Time-varying Environment Research Articles

Intraspecific competition of predator for prey with variable rates in protected areas

In this present study, we systematically explore the periodicity (almost periodic nature) of a dynamical system in time-varying environment, which portrays a special case of prey–predator model governed by non-autonomous differential equations. In particular, we investigate the dynamical characteristics of the underlying prey–predator model by considering modified Leslie–Gower-type model with Crowley–Martin functional response with time-dependent periodic variation of model parameters in a prey reserve area. We show the existence of globally stable periodic solutions. This perpetual prey oscillation results in persistent interference among predator, causing reduced feeding rate at high prey density. A comparative study between the two methods used to prove the existence of periodic (almost periodic) solution of the considered non-autonomous system is also discussed. After showing permanence, existence, uniqueness and global attractivity of the periodic (almost periodic) solution analytically, we demonstrate the typical prey and predator dynamics in time-varying environment using several numerical examples. Partial rank correlation coefficient technique is performed to address how the model output is affected by changes in a specific parameter disregarding the uncertainty over the rest of the parameters.

Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

In robotics, the tough problem about the dynamic target capturing consists of tracking the target by the robot manipulator and grasping the target by the robot finger. For the sake of space, this article deals with only the first problem, tracking the dynamic target by the robot manipulator. The traditional approaches of capturing the dynamic target may work well when they are employed in low-dimensional space by reinforcement learning or physical modeling. However, they fail to work well in high-dimensional space. The traditional approaches have four limitations with respect to Cartesian space, configuration space, reinforcement learning, and physical modeling. To overcome these limitations, this article implements improved dynamic A* algorithm in high-dimensional configuration space map to capture the target. First, a space injection model injects the collision detection and target position from the Cartesian space into the configuration space to construct a high-dimensional map. Then, the target capturing method including the improved dynamic A* algorithm is applied on the map to track and capture the target. Finally, the experiment performed in time-varying environment and the dynamic target achieves a reliable result. This article has proposed an approach that makes the robot manipulator motion planning more accurate in high-dimensional dynamic configuration space. This approach enables the multi-joint manipulator to avoid the obstacle while tracking the target in high-dimensional configuration space. It takes the advantages of heuristic algorithms in the process of target capturing method designing. It adds precision and speed to target tracking. The success of the approach may apply to any industrial robot tracking target, surgical operation, and space probes. And, it may lay a solid foundation for dynamics control with a scope for future investigations.

Formula omitted]: Adaptive control of data rate, transmission power, and connection interval in bluetooth low energy

This work investigates run-time parameter control for Bluetooth Low Energy (BLE), based on the latest Bluetooth specification 5 released in 2016. Compared to the previous version, Bluetooth specification 5 provides more options for data rate and transmission power and gives room for adaptive parameter control to improve BLE performance in time-varying wireless environments. To the best of our knowledge, this work is the first systematic study of this regime in the research community. We experimentally study how more parameter options in Bluetooth specification 5 impact BLE performance. Based on the preliminary study, we design AdaptaBLE that dynamically adjusts connection interval, data rate, and transmission power together to satisfy quality of service (QoS) requirements without wasting energy. Through extensive performance evaluation on off-the-shelf BLE chips, we show that AdaptaBLE provides stable performance regardless of wireless environments and reduces QoS failures by 5 times with similar energy consumption compared to the state-of-the-art parameter control scheme for BLE.

