Search Results (865)

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

Accurate traffic flow forecasting is a fundamental component of Intelligent Transportation Systems and proactive urban mobility management. However, the inherent complexity of urban traffic flow, characterized by non-stationary dynamics and multi-scale temporal dependencies, poses significant modeling challenges. Existing spatio-temporal models often struggle to reconcile the discrepancy between static physical road constraints and highly dynamic, state-dependent spatial correlations, while their reliance on fixed temporal receptive fields limits the capacity to disentangle overlapping periodicities and stochastic fluctuations. To bridge these gaps, this study proposes a novel Multi-scale Graph-Decoupling Spatial–temporal Network (MS-GSTN). MS-GSTN leverages a Hierarchical Moving Average decomposition module to recursively partition raw traffic flow signals into constituent patterns across diverse temporal resolutions, ranging from systemic daily trends to high-frequency transients. Subsequently, a Tri-graph Spatio-temporal Fusion module synergistically models scale-specific dependencies by integrating an adaptive temporal graph, a static spatial graph, and a data-driven dynamic spatial graph within a unified architecture. Extensive experiments on four large-scale real-world benchmark datasets demonstrate that MS-GSTN consistently achieves superior forecasting accuracy compared to representative state-of-the-art models. Quantitatively, the proposed framework yields an overall reduction in Mean Absolute Error of up to 6.2% and maintains enhanced stability across multiple forecasting horizons. Visualization analysis further confirms that MS-GSTN effectively identifies scale-dependent spatial couplings, revealing that long-term traffic flow trends propagate through global network connectivity while short-term variations are governed by localized interactions. Full article

The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value. Full article

Liquid coolant jets are commonly used to remove excess heat from workpieces during grinding. There is a pressing need to reduce energy waste that contributes to environmental heat pollution and to limit the spread of oil-based coolants and mist formation. As a liquid jet issues from a nozzle and enters the surrounding air, surface instabilities develop, causing the jet to break into droplets. This breakup diminishes the jet’s ability to deliver maximum momentum to the workpiece and grinding wheel in grinding operations, thereby reducing cooling efficiency. The presence of moving ambient air near the workpiece and rotating grinding wheel further complicates cooling. First, the study investigates jet breakups in stationary air, predicting breakup lengths with reasonable agreement to experiments at varying jet velocities using the Reynolds Averaged Navier–Stokes (RANS) method equipped with Shear Stress Transport (SST) k-ω model of turbulence. The coolant jet breakup length for a jet normal to the grinding wheel is different from that for a free jet and affected by the proximity of grinding wheel to nozzle that was not evaluated in prior studies. Simulations were performed using Ansys Fluent software 2023R1, with careful tuning of numerical schemes and selection of breakup criteria. The results include analysis of jet breakup phenomena in presence of rotating grinding wheel and workpieces, determination of breakup lengths across a range of Weber numbers, and effects of nozzle design. Full article

Non-stationary, multi-component vibration signals in rotating machinery are easily contaminated by strong background noise, which masks weak fault features and degrades diagnostic reliability. This paper proposes a joint denoising method that combines an improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and an improved wavelet thresholding scheme. ICFO, enhanced by Chebyshev chaotic initialization, a longitudinal–transverse crossover fusion mutation operator, and a thinking innovation strategy, is used to adaptively optimize the SVMD penalty factor and number of modes. The optimized SVMD decomposes the noisy signal into intrinsic mode functions, which are classified into effective and noise-dominated components via the Pearson correlation coefficient. An improved wavelet threshold function, whose threshold is modulated by the sub-band signal-to-noise ratio, is then applied to the effective components, and the denoised signal is reconstructed. Simulation experiments on nonlinear, non-stationary signals with different noise levels (SNR = 1–20 dB) show that the proposed method consistently achieves the highest SNR and lowest RMSE compared to VMD, SVMD, VMD–WTD, CFO–SVMD, and WTD. Tests on CWRU bearing data and gearbox vibration signals with added −2 dB Gaussian white noise further confirm that the method yields the lowest residual variance ratio and highest signal energy ratio while preserving key fault characteristic frequencies. Full article

Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric fluctuations and seasonal variability, makes short-term GI prediction a challenging task. To overcome these limitations, this work introduces a new hybrid forecasting architecture referred to as WTX–CBO, which integrates a Wavelet Transform (WT)-based decomposition module, an encoder–decoder Transformer model, and an XGBoost regressor, optimized using the Chaotic Billiards Optimizer (CBO) combined with the Adam optimization algorithm. In the proposed architecture, WT decomposes solar irradiance data into multi-scale components, capturing both high-frequency transients and long-term seasonal patterns. The Transformer module effectively models complex temporal and spatio-temporal dependencies, while XGBoost enhances nonlinear learning capability and mitigates overfitting. The CBO ensures efficient hyperparameter tuning and accelerated convergence, outperforming traditional meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Comprehensive experiments conducted on real-world GI datasets from diverse climatic conditions demonstrate the outperformance of the proposed model. The WTX–CBO ensemble consistently outperformed benchmark models, including LSTM, SVR, standalone Transformer, and XGBoost, achieving improved accuracy, stability, and generalization capability. The proposed WTX–CBO framework is designed as a high-accuracy decision-support forecasting tool that provides short-term global irradiance predictions to enable intelligent energy management, predictive charging, and adaptive control strategies in solar-powered applications, including solar electric vehicles (SEVs), rather than performing end-to-end vehicle or photovoltaic power simulations. Overall, the proposed hybrid framework provides a robust and scalable solution for short-term global irradiance forecasting, supporting reliable PV integration, smart charging control, and sustainable energy management in next-generation solar systems. Full article

This paper studies the stationary incompressible Navier-Stokes equations with mixed boundary conditions using a velocity-pressure finite element formulation. We first establish a variational framework and prove existence of solutions under suitable regularity assumptions, followed by a Galerkin discretization with error estimates. Three iterative algorithms (the Stokes, Newton, and Oseen schemes) are then analyzed, with stability conditions and error bounds derived for each. Numerical experiments confirm the theoretical results: all methods achieve second-order convergence for velocity and pressure. Among the three schemes, the Newton iteration is the most efficient in terms of computational time, while the Oseen iteration exhibits the strongest robustness with respect to decreasing viscosity coefficients. Full article

Accurate Short-Term Load Forecasting (STLF) is paramount for the stable and economical operation of power systems, particularly in the context of high renewable energy penetration, which exacerbates load volatility and non-stationarity. The prevailing advanced “decomposition–ensemble” paradigm, however, faces two significant challenges when processing non-stationary signals: (1) The performance of Variational Mode Decomposition (VMD) is highly dependent on its hyperparameters (K, α), and traditional meta-heuristic algorithms (e.g., GA, GWO, PSO) are prone to converging to local optima during the optimization process; (2) Deep learning predictors struggle to dynamically weigh the importance of multi-dimensional, heterogeneous features (such as the decomposed Intrinsic Mode Functions (IMFs) and external climatic factors). To address these issues, this paper proposes a novel, adaptive hybrid forecasting framework, namely IRIME-VMD-CDA-LSTNet. Firstly, an Improved Rime Optimization Algorithm (IRIME) integrated with a Gaussian Mutation strategy is proposed. This algorithm adaptively optimizes the VMD hyperparameters by targeting the minimization of average sample entropy, enabling it to effectively escape local optima. Secondly, the optimally decomposed IMFs are combined with climatic features to construct a multi-dimensional information matrix. Finally, this matrix is fed into an innovative Cross-Dimensional Attention (CDA) LSTNet model, which dynamically allocates weights to each feature dimension. Ablation experiments conducted on a real-world dataset from a distribution substation demonstrate that, compared to GA-VMD, GWO-VMD, and PSO-VMD, the proposed IRIME-VMD method achieves a reduction in Root Mean Square Error (RMSE) of up to 18.9%. More importantly, the proposed model effectively mitigates the “prediction lag” phenomenon commonly observed in baseline models, especially during peak load periods. This framework provides a robust and high-accuracy solution for non-stationary load forecasting, holding significant practical value for the operation of modern power systems. Full article

This study develops an interpretable, data-driven framework for forecasting daily MDA8 ozone levels in the Beijing–Tianjin–Hebei (BTH) region, integrating statistical diagnostics, XGBoost-based SHAP feature interpretation, and the Temporal Fusion Transformer (TFT). Using two years of pollutant and meteorological data from 56 monitoring stations, we identify a dual temporal structure: ozone, temperature, and pressure follow non-stationary annual cycles, while eight other variables show stationary, autocorrelated short-term fluctuations. SHAP analysis reveals that temperature, followed by relative humidity, NO₂, particulate matter, and pressure, are key predictors, in line with photochemical mechanisms. A hierarchical ablation experiment shows that multivariate models outperform bivariate ones, and meteorological variables improve predictions more than primary pollutants. The inclusion of five pollutant variables worsens performance due to multicollinearity. The XGBoost-TFT hybrid model, which compresses covariates into a single index, achieves the best performance (median R² = 0.686), outperforming raw-input models. These results validate the framework’s interpretability and alignment with photochemical mechanisms. Full article

