Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \( \mathcal{O}(N^2d) \) to \( \mathcal{O}(Nr^2) \), with \( r

Ensuring high-quality water resources is crucial for sustainable urban development, public health, and resilient city infrastructure, yet traditional anomaly detection methods struggle with the highly variable, non-stationary, and concept-drifting nature of urban water quality data streams. This study proposes a Physics-Informed Dynamic Ensemble Learning (PIDEL) framework, an artificial intelligence approach that combines diverse classical and deep learning models with Physics-Informed Neural Networks (PINNs) embedding convection–diffusion constraints, a Genetic Algorithm (GA) for ensemble optimization, and a Jensen–Shannon Divergence (JSD) based mechanism for dynamic model switching. Applied to a real-world urban water quality dataset, PIDEL achieves an F1-score of 0.95, representing a 59% improvement over the best static ensemble, while reducing false alarms by 73% compared to traditional methods and maintaining F1-scores above 0.9 across all sliding windows. The framework processes each 60-minute window in approximately 2.3 s on standard hardware, demonstrating its suitability for real-time deployment in smart city water systems. These results highlight that integrating physics-informed constraints with dynamic ensemble learning can substantially enhance the reliability, interpretability, and operational value of automated water quality anomaly detection for urban utilities. • Novel LSTM-PINN integrates physics constraints with deep learning for anomaly detection. • Dynamic ensemble adapts to concept drift via Jensen–Shannon divergence-based switching. • Genetic algorithm optimizes ensemble, achieving 95% F1-score and 59% improvement. • Optimal physics loss coefficient ( λ = 0 . 35 ) balances physical and data-driven learning. • Real-time processing (2.3 s per window) enables practical smart city water monitoring.

Incomplete time series data is a common problem in real-world application scenarios. Recent research has taken the approach of separating interpolation and anomaly detection, which is not interactive and performs poorly. On the other hand, interpolation using traditional methods relies on a large amount of a priori knowledge, and using deep learning methods takes up a large amount of computational resources and is inefficient. In this study, we propose a correlation-aware diffusion model that successfully bypasses the above problems. Our approach focuses on capturing deep multivariate correlations from limited incomplete data and use low-frequency component to guide generation. Experiments on four realistic scenario datasets covering three domains show that our method achieves better anomaly detection results than existing methods for various missing rates.

This paper presents SPDM, an innovative spatiotemporal-periodic diffusion model for multivariate time series interpolation, which addresses the challenges of spatiotemporal dependency and periodicity inherent in real-world datasets. Unlike prior methods, SPDM integrates conditional features capturing spatiotemporal correlations and geographic relationships, enhancing the model's ability to account for complex interdependencies within time series data. A noise prediction module, leveraging Fast Fourier Transform, decomposes time series into periodic components, thereby enabling the model to capture both intra- and inter-period dynamics and inter-channel correlations effectively. Experimental results on multiple industrial datasets show that SPDM outperforms state-of-the-art methods across various missing data scenarios, highlighting its robustness and effectiveness. This work establishes a new approach to time series interpolation by combining conditional information construction with periodicity-aware diffusion modeling, offering promising insights for further applications in time series analysis.

Accurate wind power forecasting is critical for maintaining stable power grids, yet the inherent variability of wind and limited data availability for new wind farms present significant challenges. To address these issues, we present a novel artificial intelligence framework that integrates a self-attention enhanced Spatiotemporal Long Short-Term Memory (ST-LSTM) network with Model-Agnostic Meta-Learning (MAML), termed as the Meta-Learning Spatiotemporal Attention Long Short-Term Memory framework (MAML-STALSTM). This deep learning combination enables the model to effectively capture long-range spatiotemporal dependencies while rapidly adapting to new wind farm configurations or changing wind conditions with minimal training data. By employing rigorous data preprocessing techniques and ensuring temporal separation in data splitting, we mitigate potential data leakage and enhance the model’s generalizability. Extensive experiments conducted on both onshore and offshore wind farm datasets demonstrate that our artificial intelligence approach outperforms established baseline models, particularly excelling in data-scarce environments. Ablation studies highlight the crucial roles of the self-attention mechanism and meta-learning in improving forecasting accuracy, adaptation speed, and model robustness. These results emphasize the practical benefits of our approach in enhancing grid stability and supporting the seamless integration of wind energy, thereby contributing significantly to the advancement of sustainable energy solutions.

