantical relations within topics as well as their corresponding contextual information in conversation, to produce context-dependent topic representations at the word-level and turn-level. Besides, CCTA introduces a context-controlled topic transition strategy, utilizing contextual topics to yield relevant transition words. Extensive experimental results on two benchmark multi-turn conversation datasets validate the superiority of our proposal on generating coherent and informative responses against the state-of-the-art baselines. We also find that topic transition modeling can work as an auxiliary learning task to boost the response generation.  相似文献   

16.
Abnormal event detection in videos plays an essential role for public security. However, most weakly supervised learning methods ignore the relationship between the complicated spatial correlations and the dynamical trends of temporal pattern in video data. In this paper, we provide a new perspective, i.e., spatial similarity and temporal consistency are adopted to construct Spatio-Temporal Graph-based CNNs (STGCNs). For the feature extraction, we use Inflated 3D (I3D) convolutional networks to extract features which can better capture appearance and motion dynamics in videos. For the spatio graph and temporal graph, each video segment is regarded as a vertex in the graph, and attention mechanism is introduced to allocate attention for each segment. For the spatial-temporal fusion graph, we propose a self-adapting weighting to fuse them. Finally, we build ranking loss and classification loss to improve the robustness of STGCNs. We evaluate the performance of STGCNs on UCF-Crime datasets (total 128 h) and ShanghaiTech datasets (total 317,398 frames) with the AUC score 84.2% and 92.3%, respectively. The experimental results also show the effectiveness and robustness with other evaluation metrics.  相似文献   

17.
A useful identity expressing the derivative of an unknown variable xk of X with respect to an entry in the coefficient matrix of a linear system AX = B is presented. If the derivatives of xk with respect to each entry of A or their combinations are required, then the identity avoids the repeated solution of the linear equations, and may result in a symbolic solution provided A-1 is known. A method to measure the sensitivity in an n-port linear system is proposed, and its relationships to Kron's method of tearing and Branin's formulae are also discussed.  相似文献   

18.
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19.
A more rigorous derivation for the generalized block pulse operational matrices is proposed in this paper. The Riemann-Liouville fractional integral for repeated fractional (and operational) integration is integrated exactly, then expanded in block pulse functions to yield the generalized block pulse operational matrices. The generalized block pulse operational matrices perform as s-α(α\s>;0,α∈R) in the Laplace domain and as fractional (and operational) integrators in the time domain. Also, the generalized block pulse operational matrices of differentiation which correspond to sα(α\s>;0,α∈R) in the Laplace domain are derived. Based on these results, the inversions of rational and irrational transfer functions are proposed in a simple, accurate and efficient way.  相似文献   

20.
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