Search Results (503)

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

Accurate photovoltaic (PV) panel characterization is critical for optimizing renewable energy systems, but it is often hindered by the high cost of commercial tracers or the slow, error-prone nature of manual methods. This paper presents a low-cost, Arduino-based I–V curve tracer that overcomes these limitations through fully automated resistive load switching. By integrating a relay-controlled resistor bank managed by a single microcontroller, the system eliminates the need for manual intervention, enabling rapid and repeatable measurements in just 45 s. This rapid acquisition is a key advantage over manual systems, as it minimizes the impact of fluctuating environmental conditions and ensures the resulting I–V curve represents a stable operating point. Compared to commercial alternatives, our open-source solution offers significant benefits in cost, portability, and flexibility, making it ideal for field deployment. The system’s use of fixed, stable resistive loads for each measurement point also ensures high repeatability and straightforward comparison with theoretical models. Experimental validation demonstrated high agreement with a single-diode PV model, achieving a mean absolute percentage error (MAPE) of 4.40% against the manufacturer’s data. Furthermore, re-optimizing the model with field-acquired data reduces the MAPE from 18.23% to 7.06% under variable irradiance. This work provides an accessible, robust, and efficient tool for PV characterization, democratizing access for research, education, and field diagnostics. Full article

Continuous SiC fiber-reinforced SiC matrix composites (SiC/SiC), as structural heat protection integrated materials, are often used in parts for large-area heat protection and sharp leading edges, and there are a variety of low-velocity impact events in their service. In this paper, a drop hammer impact test was conducted using narrow strip samples to simulate the low-velocity impact damage process of sharp-edged components. During the test, different impact energies and impact times were set to focus on investigating the low-velocity impact damage characteristics of 2.5D SiC/SiC composites. To further analyze the damage mechanism, computed tomography (CT) was used to observe the crack propagation paths and distribution states of the composites before and after impact, while scanning electron microscopy (SEM) was employed to characterize the differences in the micro-morphology of their fracture surfaces. The results show that the in-plane impact behavior of a 2.5D needled SiC/SiC composite strip samples differs from the conventional three-stage pattern. In addition to the three stages observed in the energy–time curve—namely in the quasi-linear elastic region, the severe load drop region, and the rebound stage after peak impact energy—a plateau stage appears when the impact energy is 1 J. During the impact process, interlayer load transfer is achieved through the connection of needled fibers, which continuously provide significant structural support, with obvious fiber pull-out and debonding phenomena. When the samples are subjected to two impacts, damage accumulation occurs inside the material. Under conditions with the same total energy, multiple impacts cause more severe damage to the material compared to a single impact. Full article

This study presents an advanced energy demand management approach within residential microgrids using bipartite models for optimal demand response. The methodology relies on linear programming, specifically the Simplex algorithm, to optimize power distribution while minimizing costs. The model aims to reduce residential energy consumption by flattening the demand curve through demand response programs. Additionally, the Internet of Things (IoT) is integrated as a communication channel to ensure efficient energy management without compromising user comfort. The research evaluates energy resource allocation using bipartite graphs, modeling the generation of energy from renewable and conventional high-efficiency sources. Various case studies analyze scenarios with and without market constraints, assessing the impact of demand response at different levels (5%, 10%, 15%, and 20%). Results demonstrate a significant reduction in reliance on external grids, with optimized energy distribution leading to potential cost savings for consumers. The findings suggest that intelligent demand response strategies can enhance microgrid efficiency, supporting sustainability and reducing carbon footprints. Full article

As a pivotal economic province in China, Jiangsu’s efforts in civil building energy conservation are critical to achieving the national “dual carbon” goals. This paper proposes a hybrid model that integrates wavelet transform, support vector regression (SVR), and extreme learning machine (ELM) to predict the civil building energy consumption of Jiangsu Province. Based on data from statistical yearbooks, the historical energy consumption of civil buildings is calculated. Through a grey relational analysis (GRA), the key factors influencing the civil building energy consumption are identified. The wavelet transform technique is then applied to decompose the energy consumption data into a trend component and a fluctuation component. The SVR model predicts the trend component, while the ELM model captures the fluctuation patterns. The final prediction results are generated by combining these two predictions. The results demonstrate that the hybrid model achieves superior performance with a Mean Absolute Percentage Error (MAPE) of merely 1.37%, outperforming both individual prediction methods and alternative hybrid approaches. Furthermore, we develop three prospective scenarios to analyze civil building energy consumption trends from 2023 to 2030. The analysis reveals that the observed patterns align with the Environmental Kuznets Curve (EKC). These findings provide valuable insights for provincial governments in future policy-making and energy planning. Full article