Hyperspectral images (HSIs) offer rich spectral–spatial information but pose challenges due to a high dimensionality and noise. This letter proposes a shape-adaptive high-order tensor decomposition (SAHTD) framework for hyperspectral feature extraction. The SAHTD employs a shape-adaptive sampling strategy based on a simplified local polynomial approximation and intersection confidence interval (LPA-ICI) to enhance edge segmentation. It integrates tensor Tucker decomposition and a nuclear norm-constrained regression model to extract discriminative features while preserving low-rank structures. Experiments on the Pavia University and Houston 2013 datasets demonstrate that the SAHTD achieves superior classification performance compared to relevant methods, validating its effectiveness for HSI analysis.

Deep learning is a powerful technique for semantic change detection (SCD) of bitemporal remote sensing images. In this work, we propose to improve SCD accuracy using deep learning with frequency feature enhancement. Specifically, we develop a frequency feature enhancement module that aims to enhance the performance of both binary change detection and semantic segmentation, two main key components for obtaining high SCD accuracy, by integrating the Fourier transform and attention mechanisms. Experimental results on the SECOND and LandSat-SCD datasets demonstrate the effectiveness of the proposed method, and it achieves high resolution for change boundaries.

Wind power prediction is a challenging task due to the high variability and uncertainty of wind generation and weather conditions. Accurate and timely wind power prediction is essential for optimal power system operation and planning. In this paper, we propose a novel Adaptive Expert Fusion Model (EFM+) for online wind power prediction. EFM+ is an innovative ensemble model that integrates the strengths of XGBoost and self-attention LSTM models using dynamic weights. EFM+ can adapt to real-time changes in wind conditions and data distribution by updating the weights based on the performance and error of the models on recent similar samples. EFM+ enables Bayesian inference and real-time uncertainty updates with new data. We conduct extensive experiments on a real-world wind farm dataset to evaluate EFM+. The results show that EFM+ outperforms existing models in prediction accuracy and error, and demonstrates high robustness and stability across various scenarios. We also conduct sensitivity and ablation analyses to assess the effects of different components and parameters on EFM+. EFM+ is a promising technique for online wind power prediction that can handle nonstationarity and uncertainty in wind power generation.

Deep learning techniques have been used to build velocity models (VMs) for seismic traveltime tomography and have shown encouraging performance in recent years. However, they need to generate labeled samples (i.e., pairs of input and label) to train the deep neural network (NN) with end-to-end learning, and the real labels for field data inversion are usually missing or very expensive. Some traditional tomographic methods can be implemented quickly, but their effectiveness is often limited by prior assumptions. To avoid generating and/or collecting labeled samples, we propose a novel method by integrating deep learning and dictionary learning to enhance the VMs with low resolution by using the traditional tomography-least square method (LSQR). We first design a type of shallow and simple NN to reduce computational cost followed by proposing a two-step strategy to enhance the VMs with low resolution: (1) Warming up. An initial dictionary is trained from the estimation by LSQR through dictionary learning method; (2) Dictionary optimization. The initial dictionary obtained in the warming-up step will be optimized by the NN, and then it will be used to reconstruct high-resolution VMs with the reference slowness and the estimation by LSQR. Furthermore, we design a loss function to minimize traveltime misfit to ensure that NN training is label-free, and the optimized dictionary can be obtained after each epoch of NN training. We demonstrate the effectiveness of the proposed method through the numerical tests on both synthetic and field data.

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of large-scale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. In this paper, we introduce a new wild large-scale latent fingerprint database that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new database consists of 1,318 unique fingerprint instances captured in all above mentioned settings. A total of 2,636 reference fingerprints from optical and capacitive sensors, 1,318 fingerphotos from smartphones, and 9,224 latent fingerprints from each of the 132 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. In addition, we provide an extensive set of analysis of various subset evaluations to highlight open challenges for future directions in latent fingerprint recognition research.

Blade icing detection is vital for wind turbines in cold climates, as it can prevent revenue loss and power degradation. Many machine learning models have been proposed to improve the detection of blade icing; however, earlier studies do not adequately address these issues due to the dynamics of sensor correlations and the imbalance of blade icing data, resulting in low precision and a high false alarm rate. In this study, we aim to address both of these challenges in order to identify blade icing more accurately. On this premise, we develop a spatial–temporal graph convolutional network (SGCN) that leverages the graph convolutional network for adaptively analyzing the dynamics of sensor correlations and a distance-based classifier to improve imbalanced learning. Experiments on the public UEA time series classification datasets and the real-world wind turbine datasets indicate that SGCN is capable of state-of-the-art accuracy, especially in the case of extremely imbalanced data.

