Search Results (315)

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

This research presents a reinforcement learning framework for stable quadruped locomotion using Proximal Policy Optimization (PPO). We address critical challenges in articulated robot control—including mechanical complexity and trajectory instability by implementing a 12-degree-of-freedom model in PyBullet simulation. Our approach features three key innovations: (1) a hybrid reward function ($R_t=0.72 · e^{-\Delta {\mathrm{CoG}}_t} + 0.25 · v_t - 0.11 · ‖{\tau }_t‖$) explicitly prioritizing center-of-gravity (CoG) stabilization; (2) rigorous benchmarking demonstrating Adam’s superiority over SGD for policy convergence (68% lower reward variance); and (3) a four-metric evaluation protocol quantifying locomotion quality through reward progression, CoG deviation, policy loss, and KL-divergence penalties. Experimental results confirm an 87.5% reduction in vertical CoG oscillation (from 2.0″ to 0.25″) across 1 million training steps. Policy optimization achieved −6.2 × 10⁻⁴ loss with KL penalties converging to 0.13, indicating stable gait generation. The framework’s efficacy is further validated by consistent CoG stabilization during deployment, demonstrating potential for real-world applications requiring robust terrain adaptation. Full article

As power systems evolve rapidly, cables, essential for electric power transmission, demand accurate and timely fault diagnosis to ensure grid safety and stability. However, current cable fault diagnosis technologies often struggle with incomplete feature extraction from complex fault signals and inefficient parameter tuning in diagnostic models, hindering efficient and precise fault detection in modern power systems. To address these, this paper proposes a data-driven strategy for cable fault diagnosis in substation secondary circuits, enhanced by effective parameter optimization. Initially, wavelet packet decomposition is employed to finely divide collected cable fault current signals into multiple levels and bands, effectively extracting fault feature vectors. To tackle the challenge of selecting penalty and kernel parameters in Support Vector Machine (SVM) models, an improved Golden Jackal Optimization (GJO) algorithm is introduced. This algorithm simulates the predatory behavior of golden jackals in nature, enabling efficient global optimization of SVM parameters and significantly improving the classification accuracy and generalization capability of the fault diagnosis model. Simulation verification using real cable fault cases confirms that the proposed method outperforms traditional techniques in fault recognition accuracy, diagnostic speed, and robustness, proving its effectiveness and feasibility. This study offers a novel and efficient solution for cable fault diagnosis. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power grid dispatching, is essential for maintaining the grid’s long-term stable operation. Traditional fault diagnosis methods encounter challenges such as limited samples and data quality issues under complex operating conditions. To overcome these problems, this study proposes a fault sample data enhancement method based on the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Firstly, a simulation model of the AC/DC hybrid system is constructed to obtain the original fault sample data. Then, through the adoption of the Wasserstein distance measure and the gradient penalty strategy, an improved WGAN-GP architecture suitable for feature learning of the AC/DC hybrid system is designed. Finally, by comparing the fault diagnosis performance of different data models, the proposed method achieves up to 100% accuracy on certain fault types and improves the average accuracy by 6.3% compared to SMOTE and vanilla GAN, particularly under limited-sample conditions. These results confirm that the proposed approach can effectively extract fault characteristics from complex fault data. Full article

The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods. Full article

To address the challenges of weak fault features and strong non-stationarity in early-stage vibration signals, this study proposes a novel fault diagnosis method combining enhanced variational mode decomposition (VMD) with a structurally improved GoogLeNet. Specifically, an improved wild horse optimizer (IWHO) with tent chaotic mapping is employed to automatically optimize critical VMD parameters, including the number of modes K and the penalty factor α, enabling precise decomposition of non-stationary signals to extract weak fault features. The vibration signal is decomposed, and the top five intrinsic mode functions (IMFs) are selected based on the kurtosis criterion. Time–frequency features are then extracted from these IMFs and input into a modified GoogLeNet classifier. The GoogLeNet structure is improved by replacing standard n × n convolution kernels with cascaded 1 × n and n × 1 kernels, and by substituting the ReLU activation function with a parameterized TReLU function to enhance adaptability and convergence. Experimental results on two public rolling bearing datasets demonstrate that the proposed method effectively handles non-stationary signals, achieving 99.17% accuracy across four fault types and maintaining over 95.80% accuracy under noisy conditions. Full article

The accurate identification of pulmonary nodules is critical for the early diagnosis of lung diseases; however, this task remains challenging due to inadequate feature representation and limited localization sensitivity. Current methodologies often utilize channel attention mechanisms and intersection over union (IoU)-based loss functions. Yet, they frequently overlook spatial context and struggle to capture subtle variations in aspect ratios, which hinders their ability to detect small objects. In this study, we introduce an improved YOLOV11 framework that addresses these limitations through two primary components: a spatial squeeze-and-excitation (SSE) module that concurrently models channel-wise and spatial attention to enhance the discriminative features pertinent to nodules and explicit aspect ratio penalty IoU (EAPIoU) loss that imposes a direct penalty on the squared differences in aspect ratios to refine the bounding box regression process. Comprehensive experiments conducted on the LUNA16, LungCT, and Node21 datasets reveal that our approach achieves superior precision, recall, and mean average precision (mAP) across various IoU thresholds, surpassing previous state-of-the-art methods while maintaining computational efficiency. Specifically, the proposed SSE module achieves a precision of 0.781 on LUNA16, while the EAPIoU loss boosts mAP@50 to 92.4% on LungCT, outperforming mainstream attention mechanisms and IoU-based loss functions. These findings underscore the effectiveness of integrating spatially aware attention mechanisms with aspect ratio-sensitive loss functions for robust nodule detection. Full article

