Unnecessary Signal Research Articles

Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations

Abstract This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism, instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.

FCPN: Pruning redundant part-whole relations for more streamlined pattern parsing

Cropping-and-segmenting pattern parsers often combine diverse inner correlations into a single metric/scheme, resulting in over-generalizations and redundant representations. It is proposed to streamline pattern parsing by using presenting a redundant association elimination network (RAEN) with capsule attention twisters (CATs) and capsule-attention routing agreement (CARA). CATs trim delicate relationships between parts and wholes that are weak and interchangeable. Senior entities can only be updated by primary entities that meet the requirements of inter-part diversity and intra-object cohesiveness. In order to enhance results, CARA is designed to protect against the unnecessary voting signals of traditional routing protocols. Experiments involving facial and human segmentation show that RAEN is better than current remarkable methods, particularly for defining detailed semantic boundaries.

Detecting loss of excitation condition of synchronous generator in the presence of unified power flow controller based on data mining method

Loss-of-excitation (LOE) conditions may cause devastating effects on both the generator and power system. If LOE is not meticulously detected, two dangerous events may emerge: 1. unnecessary trip signal, 2. not detecting this abnormal condition or detecting it with time delay. Both mentioned flaws have considerable harmful consequences on the generator and power system. Conventional techniques for detecting LOE conditions are based on impedance measurement. The presence of flexible alternating current transmission (FACT) system devices can considerably affect the impedance characteristic of the system and as a result, the operation of impedance-based relays. This paper simulates LOE and stable power swing (SPS) conditions causing failures and errors in the LOE protection in the presence or absence of a unified power flow compensator (UPFC), and parameters with different behaviors are determined. Then, the most sensitive parameters are combined using an intelligent method. The single parameter, achieved from a combination of features in the previous process, is applied to a learning vector quantization (LVQ) neural network. The LVQ is a type of artificial neural network that is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm. The simulation results show that, in a single machine infinitive bus (SMIB) test system, R-X directional scheme has 2 mal-operations and the proposed algorithm has no mal-operation without UPFC in the system. With UPFC in the system, the R-X directional scheme has four mal-operations and the proposed algorithm has no mal-operation. In the IEEE 9-bus test system, the R-X directional scheme has five mal-operations and the proposed algorithm has no mal-operation. Moreover, applying the proposed algorithm, LOE is detected very quickly in the presence or absence of the UPFC in the system and this approach prevents electrical and mechanical stresses enforced to the machine and power system. © 2017 Elsevier Inc. All rights reserved.

ECCANS: Enhanced CRITIC-based Context-Aware Network Selection algorithm for 5G HetNet

The ability of mobility management to provide seamless communication and better quality of service (QoS) to the multiservice multimode mobile node (MMMN), which can run multiple services simultaneously during roaming in ultra-dense heterogeneous networks with different radio access technologies, is a major challenge in implementing 5G and beyond. Ultra-dense heterogeneous networks (HetNet), restrictions on multiservice multimode mobile terminals, and user mobility all lead to a number of serious problems, such as the ping-pong effect, unnecessary handovers, and decreasing cell edge spectral efficiency. The dense deployment of small base stations also causes a high probability of frequent handovers in HetNet, which has led to a significant escalation in energy consumption resulting from unnecessary signaling, increased network overhead, and higher energy usage by network equipment. This increased energy requirement may lead to potentially increase the emissions of greenhouse gases to meet the energy production needs. Several handover decision algorithms have been proposed, but most of them deal with only running a single service at a time on a mobile node. In this paper, authors propose a new network selection model for MMMN which may run multiple services simultaneously. This model uses context-aware information about the user’s preferences, characteristics, and needs of the services, limitations of mobile terminal, and network conditions of an ultra-dense HetNet to choose the best network. The fundamental principle of this model is based on the comprehensive network criteria weight computed by the integration of Enhanced CRiteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy analytic hierarchy process (FAHP), and group decision-making (GDM) techniques and the service priority of running services. For computing the objective weight of criteria, a novel improved CRITIC method has been proposed. At the same time, sigmoid utility functions and linear utility functions have been used to figure out the value of network criteria for running multiple services on the mobile node. Finally, the best network from HetNet for MMMN is selected using the technique for order performance by similarity to ideal solution (TOPSIS) and threshold by computing a comprehensive utility value network criteria decision matrix and comprehensive network criteria weight. The simulation results show that the proposed model increases the selection probabilities of an appropriate network according to the service requirements and network conditions. It also reduces unnecessary handovers and improves QoS while satisfying users’ preferences, service requirements, and terminal constraints, in solving the MMMN network selection problem.

