Search Results (11)

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

This paper presents a study on the development of a hybrid modeling framework for the optimal operation of microgrids based on renewable energy resources. Accurate prediction of both renewable energy generation and consumer demand is crucial for the efficient management of renewable energy-based microgrids. The proposed hybrid modeling framework integrates a high-resolution physical model for forecasting renewable energy sources (solar and wind), a data-driven model for renewable energy prediction, and a hybrid forecasting model that combines both physical and data-driven approaches. Additionally, the framework incorporates a consumer demand model to further optimize grid operations. In this research, a hybrid prediction model was developed to enhance the accuracy of solar and wind power generation forecasts. The hybrid model leverages the complementary strengths of both physical and data-driven models. When historical data are insufficient, the physical model generates synthetic training data to improve the learning process of the data-driven model. Moreover, in cases where the data-driven model exhibits limited predictive accuracy due to insufficient training data, the physical model provides reliable forecasts, ensuring robust performance under various conditions. When sufficient real-world data are available, the Weighted Inverse Error Weighting (WIEW) strategy is applied to dynamically integrate the outputs of both models, significantly enhancing forecasting accuracy. Furthermore, a digital twin platform was implemented to operate and simulate each model, and a validation system for the digital twin platform and models was established using Software-in-the-Loop Simulation (SILS) and Power Hardware-in-the-Loop Simulation (PHILS) techniques. This study focuses on the development and validation of a hybrid model designed to improve the accuracy of solar and wind power generation forecasts for renewable energy microgrids. Full article

With the increasing congestion in orbital environments, on-orbit observation has become critical for spacecraft safety. This study investigated the observation performance of space-based inverse synthetic aperture lidar (ISAL) for monitoring on-orbit targets and space debris in geostationary Earth orbit (GEO) and low Earth orbit (LEO). Using STK simulations, the performances under fly-around and fly-by scenarios were evaluated based on three key parameters: minimum imaging time, pulse repetition frequency (PRF), and signal-to-noise ratio (SNR). The results reveal that while the GEO provided a high PRF and SNR for fly-around observations, longer imaging times limited its practical application, making the fly-by mode more suitable. In contrast, the LEO provided stable fly-around observations with lower system requirements, but the fly-by mode suffered from high PRF demands and a low SNR due to the high relative angular velocity of the target. This study further simulated fly-by observations for actual space debris in both the GEO and LEO, validating ISAL’s performance under different conditions. These findings offer valuable insights into the selection of observation modes and the optimization of ISAL’s performance in on-orbit target and debris monitoring, serving as a foundation for future space-based monitoring systems. Full article

Cellulose-derived battery separators have emerged as a viable sustainable alternative to conventional synthetic materials like polypropylene and polyethylene. Sourced from renewable and biodegradable materials, cellulose derivatives—such as nanofibers, nanocrystals, cellulose acetate, bacterial cellulose, and regenerated cellulose—exhibit a reduced environmental footprint while enhancing battery safety and performance. One of the key advantages of cellulose is its ability to act as a hybrid separator, using its unique properties to improve the performance and durability of battery systems. These separators can consist of cellulose particles combined with supporting polymers, or even a pure cellulose membrane enhanced by the incorporation of additives. Nevertheless, the manufacturing of cellulose separators encounters obstacles due to the constraints of existing production techniques, including electrospinning, vacuum filtration, and phase inversion. Although these methods are effective, they pose challenges for large-scale industrial application. This review examines the characteristics of cellulose and its derivatives, alongside various processing techniques for fabricating separators and assessing their efficacy in battery applications. Additionally, it will consider the environmental implications and the primary challenges and opportunities associated with the use of cellulose separators in energy storage systems. Ultimately, the review underscores the significance of cellulose-based battery separators as a promising approach that aligns with the increasing demand for sustainable technologies in the energy storage domain. Full article

Coastal aquifers are critical freshwater resources that face increasing threats from contamination and saltwater intrusion. Traditional approaches for characterizing these aquifers are challenged by complex dynamics, high-dimensional parameter spaces, and significant computational demands. This study presents an innovative method that combines an Auto-Regressive Convolutional Neural Network (AR-CNN) surrogate model with the Iterative Local Updating Ensemble Smoother (ILUES) for the joint inversion of contamination source parameters and hydraulic conductivity fields. The AR-CNN surrogate model, trained on synthetic data generated by the SEAWAT model, effectively approximates the complex input–output relationships of coastal aquifer systems, substantially reducing computational burden. The ILUES framework utilizes observational data to iteratively update model parameters. A case study involving a heterogeneous coastal aquifer with multipoint pollution sources demonstrates the efficacy of the proposed method. The results indicate that AR-CNN-ILUES successfully estimates pollution source strengths and characterizes the hydraulic conductivity field, although some limitations are observed in areas with sparse monitoring points and complex geological structures. Compared to the traditional SEAWAT-ILUES framework, the AR-CNN-ILUES approach reduces the total inversion time from approximately 70.4 h to 16.2 h, improving computational efficiency by about 77%. These findings highlight the potential of the AR-CNN-ILUES framework as a promising tool for efficient and accurate characterization of coastal aquifers. By enhancing computational efficiency without significantly compromising accuracy, this method offers a viable solution for the sustainable management and protection of coastal groundwater resources. Full article