The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics. Full article

This paper proposes a novel navigation approach for Unmanned Tracked Vehicles (UTVs) using prior-based ensemble deep reinforcement learning, which fuses the policy of the ensemble Deep Reinforcement Learning (DRL) and Dynamic Window Approach (DWA) to enhance both exploration efficiency and deployment safety in unstructured off-road environments. First, by integrating kinematic analysis, we introduce a novel state and an action space that account for rugged terrain features and track–ground interactions. Local elevation information and vehicle pose changes over consecutive time steps are used as inputs to the DRL model, enabling the UTVs to implicitly learn policies for safe navigation in complex terrains while minimizing the impact of slipping disturbances. Then, we introduce an ensemble Soft Actor–Critic (SAC) learning framework, which introduces the DWA as a behavioral prior, referred to as the SAC-based Hybrid Policy (SAC-HP). Ensemble SAC uses multiple policy networks to effectively reduce the variance of DRL outputs. We combine the DRL actions with the DWA method by reconstructing the hybrid Gaussian distribution of both. Experimental results indicate that the proposed SAC-HP converges faster than traditional SAC models, which enables efficient large-scale navigation tasks. Additionally, a penalty term in the reward function about energy optimization is proposed to reduce velocity oscillations, ensuring fast convergence and smooth robot movement. Scenarios with obstacles and rugged terrain have been considered to prove the SAC-HP’s efficiency, robustness, and smoothness when compared with the state of the art. Full article

This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (F₁), feature-driven (F₂), and hybrid (F₃)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F₂ and avoiding F₁’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems. Full article

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource characteristics of different virtual power plants (VPPs) within a cooperative alliance, we propose a multi-VPP interaction and sharing architecture accounting for electricity–carbon interaction. An optimization model for VPPC is developed based on the asymmetric Nash bargaining theory. Finally, the proposed model is solved using an alternating-direction method of multipliers (ADMM) algorithm featuring an improved penalty factor. The research results show that P2P trading within the VPPC achieves resource optimization and allocation at a larger scale. The proposed distributed ADMM solution algorithm requires only the exchange of traded electricity volume and price among VPPs, thus preserving user privacy. Compared with independent operation, the total operation cost of the VPPC is reduced by 20.37%, and the overall proportion of new energy consumption is increased by 16.83%. The operation costs of the three VPPs are reduced by 1.12%, 20.51%, and 6.42%, respectively, while their carbon emissions are decreased by 4.47%, 5.80%, and 5.47%, respectively. In addition, the bargaining index incorporated in the proposed (point-to-point) P2P trading mechanism motivates each VPP to enhance its contribution to the alliance to achieve higher bargaining power, thereby improving the resource allocation efficiency of the entire alliance. The ADMM algorithm based on the improved penalty factor demonstrates good computational performance and achieves a solution speed increase of 15.8% compared to the unimproved version. Full article

To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intricate state–action interactions. In particular, closed-loop experiments, which involve continuous interaction between an agent and their driving environment, serve as a critical framework for improving the practical applicability of DRL algorithms in autonomous driving systems. This study empirically analyzes the capabilities of several representative DRL algorithms—namely DDPG, SAC, TD3, PPO, TQC, and CrossQ—in handling various urban driving scenarios using the CARLA simulator within a closed-loop framework. To evaluate the adaptability of each algorithm to geographical variability and complex traffic laws, scenario-specific reward and penalty functions were carefully designed and incorporated. For a comprehensive performance assessment of the DRL algorithms, we defined several driving performance metrics, including Route Completion, Centerline Deviation Mean, Episode Reward Mean, and Success Rate, which collectively reflect the quality of the driving in terms of its completeness, stability, efficiency, and comfort. Experimental results demonstrate that TQC and SAC, both of which adopt off-policy learning and stochastic policies, achieve superior sample efficiency and learning performances. Notably, the presence of geographically variant features—such as traffic lights, intersections, and roundabouts—and their associated traffic rules within a given town pose significant challenges to driving performance, particularly in terms of Route Completion, Success Rate, and lane-keeping stability. In these challenging scenarios, the TQC algorithm achieved a Route Completion rate of 0.91, substantially outperforming the 0.23 rate observed with DDPG. This performance gap highlights the advantage of approaches like TQC and SAC, which address Q-value overestimation through statistical methods, in improving the robustness and effectiveness of autonomous driving in diverse urban environments. Full article

Accurate forecasting of photovoltaic (PV) power generation is crucial for optimizing grid operation and ensuring a reliable power supply. However, the inherent volatility and intermittency of solar energy pose significant challenges to grid stability and energy management. This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. The model first uses CEEMDAN to decompose PV power data into Intrinsic Mode Functions (IMFs), capturing complex nonlinear features. Then, the CSSSA is employed to optimize VMD parameters, particularly the number of modes and the penalty factor, ensuring optimal signal decomposition. Subsequently, BILSTM is used to model time dependencies and predict future PV power output. Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively. Full article