Real-world Disturbances Research Articles

An Accelerated Dual-Integral Structure Zeroing Neural Network Resistant to Linear Noise for Dynamic Complex Matrix Inversion

The problem of inverting dynamic complex matrices remains a central and intricate challenge that has garnered significant attention in scientific and mathematical research. The zeroing neural network (ZNN) has been a notable approach, utilizing time derivatives for real-time solutions in noiseless settings. However, real-world disturbances pose a significant challenge to a ZNN’s convergence. We design an accelerated dual-integral structure zeroing neural network (ADISZNN), which can enhance convergence and restrict linear noise, particularly in complex domains. Based on the Lyapunov principle, theoretical analysis proves the convergence and robustness of ADISZNN. We have selectively integrated the SBPAF activation function, and through theoretical dissection and comparative experimental validation we have affirmed the efficacy and accuracy of our activation function selection strategy. After conducting numerous experiments, we discovered oscillations and improved the model accordingly, resulting in the ADISZNN-Stable model. This advanced model surpasses current models in both linear noisy and noise-free environments, delivering a more rapid and stable convergence, marking a significant leap forward in the field.

PADVG: A Simple Baseline of Active Protection for Audio-Driven Video Generation

Over the past few years, deep generative models have significantly evolved, enabling the synthesis of realistic content and also bringing security concerns of illegal misuse. Therefore, active protection for generative models has been proposed recently, aiming to generate samples with hidden messages for future identification while preserving the original generating performance. However, existing active protection methods are specifically designed for generative adversarial networks (GANs), restricted to handling unconditional image generation. We observe that they get limited identification performance and visual quality when handling audio-driven video generation conditioned on target audio and source input to drive video generation with consistent context, e.g., identity and movement, between frame sequences. To address this issue, we introduce a simple yet effective active P rotection framework for A udio- D riven V ideo G eneration, named PADVG. To be specific, we present a novel frame-shared embedding module in which messages to hide are first transformed into frame-shared message coefficients. Then, these coefficients are assembled with the intermediate feature maps of video generators at multiple feature levels to generate the embedded video frames. Besides, PADVG further considers two visual consistent losses: (i) intra-frame loss is utilized to keep the visual consistency with different hidden messages; (ii) inter-frame loss is used to preserve the visual consistency across different video frames. Moreover, we also propose an auxiliary denoising training strategy through perturbing the assembled features by learnable pixel-level noise to improve identification performance, while enhancing robustness against real-world disturbances. Extensive experiments demonstrate that our proposed PADVG for audio-driven video generation can effectively identify the generated videos and achieve high visual quality.

Occluded pedestrian re-identification based on Multiple Fusion and Semantic feature Mining

The field of person re-identification (re-ID) encounters challenges such as inter-person similarity and occlusion. In this paper, we introduce an occluded pedestrian re-ID method based on Multiple Fusion and Semantic feature Mining (MFSM) to mitigate the adverse effects of occlusion on feature representation. Specifically, we devise a Multi-branch Strategic Enhancement Module (SEM) to bolster the robustness of data augmentation in addressing the issues posed by data memorization more consistently. This module emulates various real-world disturbances by implementing distinct and independent augmentation strategies. Furthermore, we propose the Triplet Cross Unit (TCU) to comprehensively exploit and consolidate both regional and relational visual cues, as well as multi-level features. The TCU facilitates transformers in acquiring an early understanding of translation invariance in images by transferring local patterns such as edges, textures, and colors from the lower CNN layers to the shallower transformer layers. Simultaneously, deeper transformer features offer more abstracted semantic visual representations that complement CNN's high-level semantic features. Lastly, we introduce the Global Squeeze-Excitation Fusion (GSEF) module to address the challenge of global feature segregation across different models in final outputs. The GSEF selectively merges features based on attention mechanisms, prioritizing the comprehensive utilization of valid global features. Extensive experiments demonstrate the state-of-the-art performance of our model in handling pedestrian occlusion, thus validating the efficacy of our method.

Bimetallic iron-nickel phosphide as efficient peroxymonosulfate activator for tetracycline hydrochloride degradation: Performance and mechanism

Sulfate radical-based advanced oxidation processes with (SR-AOPs) are widely employed to degrade organic pollutants due to their high efficiency, cost-effectiveness and safety. In this study, a highly active and stable FeNiP was successfully prepared by reduction and heat treatment. FeNiP exhibited high performance of peroxymonosulfate (PMS) activation for tetracycline hydrochloride (TC) removal. Over a wide pH range, an impressive TC degaradation efficiency 97.86% was achieved within 60 min employing 0.1 g/L FeNiP and 0.2 g/L PMS at room temperature. Both free radicals of SO4·-, ·OH, ·O2− and non-free radicals of 1O2 participated the TC degradation in the FeNiP/PMS system. The PMS activation ability was greatly enhanced by the cycling between Ni and Fe bimetal, and the active site regeneration was achieved due to the existence of the negatively charged Pn−. Moreover, the FeNiP/PMS system exhibited substantial TC degradation levels in both simulated real-world disturbance scenarios and practical water tests. Cycling experiments further affirmed the robust stability of FeNiP catalyst, demonstrating sustained degradation efficiency of approximately 80% even after four cycles. These findings illuminate its promising potential across natural water bodies, presenting an innovative catalyst construction approach for PMS activation in the degradation of antibiotic pollutants.