With high spectral resolution, hyperspectral image(HSI) data will result in the Hughes phenomenon, which brings a huge challenge to hyperspectral image classification(HIC). Feature extraction can be applied to address this problem. But several traditional methods often ignore the spatial structure information of HSI data. In this paper, we propose a tensor nuclear norm based matrix regression based projections(TNMRP) for feature extraction of hyperspectral images. Firstly, TNMRP preprocesses the data by a filling method. Then, it automatically builds the graph of block-tensor samples and uses the optimal sparse coding coefficients to obtain the weight matrix. Finally, based on tensor representation, TNMRP calculates the optimal projection matrix. Experiments of classification on Indian Pines and Pavia University databases demonstrate the effectiveness of our proposed method.

Change detection is an important branch in remote sensing image processing. Deep learning has been widely used in this field. In particular, a wide variety of attention mechanisms have made great achievements. However, some models have become increasingly complex and large, often unfeasible for edge applications. This poses a major obstacle to industrial applications. In this paper, to solve the above challenges, we propose a Lightweight network structure to improve results while taking into account efficiency. Specifically, first, the shallow features are extracted by using the spatial exchange and change exchange of the down-sampling bi-temporal channel of the three-layer EfficientNet backbone network, and then the shallow features are used for low-dimensional skip-connection. After that, a hybrid dual-temporal data module is designed to mix the dual-temporal phase into a single image, then the high-dimensional low-pixel image is restored through the up-sampling. Finally the final change map is generated through the pixel-level classifier. Our method was evaluated on public datasets by evaluation indicators such as OA, IoU, F1, Recall, Precision.

Deep learning techniques have been widely used for semantic change detection (SCD) of remote sensing images (RSIs) and have shown encouraging performance. In this paper, we propose a novel neural network by embedding the difference enhancement (DE) module into the adjacent layers of ResNet for SCD of RSIs (DESNet), which can pay more attention to the changes of bi-temporal RSIs. Furthermore, we deploy the module of multi-scale parallel sampling spatial-spectral non-local (SSN) after feature extraction, which can effectively improve the robustness to large-scale changes and the integrity of the changed objects by fusing global features that sampled from the multi-scale feature space. The experimental tests demonstrate that our DESNet can achieve state-of-the-art accuracy on the SECOND dataset and the LandSat-SCD dataset.

In recent years, several novel satellite platforms and sensors have been proposed for the Earth Radiation Budget (ERB). Simulating the sensor-measured signals could be helpful for optimizing the settings of sensors and exploring their potential in ERB. The anisotropic factor, depicting the anisotropy of Earth’s radiation, is essential in the simulation. However, developing angular distribution models involves complex procedures of data preparation, processing, and modeling. This study, targeting at simplifying the procedure of simulating the signals of ERB sensors, proposed a suit of models for estimating the longwave anisotropic factors directly from the Earth’s radiative fluxes. The models were developed with CERES/Terra data sensed in rotating azimuth plane (RAP) mode during 2000-2005 and the artificial neural network (ANN) algorithm, and tested with 12 monthly of CERES/Terra data collected in RAP and cross-track mode during 2021-2022, respectively. Models were developed for 10 scene types based on Earth’s surface types, and compared with the operational ANN ADMs. Results showed that the longwave anisotropic factors were accurately estimated with the correlation coefficient (r) varying between 0.84 and 0.98 and MAPE within 1.20% for the test dataset, and the approach proposed in this study had comparable performance with the ANN ADMs. With the estimated anisotropic factors, the sensor-measured radiances were accurately retrieved with r=1.00 and MAPE=0.53%. Therefore, the proposed approach is promising in accurate and efficient simulations of novel ERB platforms and sensors like the Moon-based Earth Radiation.

The accurate extraction of surface rivers is of great significance to ecology, residence and so on. In view of the incomplete recognition of river edge contour in the surface river extraction of remote sensing image in the classical deep learning network U-Net, the ability of the network to learn and retain the detailed information of feature map is enhanced by strengthening the attention mechanism and introducing the densely connected Atrous Spatial Pyramid Pooling on the basis of U-Net. The experimental results show that the Pixel Accuracy of water extraction results by this method is 92.1%, and the Mean Intersection Over Union is up to 90.3%, the improved algorithm can effectively extract accurate surface river information.