Transient model and its application to investigate the injection mode and periodical operation of transpiration cooling with liquid coolant phase change

To study the stability and reliability of transpiration cooling with phase change in complicated and changeable thermal environment, this paper suggests a transient mathematical model and numerically investigates the dynamic evolution of phase change and transient behavior of fluid–structure coupling heat transfer under three typical time-varying thermal environments. First, the feasibility and reliability of two coolant injection methods are analyzed and compared. The results indicates that the coolant injection based on pressure control is stable only when the heat flux imposed on transpiration cooling structure is relatively low and phase change occurs at hot side, while the coolant injection based on mass flow rate control is absolutely stable no matter how the external thermal environment changes. Then, the effects of fluctuated operation condition on the cooling performance of system and the transport characteristic of coolant are further studied. Although without affecting the stability of the transpiration cooling system in the long term, the fluctuation of coolant injection and external heat flux will result in the fluctuation of structure temperature and coolant driving force, and thereby cause significant deterioration of cooling performance, which will increase the ablation risk and coolant driving cost of transpiration cooling system.

A Carrier Tracking Loop Using Adaptive Strong Tracking Kalman Filter in GNSS Receivers

The Kalman filter (KF) has been widely used in the carrier track to improve the tracking performance of receivers under challenging environments. The KF-based tracking assumes that the noise statistics are exactly known in advance and kept fixed during the whole iteration process. However, the noise statistics are difficult to know accurately and the fixed noise statistics cannot reflect the practical situations under time-varying environments. To further enhance the performance of carrier tracking, the adaptive strong tracking Kalman filter (STKF) is proposed. The adaptive fading factors are employed in the state prediction covariance to adjust the Kalman gain. Moreover, to improve the accuracy of fading factors in STKF tracking, the measurement noise covariance is adjusted based on the $C/N_{0}$ estimations. In addition, the working state is checked, and fading factors are used only when the system is not steady. The proposed algorithm has been implemented in the software receiver. The test results demonstrate that the proposed method has more superior tracking performance under challenging environments than other tracking methods.

Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction

With the rapid development of information theoretic learning, the maximum correntropy criterion (MCC) has been widely used in time series prediction area. Especially, the kernel recursive least squares (KRLS) based on MCC is studied recently due to its online recursive form and the ability to resist noise and be robust in non-Gaussian environments. However, it is not always an optimal choice that using the correntropy, which is calculated by default Gaussian kernel function, to describe the local similarity between variables. Besides, the computational burden of MCC based KRLS will raise as data size increases, thus causing difficulties in accommodating time-varying environments. Therefore, this paper proposes a quantized generalized MCC (QGMCC) to solve the above problem. Specifically, a generalized MCC (GMCC) is utilized to enhance the accuracy and flexibility in calculating the correntropy. In order to solve the problem of computational complexity, QGMCC quantizes the input space and upper bounds the network size by vector quantization (VQ) method. Furthermore, QGMCC is applied to KRLS and forming a computationally efficient and precisely predictive algorithm. After that, the improved algorithm named quantized kernel recursive generalized maximum correntropy (QKRGMC) is set up and the derivation process is also given. Experimental results of one benchmark dataset and two real-world datasets are present to verify the effectiveness of the online prediction algorithm.

The Optimal Resource Self-configuration Method of Cognitive Network for Survivability Enhancement

In view of that general lack of intelligence and flexibility of the existing network resource allocation methods in the case of time-varying environments and diversified requirements, an efficient self-configuration method is put forward to optimize the allocation of resources and improve the survivability of system. First of all, the utility function of consumption domain is introduced as an indicator to pre-arrange the priority of user’s QoS, as a result, the utility maximization of the system under resource constraint is obtained. Then, based on this definition, a multidimensional dynamic programming framework is proposed to define and describe the self-configuration process, and the problem model is constructed under certain constraints. Furthermore, the adaptive adjustment and configuration of resources are implemented by determining the priority sequence of user services, finding the optimal resource configuration scheme, and optimizing the time configuration window. Finally, The simulation results show that the proposed method is superior to the traditional resource allocation scheme in terms of system reliability, connectivity, broadband utilization, average response time and transmission rate, which improves the system’s ability to adapt to the environment intelligently and survivability effectively.

Fuzzy Learning for Multi-Dimensional Adaptive Physical Layer Authentication: A Compact and Robust Approach

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.

