24 research outputs found
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Full waveform inversion (FWI) infers the subsurface structure information
from seismic waveform data by solving a non-convex optimization problem.
Data-driven FWI has been increasingly studied with various neural network
architectures to improve accuracy and computational efficiency. Nevertheless,
the applicability of pre-trained neural networks is severely restricted by
potential discrepancies between the source function used in the field survey
and the one utilized during training. Here, we develop a Fourier-enhanced deep
operator network (Fourier-DeepONet) for FWI with the generalization of seismic
sources, including the frequencies and locations of sources. Specifically, we
employ the Fourier neural operator as the decoder of DeepONet, and we utilize
source parameters as one input of Fourier-DeepONet, facilitating the resolution
of FWI with variable sources. To test Fourier-DeepONet, we develop two new and
realistic FWI benchmark datasets (FWI-F and FWI-L) with varying source
frequencies and locations. Our experiments demonstrate that compared with
existing data-driven FWI methods, Fourier-DeepONet obtains more accurate
predictions of subsurface structures in a wide range of source parameters.
Moreover, the proposed Fourier-DeepONet exhibits superior robustness when
dealing with noisy inputs or inputs with missing traces, paving the way for
more reliable and accurate subsurface imaging across diverse real conditions
Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead
Sensing-as-a-service is anticipated to be the core feature of 6G perceptive
mobile networks (PMN), where high-precision real-time sensing will become an
inherent capability rather than being an auxiliary function as before. With the
proliferation of wireless connected devices, resource allocation in terms of
the users' specific quality-of-service (QoS) requirements plays a pivotal role
to enhance the interference management ability and resource utilization
efficiency. In this article, we comprehensively introduce the concept of
sensing service in PMN, including the types of tasks, the
distinctions/advantages compared to conventional networks, and the definitions
of sensing QoS. Subsequently, we provide a unified RA framework in
sensing-centric PMN and elaborate on the unique challenges. Furthermore, we
present a typical case study named "communication-assisted sensing" and
evaluate the performance trade-off between sensing and communication procedure.
Finally, we shed light on several open problems and opportunities deserving
further investigation in the future
Communication-Assisted Sensing in 6G Networks
The exploration of coordination gain achieved through the synergy of sensing
and communication (S&C) functions plays a vital role in improving the
performance of integrated sensing and communication systems. This paper focuses
on the optimal waveform design for communication-assisted sensing (CAS) systems
within the context of 6G perceptive networks. In the CAS process, the base
station actively senses the targets through device-free wireless sensing and
simultaneously transmits the pertinent information to end-users. In our
research, we establish a CAS framework grounded in the principles of
rate-distortion theory and the source-channel separation theorem (SCT) in lossy
data transmission. This framework provides a comprehensive understanding of the
interplay between distortion, coding rate, and channel capacity. The purpose of
waveform design is to minimize the sensing distortion at the user end while
adhering to the SCT and power budget constraints. In the context of target
response matrix estimation, we propose two distinct waveform strategies: the
separated S&C and dual-functional waveform schemes. In the former strategy, we
develop a simple one-dimensional search algorithm, shedding light on a notable
power allocation tradeoff between the S&C waveform. In the latter scheme, we
conceive a heuristic mutual information optimization algorithm for the general
case, alongside a modified gradient projection algorithm tailored for the
scenarios with independent sensing sub-channels. Additionally, we identify the
presence of both subspace tradeoff and water-filling tradeoff. Finally, we
validate the effectiveness of the proposed algorithms through numerical
simulations
Sensing With Random Signals
Radar systems typically employ well-designed deterministic signals for target
sensing. In contrast to that, integrated sensing and communications (ISAC)
systems have to use random signals to convey useful information, potentially
causing sensing performance degradation. This paper analyzes the sensing
performance via random ISAC signals over a multi-antenna system. Towards this
end, we define a new sensing performance metric, namely, ergodic linear minimum
mean square error (ELMMSE), which characterizes the estimation error averaged
over the randomness of ISAC signals. Then, we investigate a data-dependent
precoding scheme to minimize the ELMMSE, which attains the {optimized} sensing
performance at the price of high computational complexity. To reduce the
complexity, we present an alternative data-independent precoding scheme and
propose a stochastic gradient projection (SGP) algorithm for ELMMSE
minimization, which can be trained offline by locally generated signal samples.
