Time-varying Leakage Research Articles

Global exponential synchronization of switching neural networks with leakage time-varying delays

In this paper, the synchronization problem of a class of switching neural networks with leakage time-varying delays is studied. A system solution-based direct analysis method is proposed to derive the sufficient conditions of global exponential synchronization for master–slave systems. Firstly, the state variable expression of the error system is derived by constructing a suitable regulation function, in which the leakage delays are explicitly transformed outside the state variable. Then, on this basis, the corresponding synchronization conditions is obtained by using the transition condition ingeniously. In addition, the obtained sufficient conditions contain only some simple linear scalar inequalities, which is different from most publications and greatly reduces the computational complexity. Finally, the reliability of the theoretical results is verified by numerical simulation. It is worth noting that the synchronization problem of switching neural networks with time-varying leakage delays is studied for the first time in this paper, and the method adopted does not need to construct any Lyapunov–Krasovskii functionals, which simplifies the proof process.

Experimental and Numerical Study for Gas Release and Dispersion on Offshore Platforms

Accidental gas release is a major triggering event for the offshore oil and gas industry. This paper focuses on the experimental and numerical investigation for dispersion behavior of released gas on offshore platforms. For this purpose, an experimental system is designed and developed to investigate gas release and dispersion. A series of experiments are carried out, among which the scenarios with constant leakage rates and time-varying leakage rates are both emphasized. The gas concentrations at different sampling points are obtained to study the dispersion behavior and accumulation characteristics of the released gas. Furthermore, a numerical computational fluid dynamics model is established to replicate the experimental scenarios. Good agreement between experimental data and CFD simulation results is observed by calculating a series of statistical performance measures. The developed numerical model is subsequently utilized to investigate a gas release scenario on a practical offshore platform, in which a fully transient leakage rate is adopted considering the response of process protection measures. The developed numerical model could provide support for risk assessment and optimization of contingency plans against gas release accidents in offshore facilities.

Fixed-Time Stabilization of Leakage-Delayed Neural Networks Modeled by Delayed Filippov Systems: Leakage-Delay-Dependent Settling-Time

This paper considers the periodicity and fixed-time stabilization of discontinuous neural networks with time-varying leakage and discrete delays, which is more general. Before considering the stability, the existence of periodic solutions in the sense of Filippov should be proved. So, firstly, based on the set-valued mapping, coincidence theorem, Hölder inequality and supremum-infimum principle, the investigation on the leakage-delay-dependent periodic solutions is obtained, which can not only complement the periodic solutions of leakage-delayed neural networks, but also is absolutely different from the almost-periodic solution results. Secondly, by virtue of the integral inequality, new fixed-time stability lemmas of Filippov systems are established, which involve more relaxed assumptions and do not need to input extra parameters. Thirdly, leakage-delay-dependent results on fixed-time stabilization of the proposed neural networks are established via the new fixed-time stability lemmas under modified non-chattering control laws. It is worth mentioning that the settling-times are relevant to the leakage delays, which have not been reported in the literature. Finally, the validity of the proposed results is demonstrated by two numerical examples.

Robust stability and passivity analysis for discrete-time neural networks with mixed time-varying delays via a new summation inequality

<abstract><p>The summation inequality is essential in creating delay-dependent criteria for discrete-time systems with time-varying delays and developing other delay-dependent standards. This paper uses our rebuilt summation inequality to investigate the robust stability analysis issue for discrete-time neural networks that incorporate interval time-varying leakage and discrete and distributed delays. It is a novelty of this study to consider a new inequality, which makes it less conservative than the well-known Jensen inequality, and use it in the context of discrete-time delay systems. Further stability and passivity criteria are obtained in terms of linear matrix inequalities (LMIs) using the Lyapunov-Krasovskii stability theory, coefficient matrix decomposition technique, mobilization of zero equation, mixed model transformation, and reciprocally convex combination. With the assistance of the LMI Control toolbox in Matlab, numerical examples are provided to demonstrate the validity and efficiency of the theoretical findings of this research.</p></abstract>

A Low-Noise and Fast-Settling UHF RFID Receiver With Digitally Controlled Leakage Cancellation

