Showing papers on "Bernoulli's principle published in 2023"
TL;DR: In this paper , the authors investigated the exponential synchronization of Markovian jump neural networks (MJNNs) with time-varying delays via stochastic sampling and looped functional approach.
Abstract: In this article, the exponential synchronization of Markovian jump neural networks (MJNNs) with time-varying delays is investigated via stochastic sampling and looped-functional (LF) approach. For simplicity, it is assumed that there exist two sampling periods, which satisfies the Bernoulli distribution. To model the synchronization error system, two random variables that, respectively, describe the location of the input delays and the sampling periods are introduced. In order to reduce the conservativeness, a time-dependent looped-functional (TDLF) is designed, which takes full advantage of the available information of the sampling pattern. The Gronwall–Bellman inequalities and the discrete-time Lyapunov stability theory are utilized jointly to analyze the mean-square exponential stability of the error system. A less conservative exponential synchronization criterion is derived, based on which a mode-independent stochastic sampled-data controller (SSDC) is designed. Finally, the effectiveness of the proposed control strategy is demonstrated by a numerical example.
13 citations
TL;DR: In this paper , a new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed for the short-term prediction of power load.
11 citations
TL;DR: In this article , the authors used Gabor filters with an enhanced Deep Belief Network (E-DBN) with multiple classification methods, including Gaussian-Bernoulli (GB) and Bernoulli-Benoulli(BB) RBMs, to classify lung CT images.
10 citations
TL;DR: In this paper , an asymmetric Lyapunov-Krasovskii functional (LKF) was proposed to address the leader-following consensus of multi-agent systems subject to deception attacks.
8 citations
01 Jan 2023
TL;DR: In this article , Bernoulli sub-equation function method was applied to some partial differential equations with high nonlinearity and new travelling wave solutions, such as mixed dark-bright soliton, exponential and complex domain, were reported.
Abstract: Abstract This paper applies a powerful scheme, namely Bernoulli sub-equation function method, to some partial differential equations with high non-linearity. Many new travelling wave solutions, such as mixed dark-bright soliton, exponential and complex domain, are reported. Under a suitable choice of the values of parameters, wave behaviours of the results obtained in the paper – in terms of 2D, 3D and contour surfaces – are observed.
7 citations
TL;DR: In this paper , an outlier-resistant sequential fusion problem is concerned for cyber-physical systems with quantized measurements under denial-of-service attacks, and tailored saturation functions are dedicatedly introduced to filter structures at both local and fusion stages, thereby keeping satisfactory fusion performance.
4 citations
TL;DR: In this paper , the functional matrix of integration via Bernoulli wavelets was developed and a competent numerical scheme was generated to solve the nonlinear system of singular differential equations which is Lane Emden form by NBCT with different physical conditions.
4 citations
TL;DR: In this paper , an extended/group target tracking algorithm based on B-spline is proposed, where the extension of an extended or a group target was modeled as a spatial probability distribution characterized by the control points of a Bspline function that was then jointly propagated with the measurement rate model and kinematic component model over time using the Poisson multi-Bernoulli mixture (PMBM) filter framework.
Abstract: This study provides a solution for multiple group/extended target tracking with an arbitrary shape. Many tracking approaches for extended/group targets have been proposed. However, these approaches make assumptions about the target shape, which have limitations in practical applications. To address this problem, in this work, an extended/group target tracking algorithm based on B-spline is proposed. Specifically, the extension of an extended or a group target was modeled as a spatial probability distribution characterized by the control points of a B-spline function that was then jointly propagated with the measurement rate model and kinematic component model over time using the Poisson multi-Bernoulli mixture (PMBM) filter framework. In addition, an amplitude-aided measurement partitioning approach is proposed to improve the accuracy caused by distance-based approaches. The simulation results demonstrate that the extension, shape and orientation of targets can be estimated better by the proposed algorithm, even if the shape changes. The tracking performance is also improved by about 10% and 13% compared to the other two algorithms.
3 citations
TL;DR: In this paper , stability in an inverse problem of determining three spatially varying functions including the source term and the mass density for a curved plate by the Riemannian geometrical approach is derived by the Carleman estimates and observability inequalities.
Abstract: We consider stability in an inverse problem of determining three spatially varying functions including the source term and the mass density for a curved plate by the Riemannian geometrical approach. The stability is derived by the Carleman estimates and observability inequalities. Two kinds of boundary conditions are considered: one is the hinged boundary conditions and the other is the clamped boundary conditions. In particular, the case of the Euler–Bernoulli plate is included.
3 citations
TL;DR: In this article , a mode-dependent reduced-order filtering problem for semi-Markovian jump systems with time-varying delay and external disturbance is described, where the measurement output is vulnerable to randomly occurring false data injection attacks.