Finally, we demonstrate the superiority of the proposed methods by simulations.Comment: 6 pages, 5 figures, submitted to ICASSP 202
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
The integrated sensing and communication (ISAC) technique has the potential
to achieve coordination gain by exploiting the mutual assistance between
sensing and communication (S&C) functions. While the sensing-assisted
communications (SAC) technology has been extensively studied for high-mobility
scenarios, the communication-assisted sensing (CAS) counterpart remains widely
unexplored. This paper presents a waveform design framework for CAS in 6G
perceptive networks, aiming to attain an optimal sensing quality of service
(QoS) at the user after the target's parameters successively ``pass-through''
the SC channels. In particular, a pair of transmission schemes, namely,
separated S&C and dual-functional waveform designs, are proposed to optimize
the sensing QoS under the constraints of the rate-distortion and power budget.
The first scheme reveals a power allocation trade-off, while the latter
presents a water-filling trade-off. Numerical results demonstrate the
effectiveness of the proposed algorithms, where the dual-functional scheme
exhibits approximately 12% performance gain compared to its separated waveform
design counterpart
‘ZhongPan 101’ and ‘ZhongPan 102’: Two Flat Peach Cultivars From China
Flat peach [Prunus persica (L.) Batsch var. platycarpa] is a variant of ordinary peach with a unique flat shape. It is well known for its shape and delicious fruits (Miao et al. 2022). Although flat peach has a long history of cultivation in China, until the beginning of the 20th century, flat peach was only distributed as a minor variety in the main peach-producing areas of China. In terms of flat peach cultivars, only 46 of the 709 peach cultivars listed in Peach Genetic Resource in China (Wang et al. 2012) are flat peach cultivars, and most of them are flat landraces. Several problems have been noted previously in flat peach cultivars, including poor closure of the blossom end (blossom-end scarring in mild cases and cracking in severe cases), cracked stone in some cultivars (loss of commercial value in severe cases), nonsymmetrical fruit shape, small flesh, and low yield (Wang 2021). Many of the shortcomings of flat peach cultivars are intrinsic problems of the cultivars, which are difficult to improve through cultivation measures. This is the key factor limiting the large-scale promotion of flat peach cultivation in China.
For many years, peach breeders in China have been devoted to the genetic improvement of flat peach, and some improved flat peach cultivars have been released, for instance, ‘Pocket Zaoban’ (Jiang et al. 2007) and ‘124 Pantao’ (Ma et al. 2003). However, problems persist in these cultivars, including small fruits, soft flesh, and blossom-end cracks. Only a few flat peach cultivars have good overall performance. In recent years, the Zhengzhou Fruit Research Institute (ZFRI), Chinese Academy of Agricultural Sciences (CAAS), identified genetic sources of flat peach with slow or nonmelting flesh, a well-closed blossom end, and little or no cracking. They were hybridized with high-quality peach and nectarine cultivars or selections. After multiple generations of improvement, breakthroughs were made in early flat peach breeding, and a series of flat peach cultivars with excellent comprehensive traits have been produced. These cultivars are favored by fruit farmers in the main peach-producing areas in China. Hence, the main problems in flat peach cultivation are expected to be solved, which will help expand the cultivation area of flat peach.
‘ZhongPan 101’ and ‘ZhongPan 102’ are two yellow-flesh flat peach cultivars 45 released from the ZFRI, CAAS. These two cultivars produce large, well-shaped, high-quality fruits with a completely closed stylar end and high yield. Three years of evaluation has confirmed that the peach trees of the two cultivars are stable. ‘ZhongPan 101’ and ‘ZhongPan 102’ were well adapted to climate of the middle and lower reaches of the Yellow River; have performed well in Henan, Jiangsu, and Anhui Provinces; and are suggested for trial wherever ‘ZhongYouPan 9’ is grown
The degrees-of-freedom in monostatic ISAC channels: NLoS exploitation vs. reduction
The degrees of freedom (DoFs) attained in monostatic integrated sensing and communications (ISAC) are analyzed. Specifically, monostatic sensing aims for extracting targetorientation information from the line of sight (LoS) channel between the transmitter and the target, since the Non-LoS (NLoS) paths only contain clutter or interference. By contrast, in wireless communications, typically, both the LoS and NLoS paths are exploited for achieving diversity or multiplexing gains. Hence, we shed light on the NLoS exploitation vs. reduction tradeoffs in a monostatic ISAC scenario. In particular, we optimize the transmit power of each signal path to maximize the communication rate, while guaranteeing the sensing performance for the target. The non-convex problem formulated is firstly solved in closed form for a single-NLoS-link scenario, then we harness the popular successive convex approximation (SCA) method for a general multiple-NLoS-link scenario. Our simulation results characterize the fundamental performance tradeoffs between sensing and communication, demonstrating that the available DoFs in the ISAC channel should be efficiently exploited in a way that is distinctly different from that of communication-only scenarios
Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China
Soil organic matter (SOM) is a key index of soil fertility. Visible and near-infrared (VNIR, 350–2500 nm) reflectance spectroscopy is an effective method for modeling SOM content. Characteristic wavelength screening and spectral transformation may improve the performance of SOM prediction. This study aimed to explore the optimal combination of characteristic wavelength selection and spectral transformation for hyperspectral modeling of SOM. A total of 219 topsoil (0–20 cm) samples were collected from two soil types in the East China. VNIR reflectance spectra were measured in the laboratory. Firstly, after spectral transformation (inverse-log reflectance (LR), continuum removal (CR) and first-order derivative reflectance (FDR)) of VNIR spectra, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variables elimination (UVE) algorithms. Secondly, the SOM prediction models were constructed based on the partial least squares regression (PLSR), random forest (RF) and support vector regression (SVR) methods using the full spectra and selected wavelengths, respectively. Finally, optimal SOM prediction models were selected for two soil types. The results were as follows: (1) The CARS algorithm screened 40–125 characteristic wavelengths from the full spectra. The UVE algorithm screened 105–884 characteristic wavelengths. (2) For two soil types and full spectra, CARS and UVE improved the SOM modeling precision based on the PLSR and SVR methods. The coefficient of determination (R2) value in the validation of the CARS-PLSR (PLSR model combined with CARS) and CARS-SVR (SVR model combined CARS) models ranged from 0.69 to 0.95, and the relative percent deviation (RPD) value ranged from 1.74 to 4.31. Lin’s concordance correlation coefficient (LCCC) values ranged from 0.83 to 0.97. The UVE-PLSR and UVE-SVR models showed moderate precision. (3) The PLSR and SVR modeling accuracies of Paddy soil were better than those for Shajiang black soil. RF models performed worse for both soil types, with the R2 values of validation ranging from 0.22 to 0.68 and RPD values ranging from 1.01 to 1.60. (4) For Paddy soil, the optimal SOM prediction models (highest R2 and RPD, lowest root mean square error (RMSE)) were CR-CARS-PLSR (R2 and RMSE: 0.97 and 1.21 g/kg in calibration sets, 0.95 and 1.72 g/kg in validation sets, RPD: 4.31) and CR-CARS-SVR (R2 and RMSE: 0.98 and 1.04 g/kg in calibration sets, 0.91 and 2.24 g/kg in validation sets, RPD: 3.37). For Shajiang black soil, the optimal SOM prediction models were LR-CARS-PLSR (R2 and RMSE: 0.95 and 0.93 g/kg in calibration sets, 0.86 and 1.44 g/kg in validation sets, RPD: 2.62) and FDR-CARS-SVR (R2 and RMSE: 0.99 and 0.45 g/kg in calibration sets, 0.83 and 1.58 g/kg in validation sets, RPD: 2.38). The results suggested that the CARS algorithm combined CR and FDR can significantly improve the modeling accuracy of SOM content
Impact of Desulfovibrio ferrophilus IS5 biocorrosion time on X80 carbon steel mechanical property degradation
Three different incubation times (7, 14 and 21 d) were used to immerse X80 carbon steel dogbone coupons in deoxygenated enriched artificial seawater at 28 °C inoculated with Desulfovibrio ferrophilus (strain IS5), a very corrosive sulfate reducing bacterium to cause microbiologically influenced corrosion (MIC). It was found that 21 d immersion had the largest weight loss (24.8 mg cm−2 equivalent to 0.55 mm/year corrosion rate), smallest sessile cell count (6.8 × 108 cell/cm2), deepest pit depth (30.6 μm), and most severe mechanical degradation (9 % loss in ultimate strength and 18 % loss in ultimate strain). Due to nutrient depletion in the static incubation system, corrosion rate and sessile cell count were the highest at 0.82 mm/year and 1.1 × 109 cell cm−2, respectively at 7 d. The results from scanning electron microscopy, X-ray photoelectron spectroscopy and confocal laser scanning microscopy analyses and electrochemical corrosion measurements supported the sessile cell counts and biocorrosion weight loss data
Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands.
Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0-20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350-2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation (RPD) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction (R2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy (R2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra (R2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR (R2 and RMSE: 0.92 and 1.91 g/kg in validation set, RPD: 3.23). The optimal SI-based model was model SI3-SVR (R2 and RMSE: 0.87 and 2.40 g/kg in validation set, RPD: 2.57) and model SI-SVR (R2 and RMSE: 0.84 and 2.63 g/kg in validation set, RPD: 2.35)