Simulation Experiments Research Articles

Novel Logarithmic Sliding Mode Control of Chaos in Seventh‐Order Power Systems

ABSTRACTIn seventh‐order power systems, chaotic oscillations can significantly compromise system stability, arising from intricate interactions among system parameters and initial conditions. This study introduces a robust chaos control strategy tailored for such high‐dimensional systems. The approach begins by integrating a dynamic model of an energy storage device, specifically designed to absorb surplus active power, thereby constructing a controlled ninth‐order power system with coupled dynamics. To further enhance stability and rapid response, we propose a novel logarithmic sliding mode control (LSMC) surface. This innovation incorporates natural logarithms to achieve a high‐gain effect at the equilibrium points of the reduced‐order sliding mode system, facilitating rapid re‐stabilization of the power system. Additionally, we employ the super‐twisting algorithm to address the inherent chattering issues associated with traditional sliding mode control. Theoretical analyses and simulation experiments validate the effectiveness of our proposed method, demonstrating its capability to swiftly restore system stability.

Potential risk of heavy metals release in sediments and soils of the Yellow River Basin (Henan section): A perspective on bioavailability and bioaccessibility.

The ecology of watersheds plays an important role in regulating regional climate and human activities. The sediment-soil system in the middle and lower reaches of the Yellow River Basin (Henan section) was explored. The spatial distribution characteristics of heavy metals (HMs) showed that tributaries, which are affected by anthropogenic activities, contain higher concentrations of HMs than the main channel. Sequential extraction experiments indicated that Cd had the strongest potential to be released, followed by Mn. In vitro simulation experiments showed that gastric and pulmonary fluids rendered these two orders of magnitude more bioaccessible compared to sweat. Moreover, Cd exhibited the highest bioaccessibility in both gastric and lung fluids. When bioaccessibility was considered in the evaluation of health risks, more than 82 % of reductions in non-carcinogenic and carcinogenic risk indices were observed in children and adults. A positive matrix factorization model was utilized to determine the potential sources of HMs: industrial sources, natural sources, and mixed agricultural and transportation sources were identified as the three main sources of HMs in sediments and soils. In addition, mining activities were also an HMs source in sediments.

Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms

Abstract. Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. Retrieving consistent high-resolution ADT and SST information from space is challenging, due to instrument limitations, sampling constraints, and degradations introduced by the interpolation algorithms used to obtain gap-free (L4) analyses. To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. Building upon recent findings, we conduct an Observing System Simulation Experiment (OSSE) relying on Copernicus numerical model outputs (with respective temporal and spatial resolutions of 1 d and 1/24°), and we present a strategy for further refinements. Previous OSSEs combined low-resolution L4 satellite equivalent ADTs with high-resolution “perfectly known” SSTs to derive high-resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high-resolution SST and ADT from synthetic, satellite equivalent L4 products. This modification allows us to evaluate the potential enhancement in the ADT and SST mapping while integrating dynamical constraints through tailored, physics-informed loss functions. The neural networks are thus trained using OSSE data and subsequently applied to the Copernicus Marine Service satellite-derived ADTs and SSTs, allowing us to reconstruct super-resolved ADTs and geostrophic currents at the same spatiotemporal resolution of the model outputs employed for the OSSE. A 12-year-long time series of super-resolved geostrophic currents (2008–2019) is thus presented and validated against in situ-measured currents from drogued drifting buoys and via spectral analyses. This study suggests that CNNs are beneficial for improving standard altimetry mapping: they generally sharpen the ADT gradients, with consequent correction of the surface currents direction and intensities with respect to the altimeter-derived products. Our investigation is focused on the Mediterranean Sea, quite a challenging region due to its small Rossby deformation radius (around 10 km).

Dynamic event-triggered observer design for quadcopters with uncertainties

This paper investigates the problem of estimating all 12 states and lumped uncertainties for quadcopters with unknown disturbances. A novel dynamic event-triggered mechanism is proposed for the construction of extended state observers regarding quadcopters with nonlinear control gain matrix. To attenuate the undesired uncertainty oscillation in the conventional extended state observer, an improved estimation method is developed to acquire higher estimation accuracy. Apart from validating the uniform ultimate boundedness of estimation errors, the proposed scheme is also free of Zeno behaviour. Comparative experiments in simulation demonstrate the effectiveness of the proposed observer, which shows outstanding performance in state estimation and communication saving. Hardware experiments for the quadcopter are also conducted to prove the practicability of the proposed dynamic event-triggered observer.