UHF RFID reader suffers from a strong transmitter (Tx) leakage signal coupled to a receiver (Rx), especially for an area-compact single-antenna implementation. Then, several Tx leakage cancellation schemes have been developed not to saturate the Rx and to minimize the performance degradation. This brief proposes a UHF RFID reader Rx with a low noise performance and a fast settling time by employing a digitally controlled leakage cancellation method in both passive and active realizations. A digitally-controlled passive vector reflector enhances the Tx to Rx isolation ratio by providing the optimum load impedance. An active anti-leakage feedback loop adaptively tracks the time-varying leakage and generate the phase-inverted leakage replica to suppress the leakage. The prototype chip is fabricated in a CMOS 55 nm process and the area of the chip is 2.1 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times1.8$ </tex-math></inline-formula> mm. The passive leakage cancellation (PLC) with a vector reflector endures the 1 dBm leakage with a low Rx noise figure of 12.5 dB and 35.7 mW power consumption. The active leakage cancellation (ALC) with an anti-leakage feedback loop covers 6 dBm leakage with a fast settling time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.6~\mu \text{s}$ </tex-math></inline-formula> and 58 mW power consumption.

A Novel Magnetic Flux Leakage Testing Method Based on AC and DC Composite Magnetization

The flexible printed coil array (FPC) is employed extensively because of its flexibility in non-destructive testing (NDT). To make the inductive coil detect both external and internal defects under static conditions in magnetic flux leakage testing (MFL), this paper proposes a method based on the AC and DC composite magnetization. The AC magnetization changes the magnetic field strength in the vicinity of external defects. The DC magnetization makes the defects generate static leakage magnetic field. The permeability in the skin layer is also altered by the AC magnetization which affects the transmission of leakage magnetic field caused by internal defects. Therefore, external and internal defects can generate a time-varying leakage magnetic field. Simulations and experiments are carried out to validate the feasibility of this method. The results indicate that the method based on the AC and DC composite magnetization can be applied to detect both external and internal defects.

A novel approach for hazardous area identification of toxic gas leakage accidents on offshore facilities

Some attempts have been made to predict the adverse effects of released toxic gas on individuals. However, the classification of the hazardous area considering the dynamic changes and focusing on the whole facility remains a challenging issue. This paper presents a novel approach to classify the hazardous areas on offshore facilities in toxic gas leakage and dispersion accident. By introducing the grid-based concept, each location and its corresponding evacuation trajectory to a fixed destination are considered in this approach. Instead of just considering the accumulation characteristics of released toxic gas, the adverse effects are predicted with a combination of the dynamic factors, including the emergency evacuation and the temporal-spatial variation of toxic gas profile. The proposed approach is applied to a hypothetical H2S leakage and dispersion accident on an offshore platform, a time-varying leakage profile is adopted considering the interference of the process protection measures, an emergency evacuation time model is established with the guideline from International Maritime Organization (IMO). The results suggest that the proposed approach can improve the accuracy of hazardous areas identification and provide support for optimizing emergency plans against toxic gas leakage and dispersion accident on offshore facilities.

Existence and global exponential stability of anti-periodic solutions for quaternion-valued cellular neural networks with time-varying delays

In this paper, we are concerned with a class of quaternion-valued cellular neural networks with time-varying transmission delays and leakage delays. By applying a continuation theorem of coincidence degree theory and the Wirtinger inequality as well as constructing a suitable Lyapunov functional, sufficient conditions are derived to ensure the existence and global exponential stability of anti-periodic solutions via direct approaches. Our results are completely new. Finally, numerical examples are also provided to show the effectiveness of our results.

Exponential stability of discrete-time uncertain neural networks with multiple time-varying leakage delays

In this paper, we investigate a new exponential stability criterion for uncertain discrete-time neural networks with both multiple leakage time-varying delays and discrete time-varying delays. Here for the first time leakage time varying delay is taken to be a multiple one and this does not exist in the literature. Based on the discrete-type Halanay inequality and some inequality techniques, a novel exponential stability criterion for uncertain discrete-time neural networks is derived in terms of LMIs. Then the LMIs can be easily manipulated by using LMI Control toolbox in Matlab. Finally few numerical examples along with simulative responses are given to strengthen our proposed stability results.