3 citations
TL;DR: In this article , a numerical approach in applying the differential quadrature method (DQM) to the stability of cylindrical shells subjected to axial flow is presented.
TL;DR: Wang et al. as discussed by the authors proposed a new self-adaptive time-varying grey Bernoulli prediction model, deduce the time response formula of the model, and explore the relationship between model parameters and model accuracy.
TL;DR: In this paper , the authors proposed a nonlinear fractional polynomial optimisation model for the serial production line with two Bernoulli machines, with an aim to minimize the total energy consumption under a desired production rate.
Abstract: In recent years, the topic of sustainable manufacturing system design with energy saving features has received increasing attention. For a typical production line setting, the machines are subject to random breakdown and restart besides blockage and starvation, and they are usually connected via buffer areas with finite capacity. In the present study, the energy consumption of the serial production line with two Bernoulli machines is considered, with an aim to minimise the total energy consumption under a desired production rate. The energy consumption of the two-machine system is composed of the energy needed to setup, to remain idle, and for actual manufacturing. In order to minimise the total energy expenditure, a nonlinear fractional polynomial optimisation model is constructed, which is first converted to a nonlinear polynomial optimisation problem. Then the property of the total energy cost is analysed via the sum of squares (SOS) method. To speed up the solving process, a new heuristic approach named energy consumption saving (ECS) algorithm is proposed considering the monotonicity and local optimality of the energy cost function. Finally, by presenting optimal configurations of the production line with different throughputs, buffer capacities, and energy parameters, a simulation-based study is performed to validate the SOS and ECS algorithms.
TL;DR: In this article , an event-triggered mechanism is designed to decide when to communicate the current sampled-data, and a sufficient condition is derived in terms of linear matrix inequalities (LMIs) by introducing time-varying Lyapunov-Krasovskii Functional (LKF), ensuring mean square consensus for the resulting closed-loop systems.
Abstract: This paper focuses on the issues of non-fragile event-triggered consensus problem for nonlinear multiagent systems (MASs) with external disturbance subjected to randomly occurring packet losses and periodic Denial of Service (DoS) attacks. Under connected communication topology, each agent exchanges information with its neighbors. In the presence of packet losses and DoS attacks, an event-triggered mechanism is designed to decide when to communicate the current sampled-data. Randomness is solved by using Bernoulli distribution in a stochastic way and the external disturbance is handled by using H∞ performance. A sufficient condition is derived in terms of linear matrix inequalities (LMIs) by introducing time-varying Lyapunov–Krasovskii Functional (LKF), ensuring mean square consensus for the resulting closed-loop systems. Finally, to show the effectiveness and applicability of proposed control scheme, two numerical simulations are presented.
TL;DR: In this paper , a Transformer-based Conditional Mixture of Bernoulli Network (TCMBN) is proposed for multi-label prediction in real-world data.
Abstract: Streams of irregularly occurring events are commonly modeled as a marked temporal point process. Many real-world datasets such as e-commerce transactions and electronic health records often involve events where multiple event types co-occur, e.g. multiple items purchased or multiple diseases diagnosed simultaneously. In this paper, we tackle multi-label prediction in such a problem setting, and propose a novel Transformer-based Conditional Mixture of Bernoulli Network (TCMBN) that leverages neural density estimation to capture complex temporal dependence as well as probabilistic dependence between concurrent event types. We also propose potentially incorporating domain knowledge in the objective by regularizing the predicted probability. To represent probabilistic dependence of concurrent event types graphically, we design a two-step approach that first learns the mixture of Bernoulli network and then solves a least-squares semi-definite constrained program to numerically approximate the sparse precision matrix from a learned covariance matrix. This approach proves to be effective for event prediction while also providing an interpretable and possibly non-stationary structure for insights into event co-occurrence. We demonstrate the superior performance of our approach compared to existing baselines on multiple synthetic and real benchmarks.
TL;DR: Wang et al. as mentioned in this paper proposed a fractional accumulation method and constructed a novel fractional discrete grey Bernoulli model DGFGBM (1,1,tα), and the manta ray foraging optimization (MRFO) algorithm is used to find the parameters of the new model, and the carbon emission data of Shaanxi Province in China and three of its cities are applied to verify the effectiveness of the model.
Abstract: To accurately predict CO2 emissions, this paper proposes a novel fractional accumulation method and constructs a novel fractional discrete grey Bernoulli model DGFGBM (1,1,tα). The manta ray foraging optimization (MRFO) algorithm is used to find the parameters of the new model, and the carbon emission data of Shaanxi Province in China and three of its cities are applied to verify the effectiveness of the new model. The CO2 emissions of the four regions from 2020 to 2024 are predicted, and the following conclusions can be drawn. The new fractional accumulation method is effective and reasonable and can make full use of new information and improve the prediction accuracy of the model. Compared with three other optimization algorithms, MRFO has more advantages in finding the optimal parameters of the model. Compared with other grey models and the ARIMA model, the DGFGBM (1,1,tα) model has better prediction performance. The prediction results show that from 2020 to 2024, the CO2 emissions of Shaanxi Province are expected to increase by 94.6278 million tons, an increase of 31.8%, and the CO2 emissions of Xi'an, Xianyang and Baoji are expected to increase by 22.4901 million tons, 16.3129 million tons and 7.3271 million tons, respectively, with growth rates of 45.81%, 46.51% and 34.07%, respectively.