Secure Control of Networked Control Systems Under Replay Attacks: A Switching‐Like Approach

ABSTRACTThis paper focuses on the secure control design of networked control systems (NCSs) under replay attacks. Mitigating replay attacks is a challenging task since the statistical similarity between replayed signals and real observations makes them difficult to distinguish. Given the persistence of potential attackers and the probability of individual attack failures, a novel switching‐based replay attack model is introduced. By employing this replay attack model, NCSs vulnerable to replay attacks can be transformed into a class of switching‐like systems. Within the framework of the average dwell time switching strategy and multiple Lyapunov stability theory, sufficient conditions for the mean‐square exponential stability of the underlying system are presented. Compared with the existing control schemes using the delay‐based replay attack model, the proposed secure control method effectively reduces the impact of replay attacks on the control performance. The effectiveness of the proposed approach is validated through simulation experiments carried out on two practical systems.

Large-Area Coverage Path Planning Method Based on Vehicle–UAV Collaboration

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle–UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle–UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency.

Transformer Architecture for Micromotion Target Detection Based on Multi-Scale Subaperture Coherent Integration

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network.

Finite-time path following control for UUV with unknown model constraints

ABSTRACT This paper focuses on the path-following problem of an underactuated unmanned underwater vehicle (UUV) affected by external time-varying disturbances and unknown model constraints. A control strategy integrating the finite-time line-of-sight method (FTLOS), radial basis function (RBF) neural network and switching nonsingular terminal sliding mode (SNTSMC) is proposed. Initially, the FTLOS formulates the path following guidance law, making position errors converge to a small area near zero in finite time. The SNTSMC stabilizes velocity errors, ensuring they converge to zero vicinity more precisely. Next, the RBF neural network approximates the combined unknown term, which consists of unknown model constraints and external time-varying disturbances. The approximation term is then compensated into the control force and torque. Finally, a simulation experiment is conducted on the NUTU UUV. The results verify that the UUV can follow the desired path within a finite time and stay on it.

Digital Hydraulic Transformer Concepts for Energy-Efficient Motion Control

Hydraulic linear drive systems with conventional proportional valves result in poor energy efficiency due to resistance control. In systems with multiple actuators connected to one common pressure supply, a load-sensing strategy is often used to reduce these throttling losses. However, like conventional cylinder actuators, common load-sensing systems are also not able to recuperate the energy, which is actually released when a dead load is lowered. In order to overcome these drawbacks, in this paper, new concepts of a digital hydraulic smart actuator and a load-sensitive pressure supply unit are presented, which are qualified to reduce throttling losses and, furthermore, to harvest energy from the load. According to previous research, the basic concepts used in this contribution promise energy savings in the range of 30% for certain applications, which is one of the main motivations for this study. The operating principles are based on a parallel arrangement of multiple hydraulic switching converters, representing so-called digital hydraulic transformers. Furthermore, the storage module of the presented load-sensitive pressure supply unit is able to boost the hydraulic power in the common pressure rail beyond the maximum power of the primary motor. For exemplary operating cycles of the smart actuator and the pressure supply unit, a significant reduction in the energy consumption could be shown by simulation experiments, which offers a new perspective for energy-efficient motion control.

Calibration and verification of DEM parameters for asphalt mixture based on experimental design method and bulk experiments

In order to obtain the parameters in the discrete element simulation of asphalt mixture, a calibration of parameters was conducted based on pile-up experiments and the Johnson–Kendall–Roberts (JKR) model. By combining physical experiments with simulation tests, three parameters that significantly affect the angle of repose (AOR) were selected from eight parameters using the Plackett–Burman (PB) experiment, namely the particle-particle rolling friction coefficient, JKR surface energy, and particle-particle static friction coefficient. The steepest ascent experiment was then used to determine the optimal range of the three significant parameters. Finally, a second-order regression model of the AOR and significant parameters was established through Box–Behnken design (BBD) experiments based on the principle of response surface methodology (RSM), with a model P-value of <0.0001. Using the experimentally measured real AOR value of 37.05° as the target value, the significant parameters were optimized, resulting in the optimal parameter combination solution: particle-particle rolling friction coefficient of 0.103, JKR surface energy of 6.55 J/m2, and particle-particle static friction coefficient of 0.495. The optimal parameter combination was tested in three simulation experiments, yielding a mean AOR of 37.16°, with a relative error of 0.44% compared to the physical experiment AOR, thus verifying the reliability of the optimal parameter combination.

