Adaptive Time Research Articles

A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering

The vibration signal of rolling bearing with variable operating conditions contains complex interference components, which will cause low fault diagnosis accuracy, especially in strong noise case. To solve this problem, we proposed a noise reduction method of rolling bearing with variable operating based on empirical wavelet transform and adaptive time-frequency peak filtering (EWT-ATFPF). Firstly, empirical wavelet transform is used to obtain different frequency intrinsic mode functions (IMFs). Secondly, a modified adaptive window length formula for time-frequency peak filtering (TFPF) is constructed by combining the sampling ratio index and a fault sensitivity indicator that calculated by kurtosis and correlation coefficients of IMFs, which can better characterize the impact components. Thirdly, to balance noise reduction effect and the fidelity of IMFs, we proposed an improved TFPF method by adaptively adjusting its windows length. The adaptive method could be carried out using the proposed fault sensitivity indicator and window length formula, and the denoising IMFs could be obtained by ATFPF. Finally, the denoising vibration signal is reconstructed by using the denoising IMFs. The performance of fault diagnosis of the proposed method is verified by using simulated signal and bearing fault test data. The results show that the proposed EWT-ATFPF method could effectively achieve noise reduction under variable operating conditions.

An adaptive time–frequency demodulation method and its applications in rolling bearing fault diagnosis

Rolling bearings are critical and easily damaged components of mechanical equipment. In practical engineering applications, the collected signals typically contain a large amount of noise, which makes fault diagnosis difficult. Based on this, this paper proposes an adaptive time–frequency demodulation method for rolling bearing diagnosis. The proposed method first obtains the complex envelope of the vibration signal in the time–frequency domain using the S transform (ST), and the time–frequency coefficient of ST can be used as the complex envelope, which is proved in detail in this paper. Subsequently, the complex envelope of the optimal slice frequency is obtained by frequency slicing to significantly weaken the interference of irrelevant noise and highlight the fault characteristics. An indicator is proposed to adaptively select an optimal slice frequency component that contains the most fault information. Finally, the slice envelope spectrum of the optimal slice frequency is obtained using Fourier transform for fault diagnosis. The feasibility of the proposed method is verified using the simulated signal. The application results of the bearing inner and outer ring fault experimental signals indicate that the proposed method is more accurate and effective for bearing fault diagnosis. Comparisons with other commonly used methods also verified the superiority of the proposed method.

Analysis of a new adaptive time filter algorithm for the unsteady Stokes/Darcy model

In this article, we propose a new adaptive time filter algorithm for the unsteady Stokes/Darcy model. Firstly, we present a first order θ-scheme with variable time step which is one parameter family of Linear Multi-step method. Furthermore, using time filter scheme, the convergence order is increased to the second order without almost increasing the extra computation. Moreover, we construct decoupled and coupled adaptive algorithms to improve the computational efficiency. Theoretically, we analyze stability and the second-order accuracy of Linear Multi-step method plus time filter with non-increasing variable time step, respectively. Finally, numerical experiments are given to verify theoretical results, including effectiveness, convergence and efficiency.

Challenges and instructor strategies for transitioning to online learning during and after the COVID-19 pandemic: a review of literature

The COVID-19 pandemic triggered an unprecedented shift to online learning, significantly impacting the higher education landscape. This paper examines the challenges faced by faculty and students during the rapid transition to online instruction and explores best practices for delivering effective online courses. The increased adoption of online learning created stress for faculty and resulted in academic setbacks for students. Although challenges are present strategies exist to help faculty create rich online learning environments. One important element is engagement, which looks at both student engagement with the material and with their classmates and faculty. In addition to working on student engagement the faculty were now in a position that required a new type of expertise to manage online interactions, which can be much different from their experiences in traditional classrooms. Insufficient time for proper course adaptation and limited knowledge of online teaching methods added to these challenges. Effective online delivery requires careful planning, utilization of advanced instructional technologies, and creating an immersive and interactive learning environment. Faculty must also adapt their teaching strategies to accommodate the unique challenges of online instruction. This review highlights the significance of a quality learning management system (LMS) as the backbone of online courses. An effective LMS facilitates course management, content delivery, and student interaction. Future considerations include providing comprehensive faculty support and training, promoting effective communication and collaboration among students, and incorporating interactive elements into online lessons. The following will provide lessons learned from the COVID-19 pandemic which will help faculty to improve their instructional competence and social presence in the online classroom.

Parametric effects of light acting via multiple photoreceptors contribute to circadian entrainment in Drosophila melanogaster.

