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Transient protection has the advantage of ultra-fast operation. However, traditional transient protection is susceptible to the influences of transitional resistance and the initial fault angle, resulting in issues with sensitivity and reliability under weak fault conditions. To address these issues, a new method of transient voltage protection is proposed by mining the equipment configuration information of the bus and line, as well as the frequency characteristics of the line boundary. To solve the problem that single-terminal transient line protection cannot distinguish between line fault and bus fault and the fault of adjacent line head end, a new fault feature is constructed by introducing a difference of instantaneous amplitude of the high-frequency signals between line voltage and bus voltage. To solve the problem that transient protection is susceptible to the influence of fault condition attributes, the fusion of fault characteristics with fault condition attributes results in synthetic fault characteristics, which contain fault attribute information. This approach overcame the adverse effects of fault attributes on transient protection and enhances protection reliability. Transient voltage high-frequency instantaneous amplitudes are obtained and integrated using the Variational Mode Decomposition Hilbert Transform (VMD-HT) to increase the stability of high-frequency fault signal detection. This algorithm solved the original problem of 42.2% cross-data, 41.6% false operations, and 8.9% refusal to operate. It protects both the full length of a transmission line and a busbar at the same time, with twice protection zone comparing with traditional protection. It is simpler, more reliable and more economical. Extensive ATP-Draw simulation experiments have validated the accuracy and reliability of the proposed algorithm.

Simulating electromagnetic (EM) fields can obtain the EM responses of geoelectric models at different times and spaces, which helps to explain the dynamic process of EM wave propagation underground. EM forward modeling is regarded as the engine of inversion. Traditional numerical methods have certain limitations in simulating the EM responses from large-scale geoelectric models. In recent years, the emerging physics-informed neural networks (PINNs) have given new solutions for geophysical EM field simulations. This paper conducts a preliminary exploration using PINN to simulate geophysical frequency domain EM fields. The proposed PINN performs self-supervised training under physical constraints without any data. Once the training is completed, the responses of EM fields at any position in the geoelectric model can be inferred instantly. Compared with the finite-difference solution, the proposed PINN performs the task of geophysical frequency domain EM field simulations well. The proposed PINN is applicable for simulating the EM response of any one-dimensional geoelectric model under any polarization mode at any frequency and any spatial position. This work provides a new scenario for the application of artificial intelligence in geophysical EM exploration.

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

This study proposes two curves that depict the vehicle–bridge contact force in a novel transportation system named AERORail, which is a lightweight cable-supported structure in which the rails and the prestressed cable form the load bearing system. Based on the contact force identified from a full-scale AERORail system, single and double-valley curves were obtained as the idealized contact force model for large- and small-span AERORail systems, respectively. This was achieved by utilizing the Bezier curves and the least squares method. The proposed curves were verified through a moving load model from a previous study under various spans and speeds. Moreover, the structural response of the AERORail structure under high-speed vehicle passing was explored using the idealized contact force model. The simulation results show that the proposed contact force model can predict the displacement response of 5 m and 15 m spans with a relative error of less than 5%, proving that the model can be used for dynamic analysis of AERORail.

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.

It is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basis for the indoor air quality system to control the PM2.5 concentration. One approach is to introduce the CatBoost algorithm based on rank lifting training into the classification and prediction of indoor PM2.5 concentration. Using actual monitoring data from office building, we consider previous indoor PM2.5 concentration, indoor temperature, relative humidity, CO2 concentration, and illumination as input variables, with the output indicating whether indoor PM2.5 concentration exceeds 25 μg/m3. Based on the CatBoost algorithm, we construct an intelligent classification prediction model for indoor PM2.5 concentration. The model is evaluated using actual data and compared with the multilayer perceptron (MLP), gradientboosting decision tree (GBDT), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN) models. The CatBoost algorithm demonstrates outstanding predictive performance, achieving an impressive area under the ROC curve (AUC) of 0.949 after hyperparameters optimition. Furthermore, when considering the five input variables, the feature importance is ranked as follows: previous indoor PM2.5 concentration, relative humidity, CO2, indoor temperature, and illuminance. Through verification, the prediction model based on CatBoost algorithm can accurately predict the indoor PM2.5 concentration level. The model can be used to predict whether the indoor concentration of PM2.5 exceeds the standard in advance and guide the air quality control system to regulate.

