Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System, a novel diffusion adversarial framework designed to address these challenges by: (1) employing a transformer-based global dependency capturing module to learn long-range spatio-temporal correlations from sparse trajectories; (2) constructing a generative imputation model that conditions on easily observed tidal covariates to progressively refine imputed salinity fields; and (3) using a scheduler diffusion method to enhance the model's robustness. This unified architecture exploits the periodic nature of tidal signals as a proxy for unmeasured physical drivers, without the need for additional equipment. We evaluate OASIS on four benchmark datasets, including one real-world measurement from Fort Pierce Inlet and three simulated Gulf of Mexico trajectories. Results show consistent improvements over both traditional and neural baselines, achieving up to 52.5% reduction in MAE compared to Kriging. We also develop a lightweight, web-based deployment system that enables salinity imputation through interactive and batch interfaces, available at: https://github.com/yfeng77/OASIS.

In the realm of image generation, the quest for realism and customization has never been more pressing. While existing methods like concept sliders have made strides, they often falter when it comes to non-AIGC images, particularly images captured in real-world settings. To bridge this gap, we introduce Beyond Sliders, an innovative framework that integrates GANs and diffusion models to facilitate sophisticated image manipulation across diverse image categories. Improved upon concept sliders, our method refines the image through fine-grained guidance—both textual and visual—in an adversarial manner, leading to a marked enhancement in image quality and realism. Extensive experimental validation confirms the robustness and versatility of Beyond Sliders across a spectrum of applications.

Traditional deep learning reconstruction (DLR) methods have been sparsely applied in practical low-dose computed tomography (LDCT) imaging, as they heavily rely on the similarity between the latent distributions of data features. However, in real LDCT imaging scenarios, the distribution of data features is highly diverse and complex, which limits the generalizability of existing DLR methods. Recently, diffusion models have shown great potential in the field of LDCT imaging, and some early studies have used them to address the domain generalization problem. However, they still face challenges such as high time consumption, difficulties in training with high resolution, and performance degradation in denoising scenario. In this paper, we propose a novel domain progressive LDCT imaging framework with an iterative partial diffusion model (IPDM) as the core. Firstly, the derived IPDM theoretical framework supports completing the denoising task by iterating a small part of the complete diffusion model, utilizing the strong generation ability of the diffusion model while alleviating time consumption and convergence difficulties. Secondly, a derived condition guided sampling method alleviates sampling bias caused by deviations of the predictive data gradient and Langevin dynamics. Finally, an adaptive weight strategy based on pixel-wise noise estimation can gradually adjust guided intensity. Extensive testing on diverse datasets reveals that our method outperforms traditional iterative reconstructions, unsupervised, and some supervised DLR methods in visual and quantitative evaluations, closely matching the performance of state-of-the-art supervised DLR techniques. Additionally, our IPDM was trained using practical normal-dose CT data, rather than the tested LDCT data. This enables our method to have better generalization ability compared to traditional DLR methods in practical imaging scenarios. Source code is available at https://github.com/LFY1998/IPDM-PyTorch.

Graph neural networks (GNNs), a class of deep learning models designed for performing information interaction on non-Euclidean graph data, have been successfully applied to node classification tasks in various applications such as citation networks, recommender systems, and natural language processing. Graph node classification is an important research field for node-level tasks in graph data mining. Recently, due to the limitations of shallow GNNs, many researchers have focused on designing deep graph learning models. Previous GNN architecture search works only solve shallow networks (e.g., less than four layers). It is challenging and nonefficient to manually design deep GNNs for challenges like over-smoothing and information squeezing, which greatly limits their capabilities on large-scale graph data. In this article, we propose a novel neural architecture search (NAS) method for designing deep GNNs automatically and further exploit the application potential on various node classification tasks. Our innovations lie in two aspects, where we first redesign the deep GNNs search space for architecture search with a decoupled mode based on propagation and transformation processes, and we then formulate and solve the problem as a multiobjective optimization to balance accuracy and computational efficiency. Experiments on benchmark graph datasets show that our method performs very well on various node classification tasks, and exploiting large-scale graph datasets further validates that our proposed method is scalable.

