Search Results (3,426)

Synthetic aperture radar (SAR) provides all-weather and day-and-night imaging capabilities, but aircraft detection in SAR images remains challenging because of discontinuous target scattering responses, complex background clutter, shallow-detail attenuation, and unstable localization of small targets. In particular, high-frequency SAR components may contain both target-related scattering discontinuities and clutter fluctuations, making direct frequency enhancement susceptible to background interference. To address these issues, this paper proposes WSF-YOLO, a wavelet-guided spatial–frequency joint detection method based on YOLO11n. First, a wavelet-guided spatial–frequency feature extraction module (WGStem) selectively incorporates frequency information through gated residual fusion, reducing structural-detail loss during downsampling. Second, a spatial–frequency collaborative feature enhancement module (SFC3k2) continuously strengthens aircraft contours, edge responses, and local scattering differences in the intermediate stages of the backbone. Finally, a dynamic-ratio joint regression loss (DRCN-Loss) combines complete intersection over union (CIoU) and normalized Wasserstein distance (NWD) with training-dependent weights to improve localization stability for small targets and low-overlap predictions. Experimental results show that WSF-YOLO achieves 95.6% mean average precision at an intersection-over-union threshold of 0.5 (${\mathrm{mAP}}_{50}$) and 71.2% mean average precision averaged over thresholds from 0.5 to 0.95 in increments of 0.05 (${\mathrm{mAP}}_{50–95}$) on SAR-AIRcraft-1.0, outperforming YOLO11n by 1.4 and 6.2 percentage points, respectively. Consistent improvements on another public SAR aircraft dataset further demonstrate the effectiveness of the proposed method. Full article

The influence of mechanical noise on impact identification using fiber Bragg grating (FBG) accelerometers in complex environments remains insufficiently explored and quantitatively characterized. This study investigates how the intensity and propagation path of mechanical noise affect the output stability and impact-detection capability of a self-developed FBG accelerometer. A quantitative evaluation approach is established using wavelength-variation statistics, impact-response amplitude, and signal-to-noise ratio (SNR). Experimental results show that mechanical noise markedly increased the background wavelength fluctuation, with the maximum standard deviation reaching 16.71 pm, approximately 11 times the no-additional-noise value. By contrast, the RMS of the target impact response increased only slightly, from 0.0300 nm to 0.0353 nm, indicating that mechanical noise primarily affects baseline stability rather than the amplitude of the target impact response. Under the highest-intensity disturbance tested, the SNR remained at 22.98 dB, and the impact event remained distinguishable. The proposed multi-indicator approach provides a quantitative characterization of the FBG accelerometer response under different mechanical-noise conditions. A commercial 941B accelerometer was used as an external reference for sensitivity calibration, and its measurements were not used for direct performance comparison with the FBG accelerometer. The results provide experimental evidence of the impact-identification behavior of the proposed sensor under the investigated mechanical-noise conditions. Full article

Shipboard DC power systems increasingly employ battery energy storage systems to buffer load fluctuations, support DC bus stability, and improve power supply continuity. Accordingly, the isolated DC–DC converter interfacing the battery with the shipboard DC bus must achieve both high conversion efficiency and low battery-side current ripple. However, LLC resonant tank parameters simultaneously affect conversion losses, soft-switching characteristics, and ripple transmission, while the frequency-dependent battery impedance cannot be accurately represented by a conventional resistive load model. This paper therefore proposes a multi-objective resonant parameter optimization framework that jointly considers conversion efficiency and battery-side high-frequency current ripple. A unified converter–battery mechanistic model is established using the fundamental harmonic approximation and impedance network analysis, from which a computable battery-side ripple-current metric is derived. Multi-objective particle swarm optimization is then applied to generate a Pareto-optimal solution set and identify efficiency-oriented, ripple-oriented, and compromise designs. Experimental results obtained from a laboratory prototype verify the predicted efficiency and ripple trends, demonstrating that the proposed framework provides practical guidance for LLC resonant parameter selection in shipboard battery energy storage interfaces. Full article

