Grid-like Vibration Measurements on Power Transformer Tank during Open-Circuit and Short-Circuit Tests

In this work, the vibrations on the surfaces of the tank wall, stiffeners, and the cover of a 5 MVA transformer experimental model were measured during open-circuit and short-circuit transformer tests. Vibration measurements of a transformer tank side were conducted at discrete points using two different voltage sources in no-load test. Using interpolation functions, the RMS values of acceleration and vibration velocity are visualized and compared for each considered measurement configuration (no-load and load tests and two different excitation sources). Significant differences in mode shapes and amplitudes of vibrations at different frequencies are observed. The maximum RMS values of acceleration, velocity and displacement in the open-circuit test are 0.36 m/s2, 0.31 mm/s, and 0.42 µm, respectively. The maximum values in short-circuit test are 0.74 m/s2, 1.14 mm/s, and 1.8 µm, respectively. In the short-circuit test, the frequency component of 100 Hz is dominant. In the open-circuit test, the first few 100 Hz harmonics are significant (100 Hz, 200 Hz, and 300 Hz). In addition to the visualization of RMS values during the open-circuit and short-circuit tests, animations of the vibrations are created. Fourier analysis and phase comparison between frequency components are also used to show vibration animations at dominant frequencies in the spectrum (100 Hz harmonics). The visualization of the vibrations at the tank wall surfaces is transferred into 3D space in such a way that all 15 surfaces are mapped to the spatial coordinates of the surfaces so that a 3D model of the acceleration, vibration velocity, and displacement of the transformer tank is shown.

The resting frequency of echolocation signals changes with body temperature in the hipposiderid bat, Hipposideros armiger

Doppler shift (DS) compensating bats adjust in flight the second harmonic of the constant-frequency component (CF2) of their echolocation signals so that the frequency of the Doppler shifted echoes returning from ahead is kept constant with high precision (0.1-0.2%) at the so-called reference frequency (fref). This feedback adjustment is mediated by an audio-vocal control system which correlates with a maximal activation of the foveal resonance area in the cochlea. Stationary bats adjust the average CF2 with similar precision at the resting frequency (frest), which is slightly below the fref. Over a variety of time periods (from minutes up to years) variations of the coupled fref and frest have been observed, and were attributed to age, social influences and behavioural situations in rhinolophids and hipposiderids, and to body temperature effects and flight activity in Pteronotus parnellii. We assume that, for all DS compensating bats, a change in body temperature has a strong effect on the activation state of the foveal resonance area in the cochlea which leads to a concomitant change in emission frequency. We tested our hypothesis in a hipposiderid bat, Hipposideros armiger, and measured how the circadian variation of body temperature at activation phases affected frest. With a miniature temperature logger, we recorded the skin temperature on the back of the bats simultaneously with echolocation signals produced. During warm-up from torpor strong temperature increases were accompanied by an increase in frest, of up to 1.44 kHz. We discuss the implications of our results for the organization and function of the audio-vocal control systems of all DS compensating bats.

A new multipath channel estimation and mitigation using annihilation filter combined tracking loop implementation in software GPS receivers

Abstract: The multipath effect causes severe degradation in the positioning of commercial GPS receivers. Due to multipath error, the positioning accuracy could reach a few 10 meters. If the cumulative Multipath delay is less than 0.1-0.35 chips, then it is difficult to mitigate in GPS receivers. This causes severe degradation in GPS signals and can cause a measurement bias. To alleviate this problem, the estimation of multipath parameters using annihilating filter and its mitigation in the GPS tracking loop is proposed in this work. The estimation of randomly generated multipath signals can be performed in the receiver with a lower sampling rate when compared to the larger bandwidth of the GPS baseband signal. Here, the frequency components of the Multipath signal in superimposed complex exponentials have been transformed from the time delay and the amplitude of the path observables. The Rayleigh fading model in the urban scenario has been simulated in which the amplitude and the phase of the number of paths (i.e., the frequency component of superimposed complex exponentials) are set and this fading signal is convolved with GPS signal that forms the multipath faded signal. In the GPS receiver post-processing stage, with the help of the annihilation filter, the multipath components are estimated, then an inverse/adaptive filter and compensation technique are further applied to mitigate the multipath component. The mean square error with the different number of paths with noisy environments is analyzed utilizing the cadzaw denoising algorithm. The simulation results of the proposed technique employed in the tracking module of the software GPS receiver under severe multipath conditions indicate a substantial enhancement in the performance of the GPS receiver with minimal code and carrier phase error when compared to the least squares and adaptive blind equalization channel techniques. Moreover, the positioning accuracy is also calculated with the inclusion of multipath components in two satellites out of six satellites used in the simulation, the results showed that the annihilation filter improved the mean position accuracy up to 9.3023 meters.

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction

Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable performances compared to iterative reconstruction algorithms. However, the performance of these methods may severely deteriorate when the degradation strength of the test image is not consistent with that of the training dataset. In addition, these methods do not pay enough attention to the characteristics of different degradation levels, so solely extending the training dataset with multiple degraded images is also not effective. Although training plentiful models in terms of each degradation level can mitigate this problem, extensive parameter storage is involved. Accordingly, in this paper, we focused on sparse-view CT reconstruction for multiple degradation levels. We propose a single degradation-aware deep learning framework to predict clear CT images by understanding the disparity of degradation in both the frequency domain and image domain. The dual-domain procedure can perform particular operations at different degradation levels in frequency component recovery and spatial details reconstruction. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and visual results demonstrate that our method outperformed the classical deep learning-based reconstruction methods in terms of effectiveness and scalability.