This study presents an innovative methodology to optimize the operation of distribution transformers through the estimation of hourly load curves, aimed at minimizing technical losses due to oversizing, particularly in systems lacking advanced metering infrastructure. The proposed approach combines clustering techniques, K-Means with DTW, to identify representative daily consumption patterns and a supervised model based on LightGBM to estimate hourly load curves for unmetered transformers, using customer characteristics as input. These estimated curves are integrated into a process that calculates technical losses, both no-load and load losses, for different transformer sizes, selecting the optimal rating that minimizes losses without compromising demand. Empirical validation showed accuracy levels of 95.6%, 95.29%, and 98.14% at an individual transformer, feeder, and a complete electrical system with 16,864 transformers, respectively. The application of the methodology to a real distribution system revealed a potential annual energy savings of 3004 MWh, equivalent to an estimated economic reduction of 150,238 USD. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

Precise forecasting of lithium-ion battery health status is crucial for safe, efficient, and sustainable operation throughout the battery life cycle, especially in applications like electric vehicles (EVs) and renewable energy storage systems. In this study, an improved particle swarm optimization–support vector regression (IPSO-SVR) model is proposed for dynamic hyper-parameter tuning, integrating multiple intelligent optimization algorithms (including PSO, genetic algorithm, whale optimization, and simulated annealing) to enhance the accuracy and generalization of battery state-of-health (SOH) estimation. The model dynamically adjusts SVR hyperparameters to better capture the nonlinear aging characteristics of batteries. We validate the approach using a publicly available NASA lithium-ion battery degradation dataset (cells B0005, B0006, B0007). Key health features are extracted from voltage–capacity curves (via incremental capacity analysis), and correlation analysis confirms their strong relationship with battery capacity. Experimental results show that the proposed IPSO-SVR model outperforms a conventional PSO-SVR benchmark across all three datasets, achieving higher prediction accuracy: a mean MAE of 0.611%, a mean RMSE of 0.794%, a mean MSE of 0.007%, and robustness a mean R² of 0.933. These improvements in SOH prediction not only ensure more reliable battery management but also support sustainable energy practices by enabling longer battery life spans and more efficient resource utilization. Full article

Hydrogen–gasoline dual-fuel spark ignition (SI) engines represent a promising transitional solution toward cleaner combustion and reduced carbon emissions. In a previous study, a predictive engine model was developed to simulate the performance and combustion characteristics of such systems; however, its accuracy was constrained by the use of estimated combustion parameters. This study presents an experimental validation based on high-resolution in-cylinder pressure measurements performed on a naturally aspirated SI engine operating with a 20% hydrogen energy share. The objectives are twofold: (1) to refine the combustion model using empirically derived combustion metrics, and (2) to evaluate the feasibility of moderate hydrogen enrichment in a stock engine configuration. To facilitate a more accurate understanding of how key combustion parameters evolve under different operating conditions, Vibe function was fitted to the ensemble-averaged heat release rate curves computed from 100 consecutive engine cycles at each static full-load operating point. This approach enabled the extraction of stable and representative metrics, including the mass fraction burned at 50% (MFB50) and combustion duration, which were then used to recalibrate the predictive combustion model. In addition, cycle-to-cycle variation and combustion duration were also investigated in the dual-fuel mode. The combustion duration exhibited a consistent and substantial reduction across all of the examined operating points when compared to pure gasoline operation. Furthermore, the cycle-to-cycle variation difference remained statistically insignificant, indicating that the introduction of 20% hydrogen did not adversely affect combustion stability. In addition to improving model accuracy, this work investigates the occurrence of abnormal combustion phenomena—including backfiring, auto-ignition, and knock—under enriched conditions. The results confirm that 20% hydrogen blends can be safely utilized in standard engine architectures, yielding faster combustion and reduced burn durations. The validated model offers a reliable foundation for further dual-fuel optimization and supports the broader integration of hydrogen into conventional internal combustion platforms. Full article

The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function—specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified. Full article

This study examines the symmetric and asymmetric impacts of international trade on consumption-based carbon emissions (CBEs) in the People’s Republic of China (PRC) and the United States of America (USA) from 1990 to 2018. The analysis uses autoregressive distributed lag (ARDL) and non-linear ARDL (NARDL) methodologies to capture short- and long-run trade emissions dynamics, with economic growth, oil prices, financial development and industry value addition as control variables. The findings reveal that exports reduce CBEs, while imports increase them, across both economies in the long and short run. The asymmetric analysis highlights that a fall in exports increases CBEs in the USA but reduces them in the PRC due to differences in supply chain flexibility. The PRC demonstrates larger coefficients for trade variables, reflecting its reliance on energy-intensive imports and rapid trade growth. The error correction term shows that the PRC takes 2.64 times longer than the USA to return to equilibrium after short-run shocks, reflecting systemic rigidity. These findings challenge the Environmental Kuznets Curve (EKC) hypothesis, showing that economic growth intensifies CBEs. Robustness checks confirm the results, highlighting the need for tailored policies, including carbon border adjustments, renewable energy integration and CBE-based accounting frameworks. Full article