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the existing state-of-the-art methods. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 3rd in the leaderboard, showing its effectiveness.

Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only model pairwise node relationships and are not conducive to exploring higher-order connectivity, while hypergraph provides a natural way to model high-order relations between nodes. However, recent studies show that social recommendations still face the following challenges: 1) a majority of social recommendations ignore the impact of multifaceted social relationships on user preferences; 2) the item homogeneity is often neglected, mainly referring to items with similar static attributes have similar attractiveness when exposed to users that indicating hidden links between items; and 3) directly combining the representations learned from different independent views cannot fully exploit the potential connections between different views. To address these challenges, in this article, we propose a novel method DH-HGCN++ for multiple social recommendations. Specifically, dual homogeneity (i.e., social homogeneity and item homogeneity) is introduced to mine the impact of diverse social relations on user preferences and enrich item representations. Hypergraph convolution networks with motifs are further exploited to model the high-order relations between nodes. Finally, cross-view contrastive learning is proposed as an auxiliary task to jointly optimize the DH-HGCN++. Real-world datasets are used to validate the effectiveness of the proposed model, where we use sentiment analysis to extract comment relations and employ the k-means clustering algorithm to construct the item-item correlation graph. Experiment results demonstrate that our proposed method consistently outperforms the state-of-the-art baselines on Top-N recommendations.

The SPiForest, a new isolation-based approach to outlier detection, constructs iTrees on the space containing all attributes by probability density-based inverse sampling. Most existing iForest (iF)-based approaches can precisely and quickly detect outliers scattering around one or more normal clusters. However, the performance of these methods seriously decreases when facing outliers whose nature "few and different" disappears in subspace (e.g., anomalies surrounded by normal samples). To solve this problem, SPiForest is proposed, which is different from existing approaches. First, SPiForest uses the principal component analysis (PCA) to find principal components and estimate each component's probability density function (pdf). Second, SPiForest utilizes the inv-pdf, which is inversely proportional to the pdf estimated from the given dataset, to generate support points in the space containing all attributes. Third, the hyperplane decided by these support points is used to isolate the outliers in the space. Next, these steps are repeated to build an iTree. Finally, many iTrees construct a forest for outlier detection. SPiForest provides two benefits: 1) it isolates outliers with fewer hyperplanes, which significantly improves the accuracy and 2) it effectively detects the outliers whose nature "few and different" disappears in subspace. Comparative analyses and experiments show that the SPiForest achieves a significant improvement in terms of area under the curve (AUC) when compared with the state-of-the-art methods. Specifically, our method improves by at most 17.7% on AUC when compared to iF-based algorithms.

This paper explores the application of large-scale foundation models (LSF -Models) for the improved prognostic management of smart grid power transformers. One of the foun-dational architectures of many LSF -Models, including ChatGPT, is the Transformer architecture. This architecture helps LSF-Models perform complex classification predictions and provide users with an appropriately predicted response. The advanced natural language processing techniques of the Transformer ar-chitecture interpret the nuances of power transformer-related data and efficiently extract valuable insights for fault detection and diagnosis. The multiple self-attention mechanisms of the architecture provide an enhanced form of feature extraction which expedites the creation of comprehensive models for large multiclass datasets. During testing, this fault classification method performed with 97.2 % accuracy according to the Matthews Correlation Coefficient when evaluating 45 classes of simulated power transformer internal faults and external transient disturbances. To better represent the diverse multimodal data that compose smart grid systems, this research explores a unified system that integrates multiple machine-learning models into one simple and easy-to-use interface. The backend of this interface again takes advantage of the Transformer architecture to perform conversation-based classification and to provide a response or prediction from the appropriate system-integrated model. The success of the Transformer architecture in diverse applications within the same overall system showcases its potential to analyze the wide range of data typically found throughout a robust smart grid system.

