Probabilistic energy flow (PEF) calculation is a fundamental task for the operation and planning of both natural gas systems (NGSs) and integrated energy systems considering uncertainties. Traditional Monte Carlo simulation (MCS) based PEF approach requires repeated energy flow calculations based on a large number of random samples, leading to a huge computation burden. Hence, this paper proposes a convex multiparametric programming (MPP) based fast PEF calculation method for NGSs. First, we develop an energy function based convex optimization model whose optimal solution is equivalent to the solution of deterministic energy flow equations. Then, we propose a convex MPP model with uncertain boundary conditions (such as the gas injections/loads) as the parameters. A multiparametric quadratic approximation algorithm is further introduced to solve the proposed MPP and obtain the analytical energy flow expression. This analytical expression characterizes the mapping relationship between the energy flow solution and uncertain parameters. Finally, we establish an online PEF calculation framework, in which the repeat energy flow calculations can be efficiently performed by merely substituting the uncertain parameters into the analytical energy flow expression obtained offline. Case studies on multiple NGSs verify the effectiveness of the proposed method. Note to Practitioners—This paper introduces a novel approach for efficient PEF calculation in NGSs that addresses the computational challenges posed by traditional methods. Conventional MCS-based PEF approaches involve numerous energy flow calculations with massive stochastic scenarios, resulting in significant computational burdens. The key contribution of our work is to equivalently reformulate the original PEF problem as an energy function based convex MPP model, which incorporates uncertain boundary conditions (e.g., gas injections/loads) as its parameters. Then, a multiparametric quadratic approximation algorithm is suggested to obtain the parametric solution of this MPP, which is essentially an analytical expression of the energy flow solution as a function of uncertain parameters. This allows for efficient repeated energy flow calculations by simply substituting uncertain parameters into the pre-determined expression obtained offline. Thus, the online PEF computational efficiency can be significantly improved. We conduct case studies on several benchmark NGSs to validate the effectiveness and scalability of the proposed method. Numerical results show that the online PEF computation efficiency of the proposed method is approximately 2-3 orders of magnitude faster than that of the NR-MCS method.

The rapid progression of urbanization has generated a diverse array of urban data, facilitating significant advancements in urban science and urban computing. Current studies often work on separate problems case by case using diverse data, e.g., air quality prediction, and built-up areas classification. This fragmented approach hinders the urban research field from advancing at the pace observed in Computer Vision and Natural Language Processing, due to two primary reasons. On the one hand, the diverse data processing steps lead to the lack of large-scale benchmarks and therefore decelerate iterative methodology improvement on a single problem. On the other hand, the disparity in multi-modal data formats hinders the combination of the related modal data to stimulate more research findings. To address these challenges, we propose UrbanDataLayer (UDL), a suite of standardized data structures and pipelines for city data engineering, providing a unified data format for researchers. This allows researchers to easily build up large-scale benchmarks and combine multi-modal data, thus expediting the development of multi-modal urban foundation models. To verify the effectiveness of our work, we present four distinct urban problem tasks utilizing the proposed data layer. UrbanDataLayer aims to enhance standardization and operational efficiency within the urban science research community. The examples and source code are available at https://github.com/SJTU-CILAB/udl.

In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system’s vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for generating training data. The initial states are randomly selected and the CS model is deployed for each initial state, where power system generation and loads are adjusted dynamically and power flows are redistributed to quantify the vulnerability metric. Furthermore, the proposed hybrid machine learning model deploys a combined Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve the learning precision. The classification model is trained by SVM, which divides the data into two categories with and without load shedding. Then, GBR is adopted only for the data with load shedding to determine the relationship between input power outage states and the vulnerability metric. The proposed vulnerability analysis approach is applied to several test systems and the results are analyzed. Note to Practitioners—The power system vulnerability can be quantified by cascading outage simulations. However, there are two challenges: i) there are a huge number of possible initial states and we cannot enumerate all these initial states for the cascading outage simulation. Neither can we precisely quantify the bus vulnerability. ii) The cascading outage simulation may be time-consuming for large-scale power systems, which is challenging for the online application. To address the above challenges, we expect to design a machine learning technique to predict the power system vulnerability, which can train the model in an offline way and then use it for the online application. Firstly, since there is not enough operation data from practical power systems, we develop a cascading outage simulator, using off-line AC power flows, for generating synthetic training data. Secondly, we observe that the training precision by directly applying the regression model may be very poor because the output of the machine learning model may take on an uneven distribution concerning input parameters. Thus, we propose a hybrid machine learning model with a combined classification and regression method, where the classification model is employed to remove the data without the load shedding, and the regression model then determines the relationship between input power outage states and the vulnerability metric. The proposed model and method have been tested on several systems including a practical large-scale Polish power system to show the effectiveness.

A new approach is proposed for ship detection, based on the generalized optimization of polarimetric contrast enhancement (OPCE). The new approach consists of two steps. For the first step, the OPCE problem will be solved for obtaining optimal polarizations. In this way, the received powers of a sea area are suppressed. For the second step, we construct a function which not only contains the power term but also contains a term associated with the scattering mechanism. Three optimal coefficients will be obtained such that the variance of the function values of a sea clutter area is minimal. In this way, ships on the sea area are detected. Using two examples, we demonstrate the effectiveness of the proposed approach for ship detection.

The polarimetric entropy (H) and the alpha angle (α) are two important parameters for analyzing polarimetric synthetic aperture radar data. However, for some special cases, the unstableness of the alpha angle is found; in addition, time consumption for extracting both the parameters will become quite tedious for very large images by pixelwise eigendecomposition. To overcome these shortcomings, a fast algorithm to calculate the polarimetric entropy and two new parameters are proposed in this paper. The first parameter has similar properties to the alpha angle but is stable; the second also behaves similar to the polarimetric entropy. More importantly, both new parameters can be derived very quickly. Like the unsupervisedHα classification, the image can also be classified based on the two new parameters. The NASA/JPL AIRSAR L-band data of San Francisco are applied in the experiment.