An Electromagnetic–Thermal Coupling Numerical Study of the Synchronous Generator with Second-Generation High-Temperature Superconducting Armatures

Generators with high-temperature superconducting armatures have an advantage in the fact that they can carry high currents. However, the AC loss of high-temperature superconducting (HTS) armatures is difficult to calculate precisely because HTS coils exist in a complex and time-varying electromagnetic environment. In addition, when the HTS coil is carrying a short circuit fault overcurrent, an electromagnetic–thermal simulation study of this process is required to ensure that the HTS coil is not damaged. In this paper, first, a fully coupled T-A formulation model is used to calculate the AC loss of HTS armatures. Then, the current and temperature distributions are simulated, considering the intrinsic characteristic of superconducting coated conductors, when the generator suffers the worst short circuit fault accidently. It is found that the turn with the lowest critical current quenches after 0.01 s, but the temperature rise cannot damage the coil if the circuit breaker can clear the fault quickly. The effects of the copper stabilizer thickness on the thermal stability of the HTS coil during the worst short circuit fault are also investigated. A thicker copper stabilizer improves the thermal stability of the HTS coil in the event of a short circuit fault, but the use of a simulation model is needed to make trade-offs between the engineering current density and the thermal stability of the HTS tapes. The work in this paper is necessary and can provide an important reference for manufacturing superconducting generators.

Population Dynamics in a Changing Environment: Random versus Periodic Switching.

Environmental changes greatly influence the evolution of populations. Here, we study the dynamics of a population of two strains, one growing slightly faster than the other, competing for resources in a time-varying binary environment modeled by a carrying capacity switching either randomly or periodically between states of abundance and scarcity. The population dynamics is characterized by demographic noise (birth and death events) coupled to a varying environment. We elucidate the similarities and differences of the evolution subject to a stochastically and periodically varying environment. Importantly, the population size distribution is generally found to be broader under intermediate and fast random switching than under periodic variations, which results in markedly different asymptotic behaviors between the fixation probability of random and periodic switching. We also determine the detailed conditions under which the fixation probability of the slow strain is maximal.

Robust Hierarchical Control Mechanism for Aggregated Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) are good candidates for direct load control (DLC). They can be aggregated to join the electricity market through a centralized management performed by virtual power plant (VPP). However, there are two main concerns that arise when TCLs participate in DLC. The first is related to the dispatchability of VPP against normal energy demand of individual TCLs in uncertain time-variant environments. The second refers to the tremendous increase of communication and computational requirements needed to perform DLC on a large population of TCLs. This paper introduces aggregators between the DLC controller and TCLs with a novel robust control mechanism to reconcile these concerns. The control mechanism is implemented with two layers: the upper layer suppresses the control payback effect with a quadratic optimization model, and the lower layer addresses the power trajectory tracking with a novel payback tracking error model (PTEM). The control method needs minimum sensing infrastructure since it requires power data only at the aggregation level. Our simulations resulted in a robust reference power tracking by the aggregator with a percentage root mean squared error between 3.33% – 5.69% under uncertain time-variant environments. The continuous responsiveness indicates that the aggregators manage to convert the aggregated TCLs into “manageable resources” that ensure the dispatchability of VPP.

Intent-based networks for 6G: Insights and challenges

Intent-Based Networks (IBNs), which are originally proposed to introduce Artificial Intelligence (AI) into the sixth-generation (6G) wireless networks, can effectively solve the challenges of traditional networks in terms of efficiency, flexibility, and security. IBNs are mainly used to transform users’ business intent into network configuration, operation, and maintenance strategies, which are prominent for designing the AI-enabled 6G networks. In particular, in order to meet the massive, intelligent service demands and overcome the time-varying radio propagation, IBNs can continuously learn and adapt to the time-varying network environment based on the massive collected network data in real-time. From the aspects of both the core network and radio access network, this article comprehensively surveys the architectures and key techniques of IBNs for 6G. In particular, the demonstration platforms of IBNs, such as the Apstra Operating System, Forward Networks Verification Platform, and One Convergence Service Interaction Platform, are presented. Moreover, the industrial development of IBNs is elaborated, including the emerging new products and startups to solve the problems of open data platforms, automated network operations, and preemptive network fault diagnosis. Finally, several open issues and challenges are identified as well to spur future researches.