Seismic exploration has progressively expanded into urban fringe regions and areas with intensive human activities, where anthropogenic interference has increased markedly, aggravating the background noise problem and substantially affecting the accuracy of subsequent seismic data processing. Conventional denoising methods struggle to cope with such complex noise patterns and typically rely on manually designed parameter settings, which limits their ability to suppress complex background noise in real seismic data. With the rapid development of deep learning, various neural-network-based methods have been introduced for seismic data denoising. However, existing deep learning approaches are constrained by the high resolution of seismic data and limited computational resources and therefore commonly perform denoising on cropped data patches rather than on the complete seismic section. This limitation weakens the network’s capability to perceive global contextual information, degrading denoising performance and potentially introducing blocking artifacts. To address these issues, we propose a fluctuation-prior-regularized denoising framework that explicitly decomposes the complete seismic data denoising task into global and local denoising subtasks, enabling globally consistent denoising under complex conditions. To overcome the limitations of CNNs and Transformers, we adopt Retentive Networks Meet Vision Transformers (RMT) as the backbone for feature extraction in this work. And a fluctuation-prior-constrained local denoising mechanism is introduced, allowing local patches to indirectly capture global information. In addition, a grouped regularization strategy for global and local tasks is proposed, enabling both optimization tasks to better capture their respective task-specific characteristics. Experimental results on noisy shot gathers constructed from field records and on field-recorded background noise demonstrate that the proposed DFRSeisnet achieves superior denoising performance while effectively alleviating blocking artifacts. Full article

Pepper suffers from bacterial leaf spot and yellow leaf curl, which substantially reduce crop yield and fruit quality. Traditional manual diagnosis suffers from delayed response, subjective bias and heavy labor consumption, while existing intelligent detection pipelines lack standardized preprocessing workflows, quantitative feature screening and systematic model comparison. To fill these research gaps, we built a pepper leaf dataset with 1260 samples (healthy, bacterial spot, yellow leaf curl). Three segmentation algorithms (Lab b-channel, RGB super-green, Otsu-ACWE) were quantitatively assessed to select the optimal preprocessing scheme. We extracted 32 fused visual features (27 RGB/HSV/Lab color moments + five gray-level co-occurrence matrix (GLCM) texture metrics) and adopted a random-forest classifier to eliminate seven low-contribution redundant features, retaining 25 discriminative variables. Three representative models, namely convolutional neural network (CNN), logistic regression (LR), and genetic-algorithm-optimized back-propagation neural network (GA-BP), were constructed for parallel comparison via 20 independent repeated trials, with accuracy, precision, recall, F1-score and area under the receiver operating characteristic curve (AUC) as evaluation indicators. The results verified that Lab b-channel segmentation achieved superior background separation and intact lesion edge retention. CNN yielded the best performance, with an average test accuracy of 97.67% and an average AUC of 0.999, accompanied by minimal metric standard deviations and outstanding stability. LR exhibits low computational cost and fast training, which is promising for applications with limited computing resources. In contrast, GA-BP shows weak nonlinear fitting ability and severe prediction fluctuations, making it unsuitable for high-precision diagnosis. This study proposes a standardized experimental framework to offer theoretical guidance and algorithmic references for intelligent vegetable leaf disease identification. All experiments were conducted on a dataset collected under standardized indoor single-illumination conditions; therefore, the conclusions of this study are only applicable to such controlled scenarios. Full article

This paper investigates the performance of a cascaded dual-hop relay system comprising a radio-frequency (RF) hop followed by a free-space optical (FSO) hop. The RF channel was subject to Rayleigh fading, whereas the FSO channel experienced beta-distributed dust-induced irradiance fluctuations based on experimentally obtained channel parameters. A fixed-gain amplify-and-forward (AF) relay was employed, and the FSO link operated using intensity modulation/direct detection (IM/DD) with on–off keying (OOK). Unlike conventional mixed RF/FSO studies that primarily model the optical hop through atmospheric turbulence and pointing errors, this work examined the end-to-end effect of dust-induced fading in a cascaded RF/FSO architecture. An exact integral representation of the end-to-end signal-to-noise ratio (SNR) cumulative distribution function was formulated. A finite-series closed-form approximation of the end-to-end CDF was subsequently obtained, from which corresponding closed-form approximations for the outage probability, the average bit error rate (BER), and the ergodic capacity metric were derived. The accuracy of the proposed approximations was validated through numerical integration and Monte Carlo simulations. The results show that the finite-series expressions provided highly accurate and computationally efficient performance estimates in the moderate- and high-SNR regions, while a measurable deviation may occur under very low-SNR conditions. The developed framework provides useful analytical tools for evaluating cascaded RF/FSO systems operating over beta-distributed dust-fading channels. Full article

