Time-varying Environment Research Articles

Stochastic Optimal Control for Robot Manipulation Skill Learning Under Time-Varying Uncertain Environment.

In this article, a novel stochastic optimal control method is developed for robot manipulator interacting with a time-varying uncertain environment. The unknown environment model is described as a nonlinear system with time-varying parameters as well as stochastic information, which is learned via the Gaussian process regression (GPR) method as the external dynamics. Integrating the learned external dynamics as well as the stochastic uncertainties, the complete interaction system dynamics are obtained. Then the iterative linear quadratic Gaussian with learned external dynamics (ILQG-LEDs) method is presented to obtain the optimal manipulation control parameters, namely, the feedforward force, the reference trajectory, as well as the impedance parameters, subject to time-varying environment dynamics. The comparative simulation studies verify the advantages of the presented method, and the experimental studies of the peg-hole-insertion task prove that this method can deal with complex manipulation tasks.

An energy-efficient path planning method for unmanned surface vehicle in a time-variant maritime environment

This research presents a novel method for energy-efficient path planning, aiming to enhance the endurance of unmanned surface vehicle (USV). The proposed method combines the locking sweeping (LS) method, gradient descent method, coastline expansion method, and energy consumption functions. The efficiency of constructing the energy consumption potential map was improved by optimising the LS structure. Moreover, maintaining a user-configurable distance between the USV and the coastline ensures safe paths with minimal energy consumption. The performance of the proposed method was evaluated using multiple simulations involving high-resolution electronic nautical charts and a historical time-variant sea current dataset. The results demonstrate the practicality of the method, and effectively address path planning challenges in a time-variant maritime environment.

Abstract 19: Time-Varying Exposure to Food Retailers and Cardiovascular Disease Incidence and Mortality in the Netherlands: A Nationwide Prospective Cohort Study

Background: The food environment is suggested as a risk factor of cardiovascular disease (CVD), but current evidence is inconsistent and does not account for changes in the food environment. We aimed to explore if time-varying exposure to the food environment was associated with hospitalisation and mortality due to total and specific types of CVD in The Netherlands. We hypothesized that favourable time-varying trends in food environment exposure would be associated with decreased risk of CVD, stroke, heart failure (HF), and coronary heart disease (CHD) morbidity and mortality among the Dutch general population. Methods: A prospective cohort study was performed among all Dutch residents aged 35+ years who did not change residence in 2002-2018 without CVD in 2004, identified through registry data from Statistics Netherlands (n=4,641,435; 51.3% female, 91.2% Dutch). Time-varying Food Environment Healthiness Index (FEHI) scores (range: -5 to 5) and time-varying Kernel density of six specific food retailers (food delivery outlets, fast food outlets, restaurants, local food shops, supermarkets and convenience stores) around the home location were based on bi-annual updates between 2004-2018. Hospitalisation and mortality due to overall CVD, stroke, HF, and CHD occurring between 2004 and 2020 were used as outcomes, based on hospital and death registries. Models were adjusted for age, sex, ethnic origin, household composition, income, marital status and neighbourhood urbanisation level and effect modification by age, sex, urbanization and income was studied. Results: Each unit increase in the FEHI, representing a 10% improvement, was associated with a lower hospitalisation and mortality of CVD (hospitalization Hazard Ratio (HR h )=0.90, 0.89-0.91, mortality Hazard Ratio (HR m )=0.85, 0.82-0.89), CHD (HR h =0.88, 0.85-0.91, HR m =0.80, 0.75-0.86), stroke (HR h =0.89, 0.84-0.93, HR m =0.89, 0.82-0.98), and HF (HR h =0.90, 0.84-0.96, HR m =0.84, 0.76-0.92). Increased density of local food shops, fast food outlets, supermarkets and convenience stores, and decreased density of food delivery outlets and restaurants were associated with a higher risk of CVD, CHD, stroke and HF incidence and mortality, albeit with small effect sizes. Stratified analyses suggest slightly stronger associations in highly urbanized areas, younger adults and those with higher incomes. Conclusions: Favourable trends in time-varying food retailer exposure were associated with a risk reduction in CVD hospitalisation and mortality, in particular in urbanized areas and for younger adults and those with higher incomes.

Availability and maintenance strategy under time-varying environments for redundant repairable systems with PH distributions

Systems commonly operate in time-varying environments which have essential impact on system performance, thus reliability modeling and maintenance strategy are faced with new challenges. In this study, a cold standby repairable system in a time-varying environment is investigated based on different operational levels and maintenance strategies, in which repairman's vacation and maintenance rules vary with environments. The evolution of dynamic environments is governed by a Markov process, and the successive vacation time is governed by Markovian arrival processes (MAPs). Using Phase-type (PH) distribution not only increases the flexibility of analytical properties because it can approximate to any probability distribution defined on a nonnegative real line, but also could effectively ensure the applicability of the model in engineering practice, specially all the lifetimes of components and maintenance time are subject to disparate PH distributions in diverse environments. By employing the matrix-analytic method, Kronecker operator theory and aggregated stochastic processes, some reliability indexes and the system average benefit are obtained respectively. Finally, an example of a redundant power supply is given to illustrate the validity and applicability of the proposed model.

