Time-varying Environment Research Articles

A spatial-temporal approach for corrosion prediction in time-varying marine environment

Corrosion in seawater is simultaneously influenced by multiple environmental factors including dissolved oxygen (DO), temperature, salinity, pH, and so on. These factors vary along with time and are different in different locations. The spatial-temporal variation of the actual marine environment cannot be ignored in corrosion prediction models. This paper proposes a new method for corrosion prediction in the actual time-varying marine environment which includes the design of experiments, calibration of acceleration models, and the modeling of marine environment. Acceleration models capture the effects of environmental factors and acts as the link between the environment and the corrosion process. The marine environment is described with the Kriging spatial-temporal model. Then the proposed method is used to give corrosion predictions for metals in different locations and vessels travel in different waters. This method could be helpful for corrosion resistance evaluation and environment corrosivity assessment.

Organizing principles for vegetation dynamics.

Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.

Adaptive sliding mode control for disturbed multirobot systems performing target tracking under continuously time-varying topologies

This article studies coordination control of the multirobot system for target tracking problem with disturbances in time-varying communication environment. An adaptive sliding mode control is designed to complete the target tracking task with the effect of disturbances and continuously time-varying topologies. This means that the communication between the robots is not switching among constant topologies but changes continuously along with time. The continuously time-varying topology is constructed by a set of fixed Laplacian matrices and variable scheduling functions, which is known as polytopic model structure. The designed adaptive sliding mode control can effectively lower the influence of disturbance and improve tracking performance even under the continuously time-varying topology. Furthermore, numerical simulations are given to demonstrate the effectiveness of the obtained results.

Positioning of leakages in underwater natural gas pipelines for time-varying multipath environment

Underwater natural gas pipelines (UNGPs) used to typically transport natural gas, which is a very important energy resource, can be punctured due to various reasons such as corrosion and shipping/fishing activities. Therefore, the detection and positioning of leakages has an important role in ensuring energy reliability and preventing environmental pollution. In this study, kth nearest neighbor (KNN) algorithm based fingerprint method is proposed to locate leakages in the UNGPs. The success of this method is analyzed using a realistic time-varying multipath underwater acoustic channel and the parameters of a real life UNGP. Numerical results show that for this channel, leakages can be positioned with a margin of low errors from kilometers away depending on the ambient noise, number of receivers, and sea wave height.

A stress function based model for transient thermal stresses of composite laminates in various time-variant thermal environments

We present a study of transient thermal stresses of composite laminate in this work by means of stress function based approach considering various time-variant thermal environments. Two-dimensional temperature distribution is considered for both linear and quadratic temperature distributions. For each type of distribution, we consider three different cases to examine the effect off thermal transfer point on the distribution of thermal stresses. The proposed methodology uses the principle of complementary virtual work to develop the governing equations and consider the thermomechanical coupling in its constitutive equation. The Lekhnitskii stress functions are adopted as stress fields and the mode shape functions of clamped beam are adopted as stress trial functions. By means of the Rayleigh–Ritz method, we obtain a standard eigenvalue problem and further solve the eigen problem to obtain the solutions. We find that different temperature distributions affect the inhomogeneous equations, but they don't affect the homogeneous equations in the present model. The results will demonstrate the distributions of transient thermal stresses in angle-ply and cross-ply laminates under linear and quadratic temperature distributions. The present methodology is computationally efficient and accurate in calculating the transient thermal stresses, and it can be used in thermal stress analysis of laminated structures.

Adaptive sliding mode output feedback control for dynamic positioning ships with input saturation

In this paper, a finite-time observer-based adaptive sliding mode output feedback controller is developed for dynamic positioning ship while the Coriolis matrix, hydrodynamic nonlinear damping matrix, input saturation, unmeasured states, unknown time-varying environment disturbances and transient performance are considered simultaneously. A continue finite-time state observer is proposed to estimate the unmeasured states of dynamic positioning ship in finite time and an auxiliary system is constructed to handle with the control saturation. A terminal sliding mode surface with the signal generated by the auxiliary system is designed while the good transient performance is achieved under multiple constraints and the nominal output feedback controller proposed in this paper is chatter-free guaranteeing the steady performance. In additional, an adaptive update law is introduced that the prior knowledge of disturbance is no need and the finite-time convergence of loop-system is proved strictly. Finally, simulations are given to illustrate the effectiveness and advantages of the proposed control scheme.