TL;DR: In this paper , a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter was proposed to reduce its computational complexity and make it suitable for multiple target tracking with a high number of targets.
Abstract: This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.
TL;DR: In this article , a distributed sliding mode controller is proposed for ensuring the stochastic consensus of a multi-agent system (MAS) subject to DoS attack, which may occur on each transmission channel independently and randomly according to the Bernoulli distribution.
Abstract: The consensus problem for a multi-agent system (MAS) is investigated in this paper via a sliding mode control mechanism subject to stochastic DoS attack, which may occur on each transmission channel independently and randomly according to the Bernoulli distribution. A distributed dynamic event-triggered strategy is implemented on the communication path among agents, where dynamic parameters are introduced to adjust the threshold of event-triggered condition. After that, a distributed sliding mode controller is proposed for ensuring the stochastic consensus of the MAS. Meantime, a minimization problem is solved to obtain the correct controller gain matrix. At last, a numerical example is shown to demonstrate the presented results.
TL;DR: In this paper , the authors used a novel scheme for the fractional derivative and comparison of approximate and exact solutions to solve a fractional Bernoulli equation and a chaotic system.
Abstract: The subject of this study is the solution of a fractional Bernoulli equation and a chaotic system by using a novel scheme for the fractional derivative and comparison of approximate and exact solutions. It is found that the suggested method produces solutions that are identical to the exact solution. We can therefore generalize the strategy to different systems to get more accurate results. We think that the novel fractional derivative scheme that has been offered and the algorithm that has been suggested will be utilized in the future to construct and simulate a variety of fractional models that can be used to solve more difficult physics and engineering challenges.
TL;DR: In this article , the authors consider the sequential resource allocation problem under the multi-armed bandit model in the non-stationary stochastic environment and propose a new algorithm based on a two-stage approach.
22 Mar 2023
TL;DR: Particle Thompson Sampling (PTS) as discussed by the authors is a simple and flexible approximation of Thompson sampling for solving stochastic bandit problems, which circumvents the intractability of maintaining a continuous posterior distribution in Thompson sampling by replacing the continuous distribution with a discrete distribution supported at a set of weighted static particles.
Abstract: Particle Thompson sampling (PTS) is a simple and flexible approximation of Thompson sampling for solving stochastic bandit problems. PTS circumvents the intractability of maintaining a continuous posterior distribution in Thompson sampling by replacing the continuous distribution with a discrete distribution supported at a set of weighted static particles. We analyze the dynamics of particles' weights in PTS for general stochastic bandits without assuming that the set of particles contains the unknown system parameter. It is shown that fit particles survive and unfit particles decay, with the fitness measured in KL-divergence. For Bernoulli bandit problems, all but a few fit particles decay.
31 May 2023
TL;DR: In this paper , the authors develop synthesis conditions for distributed state-and output-feedback controllers that guarantee an upper bound on the closed-loop H
Abstract: In practical networking scenarios, communication links can rarely be considered to be deterministic, yet the influence of stochastic interconnections on multi-agent systems is neglected most of the time. To bridge this gap, this paper develops synthesis conditions for distributed state- and output-feedback controllers that guarantee an upper bound on the closed-loop H 2 -norm under the effect of Bernoulli distributed packet loss. Utilizing the frameworks of Markov jump linear system and decomposable systems, the synthesis problem is formulated as a linear matrix inequality problem with complexity that scales linearly with the number of agents. Finally, the closed-loop performance is assessed in simulation studies with a signal-to-interference-plus-noise ratio based packet loss model for communication between autonomous underwater vehicles.
TL;DR: In this paper , the authors presented a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes, which is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance.
Abstract: Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.
TL;DR: In this article , the authors considered a discrete-time dynamical system over a discrete state space, which evolves according to a structured Markov model called Bernoulli Autoregressive (BAR) model.
Abstract: We consider a discrete-time dynamical system over a discrete state-space, which evolves according to a structured Markov model called Bernoulli Autoregressive (BAR) model. Our goal is to obtain sample complexity bounds for the problem of estimating the parameters of this model using an indirect Maximum Likelihood Estimator. Our sample complexity bounds exploit the structure of the BAR model and are established using concentration inequalities for random matrices and Lipschitz functions.