Design Method of a Cylindrical Skiving Tool for Internal Gear with Circular Arcs

Gear skiving is a highly productive method for manufacturing gears, especially internal gears. Circular arc internal gears are important parts of Rotary Vector (RV) reducers and harmonic reducers. This study presents the implementation of the gear skiving technique using a cylindrical tool to enhance the precision and efficiency of machining circular arc internal gears. By establishing the mathematical model for skiving a circular arc internal gear based on the conjugation theory of two surfaces, the barrel-shaped conjugate surface was solved by deducing gear meshing equations. A design method is proposed for a cylindrical skiving tool by utilizing the barrel-shaped conjugate surface with an off-center tool position along the axis. The cutting edge of the tool rake face was then obtained through cubic spline interpolation from the conjugate surface. The influence of the tool rake face offsets on the cutting rake angle and clearance angle is also discussed by defining the normal cutting plane of the tool. The correctness of the proposed cylindrical skiving tool was validated through simulation and actual skiving experiments. The experimental results demonstrated that the tooth profile error of the gear fell within ±0.004 mm, thereby satisfying the accuracy requirement for pin wheel housing gears. These research findings can contribute to advancements in novel cylindrical skiving tools.

Collaborative Sensing-Aware Task Offloading and Resource Allocation for Integrated Sensing-Communication- and Computation-Enabled Internet of Vehicles (IoV)

Integrated Sensing, Communication, and Computation (ISCC) has become a key technology driving the development of the Internet of Vehicles (IoV) by enabling real-time environmental sensing, low-latency communication, and collaborative computing. However, the increasing sensing data within the IoV leads to demands of fast data transmission in the context of limited communication resources. To address this issue, we propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism for ISCC to reduce the sensing tasks transmission delay. We formulate a joint task offloading and communication resource allocation optimization problem to minimize the total processing delay of all vehicular sensing tasks. To solve this mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes the original optimization problem into a task offloading subproblem and a resource allocation subproblem, which are solved iteratively. In the first stage, a Deep Reinforcement Learning algorithm is used to determine task offloading decisions based on the initial setting. In the second stage, a convex optimization algorithm is employed to allocate communication bandwidth according to the current task offloading decisions. We conduct simulation experiments by varying different crucial parameters, and the results demonstrate the superiority of our scheme over other benchmark schemes.

Research on automated optimization of low-carbon architectural landscape spaces based on computer vision and machine learning

Abstract In this study, computer vision and machine learning techniques are used to develop an automatic optimization method for low-carbon building landscape space. Firstly, the semantic segmentation of landscape images is carried out using U-Net network to realize the automatic extraction of key landscape features. Then, using the segmentation results, a multi-objective optimization algorithm is developed. The effectiveness of the proposed method is verified by simulation experiments, which not only significantly improves the efficiency and accuracy of landscape space optimization, but also provides valuable optimization suggestions for designers.

Reliable crash analysis: Comparing biases and error rates of empirical Bayes before-after analyses to mixed-models.

Estimating reliable causal estimates of road safety interventions is challenging, with a number of these challenges addressable through analysis choices. At a minimum, developing reliable crash modification factors (CMFs) needs to address three critical confounding factors, i.e., 1) the regression-to-the-mean (RTM) phenomenon, 2) the effect of traffic volume, and 3) the time trend for the occurrence of crashes. The current preferred crash analysis method is the empirical Bayes (EB) before-after analysis but requires complex bespoke analysis and may not be the best performing method. We compare in a simulation experiment various EB methods to a more straightforward negative binomial generalized linear mixed model (NB-GLMM) with an interaction term between treatment group and time for analysing treatment effects in crash data. Data were simulated using two broad scenarios: 1) an idealized randomized controlled design, and 2) a moderately biased site-selection scenario commonly encountered in road safety crash analyses. Both scenarios varied treatment effects, overdispersion, and sample sizes. The NB-GLMM performed best, maintaining type I error rate and providing least biased estimates across most analyses. Most standard EB methods were too liberal or generally more biased, with the exception of the EB method that incorporated a varying dispersion parameter. Incorporating mixed-effects modelling into the EB procedure improved bias. Overall, we found that using a "standard" NB-GLMM with an interaction term is sufficient for crash analysis, reducing complexity compared to bespoke EB solutions. Chosen methods should also be the least biased and possess the marginal error rates under both ideal and selection-bias conditions. Mixed-effects approaches to analysis of road safety interventions satisfy these criteria outperforming standard or other empirical Bayes approaches tested here.

Impact of frequent ARID1A mutations on protein stability provides insights into cancer pathogenesis

The ARID1A gene, frequently mutated in cancer, encodes the AT-rich interactive domain-containing protein 1 A, a key component of the chromatin remodeling SWI/SNF complex. The ARID1A protein features a conserved DNA-binding domain (ARID domain) of approximately 100 residues crucial for its function. Despite the frequency of mutations, the impact on ARID1A’s stability and contribution to cancer progression remains unclear. We analyzed five frequent missense mutations R1020S, M1022K, K1047Q, G1063V, and A1089T identified in The Cancer Genome Atlas (TCGA) to assess their effects on the stability of the ARID domain using a hybrid experimental and computational approach. By combining computational stability from web server tools, the structural dynamics from replica exchange discrete molecular simulation (rexDMD), and thermal and chemical denaturation experiments, we found that the R1020S mutation severely decreases structural stability, making it the most impactful, while M1022K has minimal effect, and others lie in between. These findings enhance our understanding of the structural-functional relationship of ARID1A missense mutations at the molecular levels and their role in cancer pathogenesis. This research paves the way for identifying and categorizing which ARID1A mutations are most pathogenic, potentially guiding the development of targeted therapies tailored to specific mutation profiles in cancer treatment.