Background: Necroptosis is associated with oncogenesis, tumor immunity and progression. This research aims to investigate the association of necroptosis-related genes with drug sensitivity and prognosis in hepatocellular carcinoma (HCC). Methods: Based on necroptosis-related signatures, HCC patients retrieved from the TCGA database were categorized. Survival outcomes, mutation profile, immune microenvironment, and drug sensitivity between HCC subtypes were further compared. Then, LASSO analysis was performed to construct a necroptosis-related prognostic signature, which was further evaluated using another independent cohort. Results: A total of 371 patients with HCC could be categorized into two necroptosis-related subtypes. About 36% of patients were allocated to subtype A, with worse survival, more mutant TP53, and a lower likelihood of immunotherapy response. In contrast, patients in subtype B had a favorable prognosis, with lower expression of immunosuppressive signatures but a lower abundance of B and CD8+ T-cell infiltration. The prognostic risk score calculated using the expression levels of nine genes involved in the necroptosis pathway (MLKL, FADD, XIAP, USP22, UHRF1, CASP8, RIPK3, ZBP1, and FAS) showed a significant association with tumor stage, histologic grade, and Child‒Pugh score. Additionally, the risk score model was proven to be accurate in both the training and independent external validation cohorts and performed better than the TNM staging system and three well-recognized risk score models. Conclusions: Based on necroptosis-related signatures, we identified two HCC subtypes with distinctive immune microenvironments, mutation profiles, drug sensitivities, and survival outcomes. A novel well-performing prognostic model was further constructed.

The algebra of split quaternions is a recently increasing topic in the study of theory and numerical computation in split quaternionic mechanics. This paper, by means of a real representation of a split quaternion matrix, studies the problem of canonical forms of a split quaternion matrix and derives algebraic techniques for finding the canonical forms of a split quaternion matrix. This paper also gives two applications for the right eigenvalue and diagonalization in split quaternionic mechanics.

The marine controlled-source electromagnetic (CSEM) method is an efficient tool for hydrocarbon exploration. The amplitudes of signals decay rapidly with the increasing offset, so signals are easily contaminated by various kinds of noise. A denoising method is critical to improve the data quality, but the diversity of noise makes denoising difficult. Specific frequency signals are transmitted for exploration requirements, and thus traditional filtering methods are not suitable. Symplectic geometry mode decomposition (SGMD), a new method to decompose signals, has an outstanding decomposition performance and noise robustness. Furthermore, it can reduce multiple types of noise by reconstructing the single components. In this study, we introduced SGMD to reduce the noise of marine CSEM data and improved the data quality significantly. The experiments show that SGMD is better than variational mode decomposition and the sym4 wavelet method.

An optimized scheme can improve the navigation accuracy of RINS without changing the inertial devices. In the multi-position stop scheme, the IMU remains stationary for most of the time, which makes motor control easier. However, the installation errors and the scale factor errors of FOG can cause platform misalignment after a certain angle of rotation around the horizontal axis, resulting in a velocity error. Continuous rotation can suppress time-varying errors better, which is of particular importance for FOG, but it can also increase the sawtooth error of the navigation output, and the error in the direction of rotation cannot be offset. To integrate the advantages of both rotation schemes, we propose an improved rotational modulation scheme for tri-axis RINS. In this scheme, the inner gimbal rotates in a two-position and four-order manner, while the middle and outer gimbals rotate continuously in the order of forward-reverse-reverse-forward. Simulation and navigation test results demonstrate that this improved rotational modulation scheme can effectively improve navigation accuracy by 50% and 25% compared with continuous rotation around the azimuth axis and a 16-position scheme with the same inertial devices, which is of great importance for RINS with FOG.

Obtaining a three-dimensional (3D) density distribution within a reasonable time is one of the most critical problems in gravity exploration. In this paper, we present an efficient 3D forward modeling and inversion method for gravity data. In forward modeling, the 3D model is discretized into multiple horizontal layers, with the gravity field at a point on the surface being the sum of the gravity fields from all layers. To calculate the gravity field from each horizontal layer, we use the fast Fourier transform (FFT) method and the Block Toeplitz with Toeplitz Blocks (BTTB) matrix, which dramatically reduces both the computation time and storage requirement. In the inversion, the observed gravity data are separated into multiple gravity components of different depths using the cutting separation method. An iterative method is used to adjust the model to fit the above gravity component for each cutting radius. The initial model is constructed from the transformation of gravity components. These methods were applied to both synthetic data and field data. The numerical simulation validated the proposed methods, and the inversion results of field data were consistent with information obtained from well logging. The computational time and memory usage were also reasonable.

