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The integration of renewable energy sources (RESs) introduces significant challenges related to uncertainty and intermittency in power grids. While Artificial Intelligence (AI) offers promising solutions for Virtual Power Plants (VPP) optimization, existing approaches often treat load forecasting, system dispatch, and demand response as loosely coupled components, limiting their ability to holistically manage these deep uncertainties. To address this, we propose a novel AI-enhanced multi-timescale optimization strategy that creates a synergistic, integrated framework. Methodologically, the approach begins with an attention-augmented Bidirectional Long Short-Term Memory (BiLSTM) model that generates high-fidelity spatiotemporal load forecasts, providing crucial spatial-aware inputs often overlooked by traditional models. These enhanced forecasts are then leveraged by a Model Predictive Control (MPC) strategy for more robust and proactive day-ahead and intraday dispatch. Crucially, the framework integrates a dynamic demand response (DDR) mechanism that is directly coupled with real-time MPC outputs, ensuring that load flexibility is mobilized based on immediate system needs rather than static signals alone. Simulations, driven by real-world operational data, confirm that this integrated strategy not only reduces operational costs and improves forecasting accuracy but also establishes a more resilient and adaptive VPP operational paradigm compared to prior AI-based methods.

Accurate and non-invasive measurement of fish phenotypic characteristics in underwater environments is crucial for advancing aquaculture. Traditional manual methods require significant labor to anesthetize and capture fish, which not only raises ethical concerns but also risks causing injury to the animals. Alternative hardware-based approaches, such as acoustic technology and active structured light techniques, are often costly and may suffer from limited measurement accuracy. In contrast, image-based methods utilizing low-cost binocular cameras present a more affordable solution, although they face challenges such as light refraction between water and the waterproof enclosure, which can cause discrepancies between image coordinates and actual positions. To address these challenges, we have developed a fish keypoint detection dataset and trained both a fish object detection model and a keypoint detection model using the RTMDet and RTMPose architectures to identify keypoints on Plectropomus leopardus. Since the binocular camera must be housed in a waterproof enclosure, we correct for birefringence caused by the water and the enclosure by applying refraction corrections to the detected keypoint coordinates. This ensures that the keypoint coordinates obtained underwater are consistent with those in air, thereby improving the accuracy of subsequent stereo matching. Once the corrected keypoint coordinates are obtained, we apply the least squares method, in conjunction with binocular stereo imaging principles, to perform stereo matching and derive the actual 3D coordinates of the keypoints. We calculate the fish body length by measuring the 3D coordinates of the snout and tail. Our model achieved 98.6% accuracy in keypoints detection (AP@0.5:0.95). Underwater tests showed an average measurement error of approximately 3.2 mm (MRPE=3.50%) for fish in a tank, with real-time processing at 28 FPS on an NVIDIA GTX 1060 GPU. These results confirm that our method effectively detects keypoints on fish bodies and measures their length without physical contact or removal from the tank. By eliminating invasive procedures, our approach not only improves measurement efficiency but also aligns with ethical standards in aquaculture. Compared to existing techniques, our method offers enhanced accuracy (reducing MRPE by 53.8% compared to baseline methods) and practicality, making it a valuable tool for the aquaculture industry.

Matched-field processing localizes underwater acoustic targets by measuring the degree of correlation between the acoustic field and replica fields. The intrusion of mesoscale eddies can induce sound speed mismatch in the matched-field process. Therefore, it is essential to investigate the impact of mesoscale eddies on matched-field localization errors. In this study, the typical vertical structure of mesoscale eddies in a certain region of the Northwestern Pacific was synthesized using the mesoscale eddy dataset META 2.0 and Argo float data. Furthermore, by employing both an idealized eddy model and composite-analysis structure of eddy, the performance of the localization algorithm was evaluated under the influence of mesoscale eddies with different structures and in different regions. The results show that under specific conditions, the distribution of localization errors exhibits certain patterns, which is beneficial for inverting eddy parameters via matched-field processing. Finally, the mechanism behind the systematic distribution of localization errors is discussed and analyzed. In the simulations, the source frequency was swept from 50 to 75 Hz with a 1 Hz step, and a circular array was employed as the receiving aperture. These findings indicate that, in the absence of small-scale interference and within a certain range of sound speed mismatch, the localization error of underwater acoustic targets increases with the strengthening of mesoscale eddy disturbances.

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid.

Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment.

Simulating the acoustic field excited by pulse sound sources holds significant practical value in computational ocean acoustics. Two primary methods exist for modeling underwater acoustic propagation in the time domain: the Fourier synthesis technique based on frequency decomposition and the time-domain underwater acoustic propagation model (TD-UAPM). TD-UAPMs solve the wave equation in the time domain without requiring frequency decomposition, providing a more intuitive explanation of the physical process of sound energy propagation over time. However, time-stepping numerical methods can accumulate numerical errors, making it crucial to improve the algorithm’s accuracy for TD-UAPMs. Herein, the time-domain wavenumber integration model SPARC was improved by replacing the second-order finite element method (FEM) with the high-order accuracy finite difference method (FDM). Furthermore, the matched interface and boundary (MIB) method was used to process the seabed more accurately. The improved model was validated using three classic underwater acoustic benchmarks. By comparing the acoustic solutions obtained using the FDM and the FEM, it is evident that the improved model requires fewer grid points while maintaining the same level of accuracy, leading to lower computational costs and faster processing compared to the original model.

Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of double-ended recording data and the dependence of traditional algorithms on accurate line parameters, this paper introduces a novel fault location algorithm for hybrid transmission lines. The method integrates a data synchronization approach with a physics-informed neural network (PINN) implemented using a backpropagation (BP) neural network architecture. First, the proposed synchronization algorithm corrects the phase misalignment between double-ended recordings. Second, a distributed-parameter fault location model is developed to derive a location function, which is then used to construct physics-informed input features. This approach reduces the need for large fault datasets, addressing the challenge of the low occurrence of faults in practice. Finally, a BP neural network employing these physics-informed features is utilized to learn the nonlinear mapping to the fault location, allowing for accurate fault location, enabling accurate positioning without requiring precise line parameters. Validation using actual line data confirms the high precision of the synchronization algorithm. Furthermore, simulations show that the proposed fault location algorithm achieves high accuracy and remains robust against variations in fault position, type, transition resistance, inception angle, and load current, making it highly practical for real engineering applications.

Research on ocean ambient noise is highly important for environment monitoring, marine mammal protection, underwater communication and navigation. In this paper, we present the long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea, at a depth of 1240 m. The data were collected from 11 th July 2022 to 31 st December 2022 together with local wind speeds ranging from 1 to 58 knots (two typhoons involved), and the processing frequency band is between 20 and 2000 Hz. The long-term mean noise level is calculated along with its skewness, kurtosis and percentile distributions. Diurnal and monthly average of noise levels are analyzed, and the large fluctuations in lower (≤100 Hz) and higher (≥400 Hz) frequencies are respectively caused by the variation of the number of nearby and distant ships and the diverse distributions of the windspeeds in individual months. We find that the noise level in winter (Dec.) is 10~11 dB higher than that in summer (Jul.) at higher frequencies. The probability densities of noise levels in the situation of a fixed wind speed are likely to obey the Burr distributions in low frequencies (50 and 100 Hz) and the Weibull distributions in high frequencies (400 and 1000 Hz). In addition, the mean noise levels for different Beaufort scales match well with the 5-dB-addtion Wenz curves, and a mathematic relationship is acquired between the noise level and wind speed in the experimental site. The results are of great representativeness, and are significant to data-driven noise modelling, evaluation and improvement of sonar performance in the region of South China Sea with an incomplete deep-water sound channel.

The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. Specifically, the U-shaped end-to-end neural network which contains down-sampling and up-sampling parts is proposed to improve the physical spatial resolution limited by the array aperture. The single target and multiple cases are considered separately. In both cases, the normalized loss on the testing sets declines rapidly, and the predicted high-resolution images own great agreement with the ground truth eventually. Further improvements in resolution are focused on, that is, compressing the predicted high-resolution image to its physical spatial resolution limitation. The results show that the trained end-to-end neural network could map high resolution targets to the impulse responses at the same location and amplitude with an uncertain target number. The proposed convolutional neural network approach could give a practical alternative to improve the physical spatial resolution of underwater sonar images.

Although previous studies have shown that repetitive transcranial magnetic stimulation (rTMS) can ameliorate addictive behaviors and cravings, the underlying neural mechanisms remain unclear. This study aimed to investigate the effect of high‐frequency rTMS with the left dorsolateral prefrontal cortex (L‐DLPFC) as a target region on smoking addiction in nicotine‐dependent individuals by detecting the change of spontaneous brain activity in the reward circuitry. We recruited 17 nicotine‐dependence participants, who completed 10 sessions of 10 Hz rTMS over a 2‐week period and underwent evaluation of several dependence‐related scales, and resting‐state fMRI scan before and after the treatment. Functional connectivity (FC) analysis was conducted with reward‐related brain regions as seeds, including ventral tegmental area, bilateral nucleus accumbens (NAc), bilateral DLPFC, and bilateral amygdala. We found that, after the treatment, individuals showed reduced nicotine dependence, alleviated tobacco withdrawal symptoms, and diminished smoking cravings. The right NAc showed increased FC with right fusiform gyrus, inferior temporal gyrus (ITG), calcarine fissure and surrounding cortex, superior occipital gyrus (SOG), lingual gyrus, and bilateral cuneus. No significant FC changes were observed in other seed regions. Moreover, the changes in FC between the right NAc and the right ITG as well as SOG before and after rTMS were negatively correlated with changes in smoking scale scores. Our findings suggest that high‐frequency L‐DLPFC‐rTMS reduces nicotine dependence and improves tobacco withdrawal symptoms, and the dysfunctional connectivity in reward circuitry may be the underlying neural mechanism for nicotine addiction and its therapeutic target.