TL;DR: In particular, this paper showed uniform Hanson-Wright inequalities and convex concentration results for simple random tensors in the spirit of recent work by Klochkov-Zhivotovskiy and Vershynin (Bernoulli 26(4):3139-3162, 2020).
Abstract: We prove extensions of classical concentration inequalities for random variables that have α-subexponential tail decay for any α ∈ (0, 2]. This includes Hanson–Wright-type and convex concentration inequalities in various situations. In particular, we show uniform Hanson–Wright inequalities and convex concentration results for simple random tensors in the spirit of recent work by Klochkov–Zhivotovskiy (Electron J Probab 25(22):30, 2020) and Vershynin (Bernoulli 26(4):3139–3162, 2020).
TL;DR: In this paper , the authors studied the existence and uniqueness of a solution to a new modification of a nonlinear fractional integro-differential equation (NFIDEq) in dual Banach space CE (E, [0, T]), which simulates several phenomena in mathematical physics, quantum mechanics, and other domains.
Abstract:
Under some suitable conditions, we study the existence and uniqueness of a solution to a new modification of a nonlinear fractional integro-differential equation (NFIDEq) in dual Banach space CE (E, [0, T]), which simulates several phenomena in mathematical physics, quantum mechanics, and other domains. The desired conclusions are demonstrated with the use of fixed-point theorems after applying the theory of fractional calculus. The validation of the provided strategy has been done by utilizing the Bernoulli matrix approach (BMA) method as a numerical method. The major motivation for selecting the BMA approach is that it combines Bernoulli polynomial approximation with Caputo fractional derivatives and numerical integral transformation to reduce the NFIDEq to an algebraic system and then derive the numerical solution; additionally, the convergence analysis indicated that the proposed strategy has more precision than other numerical methods. Finally, as a verification of the theoretical work, we apply two examples with numerical results by using [Matlab R2022b], illustrating the comparisons between the exact solutions and numerical solutions, as well as the absolute error in each case is computed.
TL;DR: In this paper , a new class of partially degenerate Hermite-Bernoulli polynomials of the first kind and generalized Gould-Hopper-partially degenerate Bernoulli (GHB) are introduced.
Abstract: In this paper, we introduce a new class of partially degenerate Hermite-Bernoulli polynomials of the first kind and generalized Gould-Hopper-partially degenerate Bernoulli polynomials of the first kind and present some properties and identities of these polynomials. A new class of polynomials generalizing different classes of Hermite polynomials such as the real Gould-Hopper, as well as the 1-d and 2-d holomorphic, ternary and polyanalytic complex Hermite polynomials and their relationship to the partially degenerate Hermite-Bernoulli polynomials of the first kind are also discussed.
TL;DR: In this article , the authors consider the effect of ship motions on the flow in the opening of a damaged ship in the case of a flooding accident and show that it is relevant to use the more complicated formulations instead of the simple and robust Bernoulli model in the numerical simulation of damaged ships in the sea.
Abstract: The survivability of a damaged RoPax ship in the case of a flooding accident can be critical, as these ships have a tendency for a rapid capsize. Various simulation tools are presently in use to study the behavior of damaged RoPax and cruise ships. Recent benchmark tests show that the numerical tools for this purpose are very useful, but their accuracy and reliability still leave something to be desired. In many numerical simulation codes for ship survivability, the water inflow and outflow through a damage opening are modeled with Bernoulli equation, which describes steady flow in an inertial frame of reference. This equation takes neither the floodwater inertia in the opening into account nor does it regard the effect of ship motions on the flow in the opening. Thus, there are some approximations involved in the use of the Bernoulli equation for this purpose. Some alternative formulations are possible. This study sheds light on the question of how relevant is it to use the more complicated formulations instead of the very simple and robust Bernoulli model in the numerical simulation of damaged ships in the sea.
TL;DR: In this paper , a numerical solution of the velocity and heat transfer on the magnetohydrodynamic suction-injection model of viscous fluid flow has been studied, and the authors used the differential transformation method and Bernoulli wavelet method to solve the highly nonlinear governing equations; applying appropriate similarity transformations and reducing governing equations to highly non-linear coupled ordinary differential equations.
Abstract: In this study, a numerical solution of the velocity and heat transfer on the magnetohydrodynamic suction–injection model of viscous fluid flow has been studied. We use the differential transformation method and Bernoulli wavelet method to solve the highly nonlinear governing equations; applying appropriate similarity transformations and reducing governing equations to highly nonlinear coupled ordinary differential equations. The objective of this analysis is to determine how the suction parameter, Hartmann number, squeeze number, thermophoresis parameter, and Prandtl number affect the velocity and temperature profiles. When the current findings are compared with those that have already been published in the literature, confident suppositions are made, and it is discovered that there is considerable agreement. Graphs have been used to discuss the influence of nondimensional characteristics on velocity and temperature.