Fuzzy Dynamic Event-Triggered Tracking Control for Semilinear Time-Delay Parabolic Systems

In this article, the fuzzy dynamic event-triggered tracking control problem of semilinear parabolic systems (SLPSs) with time-varying delay is investigated. Firstly, T-S fuzzy partial differential equation (PDE) models are introduced to describe the SLPSs. Secondly, a less conservative and more general fuzzy dynamic event-triggered strategy (DETS) is proposed to reduce communication consumption and avoid unnecessary continuous signal monitoring. Since the dynamic threshold is closely related to the currently sampled signal and the latest successfully transmitted signal, it can be promptly dynamically adjusted. In addition, on the basis of a reasonable assumption, a novel linear matrix inequality (LMI) relax technique is introduced to deal with the mismatched premise variables between the fuzzy systems and the fuzzy controller. By constructing the appropriate Lyapunov-Krasovskii candidate functional (LKCF), the criteria that the SLPSs can asymptotically track the target systems is derived, and the desired dynamic event-triggered control (DETC) gains can be obtained by solving a set of LMIs. The DETS reduces effectively communication resource consumption. Finally, the control problem of temperature distribution of catalytic rod in practical engineering application is given to verify the effectiveness and superiority of the proposed control scheme.

Security Fuzzy Control of Nonlinear Semi-Markov Switching Systems With Event-Based Mechanism and Hybrid Cyber-Attacks

This article is concerned with the security <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> fuzzy control problem for Takagi–Sugeno (T–S) fuzzy semi-Markov switching systems (SMSSs) with event-based strategy and hybrid cyber-attacks. To efficiently use network resources and avoid unnecessary signal transmission, a new mode-dependent dynamic event-triggered mechanism (ETM), whose threshold parameter changes with varying states and modes is developed for each subsystem of fuzzy SMSSs. A more practical network environment subject to deception and denial-of-service (DoS) attacks is considered, and two Bernoulli distributed variables are adopted to characterize the randomly occurring hybrid attacks in a unified framework. By the mode-dependent Lyapunov function, sufficient conditions are attained to assure that the closed-loop system is stochastically stable with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> attenuation performance. Finally, the validity and superiority of the proposed theoretical approach is demonstrated via its application to the Duffing–Van der Pols oscillator.

Byzantine Agents’ Detection in Distributed Nash Equilibrium Seeking Algorithms Using an Adaptive Event-Triggered Scheme

In this article, a resilient, adaptive event-triggered distributed Nash equilibrium seeking algorithm is investigated for a class of multimicrogrid systems. Since the strategy of each microgrid affects others’ costs through total generation cost, such interaction among microgrids is modeled as a noncooperative game. To reduce unnecessary signal transmission among the aggregators, an adaptive algorithm, where threshold parameters are adjusted based on the behavior of the estimated signals in the last two iterations, is proposed. It is observed that when false data is injected into an agent, its estimated signals begin to diverge from the equilibrium point, and as a result, the threshold value becomes increasing. Furthermore, the misbehaving agents are identified and isolated using a reputation-based mechanism and are allowed to rejoin the network when they behave normally again. To deal with arbitrary behavior of users, receding horizon scheme of predictive control is used. Finally, a set of simulation results is presented to evaluate the effectiveness of the suggested algorithm.