Pharmaceuticals and other organic matter can remain in municipal wastewater after conventional treatment, while MBBR carriers are typically made from virgin plastics. This study investigated mechanically modified post-consumer polypropylene (PP) drinking straws and perforated high-density polyethylene (HDPE) bottle caps as alternative microbial biofilm carriers for organic pollutant removal in a laboratory-scale MBBR. The reactors treated real secondary wastewater mixed with recycled sludge (1:1) and spiked with 0.1 mg/L ciprofloxacin (CIP). Three experimental series evaluated the effects of carrier filling ratio (10% and 30% PP straws) and carrier type (30% PP straws and 30% HDPE bottle caps) on pH, conductivity, UV₂₅₄ absorbance, extracellular polymeric substances (EPSs), and UV₂₈₀ absorption associated with wastewater containing CIP. A fourth series compared the more effective carrier configuration (30% HDPE bottle caps) with and without the addition of CIP. Increasing the filling ratio from 10% to 30% improved process stability and UV₂₅₄ behavior, while the 30% HDPE bottle caps showed the most favorable overall performance. At equal filling ratio, HDPE bottle caps exhibited more stable behavior under the tested conditions than PP straws, achieving lower UV₂₅₄ absorbance and more stable UV₂₈₀ absorbance. The EPS dynamics reflected the growth and life cycle of the biofilm, while the no-CIP control confirmed that the UV₂₈₀ fluctuations were related to the CIP-containing wastewater and not only to the soluble organic matter originating from the biofilm. Overall, post-consumer HDPE bottle caps showed promising potential as low-cost alternative MBBR carriers for treating ciprofloxacin-containing wastewater, while supporting the utilization of plastic waste and circular economy approaches in wastewater treatment. Full article

The drum is a key winding component of an ultra-deep shaft construction hoist, and its structural characteristics directly affect wire-rope vibration and winding behavior, potentially leading to abnormal winding phenomena such as rope jumping. Therefore, a dynamic model of wire-rope inter-turn and inter-layer transitions and a rope-jumping discrimination model are established, and the second-to-third-layer transition acceleration and the third-layer critical fleet angle in the folded-line area are selected as the dual optimization objectives. A polynomial response-surface surrogate model is constructed based on orthogonal-test screening and central composite design, and multi-objective drum-structure optimization is performed using NSGA-II combined with entropy-weighted TOPSIS. Scaled winding tests are then conducted to evaluate the dynamic tension, critical fleet angle, and winding state before and after optimization. The results show that the second-to-third-layer transition acceleration decreases by 8.2%, while the third-layer critical fleet angle in the folded-line area increases by 4.7%. The optimized drum exhibits reduced dynamic-tension fluctuations at both inter-layer transition positions, and the relative errors between the theoretical and experimental critical fleet angles are all below 2.0%. Under the tested disturbance condition, rope jumping occurs in all four tests with the original drum, whereas no rope jumping is observed with the optimized drum. Full article

In this paper, a Michelson interferometer was employed to generate interference patterns, the modulated function was extracted by digital filtering and the Laplace transform was subsequently applied to obtain the complex function $F\left(s\right)=F(\sigma +\omega i)$, which changes its zero structure in response to physical perturbations. This study presents a novel method for detecting millimetric displacements based on analyzing the movement of zeros in the complex s-plane. The proposed technique demonstrates that an unperturbed system displays two zeros on the imaginary axis, whereas the introduction of a displacement $∆x$ generates a third zero with a real component. The position of this third zero in the s-plane directly correlates with the magnitude of the perturbation. Finally, the measurement vector $s_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{s}}$, calculated as the vector difference between reference and perturbed zeros, quantifies the displacement with high precision. Experimentally, displacements ranging from 0.8 mm to 8.8 mm were introduced into one of the interferometer arms, allowing for the systematic observation of the zero migration within the complex plane. The obtained results demonstrate that this methodology transforms the displacement measurement problem into a singularity localization task in the complex domain, offering enhanced robustness against intensity fluctuations compared to conventional techniques based on Fourier analysis or fringe counting. Full article