Voltage Stability Monitoring Based on Disagreement-Based Deep Learning in a Time-Varying Environment

The traditional learning based static voltage stability monitoring methods require manual labeling of a large number of training samples. Getting these labeled training sets is expensive and time-consuming in practice. To address this issue, we propose a novel disagreement-based deep learning approach for static voltage stability monitoring. The proposed method trains a deep neural network (DNN) with a small amount of labeled data to monitor the voltage stability. Case studies on the IEEE benchmark systems show that the proposed disagreement-based deep learning achieves more than 94.03% of accuracy with only 300 labeled samples. Moreover, to deal with the time variation of network operating conditions and/or network topology, we further propose a transfer learning method that can adapt the trained DNN to the new environment swiftly. The transfer process only involves a small number of labeled samples in the new environment. Our case studies show that after the network topology changes due to line tripping, the transferred DNN achieves more than 95.48% of accuracy in the new environment by utilizing only 5 labeled samples for each category.

Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning Formulation.

The autonomous underwater glider has attracted enormous interest for underwater activities, especially in long-term and large-scale underwater data collection. In this paper, we focus on the application of gliders gathering data from underwater sensor networks over underwater acoustic channels. However, this application suffers from a rapidly time-varying environment and limited energy. To optimize the performance of data collection and maximize the network lifetime, we propose a distributed, energy-efficient sensor scheduling algorithm based on the multi-armed bandit formulation. Besides, we design an indexable threshold policy to tradeoff between the data quality and the collection delay. Moreover, to reduce the computational complexity, we divide the proposed algorithm into off-line computation and on-line scheduling parts. Simulation results indicate that the proposed policy significantly improves the performance of the data collection and reduces the energy consumption. They prove the effectiveness of the threshold, which could reduce the collection delay by at least 10% while guaranteeing the data quality.

Simultaneous orthogonal collocation decomposition method for extended Lion and Man problems

Lion and Man problems are classical examples of pursuit and evasion games. However, the traditional analytic methods and indirect numerical methods only can handle the generalization of Lion and Man problems in games with small scales and simple scenarios. In this study, we first extend the original Lion and Man problems to a more complicated and time-varying game environment. Then we propose the simultaneous orthogonal collocation decomposition (SOCD) method, which is a direct method for exploring solutions of Lion and Man problems in a complicated game environment. Compared to indirect methods, SOCD method is much easier to apply. The max-minimization problem in Lion and Man problems is decomposed into two subproblems of optimal control, which are discretized by using the orthogonal collocation method. Local solutions of the resulting nonlinear programming problems lead to the optimal control problems. We also develop the receding horizon optimization method based on SOCD method to solve Lion and Man problems online in a time-varying game environment. In this method, the whole optimization time domain is divided into several short optimization cycles, and Lion and Man problems in each cycle are based on real-time observations of the game environment. The validity of these two methods is tested by conducting numerical simulations, and the results demonstrate that these methods provide a unified framework for solving extended Lion and Man problems.

Online Learning via Offline Greedy: Applications in Market Design and Optimization

Motivated by online decision-making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on offline combinatorial problems that are amenable to a constant factor approximation using a greedy algorithm that is robust to local errors. For such problems, we provide a general framework that efficiently transforms offline robust greedy algorithms to online ones using Blackwell approachability. We show that the resulting online algorithms have O(T^{1/2}) (approximate) regret under the full information setting. We further introduce a bandit extension of Blackwell approachability that we call Bandit Blackwell approachability. We leverage this notion to transform greedy robust offline algorithms into a O(T^{2/3})(approximate) regret in the bandit setting. Demonstrating the flexibility of our framework, we apply our offline-to-online transformation to several problems at the intersection of revenue management, market design, and online optimization, including product ranking optimization in online platforms, reserve price optimization in auctions, and submodular maximization. We show that our transformation, when applied to these applications, leads to new regret bounds or improves the current known bounds.

Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controllers for Unmanned Aerial Vehicles in Time-Varying Environments

Formation control techniques have been widely implemented in networked multirobot systems. In this article, we present a novel framework for swarm multiagent systems based on the relative-position output feedback consensus supported with the new concept of adaptive strictly negative imaginary consensus controllers, leveraging the learning capability of artificial neural networks. For experimental validation, we consider the case of two quadcopters moving together while carrying a dynamic load. We employ Kharitonov's theorem to study the stability of the proposed adaptive control systems. Finally, a rigorous real-time experimental study is conducted to highlight the merits of the proposed formation control algorithms.

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment

An AI-Based Planning Framework for HAPS in a Time-Varying Environment

A High-Altitude Pseudo-Satellite (HAPS) is a fixed-wing, solar-powered Unmanned Aerial Vehicle (UAV) developed to become a flexible alternative to satellites with fixed-orbits for monitoring ground activities over long periods of time. However, given its lightweight build and weak electro-motors, the platform is rather sensitive to weather and cannot fly around hazardous weather zones swiftly. In this work, we formulate the problem of planning missions for multiple HAPS as a hybrid planning problem expressed in PDDL+. The formulation also considers the problem of modeling the platform dynamics, the time-varying environment, and the heterogeneous tasks that need to be carried out. Additionally, we propose a framework that combines a PDDL+ automated planner with an Adaptive Large Neighborhood Search (ALNS) approach, developed to couple the automated planner with a meta-heuristic that is specific for the problem. The task and motion planning are done in an intertwined way within the framework, preserving hence a common decision/search space. We validate our approach with a third-party realistic simulator for HAPS, as well as with a set of benchmark tests, showing that our integrated approach produces executable mission plans.

From winter to summer and back: Lessons from the parameterization of a seasonal food web model for the Białowieża forest.

Dynamic food web models describe how species abundances change over time as a function of trophic and life-history parameters. They are essential to predicting the response of ecosystems to perturbations. However, they are notoriously difficult to parameterize, so that most models rely heavily either on allometric scaling of parameters or inverse estimation of biomass flows. The allometric approach makes species of comparable body mass have near-identical parameters which can generate extinctions within a trophic level. The biomass flow approach is more precise, but is restricted to steady-states, which is not appropriate for time-varying environments. Adequately parameterizing large food webs of temperate and arctic environments requires dealing both with many species of similar sizes and a strongly seasonal environment. Inspired by the rich empirical knowledge on the vertebrate food web of the Białowieża forest, we parameterize a bipartite food web model comprising 21 predators and 124 prey species. Our model is a non-autonomous coupled ordinary differential equations system that allows for seasonality in life-history and predation parameters. Birth and death rates, seasonal descriptions of diet for each species, food requirements and biomass information are combined into a seasonal parameterization of a dynamic food web model. Food web seasonality is implemented with time-varying intrinsic growth rate and interaction parameters, while predation is modelled with both type I and type II functional responses. All our model variants allow for >80% persistence in spite of massive apparent competition, and a quantitative match to observed (seasonal) biomasses. We also identify trade-offs between maximizing persistence, reproducing observed biomasses, and ensuring model robustness to sampling errors. Although multi-annual cycles are expected with type II functional responses, they are here prevented by a strong predator self-regulation. We discuss these results and possible improvements on the model. We provide a general workflow to parameterize dynamic food web models in seasonal environments, based on a real case study. This may help to better predict how biodiverse food webs respond to changing environments.