Fatigue stress estimation of metro bogie frame through frequency response functions by using limited sensors

In this paper, the fatigue state estimation of the metro bogie frame is discussed based on limited sensors through frequency domain methods. The middle-frequency and high-frequency vibration fatigue damage cases of one metro bogie frame is discussed. The main excitations come from the wheel/rail interactions including wheel out-of-roundness and rail irregularities. The most common modal vibration of the bogie frame, where the transom bending deformation is dominant, is taken as the research object to study the possibilities of fatigue failure monitoring on concerned points of the bogie frame transom focusing on specific transfer paths. To eliminate unnecessary input signals which are not closely related to the occurrence of large stress amplitudes, the coherence analysis of different loads including the transmission load, carbody load and wheel-rail impact load is conducted through various methods including the short-time Fourier transform spectrum analysis, the magnitude-squared coherence analysis and the partial directed coherence analysis. Then the power spectral densities (PSDs) of the measured accelerations and cross power spectral densities (CPSDs) are calculated to obtain the transfer functions, whose output is the Y-axis stress at the concerned point. The stress PSDs are obtained using the instantly measured input accelerations and the prepared transfer function. Then the spectral moments and the stress amplitudes distribution functions are calculated through the frequency domain method to overcome the computational burden. The stress reconstructions based on inverse fast Fourier transform are analyzed and the fatigue damage results show good consistency with the experimental ones.

Event-Triggered Extended Dissipativity Fuzzy Filter Design for Nonlinear Markovian Switching Systems against Deception Attacks

This article is concerned with the adaptive-event-triggered filtering problem as it relates to a class of nonlinear discrete-time systems characterized by interval Type-2 fuzzy models. The system under investigation is susceptible to Markovian switching and deception attacks. It is proposed to implement an improved event-triggering mechanism to reduce the unnecessary signal transmissions on the communication channel and formulate the extended dissipativity specification to quantify the transient dynamics of filtering errors. By resorting to the linear matrix inequality approach and using the information on upper and lower membership functions, stochastic analysis establishes sufficient conditions for the existence of the desired filter, ensuring the mean-squared stability and extended dissipativity of the augmented filtering system. Further, an optimization-based algorithm (PSO) is proposed for computing filter gains at an optimal level of performance. The developed scheme was finally tested through experimental numerical illustrations based on a single-link robot arm and a lower limbs system.

Detection of Loss-of-Excitation in Synchronous Machines using Hybrid GMDH Neural Network Intelligent Algorithm in the Presence of UPFC in Power System

Faults in the power system should be detected as soon as possible. Otherwise, some defects in the protection system can result in imposing considerable destructive damage to the system equipment. The excitation system of a synchronous generator plays a significant role in the proper operation and control of the generator. Loss-of-excitation (LOE) may cause harmful effects on both the generator and power system. If LOE is not correctly detected, an unnecessary trip signal may be issued, resulting in the overloading of parallel generators and even a system blackout. Conventional techniques for detecting this fault are based on impedance measurement. Despite the simple structure of these techniques, they are sometimes unable to detect some faults correctly. The presence of flexible alternating current transmission system devices can significantly affect the impedance characteristic of the system and as a result, the operation of impedance relays. In this article, first, LOE and other bugs causing failures and errors in the protection of LOE are simulated in the presence and absence of a unified power flow compensator (UPFC), and parameters with different behavior are determined. Then, the most sensitive parameters are sorted using an intelligent method based on their sensitivity. Once the sort order table is obtained, the first two parameters are applied to a ground data handling neural network method, and LOE is detected. The simulation results show that the proposed algorithm is significantly reliable in detecting LOE faults. Moreover, LOE is detected very quickly in the presence or absence of the UPFC in the system by preventing the tolerance of electrical and mechanical stresses by the machine and system; thus, the maloperation of the protection system is avoided.