Recent progress of morphable 3D mesostructures in advanced materials

Soft robots complement the existing efforts of miniaturizing conventional, rigid robots, and have the potential to revolutionize areas such as military equipment and biomedical devices. This type of system can accomplish tasks in complex and time-varying environments through geometric reconfiguration induced by diverse external stimuli, such as heat, solvent, light, electric field, magnetic field, and mechanical field. Approaches to achieve reconfigurable mesostructures are essential to the design and fabrication of soft robots. Existing studies mainly focus on four key aspects: reconfiguration mechanisms, fabrication schemes, deformation control principles, and practical applications. This review presents a detailed survey of methodologies for morphable mesostructures triggered by a wide range of stimuli, with a number of impressive examples, demonstrating high degrees of deformation complexities and varied multi-functionalities. The latest progress based on the development of new materials and unique design concepts is highlighted. An outlook on the remaining challenges and open opportunities is provided.

An overview on evolving systems and learning from stream data

Evolving systems unfolds from the interaction and cooperation between systems with adaptive structures, and recursive methods of machine learning. They construct models and derive decision patterns from stream data produced by dynamically changing environments. Different components that assemble the system structure can be chosen, being rules, trees, neurons, and nodes of graphs amongst the most prominent. Evolving systems relate mainly with time-varying environments, and processing of nonstationary data using computationally efficient recursive algorithms. They are particularly appropriate for online, real-time applications, and dynamically changing situations or operating conditions. This paper gives an overview of evolving systems with focus on system components, learning algorithms, and application examples. The purpose is to introduce the main ideas and some state-of-the-art methods of the area as well as to guide the reader to the essential literature, main methodological frameworks, and their foundations.

Dynamic loss of HTS field windings in rotating electric machines

High-temperature superconducting (HTS) coated conductors (CCs) are frequently applied under complex electromagnetic fields to develop powerful, compact and efficient rotating electric machines. In such electric machines, field windings constructed by HTS CCs are adopted to increase the magnetic loading of the machines. The HTS field windings work with DC currents and due to the time-varying magnetic field environment, dynamic losses occur. In addition to the AC magnetic field, there is a large DC background field, which is caused by the self-field of the HTS field windings. This paper investigates the dynamic loss in HTS CCs using an H-formulation based numerical model for a wide range of combined DC and AC magnetic fields under various load conditions, and two different methods have been used for calculating dynamic loss. The results show that a DC background field plays a vital role to accurately predict the dynamic losses in HTS CCs. A DC background field of 75 mT can triple the dynamic loss as compared to only applying an AC magnetic field. In addition, the theoretical definition for the dynamic region for the case of solely an AC field has been found inapplicable in the case of a DC background field. Finally, a case study is done based on our double claw pole power generator to estimate the dynamic loss in an actual rotating machine, which was found to be 13.3 W. A low dynamic loss was achieved through the generator field winding design, which prevents high magnetic field fluctuations in the winding, since it is located at a distance from the air gap and armature coils. Furthermore, the rotational speed is very low and hence the resultant magnetic field frequency is low as well.

Robust Control of Underwater Vehicle-Manipulator System Using Grey Wolf Optimizer-Based Nonlinear Disturbance Observer and H-Infinity Controller