Energy-Optimal Unmanned Aerial Vehicles Motion Planning and Control Based on Integrated System Physical Dynamics

Abstract Electric vertical-take-off-and-landing multirotor aircraft has been emerging as a revolutionary transportation mode for both manned and unmanned applications, but this technology is limited by flight time and range restrictions. In this work, an energy-efficient model-based trajectory planning and feedback control framework is developed to improve the energy performance of a multirotor unmanned aerial vehicle. Target vehicle trajectories are planned by solving a formulated energy consumption optimization problem based on a system-level model, which accommodates the integrated dynamics of key vehicle subsystems. In order to implement the generated target trajectories, the framework also includes a PID feedback control architecture for real-time trajectory following. The framework is first verified under simulation, and shows an average reduction of 10.7% in energy consumption over a range of typical hover-to-hover operations, compared to the commonly used baseline flight control architecture. Through model-based analysis, key relationships that contribute to the improvements are identified and analyzed. These results demonstrate the importance of considering and coordinating all relevant system dynamics for efficient and holistic trajectory planning and control, which is absent in existing literature. The framework also demonstrates similar performance improvement under experimental validation, with an average energy reduction of 10.2% over the baseline controller despite the presence of significant real-world disturbances including wind effect.

EMC Testing of Electricity Meters Using Real-World and Artificial Current Waveforms

In 2015, the energy measurement of some static electricity meters was found to be sensitive to specific conducted electromagnetic disturbances with very fast current changes caused by highly nonlinear loads, leading to meter errors up to several hundred percent. This article describes new results on the electromagnetic compatibility (EMC) of 16 different meters from all over Europe when exposed to real-world disturbance signals. Those test signals were obtained from household appliances and onsite measurements at metered supply points all over Europe. The results show that also the interference signals recorded onsite can cause measurement errors as large as several hundred percent, even for meters that pass the present EMC standards. This unambiguously demonstrates that the present immunity testing standards do not cover the most disturbing conducted interference occurring in present daily-life situations due to the increased use of nonlinear electronics. Furthermore, to enable the adoption of potential new test waveforms in future standards for electricity meter testing, artificial test waveforms were constructed based on real-world waveforms using a piece-wise linear model. These artificial test waveforms were demonstrated to cause meter errors similar to those caused by the original real-life waveforms they are representing, showing that they are suitable candidates for use in improved standardization of electricity meter testing.

Climate Model State Estimation Using Variants of EnKF Coupled Data Assimilation

Abstract Data assimilation (DA) experiments are performed to assess impacts of observations in climate model state estimation through the cross-domain ocean–atmosphere forecast error covariances (cross covariances). Specifically, we explore strongly and weakly coupled DA variants using the Climate Analysis Forecast Ensemble (CAFE) system. This comprises 96 ensemble members of the Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 climate model assimilating observational data from the ocean, atmosphere, and sea ice realms with the ensemble Kalman filter (EnKF). Sequences of atmospheric synoptic time-scale coupled forecasts (7 days) are carried out with model consistent initialization. Unassimilated forward-independent observations are used to quantify forecast innovation error-growth rates. The results show benefit for the slow components of the atmosphere and ocean subsurface when strongly coupling ocean observations to the atmosphere. In the present system, projecting fast atmospheric observations onto the ocean subsurface through the cross covariances benefits the oceanic and atmospheric near-surface layers; however, this leads to deterioration in the ocean subsurface. Particular variants of coupled DA are able to constrain the ocean and atmosphere. The forecasts initialized with these variants have predictability at intraseasonal time scales. Errors associated with the dominant intraseasonal mode of variability, the Madden–Julian oscillation (MJO), are decomposed into normal mode functions. Consistent with recent studies showing large MJO events are concurrent with rapid error growth associated with nonlinear interactions, we find a clear relationship between the strength of a given MJO event and the related forecast innovations. Our results demonstrate consistent system behavior in relation to capturing real-world disturbances that affect climate predictability.