Consensus of NMASs with MSTs subjected to DoS attacks under event-triggered control

The leader-following consensus (LFCs) of nonlinear multi-agent systems (NMASs) with Markov switching topologies (MSTs) subjected to denial-of-service (DoS) attacks under event-triggered (ET) control is studied. An ET strategy is applied to reduce unnecessary signal transmission among agents, save network resources, and ensure systems performance. As a result of the open communication network among agents, it is inevitably subjected to attacks that leads to changing in the communication topologies. The communication topologies among agents are modeled as MSTs, and the transfer rates (TRs) are assumed to be partially unknown. DoS attacks are the most common attacks due to their destructive, stealthy, and easy implementation, so the network attacks considered in this paper are DoS attacks. Based on the distributed control theory and Lyapunov stability theory, the Lyapunov direct method and stochastic analysis method are used to explore sufficient conditions for the systems to achieve LFCs. Finally, an example is provided to verify the effectiveness of the methods and the correctness of the results.

State estimation problem for fractional-order neural networks using event-triggered state observers

This article considers the state estimation problem for fractional-order neural networks subject to external disturbances using event-triggered state observers. For the first time, a novel method based on a discrete-time event-triggered mechanism and a nonlinear fractional-order state observer is obtained. A new discrete-time event-triggered mechanism is first proposed. Then a new nonlinear fractional-order state observer based on the newly discrete-time event-triggered mechanism is designed to estimate state vectors of the fractional-order neural networks robustly. The discrete-time event-triggered mechanism is Zeno-free, and it helps reduce unnecessary continuous signal transmission. A fractional-order-dependent condition to guarantee the existence of the discrete-time event-triggered nonlinear fractional-order state observer is established. This condition is translated into a convex optimization problem, which can be solved easily by MATLAB. The solution to this problem minimizes the attenuation levels and thus reduces the error in estimating the state vector of the fractional-order neural networks. The effectiveness of the proposed method is illustrated by two numerical examples and simulation results.

Interface Trap‐Free, 100% Yield, Wafer‐Scale, Non‐Volatile Optically‐Guided Memory Array from Cumulatively‐Stacked Small Molecules/Fluoropolymer/Copper‐Oxide Nanoparticles Heterostructure

AbstractOptically‐guided memory devices, with its photo‐response, allow for photodetector and memory functions to be combined in a single device. As a result, the issue of unnecessary signal delay can be alleviated by reducing the metal wire between the photodetector and the memory device, and the function of two different devices can be performed simultaneously, which enables an image recognition system to be miniaturized. With such advantages, optically‐guided memory is therefore considered to be highly promising as a potential key component for next‐generation applications where image detection and processing capabilities are paramount. Here, a wafer‐scale 12 × 12 dinaphtho[2,3‐b:2′,3′‐f]thieno[3,2‐b]thiophene (DNTT)‐based optically‐guided memory transistors (OMT) array with a cumulatively stacked small‐molecules/fluoropolymer/copper‐oxide nanoparticles structure is demonstrated. The proposed OMT is formed in 4 different states depending on the light intensity. Furthermore, read current (IRead) and threshold voltage (Vth) in the programming state (P‐state) of OMT is maintained stably even after 20 days. Based on the optimized DNTT thickness, a wafer‐scale 12 × 12 OMT array is fabricated consisting of 144 devices for text image detection with 100% yield. This study also demonstrates a text image detection with non‐volatile memory characteristics depending on the presence or absence of light irradiation.