To address power supply–demand mismatch in hydrogen–electric hybrid ships under load fluctuations, variations in energy states, and limited power-supply capability, this paper develops a power-deficit-constrained hierarchical energy-management strategy. Rather than directly determining the power commands of individual energy units, the upper-level PPO-Lagrangian controller adaptively regulates the ECMS equivalent factor according to system states and explicit power-deficit constraint feedback, while the lower-level ECMS performs instantaneous power allocation among the fuel cell, battery, and diesel generator. The resulting allocation determines the realized constraint cost, which is fed back to update the Lagrange multiplier and subsequent equivalent-factor regulation, thereby forming a closed-loop cross-layer coordination mechanism. Simulations were conducted using navigation data from a nearshore bulk carrier and compared with representative energy-management strategies. The results show that the proposed strategy can achieve favorable performance in power-deficit suppression, constraint feasibility, diesel-generator dependence, and operational economy under load disturbances and component degradation, providing a methodological basis for further real-time implementation and experimental validation of constrained energy management in hydrogen–electric ships. Full article

To address the strong non-stationarity, coupled multi-scale fluctuations, and insufficient parameter adaptability of net load forecasting models under high photovoltaic penetration, an ICEEMDAN-BO-BiGRU combined short-term net load forecasting model is proposed. First, the net load sequence is constructed from the actual load and photovoltaic output, and then decomposed into eight intrinsic mode function components and a residual component using improved complete ensemble empirical mode decomposition with adaptive noise. Second, a bidirectional gated recurrent unit forecasting submodel is developed for each component, and Bayesian optimization is employed to adaptively optimize the key hyperparameters. Finally, the forecasts of all components are linearly summed to obtain the final net load forecast. Experiments are conducted using the CN03 sample from the HEEW dataset. The results show that, compared with CEEMDAN-BiGRU, the proposed method reduces the MAE and RMSE by 37.84% and 35.73%, respectively, over the complete February 2022 test period, while achieving a mean R² of 0.9783, indicating lower observed forecasting errors under the reported experimental configurations. Full article

Traffic signal control plays a critical role in mitigating congestion and improving urban mobility, particularly in multi-intersection networks where fixed-time strategies cannot adapt to fluctuating demand. Although reinforcement learning has shown strong potential for adaptive signal optimization, purely learning-based controllers often rely on reward shaping rather than explicit enforcement of traffic engineering constraints, which may lead to unstable phase switching and operational inefficiencies. This study proposes a Hybrid Reinforcement Learning and Rule-based algorithm (Hybrid-RL-RB), a constraint-aware traffic signal control algorithm that combines reinforcement learning with a rule-based supervisory layer for multi-intersection traffic signal control. In the implemented version, the learning component is based on tabular Q-learning with a discretized traffic state representation, while the rule-based layer supervises the final executable signal action. The objective is to improve adaptive signal control while preserving operational feasibility through minimum green time, maximum green time, spillback protection, and phase-safety constraints. The framework was implemented in SUMO through TraCI and evaluated under three scenarios of low, medium, and high traffic demand conditions across multiple network configurations, including a real-network topology (Casablanca-OSM). Experimental results show that Hybrid-RL-RB reduces average queue length by up to 51.47% and waiting time by up to 68.10% compared with Fixed-Time control. Compared with Simple-RL, the proposed method provides modest but consistent queue reductions on the 16 × 16 network, while MaxPressure remains the strongest queue-minimization baseline. In the high-demand Casablanca-OSM scenario, Hybrid-RL-RB reduces queue length by 20.50%, reduces waiting time by 21.41%, and increases throughput by 16.83% compared with Fixed-Time control. These results indicate that explicit rule-based projection can improve the operational feasibility and extensibility of RL-based traffic signal control, although further validation with additional seeds and longer real-network simulations is required. Full article