Clustering, as one of the main tasks of machine learning, is also the core work of granular computing, namely granulation. Most of the recent granular computing based clustering algorithms only utilize the plain granule features without taking the granule structure into account, especially in information area with widespread application of algebraic structure. This paper aims at proposing an algebraic structure based clustering method from granular computing prospective. Specifically, the algebraic structure based granularity is firstly formulated based on the granule structure of an algebraic binary operator. An algebraic structure based clustering method is then proposed by incorporating congruence partitioning granules and homomorphically projecting granule structure. Finally, proof of the lattice at multiple hierarchical levels and comparative analysis of experimental cases validate the effectiveness of the proposed clustering method. The algebraic structure based clustering method can provide a general framework to perform granularity clustering using the algebraic granule structure information. It meanwhile advances the granular computing methods by combing the granular computing theory and the clustering theory.

Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated image data or LDCT images from one specific scanner may not work well for another CT scanner and image processing task. To solve such domain adaptation problem, in this study, a novel generative adversarial network (GAN) with noise encoding transfer learning (NETL), or GAN-NETL, is proposed to generate a paired dataset with a different noise style. Specifically, we proposed a method to perform noise encoding operator and incorporate it into the generator to extract a noise style. Meanwhile, with a transfer learning (TL) approach, the image noise encoding operator transformed the noise type of the source domain to that of the target domain for realistic noise generation. One public and two private datasets are used to evaluate the proposed method. Experiment results demonstrated the feasibility and effectiveness of our proposed GAN-NETL model in LDCT image synthesis. In addition, we conduct additional image denoising study using the synthesized clinical LDCT data, which verified the merit of the proposed synthesis in improving the performance of the DL based LDCT processing method.

Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit pairwise relations to mine user preferences), ignoring the impact of multifaceted social relations on user preferences (i.e., high order complexity of user relations). Moreover, an observing fact is that similar items always have similar attractiveness when exposed to users, indicating a potential connection among the static attributes of items. Here, we advocate modeling the dual homogeneity from social relations and item connections by hypergraph convolution networks, named DH-HGCN, to obtain high-order correlations among users and items. Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.

Summary Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in‐service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time‐series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data‐driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one‐hot health state indicators are appended to the historical time series so that the model learns end‐of‐life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Experiments on multivariate time‐series datasets collected from real‐world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long‐term prognostics.

Knowledge representation of networked systems is fundamental in many disciplines. To date, existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels. For example, a user may belong to diverse interest groups of a social network, resulting in multi-label networks for many applications. A multi-label network not only has multiple labels for each node, the labels are often highly correlated making existing methods ineffective or even fail to handle such correlation for node representation learning. In this article, we propose a novel multi-label graph convolutional network (MuLGCN) for learning node representation. To fully explore label-label correlation and network topology structures, we propose to model a multi-label network as two Siamese GCNs: a node-node-label graph and a label-label-node graph. The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and are seamlessly integrated in one objective function. The learned label representations can effectively preserve the intra-label interaction and node label properties, and are aggregated to enhance the node representation learning under a unified training framework. Experiments and comparisons on multi-label node classification validate the effectiveness of our proposed approach.

Noise and inconsistency commonly exist in real-world information networks, due to the inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the most recent graph convolutional networks (GCNs) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. Noisy node content, combined with sparse features, provides essential challenges for existing methods to be used in real-world noisy networks. In this article, we propose feature-based attention GCN (FA-GCN), a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. To tackle noise and sparse content in each node, FA-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each node feature. To model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes to learn and vary feature importance, with respect to their connections. By using a spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than the state-of-the-art methods in both noise-free and noisy network environments.

In traditional graph neural networks (GNNs), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learning. In reality, network topology and node content each provide unique and important information, and they are not always consistent because of noise, irrelevance, or missing links between nodes. A pure topology-driven feature aggregation approach between unaligned neighborhoods may deteriorate learning from nodes with poor structure-content consistency, due to the propagation of incorrect messages over the whole network. Alternatively, in this brief, we advocate a co-alignment graph convolutional learning (CoGL) paradigm, by aligning topology and content networks to maximize consistency. Our theme is to enforce the learning from the topology network to be consistent with the content network while simultaneously optimizing the content network to comply with the topology for optimized representation learning. Given a network, CoGL first reconstructs a content network from node features then co-aligns the content network and the original network through a unified optimization goal with: 1) minimized content loss; 2) minimized classification loss; and 3) minimized adversarial loss. Experiments on six benchmarks demonstrate that CoGL achieves comparable and even better performance compared with existing state-of-the-art GNN models.