The Existence and Global Exponential Stability of Almost Periodic Solutions for Neutral-Type CNNs on Time Scales

In this paper, neutral-type competitive neural networks with mixed time-varying delays and leakage delays on time scales are proposed. Based on the contraction fixed-point theorem, some sufficient conditions that are independent of the backwards graininess function of the time scale are obtained for the existence and global exponential stability of almost periodic solutions of neural networks under consideration. The results obtained are brand new, indicating that the continuous time and discrete-time conditions of the network share the same dynamic behavior. Finally, two examples are given to illustrate the validity of the results obtained.

Finite-time stabilization of uncertain delayed-hopfield neural networks with a time-varying leakage delay via non-chattering control

This article is concerned with the finite-time stabilization (FTSB) of a class of delayed-Hopfield neural networks with a time-varying delay in the leakage term in the presence of parameter uncertainties. To accomplish the target of FTSB, two new finite-time controllers are designed for uncertain delayed-Hopfield neural networks with a time-varying delay in the leakage term. By utilizing the finite-time stability theory and the Lyapunov-Krasovskii functional (LKF) approach, some sufficient conditions for the FTSB of these neural networks are established. These conditions, which can be used for the selection of control parameters, are in the form of linear matrix inequalities (LMIs) and can be numerically checked. Additionally, an upper bound of the settling time was estimated. Finally, our theoretical results are further substantiated by two numerical examples with graphical illustrations to demonstrate the effectiveness of the results.

Influence of Leakage Delay on Almost Periodic Solutions for BAM Neural Networks

In this paper, we deal with a class of BAM neural networks with time-varying leakage delays. By applying the exponential dichotomy of linear differential equations, fixed point theorems and differential inequality techniques, we obtain some sufficient conditions which guarantee the existence and exponential stability of almost periodic solutions for such class of BAM neural networks. An example is provided to illustrate the effectiveness of the theoretical predictions. The results obtained in this paper are completely new and complement the previously known publications.

New Delay-Dependent Stability Criteria for Impulsive Neural Networks with Additive Time-Varying Delay Components and Leakage Term

This work is concerned with the delay-dependent stability problem for uncertain impulsive neural networks (NNs) with additive time-varying delay components and leakage term. We construct a newly augmented Lyapunov–Krasovskii (L–K) functional which contains triple and four integral terms and then utilizing free-matrix-based integral inequality to bound the derivative of the Lyapunov–Krasovskii functional. Some sufficient conditions are derived to assure the delay-dependent stability of the impulsive NNs by the linear matrix inequality, which is less conservative than some existing results and can be readily verified by the convex optimization algorithms. In addition, some information of activation function ignored in previous works has been taken into account in the resulting condition. In the end, three numerical examples are provided to illustrate the effectiveness of the proposed criteria.

Impulsive discrete-time BAM neural networks with random parameter uncertainties and time-varying leakage delays: an asymptotic stability analysis

This proposal analyzes the problem of asymptotic approach on stability criteria for impulsive discrete-time BAM neural networks with random parameter uncertainties and time-varying leakage delays. Reciprocally convex combination technique is approached in this paper for the reduction of decision variables. This lemma is derived from the derivation of Jensen’s inequality. Here, the uncertainties are considered as a randomly occurring parameter uncertainty and it obey certain mutually uncorrelated Bernoulli-distributed white noise sequences. A priori indicates the occurrence of uncertain parameters in the probability which is the valuable feature. Some novelty sufficient conditions for ensuring the asymptotic stability of the addressed neural networks are attained in terms of linear matrix inequalities (LMIs) by the aid of Lyapunov–Krasovskii functionals approach, which can be easily checked by MATLAB LMI Toolbox. Finally, three illustrative examples are accomplished to manifest the effectiveness and fruitfulness of the proposed research work.

Dissipativity and passivity analysis for memristor-based neural networks with leakage and two additive time-varying delays

Abstract In this paper, the problems of dissipativity and passivity analysis for memristor-based neural networks (MNNs) with both time-varying leakage delay and two additive time-varying delays are studied. By introducing an improved Lyapunov–Krasovskii functional (LKF) with triple integral terms, and combining the reciprocally convex combination technique, Wirtinger-based integral inequality with free-weighting matrices technique, some less conservative delay-dependent dissipativity and passivity criteria are obtained. The proposed criteria that depend on the upper bounds of the leakage and additive time-varying delays are given in terms of linear matrix inequalities (LMI), which can be solved by MATLAB LMI Control Toolbox. Meanwhile, the criteria for the system with a single time-varying delay are also provided. Finally, some examples are given to illustrate the effectiveness and superiority of the obtained results.