This paper proposes a new trajectory tracking scheme for the constrained nonlinear underwater vehicle-manipulator system (UVMS). For overcoming the unmodeled uncertainties, external disturbances, and constraints of control inputs in the operation of UVMS, a modified constrained H∞ controller with a basic computed-torque controller (CTC) and a new designed nonlinear disturbance observer (NDO) are proposed. The CTC gives the nominal model-based control. The NDO is designed based on the system dynamics and used to online provide the estimation of the lumped disturbances. However, the designed NDO is an observer of biased estimation, i.e., it has a blind domain of disturbance estimation which cannot be rejected. In order to reject the biased estimation, the modified constrained H∞ controller is designed but with new features. To the best of our knowledge, the conventional H∞ robust controller is generally designed by calculating the Riccati equation offline and ignoring the constraints of control inputs made by the physical actuators, which are poor in handling the time-varying environment. In order to solve these issues, the modified constrained H∞ robust controller online optimized by grey wolf optimizer (GWO) is designed to ensure the control system has a compensation of the biased estimation, a satisfied constrained control input, and a fast calculation. In this paper, we modify the prior method of offline calculating the Riccati equation of the conventional H∞ robust controller to be an online optimization scheme and proposed a new constrained evaluation function. The new constrained evaluation function is online optimized by the GWO, which can both find out the constrained suboptimal control actions and compensate the biased estimation of the NDO for the UVMS. The whole system stability is proved. The effectiveness of the fast online calculation, tracking accuracy, and lumped disturbances rejection is shown by a series of UVMS simulations.

Robust Motion Planning in Dynamic Environments Based on Sampled-Data Hamilton–Jacobi Reachability

SUMMARYWe propose a novel formal approach to robust motion planning (MP) in dynamic environments based on reachability analysis. While traditional MP methods usually fail to provide formal robust safety and performance guarantees, our approach provably ensures safe task achievement in time-varying and adversarial environments under parametric uncertainty. We leverage recent results on Hamilton–Jacobi (HJ) reachability and differential games in order to compute offline guaranteed motion plans that are compatible with the sampled-data (SD) paradigm. Also, we synthesize online provably robust safety-preserving and target-reaching feedback controls. Unlike earlier applications of reachability analysis to MP, our methodology handles arbitrary time-varying constraints, adversarial agents such as pursuing obstacles or evading targets, and takes into account the robot’s configuration. Furthermore, we use HJ projections in order to reduce significantly the computational burden without trading off safety guarantees. The validity of this approach is demonstrated through the case study of a robot arm subject to measurement errors, which is tasked with safely reaching a goal in a known time-varying workspace while avoiding capture by an unpredictable pursuer. Finally, the performance of the approach and research perspectives are discussed.

Population switching under a time-varying environment.

We study the switching dynamics of a stochastic population subjected to a deterministically time-varying environment. Our approach is demonstrated on a problem of population establishment, which is important in ecology. At the deterministic level, the model we study gives rise to a critical population size beyond which the system experiences establishment. Notably the latter has been shown to be strongly influenced by the interplay between demographic and environmental variations. Here we consider two prototypical examples of a time-varying environment: a temporary change in the environment, and a periodically varying environment. By employing a semiclassical approximation we compute, within exponential accuracy, the change in the establishment probability and mean establishment time of the population, due to the environmental variability. Our analytical results are verified by using a modified Gillespie algorithm which accounts for explicitly time-dependent reaction rates. Importantly, our theoretical approach can also be useful in studying switching dynamics in gene regulatory networks under external variations.

Stellar escapers from M67 can reach solar-like Galactic orbits

ABSTRACT We investigate the possibility that the Sun could have been born in M67 by carrying out N-body simulations of M67-like clusters in a time-varying Galactic environment, and following the Galactic orbits of stars that escape from them. We find that model clusters that occupy similar orbits to M67 today can be divided up into three groups, which we call hot, depleted, and scattered clusters. Hot clusters are born with a high initial $z$-velocity; depleted clusters are born on dynamically cold orbits but are destroyed by GMC encounters in the Galactic disc; and scattered clusters are born on dynamically cold orbits and survive to have more than 1000 stars at an age of 4.6 Gyr. We find that all cluster models in all three cluster groups have stellar escapers that are kinematically similar to the Sun. Hot clusters have the lowest fraction of escapers with solar-like kinematics, f⊙ = 0.06 per cent, whilst depleted clusters have the highest fraction, f⊙ = 6.61 per cent. We calculate that clusters that are destroyed in the Galactic disc have a specific frequency of escapers that end up on solar-like orbits that is ∼ 2 times that of escapers from clusters that survive their journey.

Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading

In this work, we propose a new deep imitation learning (DIL)-driven edge-cloud computation offloading framework for MEC networks. A key objective for the framework is to minimize the offloading cost in time-varying network environments through optimal behavioral cloning. Specifically, we first introduce our computation offloading model for MEC in detail. Then we make fine-grained offloading decisions for a mobile device, and the problem is formulated as a multi-label classification problem, with local execution cost and remote network resource usage consideration. To minimize the offloading cost, we train our decision making engine by leveraging the deep imitation learning method, and further evaluate its performance through an extensive numerical study. Simulation results show that our proposal outperforms other benchmark policies in offloading accuracy and offloading cost reduction. At last, we discuss the directions and advantages of applying deep learning methods to multiple MEC research areas, including edge data analytics, dynamic resource allocation, security, and privacy, respectively.

Adaptive Trust Management for Soft Authentication and Progressive Authorization Relying on Physical Layer Attributes

Conventional authentication mechanisms routinely used for validating communication devices are facing significant challenges. This is mainly due to their reliance on both ‘spoofable’ digital credentials and static binary characteristic, and inevitable misdetection in physical layer authentication using time-varying attributes, leading to the cascading risks of security and trust. To circumvent these impediments, we develop an adaptive trust management based soft authentication and progressive authorization scheme by intelligently exploiting the time-varying communication link-related attribute of the transmitter to improve wireless security. First of all, the trust relationship between the transmitter and receiver is established based on the evaluation of selected physical layer attribute for fast authentication and multiple-level authorization. Through the designed trust model, the transmitter is authorized by the specific level of services/resources corresponding to its trust level, so that soft security is achieved. To dynamically update the trust level of the transmitter, we propose an online conformal prediction-based adaptive trust adjustment algorithm relying on the real-time validation of its attribute estimates at the receiver, thus resulting in progressive authorization. The performance of our scheme is theoretically analyzed in terms of its individual risk and individual satisfaction. Our simulation results demonstrate that the proposed scheme significantly improves the security performance and robustness in time-varying environments, and performs better than the static binary authentication scheme and existing physical layer authentication benchmarker.

Oil price shocks and EMU sovereign yield spreads

This study examines for the first time the relationship among the oil price shocks and the sovereign yield spreads in the EMU (which is collectively the largest oil-importer of the world), in a time-varying environment. In particular, we examine the time-varying correlation between oil price shocks and the 10-year sovereign yield spread of core and periphery countries in the EMU, by employing a scalar-BEKK framework. The main findings reveal that the correlations between sovereign yield spreads and oil price shocks are indeed time-varying and are influenced by specific economic and geopolitical events that took place during the study period. Furthermore, even though the correlation patterns are constantly low or zero prior to the Great Recession, a change is revealed in the post-2008 period, when correlations become moderate and more volatile. Finally, we do not observe noteworthy differences in the correlation behaviour between core and periphery countries to different oil price shocks. The findings of this study are particularly useful and provide valuable information to marketplace participants.

A Learning Approach for Physical Layer Authentication Using Adaptive Neural Network

In communications, innovative paradigm shifts have emerged in integrating various devices into the network to provide advanced and intelligent services. However, various security threats may occur that may not always be detected using traditional cryptographic techniques. Secure authentication is of paramount importance in modern wireless systems. This paper focusses on robust authentication in a time-varying communication environment where conventional authentication mechanisms are severely limited. We propose an Adaptive Neural Network (ANN) as an intelligent authentication process to improve detection accuracy. Specifically, a Data-Adaptive Matrix (DAM) is designed to track time-varying channel features. By utilizing a convolutional neural network as an intelligent authenticator, the proposed approach integrates deep feature extraction and attack detection, hence, leading to effective physical layer security. To evaluate the system, the ANN is prototyped on a universal software radio peripheral (USRP) and its authentication performance is evaluated in a conference room environment. Experimental results show that the ANN is effective in tackling the challenges of physical layer authentication under interference conditions, and is effective in time-varying environments.