Estimation in partially linear additive errors-in-variables model with stochastic linear restrictions

The partially linear additive model is a flexible and powerful model for evaluating the relationship between multivariate covariates and the response variable. The current paper considers the estimation problem of the model with additive measurement errors, along with some additional stochastic linear restrictions on the regression coefficients. We introduce a weighted corrected profile mixed estimator of the parametric component based on the corrected profile least squares approach and weighted mixed regression method, and further establish the asymptotic normality of the proposed estimator. A Monte Carlo simulation experiment is conducted to illustrate theoretical results, and the findings from the simulation are satisfactory. Finally, we also apply the proposed procedure to analyze the Boston housing market data.

Analysis on dynamic characteristics and control strategy of medium structure network converter in new energy medium voltage flexible interconnection project

Abstract In view of the possible voltage and current overshoot problems in the process of new energy grid connection, the dynamic characteristics analysis and control strategy of the network converter in the new medium voltage flexible interconnection project are proposed. Firstly, a synchronous control strategy based on voltage phase and amplitude compensation is proposed. Then, based on the characteristics of new AC-DC hybrid distribution network, the new energy medium voltage flexible interconnection network is analyzed. The simulation experiment reveals the dynamic response characteristics of the new energy grid converter under frequency interference and load change.

Corrective Lateral Transshipment Application in a Centralized Inventory System With Random Demand

Our research work aims to study the lateral transshipment problem as a mode of cooperation between different retailers who are located at the same level. In the first work, we apply a simulation-optimization approach based on a metamodel to search for the different measures of the initial level of replenishment. Then, by applying a series of simulation experiments by the ARENA software to select the best lateral transshipment policy. This aims to maximize the expected average global gain and minimize the average global Disservice rate. For this, several Transshipment policies will be tested, for example; non-pooling, full pooling and partial pooling policies depending on the selection of physical inventory thresholds.

Greenwood Statistic Under Distortion Measurement Errors

ABSTRACTIn this paper, we address the crucial necessity of understanding the behavior of the Greenwood statistic when dealing with data corrupted by multiplicative measurement errors. The unobservable variable is subject to a multiplicative distortion influenced by an unknown smoothing function dependent on a confounding variable. We introduce the conditional mean calibrated Greenwood statistic and its variants, including the coefficient of variation, to adapt to this complex situation. The asymptotic results we present showcase their effectiveness even when such distortions are present. Moreover, for general distributions of the unobserved variable, we extend the methodology by proposing calibrated‐modified Greenwood statistics and the coefficient of variation. These methods are demonstrated to be asymptotically efficient under various assumptions about the distortion function. Furthermore, we conduct Monte Carlo simulation experiments to assess the performance of the proposed estimators and test statistics, during which we compare our proposed test statistics with existing ones. For illustrative purposes, these methods are then applied to analyze a real‐world dataset.MSC2020 Classification: 62G05, 62G08, 62G20

Legumes reduce the effects of salt stress on co-existing grass.

Nitrogen (N) fixing legumes typically enhance the ability of coexisting non-N-fixing species to resist disease and drought, but whether legumes enhance their ability to resist salt stress remains unknown, restricting our ability to explore the potential of legumes to rehabilitate salt-affected ecosystems. We conducted a simulation experiment to examine whether and how legumes influence the response of coexisting grass to salt stress. We compared the effects of salt stress on the plant biomass, root cell viability, antioxidant enzyme activities, soil extracellular enzyme activities and microbial functional gene abundances associated with N and phosphorus (P) cycling between pure grass communities and legume-grass mixtures. We found that salt stress decreased grass biomass and the abundance of most N and P cycling genes in rhizosphere soils. However, these negative effects were smaller in legume-grass mixtures than in pure grass community. Additionally, salt stress increased the activities of soil N and P cycling enzymes, with greater positive effects observed in legume-grass mixtures than in the pure grass community. The structural equation modelling results showed that the most direct and indirect path coefficients of salt stress effects on biomass were smaller in legume-grass mixtures than in the pure grass community. Our findings provide direct evidence that legumes can reduce the negative impact of salt stress on co-existing grass community, highlighting the potential of including legumes with high N fixation abilities to restore salt-affected ecosystems.