The flexible structure of the new type of aerial track studied in this paper is formed by strings and tracks, which have obvious dynamic characteristics under the action of vehicle moving loads and is also an important factor affecting its performance. The paper uses the full-scale model of AERORail, based on the modal test under a specific span, combined with the fundamental frequency analysis of the structure, and carries out the dynamic load test to determine the change law of the contact force acting on the rail by the dynamic load. The time-domain improved algorithm based on the method of moments is used to identify and analyze the dynamic loads under different spans and vehicle speeds and to determine the correlation between the dispatched loads and the spans and vehicle speeds. It is proven that the occurrence time and frequency of the contact force are related to the passing time of the vehicle. The contact force fluctuates with the change in the vehicle speed, but with the increase in the span, the fluctuation of the contact force decreases obviously. The relevant conclusions provide support for the layout of the AERORail vehicle load. For this innovative structure, the relevant conclusions provide the basis for the application of this novel structure.

Consumers in electricity markets are becoming more proactive because of the rapid development of demand–response management and distributed energy resources, which boost the transformation of peer-to-peer (P2P) energy-trading mechanisms. However, in the P2P negotiation process, it is a challenging task to prevent private information from being attacked by malicious agents. In this paper, we propose a privacy-preserving, two-party, secure computation mechanism for consensus-based P2P energy trading. First, a novel P2P negotiation mechanism for energy trading is proposed based on the consensus + innovation (C + I) method and the power transfer distribution factor (PTDF), and this mechanism can simultaneously maximize social welfare and maintain physical network constraints. In addition, the C + I method only requires a minimum set of information to be exchanged. Then, we analyze the strategy of malicious neighboring agents colluding to attack in order to steal private information. To defend against this attack, we propose a two-party, secure computation mechanism in order to realize safe negotiation between each pair of prosumers based on Paillier homomorphic encryption (HE), a smart contract (SC), and zero-knowledge proof (ZKP). The energy price is updated in a safe way without leaking any private information. Finally, we simulate the functionality of the privacy-preserving mechanism in terms of convergence performance, computational efficiency, scalability, and SC operations.

The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) is a high-accuracy velocity inversion method, it inevitably suffers from challenging problems, including human interference, strong nonuniqueness, and high computing costs. As an efficient and accurate nonlinear algorithm, deep learning (DL) has been used to estimate velocity models. However, conventional DL is insufficient to characterize detailed structures and retrieve complex velocity models. To address the aforementioned problems, we propose a hybrid network (AG-ResUnet) involving fully convolutional layers, attention mechanism, and residual unit to estimate velocity models from common source point (CSP) gathers. Specifically, the attention mechanism extracts the boundary information, which serves as a structural constraint in network training. We introduce the structural similarity index (SSIM) to the loss function, which minimizes the misfit between predicted velocity and ground truth. Compared with FWI and other networks, AG-ResUnet is more effective and efficient. Experiments on transfer learning and noisy data inversion demonstrate that AG-ResUnet makes a generalized and robust velocity prediction with rich structural details. The synthetic examples demonstrate that our method can improve seismic velocity inversion, contributing to guiding the imaging of geological structures.

With the rapid growth of the economy and population, the desire to use groundwater supplies is increasing. In order to detect groundwater, conducting a geophysics survey is a common way to map the subsurface. This method can describe the subsurface’s physical properties. For example, the direct current (DC) resistivity method provides a resistivity map of the subsurface, in which the groundwater is normally located in low resistivity anomaly zones. However, the DC method needs to overcome the challenges of the urban environment, with its infrastructure and background noise. In this paper, we propose a pseudo-3D resistivity surveying method to solve these problems, and it is applied to situations of groundwater detection within the city environment, dam leakage, and drinking water. From the pseudo-3D resistivity surveying, we detected the low-resistivity anomaly in the area, which is interpreted as groundwater through borehole verification.

