Search Results (8)

Conventional unconditional random field (URF) models were shown to neglect in-situ monitoring data and thus misrepresent real slope stability. To address this, a conditional random field (CRF) generator was proposed, in which Fast Fourier Transform (FFT) filtering was coupled with co-Kriging to assimilate site observations. A representative three-bench slope was adopted, and the failure-mode distribution and the statistics of the factor of safety (FoS) produced by the URF, the independent random field (IRF), and the CRF were examined across bedding-dip angles of 15–75° and two cross-correlation states ($\rho$ = −0.2, 0). It was found that eliminating cross-correlation decreased the mean FoS by 0.006, increased its standard deviation by 10.26%, and raised the frequency of low-FoS events from 7.49% to 12.30%. When field constraints were imposed through the CRF, the probability of through-going failure was reduced by 12%, the mean FoS was increased by 0.01, the standard deviation was reduced by 15.38%, and low-FoS events were suppressed to 2.30%. The CRF framework was thus demonstrated to integrate stochastic analysis with field measurements, enabling more realistic reliability assessment and proactive risk management of slopes. Full article

This paper presents the development and analysis of a planar microfluidic microwave sensor featuring three circular complementary split-ring resonators (CSRRs) fabricated on an RO3035 substrate. The sensor demonstrates enhanced sensitivity in characterizing liquids contained in a fine glass capillary tube by leveraging a novel configuration: a central 5-split-ring CSRR with a drilled hole to suspend the capillary, flanked by two 2-split-ring CSRRs to improve the band-stop filtering effect. The sensor’s performance is benchmarked against another CSRR-based microwave sensor with a similar configuration. High linearity is observed (R² > 0.99), confirming its capability for precise ethanol concentration prediction. Compared to the replicated square CSRR design from the literature, the proposed sensor achieves a 35.22% improvement in sensitivity, with a frequency shift sensitivity of 567.41 kHz/% ethanol concentration versus 419.62 kHz/% for the reference sensor. The enhanced sensitivity is attributed to several key design strategies: increasing the intrinsic capacitance by enlarging the effective area and radial slot width to amplify edge capacitive effects, adding more split rings to intensify the resonance dip, placing additional CSRRs to improve energy extraction at resonance, and adopting circular CSRRs for superior electric field confinement. Additionally, the proposed design operates at a lower resonant frequency (2.234 GHz), which not only reduces dielectric and radiation losses but also enables the use of more cost-effective and power-efficient RF components. This advantage makes the sensor highly suitable for integration into portable and standalone sensing platforms. Full article

The widespread use of static converters for controlling electrical machines and the concern for electrical power quality in industrial environments provide an opportunity for utilizing these devices to enhance the power quality. In this context, this work presents a back-to-back converter model for driving induction machines. The converter is designed to correct the power factor of the point common coupling (PCC), compensate for harmonic currents (acting as an active filter), and withstand voltage sags. The necessary control system models were developed, and an alternative implementation for these functions in the converter was proposed. The results demonstrate the technical feasibility of this solution, as the converter operated within its nominal limits by compensating for harmonics and reactive power. Moreover, the equipment showed resilience to severe voltage sags. The contribution of this paper focuses on the multifunctionality of the frequency converter for driving induction machines. It emphasizes the advantage of the inverter in improving power quality in industrial environments through reactive power compensation and harmonic current compensation, thus functioning as an active power filter. Additionally, it is worth highlighting its ability to handle voltage dips. In this regard, this paper contributes by providing an operational strategy for driving the induction machine during such transients. Full article