A new method for performing a nonlinear form of manifold-oriented stochastic neighbor projection method is proposed. By the use of kernel functions, one can operate in the feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. The proposed method is termed as kernel-based manifoldoriented stochastic neighbor projection(KMSNP). By two different strategies, KMSNP is divided into two methods: KMSNP1 and KMSNP2. Experimental results on several databases show that, compared with the relevant methods, the proposed methods obtain higher classification performance and recognition rate. INTRODUCTION Kernel-based methods(kernel methods for short) have become a new hot topic in machine learning fields in recent years, their theoretical basis is statistical learning theory. Kernel methods are a class of algorithms for pattern analysis, whose best known element is the support vector machine(SVM) (Dardas and Georganas 2011).The methods skillfully introduce kernel function which not only reduces the curse of dimensionality (Cherchi and Guevara 2012, Xue et al. 2012), but also effectively solves the local minimum and incomplete statistical analysis in traditional pattern recognition methods on the premise of no additional computational capacity. As an availability way to resolve the problem of nonlinear pattern recognition, kernel methods approach the problem by mapping the data into a highdimensional feature space, where each coordinate corresponds to one feature of the data items, transforming the data into a set of points in a Euclidean space (Chen and Li 2011, Zhang et al. 2008). The theory of kernel methods can be traced back to 1909, Mercer proposed Mercer's theorem (Mercer 1909) which indicates that any ‘reasonable’ kernel function corresponds to some feature space. 1964, the use of Mercer's theorem for interpreting kernels as inner products in a feature space was introduced into machine learning by Aizerman et al. (AizermanI et al. 1964), but no sufficient importance has been attached to it. Until 1992, Vapnik et al. (Boser et al. 1992) successfully extended the SVM to the non-linear SVM by using kernel functions, it began to show its potential and advantages. Subsequently, more and more kernelbased methods were presented, such as: kernel principal component analysis(KPCA) (Xiao et al. 2012), kernel fisher discriminator(KFD) (Yang et al. 2005), kernel independent component analysis (KICA) (Zhang et al. 2013), kernel partial least squares(KPLS) (Helander et al. 2012) and so on. In this paper, we propose to use the kernel idea and present a method called kernel-based manifoldoriented stochastic neighbor projection(KMSNP) method through improving the manifold-oriented stochastic neighbor projection(MSNP) (Wu et al. 2011) technique. MSNP is based on stochastic neighbor embedding(SNE) (Hinton and Roweis 2002) and t-SNE (Maaten and Hinton 2008). The basic principle of SNE is to convert pairwise Euclidean distances into probabilities of selecting neighbors to model pairwise similarities while t-SNE uses student t-distribution to model pairwise dissimilarities in low-dimensional space. Different from SNE and t-SNE, MSNP converts pairwise dissimilarities of inputs to probability distribution related to geodesic distance in highdimensional space and uses Cauchy distribution to model stochastic distribution of features. Furthermore, it recovers the manifold structure through a linear projection by requiring the two distributions to be similar. Experiments demonstrate MSNP has unique advantages in terms of visualization and recognition task, but there are still two drawbacks in it: firstly, MSNP is an unsupervised method and lack of the idea of class label, so it is not suitable for pattern identification; secondly, since MSNP is a linear feature dimensionality reduction algorithm, it cannot effectively settle the nonlinear feature extraction problem. To overcome the disadvantages of MSNP, we have done some preliminary work. On the first, we introduced the idea of class label and presented a method called discriminative stochastic neighbor embedding analysis(DSNE) (Zheng et al. 2012, Chen Proceedings 27th European Conference on Modelling and Simulation ©ECMS Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang (Editors) ISBN: 978-0-9564944-6-7 / ISBN: 978-0-9564944-7-4 (CD) and Wang 2012). On the second, we think KMSNP can overcome the disadvantage mentioned above well. The rest of this paper is organized as follows: in Section 2, we provide a brief review of MSNP. Section 3 describes the detailed algorithm derivation of KMSNP. Furthermore, experiments on various databases are presented in Section 4. Finally, we provide some concluding remarks and describe several issues for future works in Section 5. MSNP Considering the problem of representing d-dimensional data vectors x1, x2, . . . , xN, by r-dimensional (r << d) vectors y1, y2, . . ., yN such that yi represents xi. The basic principle of MSNP is to convert pairwise dissimilarity of inputs to probability distribution related to geodesic distance in high-dimensional space, and then using Cauchy distribution to model stochastic distribution of features, finally, MSNP recovers the manifold structure through a linear projection by requiring the two distributions to be similar. Mathematically, the similarity of datapoint xi to datapoint xj is depicted as the following joint probability pij which means xi how possible to pick xj as its neighbor: exp( / 2) exp( / 2) geo ij ij geo ik k i D p D      (1) where Dij is the geodesic distance for xi and xj. In practice, MSNP calculates geodesic distance by using a two-phase method (Wu et al. 2011). Firstly, an adjacency graph G is constructed by K-nearest neighbor strategy. Secondly, the desired geodesic distance is approximated by the shortest path of graph G. This procedure is proposed in Isomap to estimate geodesic distance and the detail calculation steps can be found in (Tenenbaum et al. 2000). For low-dimensional representations, MSNP employs Cauchy distribution with  degree of freedom to construct joint probability qij. The probability qij indicates how possible point i and point j can be stochastic neighbors is defined as:

Focusing on Asian dust aerosols, the Community Radiative Transfer Model (CRTM) developed at JCSDA under NOAA/NESDIS is used to simulate the effects of dust on the observations from 10-12μm split-window channels of the Visible and InfraRed Radiometor(VIRR) on Chinese FengYun-3A (FY-3A) satellite. Based on the simulation, an infrared dust retrieving algorithm is developed with VIRR data, in which the effective radius of Asian dust is defined with the ground measurements from SKYNET. The optical thickness and height of the dust layer are retrieved simultaneously with this algorithm. The results show the optical thickness of dust layer is reliable comparing with the height. The errors in the calibration of sensors and surface temperature may have large effect on the retrieving.

The Earth Radiation Measurement (ERM) instrument onboard FengYun (FY)-3B satellite observes the Earth's atmosphere through a narrow scanning field of view (NFOV) and a wide nonscanning FOV. For each field of view, the measurements are made from two broadband channels: a total waveband channel covering 0.2-50 μm and a shortwave (SW) band covering 0.2-4.3 μm. The validation to the ERM calibration was carried out by comparing the unfiltered longwave (LW) and SW radiances from ERM with those from Clouds and Earth's Radiation Energy System (CERES) flight model 3 onboard Earth Observing System Aqua satellite. While the ERM LW and SW radiances have a good correlation with CERES data, there is a systemic bias between the two data sets. A spectral correction is made for the ERM data using the CERES data. After the correction, the error of the ERM LW radiance is reduced from -3.00 to -0.60 W/sr · m2. For the SW radiance, the bias is reduced from 6.00 to 4.00 W/sr · m2. Based on the ERM in-orbit calibration data, the stability of the ERM LW radiometric response is analyzed, and it is shown that the gains are stable with a variation of less than 1.5% during its first 9 months in orbit. However, the gains at the SW channels have larger changes and exceed 3%. These drifts might be caused by the detector degradation. Also, the NFOV scanner at the SW channel is no longer working after its 8 months in orbit.

The AMSU-B data contain the vertical distribution of the retrieved water vapor from satellite brightness temperature. The tropical cyclone Matsa event in 2005 formed from the northwest pacific is analysised by combining the AMSU-B water vapor fields with the latent heat. Results show the combination the TRMM latent heating data in the heavy precipitation area around TC, we find heat release is one of the most important factors to impact TC intensity, especially to the long-life and land load TC. And there is a time delay between the low level water vapor convergence and the middle layer latent heat, which will improve the real-time forecast of tropical cyclones.

Large-scale environmental vapor flux plays a very important role in the development of tropical cyclones. A regression method was developed to get the relationship between radiances measured by the Advanced Microwave Sounding Unit (AMSU) and water vapor content. With this formula, we get the water vapor content in the upper, middle and lower troposphere, which retrieveled by channels 183.3±1 GHz, 183.3±3 GHz and 183.3±7 GHz brightness temperatures respectively. Then, we illustrate the Typhoon Bilis (0604) to analyse the distributing and evolvement of the surrounding water vapor. By this exponentially algorithm, there are great water vapor transportations as indicated by the water vapor channel brightness temperatures. And it shows the importance of the environmental water vapor in the development of tropical cyclones.