Resilience of Error Correction Codes Against Harsh Electromagnetic Disturbances: Fault Elimination for Triplication-Based Error Correction Codes

Modern safety-critical systems rely heavily on robust communication channels. Even though these communication channels can be protected by error detection and correction codes, vulnerabilities caused by false negatives still exist. These false negatives can be caused by harsh electromagnetic disruptions and are detrimental to overall safety. This article considers the construction and structure of triplication-based error correction codes to find the most electromagnetic interference (EMI)-resilient code. Each code is tested and simulated in terms of the occurrence rate of false negatives under single-frequency disturbances. It is found that a code with inversion is significantly more robust to these disturbances. Furthermore, a systematic fault injection is also performed to search for vulnerabilities within the code itself. The systematic approach allows us to reverse engineer the expected vulnerabilities to real-world disturbances.

Intelligent optimization in model-predictive control with risk-sensitive filtering

The uncertainty issue in real-work optimization affects the level of optimization significantly. Because most future uncertainties cannot be foreseen in advance, the optimization must take the uncertainties as a risk in an intelligent way in the process of computation algorithm. Based on our risk-sensitive filtering algorithm, this study adopts a model-predictive control to construct a risk-averse, predictable model that can be used to regulate the level of a real-world system. Our model is intelligent in that the predictive model needs not to identify the system parameters in advance, and our algorithm will learn the parameters through data. When the real-world system is under the disturbance of unexpected events, our model can still maintain suitable performance. Our results show that the intelligent model designed in this study can learn the system parameters in a real-world system and minimize unexpected real-world disturbances. Through the learning process, our model is robust, and the optimal performance can still be retained even the system parameters deviate from expected, e.g., material shortage in a supply chain due to earthquake. When parameter error risks occur, the control rules can still drive the overall system with a minimal performance drop.

Constrained multivariable extremum-seeking for online fuel-efficiency optimization of Diesel engines

This work presents a new method for online fuel-efficiency optimization of Diesel engines, using constrained extremum-seeking. A two-input optimization problem, which is suitable for extremum-seeking, is integrated into a tracking control system. As a result, both air-path and fuel-path actuators are used for tracking and extremum-seeking. A key element of the proposed method is a cost function based on real-time BSFC estimation. Moreover, an existing constrained extremum-seeking method is extended to multiple output constraints. Experiments on a Euro-VI heavy-duty Diesel engine demonstrate the constraint handling, robustness with respect to real-world disturbances, and the fuel saving potential of the control design.

Power systems frequency estimation using amplitude tracking square wave for low-end protective relays

Growing emphasis on the disturbance-free electrical power distribution demands for economical and efficient smart estimation techniques in the automation architectures of the future medium voltage (MV)/ low voltage (LV) distribution networks. Unlike high-end protective relays which are typically used in the high voltage transmission systems, low-end economical protective relays are suitable for the MV/LV distribution networks. Frequency estimation is one of the pre-requisites of the electrical protective relays in such systems. The low-end protective relays demand for computationally efficient signal processing algorithms for frequency estimation thus eliminating the need to integrate sophisticated hardware components and advanced software routines. In this paper, time-domain based frequency estimation is proposed, which is focused for the implementation in the low-end relays for MV/LV distribution networks. Frequency estimation is based on the novel amplitude tracking square wave (ATSW), which is fast and sensitive to disturbances in the monitored signal. Performance of the ATSW for frequency estimation is analyzed on the recorded real-world disturbance signals as well as on the various simulated case studies. Simplicity of the ATSW for low-end relays is substantiated by comparative analysis with the existing frequency estimation techniques. In addition to the frequency estimation, versatility of the ATSW is substantiated by application for transient state detection in the AC and DC power systems.

Synthetic GNSS spoofing data generation using field recorded signals

With the increase in GNSS user base, the studies of threats and vulnerabilities of GNSS system are also increased. Among the threats, spoofing is of particular interest because of the risk associated with it. The studies on spoofing are generally limited to simulated scenarios as the real world spoofing attack is very difficult to create or spot and record for analysis. This paper presents a method of generating baseband spoofing data using real world signals by simultaneous recordings of GNSS signals using two separate receivers, where one of them simulates the receiver under attack and the other simulates the response the spoofer will be going to produce to fabricate the attack. After taking the records and merging them to create the spoofing baseband signals, it is checked against several spoofing detection methods to verify the valid spoofing attack being present in the signal. This method produces the signal recordings that have real world disturbances in it that may be difficult to simulate.The developed method has the following advantages:•It does not require very expensive hardware to produce an intermediate spoofing signal.•The user has control over the spoofing power advantage.•The same scenario can be reproduced with varying parameters.