Wind curtailment and weak inertia characteristics are two factors that shackle the permeability of wind power. An electric hydrogen production device consumes electricity to produce hydrogen under normal working conditions to solve the problem of abandoning wind. When participating in frequency regulation, it serves as a load reduction method to assist the system to rebuild a power balance and improve the wind power permeability. However, due to its own working characteristics, an electric hydrogen production device cannot undertake the high-frequency component of the frequency regulation power command; therefore, an energy storage device was selected to undertake a high-frequency power command to assist the electric hydrogen production device to complete the system frequency regulation. This paper first proposes and analyzes the architecture of a wind storage hydrogen-generating station for centralized hydrogen production with a distributed energy storage, and proposes the virtual inertia and droop characteristic mechanism of the wind storage hydrogen-generating station to simulate a synchronous unit. Secondly, an alkaline electrolysis cell suitable for large-scale engineering applications is selected as the research object and its mathematical model is established, the matching between different energy storage devices and their cooperation in power grid frequency regulation is analyzed, and a super capacitor is selected. A control strategy for the wind storage hydrogen-generating power station to participate in power grid frequency regulation with a wide time scale is then proposed. Using the first-order low-pass filter, the low-frequency component of the frequency regulation power command is realized by an electric hydrogen production device load reduction, and a high-frequency component is realized by the energy storage device. Finally, the effectiveness and rationality of the proposed control strategy are verified by establishing the simulation model of the wind storage hydrogen-generating power station with different initial wind speed states, comparing the system frequency dip values under the proposed multi-energy cooperative control strategy and a single energy device control strategy. Full article

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method. Full article

This paper provides a theoretical framework to analyze and quantify roughness effects on sensing performance parameters of surface plasmon resonance measurements. Rigorous coupled-wave analysis and the Monte Carlo method were applied to compute plasmonic reflectance spectra for different surface roughness profiles. The rough surfaces were generated using the low pass frequency filtering method. Different coating and surface treatments and their reported root-mean-square roughness in the literature were extracted and investigated in this study to calculate the refractive index sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip intensity, plasmonic dip position, and figure of merit. Here, we propose a figure-of-merit equation considering optical intensity contrast and signal-to-noise ratio. The proposed figure-of-merit equation could predict a similar refractive index sensing performance compared to experimental results reported in the literature. The surface roughness height strongly affected all the performance parameters, resulting in a degraded figure of merit for surface plasmon resonance measurement. Full article

Fractured imaging is an important target for oil and gas exploration, as these images are heterogeneous and have contain low-impedance contrast, which indicate the complexity in a geological structure. These small-scale discontinuities, such as fractures and faults, present themselves in seismic data in the form of diffracted waves. Generally, seismic data contain both reflected and diffracted events because of the physical phenomena in the subsurface and due to the recording system. Seismic diffractions are produced once the acoustic impedance contrast appears, including faults, fractures, channels, rough edges of structures, and karst sections. In this study, a double square root (DSR) equation is used for modeling of the diffraction hyperbola with different velocities and depths of point diffraction to elaborate the diffraction hyperbolic pattern. Further, we study the diffraction separation methods and the effects of the velocity analysis methods (semblance vs. hybrid travel time) for velocity model building for imaging. As a proof of concept, we apply our research work on a steep dipping fault model, which demonstrates the possibility of separating seismic diffractions using dip frequency filtering (DFF) in the frequency–wavenumber (F-K) domain. The imaging is performed using two different velocity models, namely the semblance and hybrid travel time (HTT) analysis methods. The HTT method provides the optimum results for imaging of complex structures and imaging below shadow zones. Full article

Ground-penetrating radar (GPR) is a close-range remote-sensing tool applied in a great many near-surface projects for engineering or environmental purposes. In GPR B-scans, there may exist a variety of reflections and diffractions that corresponds to different structures and targets in the subsurface media, and the noise is always embedded. To assist in the interpretation, GPR B-scans can be generally divided into two parts according to the dip attribute of the reflections, where the sub-horizontal layers and dipping structures are properly separated. In this work, we extend the f - x empirical mode decomposition (f - x EMD) to form a semi-adaptive dip filter for GPR data. In f - x domain, each frequency slice is decomposed by EMD and reconstructed to form a dipping profile and a horizontal profile respectively, where the reflections at different dips are separated adaptively. Then the noises mixed in the dipping profile are further separated by rank-deduction methods in f - x domain. The above two-step scheme constitutes the hybrid scheme, which can separate the dipping structures, sub-horizontal layers, and most of the random noise in GPR B-scans. We briefly review the basics of the f - x EMD, and then introduce the derived hybrid scheme in f - x domain. The proposed method is tested by the synthetic data, the forward simulation data, and the field data, respectively. Full article