Simulating and predicting wave propagation in porous media saturated with two fluids is an important issue in geophysical exploration studies. In this work, wave propagation in porous media with specified structures saturated with two immiscible fluids was studied, and the main objective was to establish a wave equation system with a relatively simple structure. The wave equations derived by Tuncay and Corapcioglu were analyzed first. It was found that the coefficient matrix of the equations tends to be singular due to the inclusion of a small parameter that characterizes the effect of capillary stiffening. Therefore, the previously established model consisting of three governing equations may be unstable under natural conditions. An improved model based on Tuncay and Corapcioglu’s work was proposed to ensure the nonsingularity of the coefficient matrix. By introducing an assumption in which one fluid was completely wrapped by the other, the governing equation of the wrapped fluid was degenerated. In this way, the coefficient matrix of wave equations became nonsingular. The dispersion and attenuation prediction resulting from the new model was compared with that of the original model. Numerical examples show that although the improved model consists of only two governing equations, it can obtain a result similar to that of the original model for the case of a porous medium containing gas and water, which simplifies the complexity of the calculations. However, in a porous medium with oil and water, the predictions of dispersion and attenuation produced by the original model obviously deviate from the normal trend. In contrast, the results of the improved model exhibit the correct trend with a smooth curve. This phenomenon shows the stability of the improved model and it could be used to describe wave propagation dispersions and attenuations of porous media containing two immiscible fluids in practical cases.

Natural gas hydrate is one of the most important clean energies and part of carbon cycle, due to the least carbon content. Natural gas hydrates depend on high pressure and low temperatures, located under seabed or permafrost. Small changes in temperature and pressure may lead gas hydrates to separate into water and gas, commonly as methane. As a powerful greenhouse gas, methane is much stronger than carbon dioxide. Therefore, it is necessary to detect the gas hydrates stable zone (GHSZ) before the methane gas escapes from GHSZ. Marine controlled source electromagnetic method (CSEM) is a useful tool to detect gas hydrate in offshore. The results from 3D CSEM method are a resistivity cube to describe the distribution of gas hydrates. In order to study the detectability of CSEM method, we simulate the sensitivity and resolution of marine CSEM synthetic data. By using the sensitivity and resolution, a simple statement may be quickly judged on the existence and occurrence range of the natural gas hydrate. In this paper, we compare the resolution of marine CSEM method with various transverse resistance. This information may help researchers find out whether the GHSZ exists or not.

In this paper, a new stable finite-difference (FD) method for solving elastodynamic equations is presented and applied on the Biot and Biot/squirt (BISQ) models. This method is based on the operator splitting theory and makes use of the characteristic boundary conditions to confirm the overall stability which is demonstrated with the energy method. Through the stability analysis, it is showed that the stability conditions are more generous than that of the traditional algorithms. It allows us to use the larger time step τ in the procedures for the elastic wave field solutions. This context also provides and compares the computational results from the stable Biot and unstable BISQ models. The comparisons show that this FD method can apply a new numerical technique to detect the stability of the seismic wave propagation theories. The rigorous theoretical stability analysis with the energy method is presented and the stable/unstable performance with the numerical solutions is also revealed. The truncation errors and the detailed stability conditions of the FD methods with different characteristic boundary conditions have also been evaluated. Several applications of the constructed FD methods are presented. When the stable FD methods to the elastic wave models are applied, an initial stability test can be established. Further work is still necessary to improve the accuracy of the method.

Marine controlled-source electromagnetic (CSEM) is an efficient offshore hydrocarbon exploration method that has been developed during the last 18 years. Sea Bed Logging (SBL) and towed streamer electromagnetic (TSEM) are two different data acquisition systems. We compared these two methods by using 1D sensitivity modeling and 2D Occam’s inversion. Based on this research, we tested the effect of frequency, offset range, water depth, reservoir size, and reservoir depth on the detection capability of the two acquisition methods in terms of sensitivity. In order to test the methodology clearly and simply, the geological model was extremely simplified for the inversion. The effect of these parameters on resolution was checked as another purpose. To easily evaluate our inversion results, a simple quantity was employed that we called the anomaly transverse resistance ratio. In the shallow water environment, both the SBL and the TSEM systems had a good sensitivity to the high resistivity targets. However, in the deep water environment, the SBL system had a low noise floor. Then, it could provide better detectability to the deep target. The TSEM had the advantage in terms of the horizontal resolution because of the dense in-line sampling of the electric field.

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

With the breakthroughs made by physicists in high-dimensional mathematics, it has become possible to represent and solve a number of classical mathematical physics problems using the split quaternion algebra. In this paper, we study the least squares approximation of a class of Schrödinger equations in split quaternionic mechanics and propose two algebraic algorithms to the generalized right eigen-problem for an i-Hermitian split quaternion matrix pencil by using two isomorphic mappings. Numerical examples show the effectiveness of the proposed theories and algorithms. [ABSTRACT FROM AUTHOR]