This paper proposes an approximate dynamic programming (ADP) based approach for the economic dispatch (ED) of microgrid with distributed generations (DGs). The time-variant renewable generation, electricity price and the power demand are considered as stochastic variables in this work. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo (MC) method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.

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 service. We evaluate the proposed approach on a real-world dataset and the results show it has an improvement of 34% in F-measure over the basic LSTM model.

Medical imaging is a cornerstone of modern healthcare, enabling precise diagnosis, treatment planning, and disease monitoring. Traditional machine learning (ML) approaches have significantly improved medical image analysis, yet they face challenges such as data scarcity, lack of interpretability, and variability in imaging protocols. Physics-Informed Machine Learning (PIML) offers a transformative solution by integrating fundamental physical laws, usually in partial differential equations and boundary conditions, into data-driven ML models. PIML constrains the solution space, enhances interpretability, and reduces the dependency on large, annotated datasets. This review provides an overview of the principles, methodologies, and applications of PIML in medical imaging, with a focus on imaging modalities such as MRI, CT, and ultrasound. We discuss the taxonomy of PIML approaches based on observational, inductive, and learning biases, showing their roles in enhancing model accuracy and generalization. Additionally, we explore the impact of PIML on image reconstruction, segmentation, enhancement, and anomaly detection, demonstrating its effectiveness in addressing noise, resolution, and diagnostic accuracy challenges. Despite its advantages, PIML faces challenges in the accurate representation of complex physiological processes, computational efficiency, and the integration of physics-based priors across diverse applications. This review points out future research directions including the development of hybrid models that combine PIML with deep learning techniques and large foundation models, improved benchmark datasets, and scalable algorithms for real-time applications. The findings of this review highlight PIML as a pivotal approach for advancing medical imaging, bridging the gap between theoretical models and practical implementation in clinical settings.

Prognostics predicts the future performance progression and remaining useful life (RUL) of in-service systems based on historical and contemporary data. One of the challenges in prognostics is the development of methods that are capable of handling real-world uncertainties that typically lead to inaccurate predictions. To alleviate the impacts of uncertainties and to achieve accurate degradation trajectory and RUL predictions, a novel sequence-to-sequence predictive model is proposed based on a variational autoencoder that is trained with generative adversarial networks. A long short-term memory network and a Gaussian mixture model are utilized as building blocks so that the model is capable of providing probabilistic predictions. Correlative and monotonic metrics are applied to identify sensitive features in the degradation progress, in order to reduce the uncertainty induced from raw data. Then, the selected features are concatenated with one-hot health state indicators as training data for the model to learn end of life without the need for prior knowledge of failure thresholds. Performance of the proposed model is validated by health monitoring data collected from real-world aeroengines, wind turbines, and lithium-ion batteries. The results demonstrate that significant performance improvement can be achieved in long-term degradation progress and RUL prediction tasks.

Knowledge representation of networked systems is fundamental in many disciplines. To date, most 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, e.g., 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 paper, we propose a novel multi-label graph convolutional network (MuLGCN) for learning node representation of multi-label networks. 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 they are seamlessly integrated under one objective function. The learned label representations can effectively preserve the inner-label interaction and node label properties, and are then aggregated to enhance the node representation learning under a unified training framework. Experiments validate the effectiveness of our proposed approach.

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.