Extensive efforts have been applied to develop efficient feature extraction algorithms, which aim to achieve optimal results in many fundamental tasks such as Web-based software service clustering, recommendation and composition. However, one common issue for existing methods is that mined features are problem dependent, causing poor generalization ability across different applications. Recent studies show that we can represent networked data (e.g., citation networks and social networks) as low-dimensional vectors with rich structure and content information preserved, which can then greatly facilitate many downstream tasks such as classification and clustering. In this article, we focus on the problem of Web service network embedding, which aims to learn low-dimensional vectors to represent services by encoding both Mashup-API composition structure and service functional content. We first propose a novel probabilistic topic model to predict potential links between Mashups and APIs in the service network. Then, we develop a Service Graph Convolutional Network (Service-GCN) to learn vector representations of services, where each service (e.g., Mashup or API) forms its representation through message passing between neighborhood services over the network. We evaluate the network embedding quality on two real-world datasets for downstream classification and clustering tasks. Experimental results show that the average performance of our method improves 20.7 percent (Micro-F1) in service classification and 19.0 percent (Accuracy) in Mashup clustering compared to the state-of-the-art, which verified the effectiveness of the proposed approach for learning vector representations of Web services.

In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.

Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.

Summary Condition monitoring and fault diagnosis of main bearings of large‐scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine‐tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.

Aquaculture, especially fish farming, plays a vital role in ensuring food security in the United States and worldwide. However, for fish farming to be sustainable and economically viable, drastic improvements to the current labor-intensive and resource-inefficient operations are required. The hybrid aerial/underwater robotic system (HAUCS) aims to bring fundamental innovations to how pond-based farms operate. HAUCS is an end-to-end framework that consists of three principal subsystems: 1) a team of collaborative aero-amphibious robotic sensing platforms integrated with water quality sensors; 2) a land-based home station that can provide automated charging and sensor cleaning; and 3) a backend processing center that includes a machine-learning-based water quality prediction model and farm control center. HAUCS will be capable of collaborative monitoring and decision-making on farms of varying scales. The HAUCS platform, payload, and prediction model are discussed. The initial deployment of the HAUCS framework at a land-based aquaculture fish farm is presented.

Tagging systems have been widely used as a major way of managing Web service resources. Many portals such as ProgrammableWeb and BioCatalogue allow users to create manual tags annotating Web services and their compositions (e.g., mashups). This is extremely helpful for managing and retrieving enormous Web service data. In the past few years, many tag recommendation approaches have been proposed for Web services that contain few or no tags. Most of them only exploit the textual content or tag service matrix information. Sometimes those approaches suffer from the data sparsity problem, especially when Web services have only few tags or their auxiliary textual contents are hard to be obtained. In real world, a plenty of relationships are available in recommendation systems, e.g., the composition relationship between services and the annotation relationship between mashups and tags. These multi-relational data can be utilized as additional features to improve the recommendation performance. In this paper, we exploit various types of relationships as features and propose a novel topic-sensitive approach based on the Factorization Machines for mashup tag recommendation. Factorization Machines is utilized to model the pair-wise interactions between all features and predict adequate tags for mashups. In this approach, we first obtain the latent topics of all tags as well as the description documents for mashups and APIs based on a novel probabilistic topic model. Then, a multi-relational network by mining various relationships from the Web service data is constructed. Various auxiliary informations are subsequently extracted from the network to train the Factorization Machines. The proposed model is evaluated on three real-world datasets and the experimental results show that it outperforms several state-of-the-art methods.

Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

Network embedding aims to preserve topological structures of a network using low-dimensional vectors and has shown to be effective for driving a myriad of graph mining tasks (e.g., link prediction or classification) free of the stressful feature extraction procedure. Many methods have been proposed to integrate node content and/or label information, with nodes sharing similar content/labels being close to each other in the learned latent space. To date, existing methods either consider networked instances with a single label or consider a set of labels as a whole for node representation learning. Therefore, they cannot handle network of instances containing multiple labels (i.e. multi-labels), which are ubiquitous in describing complex concepts of instances. In this article, we formulate a new multi-label network embedding (MLNE) problem to learn feature representation for networked multi-label instances. We argue that the key to MLNE learning is to aggregate node topology structures, node content, and multi-label correlations. We propose a two-layer network embedding framework to couple information for effective learning. To capture higher order label correlations, we use labels to form a high-level label-label network over a low-level node-node network, in which the label network interacts with the node network through multi-labeling relations. The low-level node-node network can be enhanced by latent label-specific features from high-level label network with well-captured high-order correlations between labels. To enable the multi-label informed network embedding, we force both node and label representations being optimized under the same low-dimensional latent space by a unified training objective. Experiments on real-world data sets demonstrate that MLNE achieves better performance compared with methods with or without considering label information.

The advent of Service-Oriented Architecture (SOA) has brought a fundamental shift in the way in which distributed applications are implemented. An overwhelming number of Web-based services (e.g., APIs and Mashups) have leveraged this shift and furthered development. Applications designed with SOA principles are typically characterized by frequent dependencies with one another in the form of heterogeneous networks, i.e., annotation relations between tags and services, and composition relations between Mashups and APIs. Although prior work has shown the utility gained by exploring these networks, their analysis is still in its infancy. This article develops an approach to learning representations of the Web service network, which seeks to embed Web services in low-dimensional continuous vectors with preserved information of the network structure, functional tags, and service descriptions, such that services with similar functional properties and network structures are mapped together in the learned latent space. We first propose a topic generative model for constructing two topic distribution networks (Mashup-Topic and API-Topic) from the service content. Then, we present an efficient optimization process to derive low-dimensional vector representations of Web services from a tri-layer bipartite network with the Mashup-Topic and API-Topic networks on two ends and the Mashup-API composition network in the middle. Experiments on real-word datasets have verified that our approach is effective to learn robust low-rank service representations, i.e., 25% F1-measure gain over the state-of-the-art in Web service recommendation task.

The service-oriented architecture makes it possible for developers to create value-added Mashup applications by composing multiple available Web services. Due to the overwhelming number of Web services online, it is often hard and time-consuming for developers to find their desired ones from the entire service repository. In the past, various approaches aim at recommending Web services for automatic Mashup creation have been proposed, i.e., TFIDF, collaborative filtering and topic model-based methods, which rely on the original service descriptions given by service providers. However, most traditional methods fail to capture the function-related features of services since words contained in service descriptions usually correspond to different intent aspects (e.g., functional and non-functional related). To tackle this problem, we propose a tag attention-based recurrent neural networks model for Web service recommendation. The model consists of two Siamese bidirectional Long Short-Term Memory (LSTM) networks, which jointly learn two embeddings representing the functional features of Web services and the functional requirements of Mashups. In addition, by considering the tags of services as functional context information, the model can learn to assign attention scores to different words in service descriptions according to their intent importance, thus words used to reveal the functional properties of Web service will be given special attention. We compare our approach with the state-of-the-art methods (e.g., RTM, Word2vec, etc.) on a real-world dataset crawled from ProgrammableWeb, and the experimental results demonstrate the effectiveness of the proposed model.

Service recommendation is a fundamental task in many application environments (e.g., Mashup creation and cloud computing). In the past, various methods have been proposed to facilitate the service selection process based on the original functional descriptions. However, the mined features from the descriptions are usually too sparse for training a well-performed model. In addition, most methods neglect to differentiate the weights of various features, while words included in descriptions usually exhibit different intentions (e.g., functional or non-functional). To address these challenges, in this paper we propose a text expansion and deep model-based approach for service recommendation. Specifically, we first expand the description of services at sentence level based on a novel probabilistic topic model that learns topics of words, sentences and descriptions in a stratified fashion. The expansion process can bridge the vocabulary gap between services and user queries with the collective semantic similarity of sentences and descriptions. Then, we propose a Long Short-Term Memory-based model to recommend services with two attention mechanisms - a functional attention mechanism that takes tags as functional prior to mine the function-related features of services and Mashups, and a contextual attention mechanism that considers Mashup requirements as application scenario to help select the most appropriate services. We evaluate the proposed approach on a real-world dataset and the results show it has an improvement of 34 percent in F-measure over the basic LSTM model.