Non-Stationary Bandit Strategy for Rate Adaptation With Delayed Feedback

Rate adaptation is an efficient mechanism to utilize the channel capacity by adjusting the modulation and coding scheme in a dynamic wireless environment. The channel feedback, such as acknowledgment/negative acknowledgment (ACK/NACK) messages or the channel measurement such as received signal strength indicator (RSSI) can be applied to the rate adaptation. Existing rate adaptation algorithms are mainly driven by heuristics. They can not achieve satisfactory transmission rates in the time-varying environment. In this paper, we focus on the rate adaptation problem in a time-division duplex (TDD) system. A multi-armed bandit (MAB) strategy is applied to learn the changes of the channel condition from both RSSI and ACK/NACK signals. A discounted upper confidence bound based rate adaptation (DUCB-RA) algorithm is proposed. We show that the performance of the proposed algorithm is converged to the optimal with mathematical proofs. Simulation results demonstrate that the proposed algorithm can adapt to the time-varying channel and achieve better transmission throughput compared to existing rate adaptation algorithms.

Online Trajectory Optimization Using Inexact Gradient Feedback for Time-Varying Environments

This article considers the problem of online trajectory design under time-varying environments. We formulate the general trajectory optimization problem within the framework of time-varying constrained convex optimization and propose a novel version of online gradient ascent algorithm (OGA) for such problems. Respecting the online nature, we carefully select the step size of OGA at each iteration so that the iterates stay feasible. Importantly, the proposed algorithm allows noisy gradients, expanding the range of practical applicability. In contrast to the most available literature, we present the offline sublinear regret of OGA up to the path length variations of the offline optimal solution, the cumulative gradient, and the error in the gradient variations. Furthermore, we establish a lower-bound on the offline dynamic regret, which defines the optimality of any trajectory. To show the efficacy of the proposed algorithm, we consider two practical problems of interest. First, a device to device (D2D) communications setting, where the goal is to design a user trajectory while maximizing its connectivity to the internet. Second, planning energy-efficient trajectories for unmanned surface vehicles (USV) under strong disturbances in ocean environments. Different from the state-of-the-art trajectory planning algorithms that entail planning and re-planning the full trajectory using the forecast data at each time instant, the proposed algorithm is entirely online and relies mostly on the ocean velocity measurements at the vehicle location. The detailed simulation results demonstrate the significance of the proposed algorithm on both synthetic and real data sets. Video result is available at https://tinyurl.com/y3ahmhsf .

Time-Varying Sparse Channel Estimation Based on Adaptive Average and MSE Optimal Threshold in STBC MIMO-OFDM Systems

Channel estimation is still a challenge for space time block coding (STBC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in time-varying environments. To estimate the channel state information (CSI) precisely without increasing complexity in any significant way, this paper utilizes the sparsity and the inherent temporal correlation of the time-varying wireless channel, and proposes a novel channel estimation method applied to STBC MIMO-OFDM systems. The proposed method consists of two schemes: adaptive multi-frame averaging (AMA) and improved mean square error (MSE) optimal threshold (IMOT). First, the temporal correlation of the time-varying channel is modeled by a linear Gauss-Markov (LGM) model, and the AMA scheme is incorporated to refine the initial estimated channel impulse response (CIR) through noise reduction. Based on the LGM model, the optimal average frame number is adaptively determined by minimizing the MSE of the denoised CIR. Then, the sparsity of the wireless channel is utilized to model the CIR as a sparse vector, and the IMOT scheme is performed to further remove the noise effect by discarding most of the noise-only CIR taps. Specifically, the IMOT scheme is achieved by recovering the CIR support across the optimal “tap-to-tap” threshold derived by minimizing the MSE of each CIR tap. Moreover, the prior confidence level of the tap to be active is calculated through multi-frame statistics to further improve the performance of the IMOT scheme. Simulation results verify that the proposed AMA-IMOT channel estimation method can achieve better performance than comparison methods.