An asymptotically exact estimate of the median noise eigenvalue of sample covariance matrices

The Dominant Mode Rejection beamformer [Abraham & Owsley (1990)] averages the noise subspace eigenvalues of the sample covariance matrix (SCM) to estimate the background noise power. This noise power estimate from snapshot deficient SCMs can be unreliable for large arrays in time-varying environments with limited snapshots. Median filtering offers robustness to outliers and subspace mismatch for these challenging problems. Recent numerical experiments [Campos Anchieta & Buck (2022)] identified a simple regression relating the median sample eigenvalue to the true background power for snapshot deficient SCMs. However, the complicated expression of the Marchenko-Pastur (MP) distribution for the noise eigenvalues of the SCM thwarted prior attempts to find a closed form estimator for the MP distribution median. This talk exploits a coordinate transform to obtain a closed-form median of the MP distribution that is exact for the leading term in the power series expansion of the distribution. The new median estimator is more accurate than the previous numerical regression when the ratio of sensors to snapshots is 2 or more. Given the central role of SCM eigenvalues in principal component analysis and constant false alarm rate detectors, the new median expression should find application in other data science algorithms for underwater acoustics. [Work supported by ONR Code 321US.]

Gene Expression Tradeoffs Determine Bacterial Survival and Adaptation to Antibiotic Stress.

To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the minimum inhibitory concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC, and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.

Differential Privacy Preservation for Continuous Release of Real-Time Location Data.

Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability.

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity problem due to the complex environment and low bandwidth of the commercial WiFi. Meanwhile, fingerprints may change unpredictably due to multipath WiFi signal propagation in time-varying environments. To tackle these problems, this paper proposes an adaptive online learning DFL method, which adaptively updates the localization model to ensure long-term accuracy and adaptability. Specifically, the CSI signals of the target located at different reference points are first collected and transformed to discriminable fingerprints using the weights of Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). After that, an online learning DFL model is built to adapt to the changes of the environment. Experimental results in a time-varying indoor environment validate the adaptability of the proposed method against environmental changes and show that our method can achieve 10% improvement over other methods.

Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier

Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.

A Novel Online Sliding Blind Deconvolution Algorithm for Satellite Microvibration Source Separation in Time-Varying Environment

A Novel Online Sliding Blind Deconvolution Algorithm for Satellite Microvibration Source Separation in Time-Varying Environment

Radar Detection Self-Evolution Based on Incremental Learning in Time-Varying Clutter Environments

Radar Detection Self-Evolution Based on Incremental Learning in Time-Varying Clutter Environments

Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids

Microgrids (MGs), as localized small power systems, can effectively provide voltage regulation services for distribution networks by integrating and managing various distributed energy resources. Existing literature employs model-based optimization approaches to formulate the voltage regulation problem of multi-MGs, which require complete system models. However, this assumption is normally impractical due to time-varying environment and privacy issues. To fill this research gap, this paper suggests a data-driven decentralized framework for the cost-effective voltage regulation of a distribution network with multi-MGs. A novel multi-agent reinforcement learning method featuring an augmented graph convolutional network and a proximal policy optimization algorithm is proposed to solve this problem. Furthermore, the techniques of critical bus and electrical distance enhance the capability of feature extractions from the distribution network, allowing for the decentralized training with privacy preserving. Simulation results based on modified IEEE 33-bus, 69-bus, and 123-bus networks are developed to validate the effectiveness of the proposed method in enabling multi-MGs to provide distribution network voltage regulation.

Optimization of two-stage thermal desorption combined with pentafluorophenyl hydrazine derivatization-gas chromatography/mass spectrometry analytical method of atmospheric carbonyl compounds

The detection method of carbonyl compounds (CCs) in the atmosphere has been a challenging problem because of its low concentration and strong volatility and reactivity. Based on the existing thermal desorption (TD) technology, the TD instrument was combined with gas chromatography/mass spectrometry (GC/MS), and pentafluorophenyl hydrazine (PFPH) was used as derivatization reagent to establish a simple, high sensitivity, good stability and simultaneous detection of 13 atmospheric carbonyl compounds, namely PFPH derivation-two-stage TD-GC/MS analysis method. The main factors affecting the TD conditions were discussed, including desorption temperature, desorption time and carrier gas flow rate. The results showed that when the tube desorption flow rate and time were 10 mL min−1 and 8 min, and the trap desorption flow rate and time were 5 mL min−1 and 8 min, the recoveries of various CCs PFPH-derivatives were relatively high. The collection efficiencies of all detected carbonyls by this method were above 85 %. The method detection limits (MDLs) of the tested carbonyls were in the range of 0.44–1.23 ng cartridge−1. Compared with the method of PFPH-solvent desorption-GC/MS, the two-stage TD-GC/MS method showed lower MDLs and more carbonyls detected at low concentrations. In brief, the two stage TD-GC/MS analysis method is of great significance for the detection of trace CCs in the time-varying atmospheric environment, and is worthy of popularization and application in atmospheric samples.