Adaptive Event-Triggered Decentralized Dynamic Output Feedback Control for Load Frequency Regulation of Power Systems With Communication Delays

In order to ensure that the power system frequency and tie-line power remain at the nominal value when the load fluctuates, while reducing the release number of decentralized sensor, this work presents a novel adaptive event-triggered scheme for the load frequency regulation via designing the decentralized dynamic output feedback controller (DOFC), where the communication delay issue is also considered due to communication constraints. Distinct from the existing ones, the proposed adaptive event-triggered strategy automatically tunes the threshold according to the local extremum of the system output signal, which can significantly reduce the number of unnecessary signal transmissions to ensure system performance. First, the proposed adaptive event-triggered transmission scheme is integrated with the decentralized DOFC and communication delays under the framework of a linear time-delay system. Then, the asymptotic stability of the closed-loop power system is analyzed through the Lyapunov stability theory and a procedure is given for the design of decentralized dynamic output load frequency controllers by solving some linear matrix inequalities (LMIs). Finally, a three-area power system is used to verify the effectiveness and usefulness of the proposed results.

Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability and average throughput in each RACH attempt. We also derive the average energy consumption in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, and the lowest average energy consumption, but the performance gain decreases with the increase of device intensity.

Event‐based finite‐time H∞ synchronization control for switched complex networks with average dwell time

AbstractThis paper investigates the problem of the event‐triggered finite‐time nonfragile synchronization control for a class of discrete time‐delayed complex networks with switched topology. To decrease the unnecessary signal transmission and save control cost, the dynamic event‐triggering scheme is employed in the controller‐to‐actuator channel where the varying triggering threshold is governed by a dynamical equation. The main purpose of this paper is to design a nonfragile synchronization controller such that the synchronization error dynamics satisfies both the finite‐time boundedness and the disturbance rejection level. By constructing appropriate Lyapunov–Krasovskii functional and using the average‐dwell‐time technique, the desired controller gains are acquired in terms of the solution to a set of linear matrix inequalities. Finally, a numerical simulation example is provided to demonstrate the effectiveness of the proposed synchronization controller design scheme.

Adaptive Fuzzy Control for a Spatial Flexible Hose System with Dynamic Event-triggered Mechanism

In this study, we develop an adaptive boundary control strategy with a dynamic event-triggered mechanism (dETM) for an aerial refueling hose system involving certain performance specifications. The controller uses the barrier Lyapunov function method, where the introduction of two boundary deflection transformations, an asymmetric scaling function and a behavior-shaping function, reduces the complexity of the stability analysis and guarantees that the system meets the required performance description. Fuzzy logic systems are developed to strengthen the adaptivity in other unmodeled dynamics. Particularly in this work, the dETM is constructed to decrease the unnecessary signal transmission between the controllers and actuators while achieving vibration suppression. The stability of the controlled systems is proved via Lyapunov analysis. Finally, we establish numerical simulations to illustrate the validity of the designed controllers.

Delay-Independent regulation of blood glucose for type-1 diabetes mellitus patients via an observer-based predictor feedback approach by considering quantization constraints

Diabetes, as a widespread chronic disease, is caused by the increase of blood glucose concentration (BGC) due to pancreatic insulin production failure and/or insulin resistance in the body. The artificial pancreas (AP) known as a closed-loop insulin delivery control system consists of a glucose sensor for BGC measurement, a control algorithm for calculation of exogenous insulin delivery rate (IDR), and an insulin infusion pump. The AP provides a closed-loop glucose-insulin regulatory system for type-1 diabetes mellitus (T1DM) patients in order to effectively reduce the high BGC level. In this paper, we aim to design a controller in order to regulate the BGC of T1DM patients at its basal value with considering meal disturbance in the system. We confront with different problems for glucose control such as sampled and quantized glucose measurement by the sensor, quantized IDR by the insulin infusion pump, and an unknown input delay of the insulin infusion pump as the actuator of the control system. The bergman minimal model (BMM) as the most famous model for describing the glucose-insulin regulatory system is reviewed and the delay-independent observer for the estimation of the system’s state vector from the sensor output (sampled and quantized BGC) is considered. Moreover, the observer works on the basis of an event-triggered strategy in order to avoid unnecessary signal updating. At the end, the IDR calculated by the proposed delay-independent truncated predictor output feedback controller (based on the observer’s output) is delivered to the T1DM patient. A sufficient condition on the insulin infusion pump delay’s upper bound value in order to guarantee the ultimate boundedness of the glucose-insulin regulatory system under the mentioned constraints is derived. Also, the nonoccurrence of Zeno behavior in the proposed control system is proved. Numerical simulation results confirm the effectiveness of the designed controller in the presence of meal disturbance, unknown input delay, and quantization errors for blood glucose regulation of the glucose-insulin regulatory system in T1DM patients. Moreover, a pre-clinical verification of the applicability of the proposed glucose controller is carried out on a large-scale and multi-compartmental model that is the base of the UVA/PADOVA Type-1 Diabetes Simulator.