Tubular pump systems (TPSs) represent a critical class of large-scale turbomachinery for low-head water transport, where mechanical reliability is often challenged by complex internal flow dynamics. Pressure pulsations in pump systems induce vibrations that adversely affect performance, emphasizing the need for effective control mechanisms to ensure stable operation. In tubular pumps, unsteady pressure pulsations are typically driven by rotor–stator interactions; however, the behavior of these pulsations in the absence of impeller rotation remains poorly understood. In this study, a novel comparative investigation is conducted to elucidate the effect of impeller rotation on pressure pulsations characteristic by examining two scenarios: normal impeller operation at rated speed and a completely stationary (zero-speed) impeller condition. Experiments were performed on a model low-head tubular pump, measuring dynamic pressures at four key locations across a range of flow rates. Time–frequency analysis using the continuous wavelet transform (CWT) and the wavelet coherence transform (WTC) was applied to delineate the unsteady pressure features. The results demonstrate that under normal rotation, pressure pulsations are dominated by pronounced periodic components at the impeller’s rotational frequency and its harmonics, with the strongest fluctuation amplitudes observed near the impeller outlet region. In contrast, with the impeller held stationary, these distinct periodic peaks vanish, replaced by broadband, irregular fluctuations. Crucially, WTC analysis revealed that significant coherence between the two operational states was confined to low frequencies (≈16.7–50 Hz), particularly at the impeller inlet, highlighting the presence of low-frequency dynamics likely associated with system-scale hydraulic compliance or inlet flow non-uniformity, independent of impeller rotation. These findings confirm the pivotal role of impeller rotation in generating periodic pressure pulsations while providing new insight into the underlying unsteady flow mechanisms in tubular pumps. Full article

Bearings are important elements of mechanical systems and the correct forecasting of their remaining useful life (RUL) is key to successful predictive maintenance. Nevertheless, noise interference during different operating conditions is also a significant problem in predicting their RUL. Existing denoising-based RUL prediction models often show degraded performance when exposed to heterogeneous and non-stationary noise, resulting in unstable feature extraction and reduced generalisation. To address the challenge of heterogeneous and non-stationary noise in bearing RUL prediction, this study proposes a hybrid framework that combines a noise-conditioned convolutional denoising autoencoder (NC-CDAE) and a temporal attention transformer (TAT). The NC-CDAE adaptively suppresses diverse noise types through conditional modulation, while the TAT captures long-term temporal dependencies to enhance degradation trend learning. This synergistic design improves both the noise robustness and temporal modelling capability of the system. To further validate the model under varying conditions, synthetic datasets with different noise intensities were generated using a conditional generative adversarial network (cGAN). Comprehensive experiments show that the proposed NC-CDAE + TAT framework achieves lower and more stable errors than state-of-the-art methods, reducing RMSE by up to 23.6% and MAE by 18.2% on average and maintaining consistent performance (an RMSE between 0.155 and 0.194) across diverse conditions. Full article

This study investigates the sailing performance and maneuverability of a fully wind-powered ship equipped with two rigid wing sails and a rudder, using Computational Fluid Dynamics (CFD). Unlike some conventional approaches that separately analyze above-water and underwater forces, this research employs a comprehensive CFD model to predict ship motion and performance under various wind directions and sail angles, from a stationary state to steady sailing. The accuracy of the CFD method is confirmed through comparison with experimental drift test data. Although the simulated drift data showed some discrepancies from the observed data due to the difficulty of accurately modeling the wind field in the simulation, the results indicate that the CFD method can effectively reproduce the ship motions observed in the experiments. Simulations reveal that the previously proposed L-shaped and T-shaped sail arrangements, which were designed to maximize thrust without considering maneuvering effects, remain effective even when ship motion is included. However, the results also show that conventional sail arrangements can achieve higher steady-state speeds due to reduced leeway-related resistance, while the L-shaped and T-shaped arrangements yield distinct steady-state leeway (drift) characteristics under heading control. These findings suggest that dynamically adjusting sail arrangements according to operational requirements may help manage the ship’s trajectory (lateral offset) and mitigate maneuvering difficulties, contributing to the practical application of fully wind-powered ships. The study provides quantitative insights into the relationship between sail arrangement, acceleration, and leeway/drift behavior, supporting the design of next-generation wind-powered ships. Full article

Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming provide closed-form solutions. Yet, their performance drops when they face non-ideal conditions such as imperfect channel state information (CSI), dynamic propagation environment, or high-dimensional system configurations, primarily due to static assumptions and computational limitations. These limitations have led to the rise of deep learning-based beamforming, where data-driven models derive beamforming solutions directly from CSI. By leveraging the representational capabilities of cutting-edge deep learning architectures, along with the increasing availability of data and computational resources, deep learning presents an adaptive and potentially scalable alternative to traditional methodologies. In this work, we unify and systematically compare our two unsupervised learning architectures for uplink receive beamforming: a simple neural network beamforming (NNBF) model, composed of convolutional and fully connected layers, and a transformer-based NNBF model that integrates grouped convolutions for feature extraction and transformer blocks to capture long-range channel dependencies. They are evaluated in a common multi-user single input multiple output (MU-SIMO) system model to maximize sum-rate across single-antenna user equipments (UEs) under 3GPP-compliant channel models, namely TDL-A and UMa. Furthermore, we present a FLOPs-based asymptotic computational complexity analysis for the NNBF architectures alongside baseline methods, namely ZFBF and MMSE beamforming, explicitly characterizing inference-time scaling behavior. Experiments for the simple NNBF are performed under simplified assumptions such as stationary UEs and perfect CSI across varying antenna configurations in the TDL-A channel. On the other hand, transformer-based NNBF is evaluated in more realistic conditions, including urban macro environments with imperfect CSI, diverse UE mobilities, coding rates, and modulation schemes. Results show that the transformer-based NNBF achieves superior performance under realistic conditions at the cost of increased computational complexity, while the simple NNBF presents comparable or better performance than baseline methods with significantly lower complexity under simplified assumptions. Full article

Emergency supplies allocation is a critical task in post-disaster response, as ineffective or delayed decisions can directly lead to increased human suffering and loss of life. In practice, emergency managers must make rapid allocation decisions over multiple periods under incomplete information and highly unpredictable demand, making robust and adaptive decision support essential. However, existing allocation approaches face several challenges: (1) Those traditional approaches rely heavily on predefined uncertainty sets or probabilistic models, and are inherently static, making them unsuitable for multi-period, dynamically allocation problems; and (2) while reinforcement learning (RL) technique is inherently suitable for dynamic decision-making, most existing RL-base approaches assume fixed demand, making them unable to cope with the non-stationary demand patterns seen in real disasters. To address these challenges, we first establish a multi-period and multi-objective emergency supplies allocation problem with demand uncertainty and then formulate it as a two-player zero-sum Markov game (TZMG). Demand uncertainty is modeled through an adversary rather than predefined uncertainty sets. We then propose RESA, a novel RL framework that uses adversarial training to learn robust allocation policies. In addition, RESA introduces a combinatorial action representation and reward clipping methods to handle high-dimensional allocations and nonlinear objectives. Building on RESA, we develop RESA_PPO by employing proximal policy optimization as its policy optimizer. Experiment results with realistic post-disaster data show that RESA_PPO achieves near-optimal performance, with an average gap of only 3.7% in terms of the objective value of the formulated problem, from the theoretical optimum derived by exact solvers. Moreover, RESA_PPO outperforms all baseline methods, including heuristic and standard RL methods, by at least 5.25% on average. Full article

Continuous cropping leads to declines in soil productivity and biodiversity, as well as a deterioration of overall phytosanitary conditions. What if we rotate the intercrops instead of the main crops? In a stationary three-year field experiment, maize was intercropped with Fabaceae (faba bean, crimson and Persian clovers, and blue-flowered alfalfa), Poaceae (winter rye, annual ryegrass, spring barley, and common oat), and Brassicaceae (white mustard, spring oilseed rape, oilseed radish, and spring Camelina) intercrops in separate growing seasons. Fabaceae intercrops developed slowly and competed poorly with weeds. The highest air-dried biomass (ADM) was produced by Persian and crimson clovers (approx. 86 g m⁻²). Intercrops of the Poaceae family, particularly rye and oats, as well as ryegrass, which was the most productive at 200 g m⁻² ADM, germinated faster and competed effectively with weeds. Brassicaceae intercrops also developed rapidly, especially mustard, Camelina, and radish (the most productive 206 g m⁻² ADM). Most intercrops competed with maize and reduced its biomass productivity; however, their competitive effects were weaker than those of weeds. A strong negative correlation between maize and weed biomass was detected (max. r = −0.946; p < 0.01). Complex evaluation index (CEI) showed that the crimson clover–annual ryegrass–spring oilseed rape rotation (CC-AR-SR) was the most productive and was effective in suppressing major weeds Echinochloa crus-galli, Chenopodium album, Polygonum lapathifolium, and Cirsium arvense, less competitive with maize (CEI 4.82), and can be used as an Integrated Pest Management tool. Full article