Circadian rhythms in physiology and behaviour have near 24 h periodicities that must adjust to the exact 24 h geophysical cycles on earth to ensure adaptive daily timing. Such adjustment is called entrainment. One major mode of entrainment is via the continuous modulation of circadian period by the prolonged presence of light. Although Drosophila melanogaster is a prominent insect model of chronobiology, there is little evidence for such continuous effects of light in the species. In this study, we demonstrate that prolonged light exposure at specific times of the day shapes the daily timing of activity in flies. We also establish that continuous UV- and blue-blocked light lengthens the circadian period of Drosophila and provide evidence that this is produced by the combined action of multiple photoreceptors which, includes the cell-autonomous photoreceptor cryptochrome. Finally, we introduce ramped light cycles as an entrainment paradigm that produces light entrainment that lacks the large light-driven startle responses typically displayed by flies and requires multiple days for entrainment to shifted cycles. These features are reminiscent of entrainment in mammalian models systems and make possible new experimental approaches to understanding the mechanisms underlying entrainment in the fly.

Forest management drives evolution of understorey herbs

Forest management has a strong impact on the forest structure and subsequently on the biotic and abiotic forest understorey environment. Forest understorey herbs can thus be expected to exhibit an evolutionary response to management-induced environmental variation (provided sufficient time for adaptation), but this has been little tested to date.Here we use one common garden, to test for genetically based variation in phenotypic traits in populations of forest herbs sampled along a forest management intensity gradient, covering protected areas, selection forests and age-class forests. Five different herbaceous species were sampled from 70 to 100 populations in three regions in Germany and were tested for genetically based variation in flowering start, proportion of flowering ramets, and plant height. Additionally, we performed structural equation modelling to study how forest management drives trait differentiation via its effects on the microenvironment.Our results show the studied forest understorey herbs varied genetically in the measured functional and phenological traits among the sampled populations. Forest management likely affected the traits in various directions and strengths depending on the species, either directly through variation in forest structural attributes or indirectly through changes in the microclimatic environment on the forest floor.In sum, we show that forest management can have evolutionary consequences for forest understorey plants. In an applied context, diverse forest management actions within landscapes are recommended as it creates heterogeneity that selects for different plant traits and thus helps conserving genetic diversity.

Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

AbstractPrognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464–75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.

Adaptive multi-scale TF-net for high-resolution time–frequency representations

A novel adaptive multi-scale time–frequency network (AMTFN) is proposed to provide high-resolution time–frequency representations for nonstationary signals. AMTFN is an end-to-end deep network, which firstly adaptively learns the comprehensive basis functions to produce time–frequency (TF) feature maps through multi-scale 1D convolutional kernels. Then, the channel attention mechanism is embedded into AMTFN to rescale the TF feature maps selectively. Thus, the subsequent residual encoder–decoder block’s energy concentration performance is greatly improved with these rescaled TF feature maps. Besides, this paper designs a new training strategy to elegantly enable the model to pay more attention to the intersections of instantaneous frequency trajectories. In the end, a series of simulations as well as real-world cases, are studied to demonstrate the effectiveness and advantages of the proposed method.

Fast Generalized Sliding Sinusoidal Transforms

Discrete cosine and sine transforms closely approximate the Karhunen–Loeve transform for first-order Markov stationary signals with high and low correlation coefficients, respectively. Discrete sinusoidal transforms can be used in data compression, digital filtering, spectral analysis and pattern recognition. Short-time transforms based on discrete sinusoidal transforms are suitable for the adaptive processing and time–frequency analysis of quasi-stationary data. The generalized sliding discrete transform is a type of short-time transform, that is, a fixed-length windowed transform that slides over a signal with an arbitrary integer step. In this paper, eight fast algorithms for calculating various sliding sinusoidal transforms based on a generalized solution of a second-order linear nonhomogeneous difference equation and pruned discrete sine transforms are proposed. The performances of the algorithms in terms of computational complexity and execution time were compared with those of recursive sliding and fast discrete sinusoidal algorithms. The low complexity of the proposed algorithms resulted in significant time savings.

Robust adaptive finite time command filtered backstepping control for uncertain output constrained strict-feedback nonlinear systems

Robust adaptive finite time command filtered backstepping control for uncertain output constrained strict-feedback nonlinear systems

A Linear-Scan-Based Wideband Single-Cut Near-Field RCS Measurement Technique With Wide Effective Angle Coverage

A linear-scan-based wideband near-field radar cross section (RCS) measurement technique for achieving wide effective-angle-coverage is proposed in this letter. Both the data preprocessing and linear near-field to far-field transformation (LNF2FFT) techniques are used to ensure the accurate measurement of wideband far-field RCS in a wide azimuthal angle range. With the linear-scan-based near-field sampling strategy, the object under test (OUT) keeps stationary during the measurement process, and the equipment of turntable is not required. Moreover, based on the 2D plane wave expansion, LNF2FFT is proposed to translate the linearly sampled near-field data into far-field RCS pattern. An adaptive time gating strategy is also proposed to deal with the varied background property in liner samplings. Serval important practical factors relating to the measurement accuracy, such as phase center calibration, background subtraction and calibration, are also discussed in detail. Two illustrative test examples are presented, in which RCS measurement with wide effective-angle-coverage of ±35° and the average wideband measurement errors less than 0.4 dB are achieved in 1 m limited measurement range.