Volitional EMG Estimation Method during Functional Electrical Stimulation by Dual-Channel Surface EMGs.

We propose a novel dual-channel electromyography (EMG) spatio-temporal differential (DESTD) method that can estimate volitional electromyography (vEMG) signals during time-varying functional electrical stimulation (FES). The proposed method uses two pairs of EMG signals from the same stimulated muscle to calculate the spatio-temporal difference between the signals. We performed an experimental study with five healthy participants to evaluate the vEMG signal estimation performance of the DESTD method and compare it with that of the conventional comb filter and Gram–Schmidt methods. The normalized root mean square error (NRMSE) values between the semi-simulated raw vEMG signal and vEMG signals which were estimated using the DESTD method and conventional methods, and the two-tailed -test and analysis of variance were conducted. The results showed that under the stimulation of the gastrocnemius muscle with rapid and dynamically modulated stimulation intensity, the DESTD method had a lower NRMSE compared to the conventional methods (p < 0.01) for all stimulation intensities (maximum 5, 10, 15, and 20 mA). We demonstrated that the DESTD method could be applied to wearable EMG-controlled FES systems because it estimated vEMG signals more effectively compared to the conventional methods under dynamic FES conditions and removed unnecessary FES-induced EMG signals.

A Novel Method for 3D Nanoscale Tracking of 100 nm Polystyrene Particles in Multi-Wavelength Evanescent Fields Microscopy – Absolute Difference Height Verification –

Total internal reflection is an optical imaging technique for nanoparticle tracking and observation employing the scattered light from an evanescent field near the interface or reference surface. Generally, the nanoparticle behavior is the three-dimensional Brownian motion in an aqueous medium. The motion can be traced by an optical microscopy, but it cannot be traced by an electron microscopy technique. In the three-dimensional nanoparticle moving position, the X and Y positions are parallel to the surface, which can be traced by the general microscopy techniques. However, the height position Z of a nanoparticle perpendicular to the surface could not be traced without the longitudinal scanning method. Here, a novel method is proposed to investigate the 3D position of nanoparticles by applying multi-wavelength evanescent fields microscopy, which has a high spatial resolution in the Z-direction without longitudinal scanning. This paper focuses on the verification of measurement in the Z-direction. A piezoelectric actuator was employed to control the nanoparticle displacement in height Z. Standard polystyrene 100 nm particles were randomly adhered on a spherical tip that connected with the piezoelectric actuator. The spherical tip was essentially made from an optical adhesive (n = 1.348) with a refractive index close to the water for decreasing the unnecessary signal from the tip-self during nanoparticle observation in the water. The proposed method could obtain the multi-wavelength scattering lights from the observed nanoparticles by an 8-bit color camera with higher than 50 frames per second recording to investigate the 3D nanoscale tracking. The X and Y positions of nanoparticles were determined by the centroid of the scattering light intensities. The height Z was determined from the logarithm ratios between the detected scattering light intensities of both wavelengths. The measurement repeatability of the absolute difference in height between nanoparticles could be measured less than ±16 nm by using the proposed method. The penetration height measurability range was approximated at 250 nm from the reference surface.