Forecasting Soil Moisture In The Soil Under The Caragana Shrubland Using Wavelet Analysis And NARX Neural Network

Purpose: It is important for sustainable use of soil water resources to forecast soil moisture in forestland of water-limited regions. There are some soil moisture models. However, there is not a better method to forecast soil moisture.Methods: The change of soil moisture with time were investigated and the data of soil moisture were divided into a low frequency and a high frequency component using wavelet analysis, and then NARX neural network was used to build model I and model II. For model I, low frequency component was the input variable, and for model II, low frequency component and high frequency component were predicted.Results: the average relative error for model I is 3.5% and for model II is 0.3%. The average relative error of predicted soil moisture in100cm layer using model II is 0.8%, then soil water content in 40 cm and 200 cm soil depth is selected and the forecast errors are 4.9 % and 0.4 %.Using model II to predict soil water is well．Conclusion: Predicting soil water will be important for sustainable use of soil water resource and controlling soil degradation, vegetation decline and crop failure in water limited regions.

Integrated framework for rapid climate stress testing on a monthly timestep

“Bottom-up” methods are increasingly used to assess the vulnerability of water systems to climate change. Central to these methods is the climate “stress test”, where the system is subjected to various climatic changes to test for unacceptable outcomes. We present a framework for climate stress testing on a monthly timestep, suitable for systems whose dominant dynamic is seasonal or longer (eg. water resource systems with carry-over storage). The framework integrates multi-site stochastic climate generation with perturbation methods and in-built rainfall runoff modelling. The stochastic generation includes a low frequency component suitable for representing multi-annual fluctuations. Multiple perturbation options are provided, ranging from simple delta change through to altered seasonality and low frequency dynamics. The framework runs rapidly, supporting comprehensive multi-dimensional stress testing without recourse to supercomputing facilities. We demonstrate the framework on a large water resource system in southern Australia. The Matlab/Octave framework is freely available for download from https://doi.org/10.5281/zenodo.5617008.

Disrupted interoception by auditory distractor: Difficulty inferring the internal bodily states?

Perception of internal bodily sensations or interoception has recently been studied under a predictive coding framework. In this framework, the brain utilizes both top-down prediction and bottom-up prediction error signals to determine the content of the perception through inferences regarding the cause of the ongoing sensation. Particularly, interoception and other exteroceptive sensory modalities are considered to share an integrated, intertwined process of inference. Thus, it is possible that exteroceptive stimuli interfere with the inference of interoception. Hence, we investigated whether auditory stimuli disrupted interoceptive inference that resulted in diminished awareness of interoception. Thirty healthy volunteers performed the heartbeat counting task with and without distractor sounds. The psychophysiological traits that would reflect the individual differences in prior prediction signals of interoception were measured as the high-frequency component of the heart rate variability (HF-HRV) at rest and trait interoceptive sensibility. The results showed that the auditory distractor diminished objective interoceptive accuracy, subjective confidence in interoception, and the intensity of the heartbeat, suggesting disrupted interoceptive inference under external stimuli. Importantly, individual differences in the distractor effect were modulated by both the HF-HRV and tendency to worry about bodily states. These findings support and extend the predictive coding account of interoception by suggesting that interoceptive inference could be disrupted by external stimuli and that such disruption may be modulated by a difference in prior predictions and its precision regarding interoception.

Seismic inversion using complex spherical-wave reflection coefficient at different offsets and frequencies

Compared with the plane-wave reflection coefficient, the spherical-wave reflection coefficient (SRC) can more accurately describe the reflected wavefield excited by a point source, especially in the case of low seismic frequency and short travel distance. However, unlike the widely used plane-wave amplitude-variation-with-offset/frequency (AVO/AVF) inversion, the practical application of spherical-wave AVO/AVF inversion in multilayer elastic media is still in the exploratory stage. One of the difficulties is how to fully use the amplitude and phase information of the complex-valued SRC and the spherical-wave response property of each frequency component to obtain the spherical-wave synthetic seismogram in multilayer elastic media. In view of this, we have developed a complex convolution model considering the amplitude and phase information of a SRC to obtain the complex synthetic seismogram of a certain frequency component. A simple harmonic superposition method is further developed. By superposing the complex synthetic seismograms of different frequency components, the synthetic seismogram of the full-frequency band can be obtained. In addition, a novel three-parameter SRC in terms of P- and S-wave moduli and density is derived. Based on the SRC and complex seismic traces with different offsets (or incidence angles) and frequency components, an inversion approach of complex spherical-wave amplitude and phase variation with offset and frequency is proposed. A noisy synthetic data example verifies the robustness of our complex spherical-wave inversion approach. Field data examples indicate that the P- and S-wave moduli estimated by the complex spherical-wave inversion approach can reasonably match the filtered well-logging data. Considering spherical waves rather than plane waves can improve the accuracy of seismic inversion results.