Optimal Aggregation Strategies for Social Learning Over Graphs

Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs. This paper investigates the effect of combination policies on the performance of adaptive social learning strategies. Using large-deviation analysis, it first derives a bound on the steady-state error probability and characterizes the optimal selection for the Perron eigenvectors of the combination policies. It subsequently studies the effect of the combination policy on the transient behavior of the learning strategy by estimating the adaptation time in the low signal-to-noise ratio regime. In the process, it is discovered that, interestingly, the influence of the combination policy on the transient behavior is insignificant, and thus it is more critical to employ policies that enhance the steady-state performance. The theoretical conclusions are illustrated by means of computer simulations.

RETRACTED ARTICLE: E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm

In the era of the Internet, information data continue to accumulate, and the explosive growth of network information explosion leads to the reduction of the accuracy of users’ access to information. To enhance the user experience and purchasing desire of e-commerce users, a e-commerce user recommendation algorithm based on social relationship characteristics and improved K-means algorithm is proposed. It combines the Automatic Time Division Dynamic Topic Model based on adaptive time slice division for building a strength calculation model in view of the characteristics of social relations. Then, it proposes an e-commerce user recommendation algorithm in view of the improved K-means algorithm to improve the accuracy of topic feature extraction and user recommendation. The experiment illustrates that there is no fluctuation in the clustering function of the improved K-means algorithm, and the highest, lowest, and average accuracy remain consistent under the three datasets, with average accuracy of 78.9%, 84.5%, and 95.9%, respectively. The community discovery-based friend recommendation algorithm presented in the study has the highest accuracy, illustrating that improving the K-means algorithm can further improve recommendation accuracy. The accuracy of the feature extraction method in view of alternative cost is 0.63, which improves the accuracy by about 9%. The results indicate that this study can provide technical support for user recommendations on e-commerce platforms.

A grand spectrum of the geomagnetic field

The spectrum of geomagnetic variations is broad and reflects multiple internal and external physical Earth processes. The geodynamo generates an internal magnetic field, dominated by the low frequency contribution of the axial dipole whose temporal variations range from geomagnetic reversals, excursions, and large-scale paleosecular variations, down to decadal and sub-annual changes. Variations in the external field arise from high frequency interactions of the solar wind with Earth’s magnetosphere and ionosphere, along with excitation within the atmospheric cavity by lightning, power systems, and radio transmissions. External variations induce a field in the conductive Earth that adds to the internal signal. A spherical harmonic analysis of dipole terms in over 100 years of observatory data allows us to show that the external field is stronger than the internal field at periods of the 11-year sunspot cycle and shorter. Using spectral estimates derived from this and other data sets by adaptive multi-spectral time series analysis, we can create a composite power spectrum that spans frequencies from 10-15 Hz to 20 kHz (periods ranging from 10 million years to 5 × 10-5 s), and powers ranging from 10-9 (nT)2/Hz to 1021 (nT)2/Hz. The different processes contributing to the spectrum are characterized by various inverse power laws across frequency bands. This Grand Spectrum quantifies the successive dominance of the many different geophysical processes with frequency, but importantly provides a compelling graphic to illustrate the complexity of the geomagnetic field.

Using Multiscale Direct Simulation Approach to Understanding Transports Behavior Inside PEM Water Electrolyzer

In this work, numerical modeling techniques were developed to predict oxygen bubble evolution through complex pore geometries to provide information of gas transport pathways inside saturated porous transport layers (PTL). Each PTL undergoes an image processing technique that uses X-ray CT images to generate accurate 3D microstructures. An example of a generated PTL structure is shown in Figure 1.Historically, meshing of the complex geometry was tedious and non-automated. Due to the random particle size across the geometry, a constant base size for meshing resulted in an inadequate mesh for accurate predictions. The Lattice-Boltzmann method is therefore more commonly used because a lattice is easier than a finite volume mesh to generate. Although sufficiently fine lattices adhered to the topology of the physical structure, simulations required a timestep between 1e-8 and 1e-10 seconds, resulting in long computational time.Meshing techniques have improved and can now be applied to complex geometries. Using the finite volume method accompanied with an adaptive mesh and adaptive time scale, provides an accurate representation of the PTL structure and larger timesteps. A volume of fluid (VoF) method was used to model two phase flow, using liquid water and gaseous oxygen, through different PTL structures. Evolution, growth, size, velocity, and surface interaction of oxygen bubbles through microstructures were studied as also shown in Figure 2. Findings from the work will provide better understanding of oxygen evolution through saturated PTLs Figure 1