Robust fault detection filter using linear matrix inequalities’ approach for shaft crack diagnosis

Detecting cracks in rotating shafts is a challenging problem when using vibration-based diagnostics. This is due to the fact that a localized crack has a minimal influence on the global vibration response of the system. To increase sensitivity and reliability, the vibration response needs to be coupled with additional sources of information such as a mathematical model of the machine. Modern control theory techniques offer system-level mathematical models for both control and diagnostics. Focusing on the latter, a new and promising approach involves the use of unknown input observers. Such observers can be designed to employ robust fault detection filters (RFDFs) for isolating fault signatures while reducing the influence of real-world disturbances and noise. For the present study, a modified design procedure coupled with robust fault detection is utilized for shaft crack detection. The filter is designed using the linear matrix inequalities (LMI) technique. The LMI approach is applied to obtain the solution of the mixed H−/H∞ optimization problem, which arises during the synthesis of the RFDF. By reformulating the LMI conditions, the proposed RFDF design procedure is simplified and thus requires less iteration steps to find the optimal solution. A new feature of the present approach involves the application of the rigid finite element method for the formulation of the mathematical model of the rotor and the shaft crack. The numerical and experimental results confirm the advantages of the designed robust fault detection filter and its ability to detect shaft cracks. The filter is minimally sensitive to measurement noise while allowing for the identification of shallow cracks (2% or 5% deep). The cracks are manifested through the observance of very subtle vibration response changes. The results also confirm the effectiveness and accuracy of the rigid finite element modeling concerning the cracked rotor.

The Robust Template Matching by Statistical Reach Feature Method

A novel method for image template matching with the robustness for various real-world disturbances is proposed. The method is based on statistical reach feature method. The method utilizes the set of signs of differences for selected pairs of points from the template image. The selection is performed from the statistical view-point. The concrete description of the method is presented plainly. The selection of thresholds in the method is argued. Experiments with various disturbances including illumination change, occlusion, and JPEG noise are shown. The analyses based on the noise models are performed to show theoretical background of the robustness of the method.

Development of an optimal crown/shape level-2 control model for rolling mills with multiple control devices

Three development stages of an online crown/shape control model are discussed. Each stage bears specific consideration and provides variable information for mill operations. Theoretical derivation of the first stage facilitates understanding of mill rolling behavior. Usage of the crown/shape model leads to the development of the linear crown control system. The second-stage development is to generate an offline simulation model which bridges the gap between theory and application. It is used to verify the theoretical model, to determine control gain factors, and even to examine stability of the control algorithm. The real-time process model is last when conducting mill setup calculations. It has to cope with uncertainties of measured devices, possible errors of the theoretical model, and real-world disturbance of mill operating conditions. Providing a stable, optimal, and accurate setup for the mill is its major responsibility. Statistical methods are used everywhere in these models from tuning to optimizing processes. This optimal linear crown/shape control system has been successfully applied to a production hot strip mill since 1992. The article describes development and application of this rolling model.

Disturbance and Tree Life History on the Shifting Mosaic Landscape

An analytical model of disturbance and plant population dynamics is developed to explore the optimal life history for a plant within a "shifting mosaic" meta—population. The population dynamics consist of short—lived recruitment events followed by longer intervals of thinning. Plants balance costs and benefits of delayed maturation time that result from cohort thinning, a correlation between maturation time and longevity, and the distribution of recruitment events in space and time. Two fundamentally different responses to disturbance are explored: (1) the plant is killed by the disturbance that allows for new recruitment (type A response), and (2) the plant may survive many such disturbances (type B response). Species maximize either the probability of being reproductively mature at the time of the next recruitment opportunity (type A) or the total number of recruitment opportunities to occur during the period of reproductive maturity (type B). Predictions of the theory are compared with the actual life histories of trees that occur in different disturbance regimes. The costs and benefits associated with delayed maturation from an energy standpoint must be weighed against the probability that a recruitment opportunity (disturbance) will occur at a particular age. Trees subject to low thinning rates should reach reproductive maturity t1 at t1 ° 0.4 x (expected disturbance interval in years). At high thinning rates, this optimum is t1 ° 0.4/(mortality rate per year). Disturbance probabilities that increase with time since the last disturbance select for maturation times that are greater than these values. Species that are not killed by disturbances have optimal maturation times that are independent of disturbance frequency. However, when such species are susceptible as juveniles, optimal maturation time does depend on disturbance frequency. This optimum maturation time is still greater than it is for the case of a mortality response (type A) to disturbance, but less than the case of no susceptibility period (type B). Application of the theory to real—world disturbance regimes results in predictions that closely match the life histories of species that actually occur there. The optimal maturation time for a gap species in temperate North American forests is 30—60 yr, a value that agrees with observed maturation time. A second test involved fire regimes where two species having very different responses to fire and life histories co—occur, Pinus resinosa and P. banksiana. The maturation times of these species both match the predicted optima for a species that survives fire (P. resinosa) vs. one that is killed by fire (P. banksiana) subjected to identical fire regimes. Different modes of dispersal are predicted to have important effects on reproductive potential, but little effect on the optimal maturation time. Application of the models to these actual cases is consistent with that prediction. The "intermediate" disturbance is predicted to be that which implies the optimum life history that coincides with the life histories of the greatest number of species.