Fault detection remains a key problem for reducing the operation and maintenance costs of marine hydrokinetic (MHK) turbines. With this in mind, a deep bidirectional long short-term memory (Bi-LSTM) network for rapid detection of MHK turbine faults is presented. The effectiveness of the proposed scheme is validated using simulated time-series sensor data gathered from a novel Fatigue, Aerodynamics, Structures, and Turbulence (FAST)-based MHK turbine simulation platform. Four-factor analysis of variance is performed along with post-hoc testing at the 95% confidence level to establish the model's capabilities. Operating conditions, input length, training pitch, and generalization pitch are used as factors in this experiment. Models are trained on data consisting of 500 baseline and 500 faulty examples of a single blade pitch imbalance. To evaluate generalization performance, models are applied, without fine-tuning, on the remaining levels of fault. Over 70% mean generalization accuracy in all cases, with an optimal accuracy over 95% given 1 second lengths of data, is observed. No significant difference is found due to varying operating conditions, indicating a robust model. Training on lower levels of fault demonstrated greater generalization capability. To the best of our knowledge, this is the first time Bi-LSTM has been applied as a building block, and that a deep learning-based method has been applied to only time-series sensor measurements for fault detection in MHK turbines.

Utilizing advanced communication technologies to facilitate power system monitoring and control, the smart grid is envisioned to be more robust and resilient against cascading failures. Although the integration of communication network does benefit the smart grid in many aspects, such benefits should not overshadow the fact that the interdependence between the communication network and the power infrastructure makes the smart grid more fragile to cascading failures. Thus, it is essential to understand the impact of such cyber-physical integration with interdependence from both positive and negative perspectives. In this paper, we develop a systematic framework to analyze the benefits and drawbacks of the cyber-physical interdependence. We use theoretical analysis and system-level simulations to characterize the impact of such interdependence. We identify two phases during the progress of failure propagation where the integrated communication and interdependence helps and hinders the mitigation of the failure, respectively, which provides practical guidance to smart grid system design and optimization.

In this paper, coordinated sliding mode control (SMC) and adaptive dynamic programming (ADP) strategy is proposed for near-space aerospace vehicle (NSASV) adaptive attitude tracking control. In this design, the NSASV attitude angle control is implemented as classical cascade control scheme with two control loops in the model. The outer one is a slow control loop for the attitude angle tracking, and the inner one is a fast control loop for the attitude angular rate tracking. Both of these two control loops are designed by using SMC, which can provide exact control performance near the operating point. To improve the control performance and robustness under parameter variations and external disturbances, ADP based supplementary control is introduced and incorporated into the inner fast control loop to provide adaptive compensation for the reference signal. Simulation study is carried out in Matlab/Simulink environment, and the results demonstrate that the proposed cooperative control could provide quite satisfied tracking performance in terms of overshoot and oscillation.

In this paper, a novel framework for dynamic event monitoring in smart grid with synchrophasor data is proposed. The fundamental principle is that the nonstationary signatures of any dynamic event in the system will be captured by the data set collected from the phase measurement units (PMUs). The framework is performed in three steps: 1) Energy function components construction by using PMU data. Each of the component in the energy function, such as the load, transmission line, and generation, are formulated explicitly; 2) Unsupervised feature learning and fusion by using the energy function components. In the feature learning stage, stacked autoencoders (SAE) is used to learn representative features from each component. Then in the feature fusion stage, a shared representation between modalities is further learned; and 3) Supervised classifier training and online application. Classifier, such as a simple neural network, is trained for monitoring the system dynamics to detect and classify events. Compared with purely data-driven methods, the proposed framework utilizes the physical basis to understand and correlate the features with different events. Meanwhile, because of learning features automatically and adaptively, the proposed method reduces the need of human labor and can be more easy to process big data in smart grid. Simulation is carried out on IEEE 39-bus system, and the results show promising effectiveness on dynamic event detection and classification.

Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.

In this paper, a novel nonlinear learning controller called fuzzy-based goal representation adaptive dynamic programming (Fuzzy-GrADP) is proposed. In the proposed GrADP method, a goal representation network is introduced to generate an adaptive internal reinforcement signal to the critic network to help the controller provide a general mapping between the input and output actions. Moreover, in the proposed architecture, the action network in the GrADP is improved by using the fuzzy hyperbolic model, which combines the merits of the fuzzy model and the neural network model. Based on the back-propagation technique, the parameters in the membership functions and the fuzzy rules are all undergo training and online adapting. The proposed controller is tested on two numerical benchmarks, and the simulation results show that the proposed controller outperforms the original adaptive dynamic fuzzy controller and the pure neural network-based GrADP controller. In addition, the proposed controller is further applied on a large multimachine power system for static var compensator damping control, where simulation results demonstrate the effectiveness of the proposed approach on real applications. Furthermore, in order to demonstrate the theoretical guarantee of the proposed method, Lyapunov stability analysis to support the proposed Fuzzy-GrADP approach has also been carried out.

This paper develops an adaptive modulation approach for power system control based on the approximate/adaptive dynamic programming method, namely, the goal representation heuristic dynamic programming (GrHDP). In particular, we focus on the fault recovery problem of a doubly fed induction generator (DFIG)-based wind farm and a static synchronous compensator (STATCOM) with high-voltage direct current (HVDC) transmission. In this design, the online GrHDP-based controller provides three adaptive supplementary control signals to the DFIG controller, STATCOM controller, and HVDC rectifier controller, respectively. The mechanism is to observe the system states and their derivatives and then provides supplementary control to the plant according to the utility function. With the GrHDP design, the controller can adaptively develop an internal goal representation signal according to the observed power system states, therefore, to achieve more effective learning and modulating. Our control approach is validated on a wind power integrated benchmark system with two areas connected by HVDC transmission lines. Compared with the classical direct HDP and proportional integral control, our GrHDP approach demonstrates the improved transient stability under system faults. Moreover, experiments under different system operating conditions with signal transmission delays are also carried out to further verify the effectiveness and robustness of the proposed approach.

Recently, the sequential attack, where multiple malignant contingencies are launched by attackers sequentially, has revealed power grid vulnerability under cascading failures. This paper systematically analyzes properties and features of N-k cascaded- based sequential line-switching attacks using a DC power flow based cascading failure simulator (DC- CFS). This paper first explains the key factors behind cascade-based attacks, then compares three adopted metrics with an original line-margin metric to compute vulnerability indexes and design sequential attacks. Two target search schemes, i.e., offline and online target search in sequential attacks, are also presented. Simulation results of N-2 to N-4 line-switching attacks have suggested that the proposed line margin metric produces stronger sequential attacks, and online target search is more effective than offline search. Reasons behind counter-intuitive load loss resulting from different metrics are also analyzed to facilitate future study on the risk of sequential attacks.

Power grids are often run near the operational limits because of increasing electricity demand, where even small disturbances could possibly trigger major blackouts. The attacks are the potential threats to trigger large-scale cascading failures in the power grid. In particular, the attacks mean to make substations/transmission lines lose functionality by either physical sabotages or cyber attacks. Previously, the attacks were investigated from substation-only/transmission-line-only perspectives, assuming attacks can occur only on substations/transmission lines. In this paper, we introduce the joint substation-transmission line perspective, which assumes attacks can happen on substations, transmission lines, or both. The introduced perspective is a nature extension to substation-only and transmission-line-only perspectives. Such extension leads to discovering many joint substation-transmission line vulnerabilities. Furthermore, we investigate the joint substation-transmission line attack strategies. In particular, we design a new metric, the component interdependency graph (CIG), and propose the CIG-based attack strategy. In simulations, we adopt IEEE 30 bus system, IEEE 118 bus system, and Bay Area power grid as test benchmarks, and use the extended degree-based and load attack strategies as comparison schemes. Simulation results show the CIG-based attack strategy has stronger attack performance.