With the increasing number of end devices and the growing heterogeneity of application demands, task orchestration in dynamic cloud–edge environments faces challenges arising from network fluctuations, resource contention, and decision uncertainty. To address these challenges, this paper proposes a Cost-Optimized Adaptive Three-Way Decision (COATWD) method for cloud–edge task orchestration. Candidate execution costs for Local Edge, Remote Edge, and Cloud are evaluated by jointly considering network transmission, task queuing, task execution, and QoS constraints, while historical prediction residuals provide empirical evidence for estimating candidate-superiority probabilities. Based on the current task characteristics and candidate resource states, decision losses are dynamically constructed, and adaptive dual thresholds are generated according to the effectiveness of historical refinement, thereby partitioning candidate relations into positive, boundary, and negative regions. Candidate relations assigned to the boundary region are further refined by matching historical information to the task type, candidate execution role, and specific Edge host. Observed execution outcomes continuously update prediction residuals and task success and failure statistics, thereby forming a closed-loop, online, adaptive orchestration process. Experimental results obtained with EdgeCloudSim 4.0 show that COATWD effectively reduces the task failure rate, average service time, and average processing time under different device scales and application workloads. Full article

Electromagnetic flowmeters are widely used for slurry flow measurement in grouting engineering, but their accuracy deteriorates under low-flow-rate conditions. To address this limitation, a laser-ranging-based grouting flow measurement method using an average-value algorithm was investigated, and a combined vertical–horizontal baffle configuration was introduced to regulate the slurry free-surface morphology and improve measurement accuracy. Laboratory tests and orthogonal experiments were conducted to evaluate the effects of the slurry water–cement ratio, stirring-shaft rotational speed, and discharge flow rate on measurement performance. Engineering-scale numerical simulations were further performed to clarify the flow-regulation mechanism of the combined baffles. The results showed that the mean relative measurement errors of all nine orthogonal experimental cases were below 4%, with a minimum value of 1.39% obtained at a water–cement ratio of 2:1, a stirring-shaft speed of 40 r/min, and a discharge flow rate of 10 L/min. Accuracy analysis indicated that the influence of the investigated factors followed the order of discharge flow rate > water–cement ratio > stirring-shaft rotational speed, with discharge flow rate being the dominant factor. The combined baffles improved measurement accuracy over a range of slurry concentrations and discharge conditions, with a maximum improvement of 6.88% at a discharge flow rate of 1 L/min. The numerical results further showed that the combined baffles disrupted the dominant tangential flow, suppressed central-vortex formation, and flattened the slurry free surface. Meanwhile, the horizontal baffles reduced the upward transmission of disturbances from the lower rotating region, thereby limiting excessive free-surface fluctuations. These findings demonstrate the potential of the proposed baffle-assisted laser-ranging method for accurate slurry flow measurement, particularly under low-flow-rate grouting conditions. Full article

To address current-density ramping, cell-voltage increase, and elevated operating stress in anion exchange membrane water electrolyzers (AEMWEs) under fluctuating wind and photovoltaic (PV) power inputs, this study develops a dynamic AEMWE model that couples voltage losses, thermal dynamics, and water-management states. A forecast-assisted reference governor based on short-term power prediction and dynamic constraints is further proposed. Using German Open Power System Data (OPSD) wind and PV, the effects of power-command correction on current density, voltage efficiency, specific energy consumption (SEC), voltage-limit exceedance, and a degradation-related stress proxy are analyzed. The steady-state benchmark distinguishes a separate 21-point empirical polarization fit (same-set RMSE 0.0059 V and MAPE 0.292%) from the mechanistic voltage-loss model actually used in the dynamic simulations (same-set RMSE 0.5063 V and MAPE 28.42%); consequently, the dynamic results are treated as model-conditioned rather than experimentally validated. In the 24 h case, explicit state-constrained smoothing reduced the maximum cell voltage from 2.2479 to 2.1501 V and SEC from 59.4755 to 58.1968 kWh kg⁻¹, but hydrogen yield and renewable-energy utilization decreased from 1.51997 to 0.54293 kg d⁻¹ and from 99.52% to 34.78%, respectively. The forecast envelope did not bind on the selected smooth day; across 30 representative days it reduced the stress proxy relative to the no-look-ahead state-constrained baseline on only 3–6 days depending on wind–PV composition, while in a diagnostic ramp-down case it activated 10 times and reduced cumulative ramping by 6.7% at the cost of a 16.9% hydrogen-yield loss. These results provide model-based exploratory evidence and separate the benefit of state projection from the incremental value of prediction. Full article