Almost automorphic solution for neutral type high-order Hopfield BAM neural networks with time-varying leakage delays on time scales

In this paper, by applying the exponential dichotomy theory on time scales, Banach contraction mapping principle and differential inequality analysis techniques, we establish some sufficient conditions for the existence and global exponential stability of almost automorphic solution for neutral type high-order Hopfield bidirectional associative memory (BAM) neural networks with time-varying leakage delays on time scales. In the addressed system, we not only consider the effects of the first-order neutral terms on networks, but also investigate the influences of the second-order neutral terms on the networks. High-order Hopfield BAM neural networks with continuously distributed leakage delays on time scales are also considered. Our results are completely new even if time scale T=R or T=Z and complementary to the previously existing results. Finally, three examples with numerical simulations are presented to illustrate the feasibility of our proposed theoretical results.

Using multi-robot active olfaction method to locate time-varying contaminant source in indoor environment

Prevention of indoor contaminant source leak disasters is becoming an urgent issue in the indoor environment and public safety fields. Quickly locating such a source is a prerequisite for effective implementation of source control, and a key basis for further guidance on evacuation and emergency rescue. Indoor contaminant sources have many forms because of source characteristic variation. The current research focuses on continuous release from a source with constant intensity and instantaneous release from a source with impulse intensity. There are few studies of sources with varying release intensity. The main goal of the present research study was to develop a source localization method for a time-varying source. A computational fluid dynamics model of leakage for an indoor time-varying contaminant source is established, together with a search strategy using multi-robot active olfaction. Based on the time-varying leakage source localization method, the robots are used to verify method effectiveness through indoor two-dimensional ventilation room simulation experiments. The influence of factors such as intensity change, position and obstacles on the localization is analyzed, which provides a foundation for further study of contaminant source identification in an unsteady flow field.

Delay-partitioning approach to stability analysis of state estimation for neutral-type neural networks with both time-varying delays and leakage term via sampled-data control

ABSTRACTThis paper mainly focuses on further improved stability analysis of state estimation for neutral-type neural networks with both time-varying delays and leakage delay via sampled-data control by delay-partitioning approach. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states and a sampled-data estimator is constructed. To fully use the sawtooth structure characteristics of the sampling input delay, sufficient conditions are derived such that the system governing the error dynamics is asymptotically stable. The design method of the desired state estimator is proposed. We construct a suitable Lyapunov–Krasovskii functional (LKF) with triple and quadruple integral terms then by using a novel free-matrix-based integral inequality (FMII) including well-known integral inequalities as special cases. Moreover, the design procedure can be easily achieved by solving a set of linear matrix inequalities (LMIs), which can be easily facilitated by using the standard numerical software. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed results.

Adaptive Exponential Synchronization for Stochastic Competitive Neural Networks with Time-Varying Leakage Delays and Reaction-Diffusion Terms

We study the exponential synchronization problem for a class of stochastic competitive neural networks with different timescales, as well as spatial diffusion, time-varying leakage delays, and discrete and distributed time-varying delays. By introducing several important inequalities and using Lyapunov functional technique, an adaptive feedback controller is designed to realize the exponential synchronization for the proposed competitive neural networks in terms of p-norm. According to the theoretical results obtained in this paper, the influences of the timescale, external stimulus constants, disposable scaling constants, and controller parameters on synchronization are analyzed. Numerical simulations are presented to show the feasibility of the theoretical results.

Pseudo Almost Periodic Solutions for High-Order Hopfield Neural Networks with Time-Varying Leakage Delays

In this paper, high-order Hopfield neural networks with time-varying leakage delays are investigated. By applying Lyapunov functional method and differential inequality techniques, a set of sufficient conditions are obtained for the existence and exponential stability of pseudo almost periodic solutions of the model. Some simulations are carried out to support the theoretical findings. Our results improve and generalize those of the previous studies.