A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multi-task. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.

Mixed Graph Neural Network-Based Fake News Detection for Sustainable Vehicular Social Networks

The rapid development of the Internet of Vehicles has substantially boosted the prevalence of vehicular social networks (VSN). However, content security has gradually been a latent threat to the stable operation of VSN. The VSN is a time-varying environment and mixed with various real or fake contents, which brings great challenges to the sustainability of VSN. To establish a sustainable VSN, it is of practical value to possess a strong ability for fake content detection. Related works can be divided into the global semantics-based approaches and the local semantics-based approaches, though both with limitations. Leveraging these two different approaches, this paper proposes a fake content detection model based on the mixed graph neural networks (GNN) for sustainable VSN. It takes GNN as the bottom architecture and integrates both convolution neural networks and recurrent neural networks to capture two aspects of semantics. Such a mixed detection framework is expected to possess a better detection effect. A number of experiments were conducted on two social network datasets for evaluation, and the results indicated that the detection effect can be improved by about 5%-15% compared with baseline methods.

Frequency domain online secondary path modelling for active noise control without auxiliary noise

The identification of the secondary path transfer function is of vital importance for active noise control (ANC) system. Online secondary path modeling method is preferable to offline modeling due to its tracking capability in time-varying acoustic environments. However, the auxiliary noise of online modeling mixed with the residual error signal diminishes the noise control performance. In this paper, we propose a frequency domain online secondary path modelling algorithm based on our previous work which achieves simultaneous acoustic paths modeling and noise control without auxiliary noise. The uniqueness of the solution is theoretically analyzed, and the computational burden is shown significantly lower than the previously proposed method. Simulations are conducted to verify that the proposed algorithm also has a better convergence behavior.

An Efficient Recognition Method for Orbital Angular Momentum via Adaptive Deep ELM.

For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a self-adapted model is a challenging problem. To address this issue, an efficient deep learning framework that uses a derived extreme learning machine (ELM) has been put forward. Different from typical neural network methods, the provided analytical machine learning model can match the different OAM modes automatically. In the model selection phase, a multilayer ELM is adopted to quantify the laser spot characteristics. In the parameter optimization phase, a fast iterative shrinkage-thresholding algorithm makes the model present the analytic expression. After the feature extraction of the received intensity distributions, the proposed method develops a relationship between laser spot and OAM mode, thus building the steady neural network architecture for the new received vortex beam. The whole recognition process avoids the trial and error caused by user intervention, which makes the model suitable for a time-varying atmospheric environment. Numerical simulations are conducted on different experimental datasets. The results demonstrate that the proposed method has a better capacity for OAM recognition.

A novel recursive least-squares adaptive method for streaming tensor-train decomposition with incomplete observations

Tensor tracking which is referred to as online (adaptive) decomposition of streaming tensors has recently gained much attention in the signal processing community due to the fact that many modern applications generate a huge number of multidimensional data streams over time. In this paper, we propose an effective tensor tracking method via the tensor-train format for decomposing high-order incomplete streaming tensors. On the arrival of new data, the proposed algorithm minimizes a weighted least-squares objective function accounting for both missing values and time-variation constraints on the underlying tensor-train cores, thanks to the recursive least-squares filtering technique and the block coordinate descent framework. Our algorithm is fully capable of tensor tracking from noisy, incomplete, and high-dimensional observations in both static and time-varying environments. Its tracking ability is validated with several experiments on both synthetic and real data.

Robust improved multiband-structured subband adaptive filter for active noise control with impulsive interference

The feedforward filtered-x least mean square algorithm is extensively implemented for active control of broadband noise. However, the control performance is substantially deteriorated due to colored noise and the presence of uncorrelated disturbances near the reference and error sensors. To tackle these issues, in this paper, a robust improved multiband-structured subband adaptive filter based on logarithmic and total least squares method is proposed for active control. Unlike the conventional method of total least squares, the proposed algorithm adopts logarithmic and Rayleigh quotient functions. The closed loop implementation of the improved multiband-structured subband adaptive filter is adopted to meet the delayless requirement. The proposed algorithm is well-suited for environments where the reference signal is highly correlated and the residual noise is contaminated by impulsive noise. Furthermore, an affine combination of the proposed algorithm is developed to meet the complementary requirements of faster convergence and improved noise reduction. Eventually, the stability and computational complexity are studied. Simulation results using measured acoustic paths in an anechoic chamber and a normal room illustrate the effectiveness of the proposed algorithm for controlling broadband noise with impulsive interference. In addition, the tracking control performance is evaluated in a time-varying acoustic environment.

Lessons learned from time reversal communications

Time reversal (TR) is a technique that utilizes spatial diversity to achieve spatial and temporal focusing in complex environments. This approach has been successfully applied to phase-coherent acoustic communications in time-varying multipath environments, providing an alternative to conventional adaptive multichannel equalizers (M-DFE). Essentially, the TR method involves combining multichannels as a spatial precombiner, effectively reducing channel complexities and achieving optimal performance. This presentation will discuss the insights gained from using the TR approach for underwater communications under diverse underwater conditions.