Laser phase noise is a critical factor that limits the range and performance of coherent lidar systems, especially in high-resolution applications such as inverse synthetic aperture lidar (ISAL), which demands stringent coherence. The effective suppression of laser phase noise is essential to enable high-resolution imaging over long distances. This paper presents a phase noise compensation technique utilizing dual reference channels (DRCs) based on concatenated generated phase (CGP) principles. The proposed method uses two reference channels with different delay lengths: a long-delay channel for coarse phase noise compensation and a short-delay channel for fine adjustments. We performed ISAL imaging experiments on stationary and rotating targets using a seed laser with a 3.41 MHz linewidth, achieving round-trip distances exceeding 110 times the laser coherence length. Imaging quality closely matched a 100 Hz narrow linewidth laser, approaching theoretical resolution limits. Compared to prior methods based on residual error linear estimation, the DRC method enhances compensation speed tenfold while maintaining accuracy. These results highlight the efficacy of the proposed DRC method in mitigating laser phase noise, significantly improving ISAL imaging performance. Full article

Traditionally, 3D modeling of marine controlled-source electromagnetic (CSEM) data (in the frequency domain) involves high-memory demand, requiring solving a large linear system for each frequency. To address this problem, we propose to solve Maxwell’s equations in a fictitious dielectric medium with time-domain finite-difference methods, with the support of the correspondence principle. As an advantage of this approach, we highlight the possibility of its implementation for execution with GPU accelerators, in addition to multi-frequency data modeling with a single simulation. Furthermore, we explore using the correspondence principle to the inversion of CSEM data by calculating the gradient of the least-squares objective function employing the adjoint-state method to establish the relationship between adjoint fields in a conductive medium and their counterparts in the fictitious dielectric medium, similar to the approach used in forward modeling. We validate this method through 2D inversions of three synthetic CSEM datasets, computed for a simple model consisting of two resistors in a conductive medium, a model adapted from a CSEM modeling and inversion package, and the last one based on a reference model of turbidite reservoirs on the Brazilian continental margin. We also evaluate the differences between the results of inversions using the steepest descent method and our proposed momentum method, comparing them with the limited-memory BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm (L-BFGS-B). In all experiments, we use smoothing by model reparameterization as a strategy for regularizing and stabilizing the iterations throughout the inversions. The results indicate that, although it requires more iterations, our modified momentum method produces the best models, which are consistent with results from the L-BFGS-B algorithm and require less storage per iteration. Full article

In response to the application requirement of wide-range high-resolution imaging of non-cooperative moving real targets by inverse synthetic-aperture ladar (ISAL), experiments were conducted on the depolarization effect of target materials, and the polarization selection of ISAL receiving and transmitting channels was discussed. Considering the impact of target depolarization and the demand for along-track interferometry, combined with beam-broaden and high-gain amplifiers, an ISAL system design method that can stably image multiple non-cooperative real targets has been proposed. Under the condition of broadening the transmitting and receiving beams to 3° in the elevation direction for non-cooperative moving vehicles, echo data with a duration of 1 s is obtained. The spatial correlation algorithm combined with along-track interferometry is used to estimate the vibration phase error. The sub-aperture Range-Doppler algorithm is used for imaging. The ISAL imaging results of the moving vehicle validated the high-resolution imaging ability of ISAL and its potential for stable imaging of non-cooperative moving real targets. Full article

Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture. Full article

Sweet potatoes have become increasingly popular among consumers due to their health benefits, and, as a result, sweet potato production has been growing rapidly over the last decade in the United States. However, the industry is facing major challenges, including the risk of disease outbreaks and adverse weather events, which could potentially have a significant impact on the market. However, the economic literature on the sweet potato commodity is limited. This study models the U.S. sweet potato market price response to supply changes and derives elasticity estimates. This information is essential for understanding the sweet potato market and for simulating the impacts of potential supply shocks, given the challenges that the industry is facing. We found that prices are highly sensitive to supply. North Carolina, the largest sweet potato producer in the country, dominates the domestic market and exerts significantly larger influences on market prices than other producing states. Full article

Uncertainty Quantification (UQ) is highly requested in computational modeling and simulation, especially in an industrial context. With the continuous evolution of modern complex systems demands on quality and reliability of simulation models increase. A main challenge is related to the fact that the considered computational models are rarely able to represent the true physics perfectly and demonstrate a discrepancy compared to measurement data. Further, an accurate knowledge of considered model parameters is usually not available. e.g., fluctuations in manufacturing processes of hardware components or noise in sensors introduce uncertainties which must be quantified in an appropriate way. Mathematically, such UQ tasks are posed as inverse problems, requiring efficient methods to solve. This work investigates the influence of model discrepancies onto the calibration of physical model parameters and further considers a Bayesian inference framework including an attempt to correct for model discrepancy. A polynomial expansion is used to approximate and learn model discrepancy. This work extends by discussion and specification of a guideline on how to define the model discrepancy term complexity, based on the available data. Application to an electric motor model with synthetic measurements illustrates the importance and promising perspective of the method. Full article