The high-power drive of an impulse sound source with drilling makes the system’s life short and difficult to integrate. This report firstly establishes the pulse discharge experimental system and finite element model, and compares and verifies the typical parameters. Second, the study examines how the energy storage capacitor’s charging voltage, discharge electrode gap, and liquid environment conductivity influence the electroacoustic performance of needle series electrodes. Subsequently, the optimal electrode configuration is identified under power constraints, yielding electroacoustic parameters and curves suitable for low-power impulsive sound sources. The findings reveal that the needle–plate electrode outperforms others in pre-breakdown duration, peak impulse wave strength, highest sound pressure level, and electroacoustic conversion efficiency. However, its higher power demand can be mitigated by lowering the charging voltage and narrowing the electrode gap. The charging voltage of the power-limited needle–plate electrode is only 3.5 kV, the impulse wave intensity reaches 1.27 MPa, and the peak system power is effectively controlled within 6.66 kW. A stable 288 dB SPL output is maintained up to 1 kHz, and above 250 dB in the wide bandwidth of 1–100 kHz. Needle–plate electrodes provide broadband excitation and high intensity SPL output despite power limitations. Full article

Currently, deep reinforcement learning has been widely applied to energy system optimization and scheduling, and the DRL method relies more heavily on historical data. The lack of historical operation data in new integrated energy systems leads to insufficient DRL training samples, which easily triggers the problems of underfitting and insufficient exploration of the decision space and thus reduces the accuracy of the scheduling plan. In addition, conventional data-driven methods are also difficult to accurately predict renewable energy output due to insufficient training data, which further affects the scheduling effect. Therefore, this paper proposes a small-sample scenario optimization scheduling method based on multidimensional data expansion. Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed method is verified by arithmetic examples. Full article

Japan’s shift to a super-aged society is reshaping household carbon footprint (HCF) in ways that vary by age, income, and region. Drawing on a two-tier national–prefectural framework, we quantify the influence of demographic shifts on HCF and evaluate inequalities, and project prefectural HCF to 2050 under fixed 2005 technology and consumption baselines. Nationally, emissions follow an inverted-U age curve, peaking at the 50–54 s (2.16 tCO₂) and dropping at both the younger and older ends. Carbon inequality—the gap between high- and low-income households—displays the opposite U shape, being the widest below 30 and above 85. Regional HCF patterns add a further layer: while the inverted U persists, its peak shifts to the 60–64 s in high-income prefectures such as Tokyo—where senior emissions rise by 44% by 2050—and to the 45–49 s in low-income prefectures such as Akita, where younger age groups cut emissions by 58%. Although spatial carbon inequality narrows through midlife, it widens again in old age as eldercare and home energy needs grow. These findings suggest that a uniform mitigation trajectory overlooks key cohorts and regions. To meet the 2050 net-zero target, Japan should integrate age-, income-, and region-specific interventions—for example, targeted carbon pricing, green finance for middle-aged consumers, and less-urban low-carbon eldercare—into its decarbonization roadmap. Full article

This study addresses the coupled influence of the threshold pressure gradient and stress sensitivity during the seepage process in low-permeability reservoirs. By integrating Laplace transform, perturbation transform, the image principle, and the superposition principle, a non-steady-state seepage model for segmented-fractured horizontal wells considering both effects is established for the first time. The analytical solution of the point source function including the threshold pressure gradient ($\lambda$) and stress sensitivity effect (permeability modulus $\alpha$) is innovatively derived and extended to closed-boundary reservoirs. The model accuracy is verified by CMG numerical simulation (with an error of only 1.02%). Based on this, the seepage process is divided into four stages: I linear flow (pressure derivative slope of 0.5), II fracture radial flow (slope of 0), III dual radial flow (slope of 0.36), and IV pseudo-radial flow (slope of 0). Sensitivity analysis indicates the following: (1) The threshold pressure gradient significantly increases the seepage resistance in the late stage (the pressure curve shows a significant upward curvature when $\lambda$ = 0.1 MPa/m); (2) Stress sensitivity dominates the energy dissipation in the middle and late stages (a closed-boundary-like feature is presented when $\alpha$ > 0.1 MPa⁻¹); (3) The half-length of fractures dominates the early flow (a 100 m fracture reduces the pressure drop by 40% compared to a 20 m fracture). This model resolves the accuracy deficiency of traditional single-effect models and provides theoretical support for the development effect evaluation and well test interpretation of fractured horizontal wells in low-permeability reservoirs. Full article