The role of internal climate variability on future streamflow projections

Uncertainty about the future impacts of climate change represents a significant barrier to implementing adaptation measures. This work explores the impact of internal climate variability on streamflow projections for 133 catchments across the eastern and northeastern United States. Using data from a single model initial-condition large ensemble (SMILE) at high spatial and temporal resolution, this work assesses the magnitude of anthropogenic climate change and internal climate variability on projected future streamflow. The impact of catchment size is studied by grouping catchments into three different size classes (less than500 km2, between 500 and 1000 km2, and greater than 1000 km2). Results show that in a warmer climate, low to middle quantiles of future streamflow will systematically decrease, while the upper quantiles will increase. Increases are largest for more extreme streamflow indices. Using three different approaches, the role of internal variability is studied to estimate the time of emergence (TOE). In this case, results show that the climate change signal of extreme floods and droughts emerges later than that of median flow quantiles, even though the changes for floods as droughts is more significant. There is a clear relationship between catchment size and TOE, with small catchments seeing an earlier TOE for floods, and a later one for droughts. These results provide insight into adaptation times for small to large watersheds.

Improved variable step adaptive filtering algorithm and its application in time delay estimation for continuous wave dual-sensor pulse signals

Continuous wave mud pulse transmission is crucial for wireless logging, but surface pump noise disrupts upstream mud pulses, hindering downhole information extraction. At a theoretical level, the dual-sensor differential denoising approach is an excellent technology for suppressing pump noise, but precisely identifying the time delay, which has a considerable impact on the denoising effect, remains a challenge. This paper provides an improved variable step-size adaptive time delay estimation method TVSS-LMSTDE to address this issue. Iteration time regulates the change in step size. The preset adjustment principle states that modifying parameters ζ and γ controls the learning features of step size. A series of stable and erratic dual-sensor continuous wave mud pulse signals interfered by various noise intensities are created in the simulation, and the performance of three different variable step-size technologies—error-oriented, nonlinear relationship, and iterative time regulation—in terms of convergence speed, estimation stability, and calculation amount are compared and analyzed. The results show that the iterative time regulation technique produces a minimal mean square error in time delay estimate, with generally steady estimation and excellent accuracy. When applied to hydraulic cycle tests, the technique accurately gets the time delay value of dual-sensor signals, giving critical support for the successful recovery of mud pulse data severely disrupted by pump noise.

Energy-efficient adaptive cruise control system with acceleration prediction via long short-term memory

An energy-efficient adaptive cruise control (ACC) system that predicts the preceding state is proposed to meet the demands of driving in different states and working conditions. The long short-term memory (LSTM) network predicts the trajectory of the preceding vehicle’s future acceleration, allowing for an adaptive time headway that takes acceleration into account. The acceleration of the preceding vehicle is also incorporated as an augmented state in the deep deterministic policy gradient (DDPG) algorithm, resulting in a combined algorithm called prediction deep deterministic policy gradient (PDDPG). A multi-objective reward function is constructed based on human driving data to evaluate the performance indexes of vehicle longitudinal control, including efficiency, safety, and economy. The rules for changing the weight of each target performance under various typical cycle conditions are determined through experiments. The Carsim-based urban, suburban and highway conditions together with the Simulink vehicle dynamics model are compared with human driving data and ACC of conventional algorithms. Based on the test results, the proposed algorithm can improve fuel efficiency by 19.36% in urban driving conditions and reduce acceleration fluctuations.

Latent Representation-Based Learning Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-Driven Soft Actuators.

The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.

Improving protection of compensated transmission line using IoT enabled adaptive auto reclosing scheme

Abstract Protection of critical components in the power sector especially the transmission line system is very important to minimize loss of revenue, resources, and productivity. Improvement of power transfer capacity and voltage profile in smart the power grid is achieved by Series Compensation (SC). However, it poses many serious issues in the protection of compensated transmission lines. The majority of faults occurring in transmission lines are transient in nature for which service continuity can be regained by an auto-reclosing scheme. An efficient auto-reclosing scheme that can provide adaptive protection with reliable operation is required to prevent transient and permanent faults in the series compensated transmission line (SCTL). This work highlights the emulation of an Internet of Things (IoT) aided adaptive auto-reclosing scheme in association with an ESP32 controller that provides adaptive dead time control. The reclosing instance has been identified by means of a synchro-check relay. A real-time IoT-aided auto-reclosing scheme has been realized using the ESP32 controller that can generate a trip/reclosing signal instantaneously using the concepts of edge computing. The projected system has been also corroborated by simulating the modeled smart power grid network with wide variations in fault context in PSCAD. The validation of the set of rules is done in MATLAB software for the proposed auto-reclosing scheme. The extracted test outcomes of the simulation and its laboratory hardware implementation indicate an appreciable reduction in dead time which reflects the efficacy of the developed protection scheme.