Adaptive dynamic programming (ADP) has been widely recognized as one of the “core methodologies” to achieve optimal control for intelligent systems in Markov decision process (MDP). Generally, ADP control design requires all the information of the system dynamics. However, in many practical situations, the measured input and output data can only represent part of the system states. This means the complete information of the system cannot be available in many real-world cases, which narrows the range of application of the ADP design. In this paper, we propose a data-driven ADP method to stabilize the system with partially observable dynamics based on neural network techniques. A state network is integrated into the typical actor-critic architecture to provide an estimated state from the measured input/output sequences. The theoretical analysis and the stability discussion of this data-driven ADP method are also provided. Two examples are studied to verify our proposed method.

Vulnerability analysis on the power grid has been widely conducted from the substation-only and transmission-line-only perspectives. In order words, it is considered that attacks can occur on substations or transmission lines separately. In this paper, we naturally extend existing two perspectives and introduce the joint-substation-transmission-line's perspective, which means attacks can concurrently occur on substations and transmission lines. Vulnerabilities are referred to as these multiple-component combinations that can yield large damage to the power grid. One such combination consists of substations, transmission lines, or both. The new perspective is promising to discover more power grid vulnerabilities. In particular, we conduct the vulnerability analysis on the IEEE 39 bus system. Compared with known substation-only/transmission-line-only vulnerabilities, joint-substation-transmission-line vulnerabilities account for the largest percentage. Referring to three-component vulnerabilities, for instance, joint-substation-transmission-line vulnerabilities account for 76.06%; substation-only and transmission-line-only vulnerabilities account for 10.96% and 12.98%, respectively. In addition, we adopt two existing metrics, degree and load, to study the joint-substation-transmission-line attack strategy. Generally speaking, the joint-substation-transmission-line attack strategy based on the load metric has better attack performance than comparison schemes.

The vulnerability analysis is vital for safely running power grids. The simultaneous attack, which applies multiple failures simultaneously, does not consider the time domain in applying failures, and is limited to find unknown vulnerabilities of power grid networks. In this paper, we discover a new attack scenario, called the sequential attack, in which the failures of multiple network components (i.e., links/nodes) occur at different time. The sequence of such failures can be carefully arranged by attackers in order to maximize attack performances. This attack scenario leads to a new angle to analyze and discover vulnerabilities of grid networks. The IEEE 39 bus system is adopted as test benchmark to compare the proposed attack scenario with the existing simultaneous attack scenario. New vulnerabilities are found. For example, the sequential failure of two links, e.g., links 26 and 39 in the test benchmark, can cause 80% power loss, whereas the simultaneous failure of them causes less than 10% power loss. In addition, the sequential attack is demonstrated to be statistically stronger than the simultaneous attack. Finally, several metrics are compared and discussed in terms of whether they can be used to sharply reduce the search space for identifying strong sequential attacks.

Due to the intermittent power generation from renewable energy in the smart grid (i.e., photovoltaic (PV) or wind farm), large frequency fluctuation occurs when the load-frequency control (LFC) capacity is not enough to compensate the unbalance of generation and load demand. This problem may become worsen when the system is in island operating. Meanwhile, in the near future, electric vehicles (EVs) will be widely used by customers, where the EV station could be treated as dispersed battery energy storage. Therefore, the vehicle-to-grid (V2G) power control can be applied to compensate for inadequate LFC capacity, thus improving the island smart grid frequency stability. In this paper, an on-line learning method, called goal representation adaptive dynamic programming (GrADP), is adopted to coordinate control of units in an island smart grid. In the controller design, adaptive supplementary control signals are provided to proportional-integral (PI) controllers by online GrADP according to the utility function. Simulations on a benchmark smart grid with micro turbine (MT), EVs and PVs demonstrate the superior control effect and robustness of the proposed coordinate controller over the original PI controller and fuzzy controller.

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.