Search Results (2,262)

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

This study aims to investigate the effects of Chan-Hom (2015) typhoon-induced variations in enthalpy flux (EF) and moisture flux (MF) on intensity variations and rainfall. Chan-Hom (2015) made landfall at Zhoushan, then changed its direction and moved towards Korea. This analysis used ERA5 reanalyzed data, encompassing daily surface latent and sensible heat flux, along with wind measurements at a height of 10 m. Furthermore, wind components and specific humidity data from the 1000–200 hPa level in ERA5 were utilized to compute the MF and MF convergence, in accordance with the equations outlined in the methodology. This study examines the correlation among typhoon intensity, precipitation, MF, and EF. The mechanism by which Typhoon Chan-Hom has caused a decline in sea surface temperature (SST) was analyzed. Typhoons need a higher EF that can affect them before landfall to maintain their intensity. The highest LHF was observed (340 W/m²) prior to typhoon landfall, indicating that LHF responds to intensity-induced wind during Chan-Hom. Typhoon-induced rainfall is mainly controlled by the MF convergence, rather than the typhoon intensity. The spatial and temporal distributions of MF and MF convergence (MFC) during typhoon formation to landfall reveal that the symmetric MFC is dominated by typhoon intensity; a symmetrical structure is observed when the intensity is high. MFC includes wind convergence and moisture advection. Wind convergence dominates the MFC during typhoons, but moisture advection forms at the eyewall. MF during the typhoon’s landfall can relate to the amount of rainfall that occurred over the land. However, the rainfall pattern changed after landfall, and the typhoon changed its direction. SST cooling observed in the study area is mainly due to the upwelling process with strong cyclonic winds. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

Compared with conventional symmetric aircraft, the oblique-wing aircraft offers significant advantages across a wide speed range due to the variable oblique angle. However, the asymmetric aerodynamic characteristics will arise from the differential leading-edge sweep between the forward and aft wings during the rotation process. This study investigates the aerodynamic effects of a conceptual oblique-wing configuration at transonic (Mach 0.85) and supersonic (Mach 1.40) flight conditions. For the baseline design, peak lift-to-drag ratio occurs at oblique angles of 30° and 60°, respectively. Analysis at Mach 0.85 reveals that the forward wing dominates the aerodynamic performance of the whole configuration. The parameter study of the leading-edge sweep confirms that the configuration combining a smaller forward-wing sweep with a larger aft-wing sweep is an effective design for achieving the balanced aerodynamic performance, namely, the forward wing with a 24° leading-edge sweepback angle and the after wing with 33° yield a high lift-to-drag ratio, achieving an optimal trade-off with rolling moment minimization. This drag reduction is achieved through the simultaneous decrease in both wave drag and induced drag. Furthermore, downwash analysis reveals that the inherent rolling moment originates from asymmetric tail loads induced by uneven downwash distribution. These findings provide guidance for the aerodynamic design of future oblique-wing aircraft. Full article

Quantum Key Distribution (QKD) is a process to establish a symmetric key between two parties using the principles of quantum mechanics. Currently, commercial QKD systems are still expensive, they require specific infrastructure, and they are impractical for deployment in portable or resource-constrained devices. In this article, we introduce the Butterfly Protocol (and its extended version) that enables QKD to be offered as a service to non-QKD-capable (portable or IoT) devices. Our key contributions include (1) resilience to the compromise of any single classical link, (2) protection against malicious QKD users, (3) implicit mutual authentication between users without relying on large post-quantum certificates, and (4) the Double Butterfly extension, which secures communication even when the underlying QKD network cannot be fully trusted. We also demonstrate how to integrate the Butterfly Protocol into TLS 1.3 and provide its initial security analysis. We present preliminary performance results and discuss the main bottlenecks in the Butterfly Protocol implementation. We believe that our solution represents a practical step toward integrating QKD into classical networks and extending its use to portable devices. Full article

This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios. Full article

In this paper, we propose FedENLC, an end-to-end noisy label correction model that performs model training and label correction simultaneously to fundamentally mitigate the label noise problem of federated learning (FL). FedENLC consists of two stages. In the first stage, the proposed model employs Symmetric Cross Entropy (SCE), a robust loss function for noisy labels, and label smoothing to prevent the model from being biased by incorrect information in noisy environments. Subsequently, a Bayesian Gaussian Mixture Model (BGMM) is utilized to detect noisy clients. BGMM mitigates extreme parameter bias through its prior distribution, enabling stable and reliable detection in FL environments where data heterogeneity and noisy labels coexist. In the second stage, only the top noisy clients with high noise ratios are selectively included in the label correction process. The selection of top noisy clients is determined dynamically by considering the number of classes, posterior probabilities, and the degree of data heterogeneity. Through this approach, the proposed model prevents performance degradation caused by incorrect detection, while improving both computational efficiency and training stability. Experimental results show that FedENLC achieves significantly improved performance over existing models on the CIFAR-10 and CIFAR-100 datasets under data heterogeneity settings along with four noise settings. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures. First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

Aim: This study examined how load size and symmetry affect trunk muscle activation patterns, vertical ground reaction forces, and estimated lumbar spine compression during overground walking in individuals with chronic low back pain (CLBP) and those without symptoms. Methods: Thirty male participants (15 with CLBP, 15 controls; ages 23–28 years) performed walking tests under four load conditions: symmetric and asymmetric carriage at 10% and 20% of body weight. Bilateral surface electromyography measured activation from seven trunk muscles (rectus abdominis, external oblique, internal oblique, latissimus dorsi, lumbar erector spinae, multifidus) and the thoracolumbar fascia region, normalized to maximum voluntary isometric contractions (%MVIC). Force plates recorded vertical ground reaction forces synchronized with heel-strike events. A repeated-measures ANOVA with Bonferroni corrections was used to analyze the effects of load configuration and magnitude. Results: Asymmetric loading at 20% body weight caused significantly higher peak vertical ground reaction forces compared to symmetric loading (mean difference = 47.3 N, p < 0.001), with a significant interaction between load magnitude and configuration (p = 0.004, ηp² = 0.26). Participants with CLBP showed consistently higher trunk muscle activation throughout the gait cycle (peak: 37% MVIC vs. 30% MVIC in controls; p < 0.001, d = 1.68), with maximum recruitment at shorter muscle lengths and 24% less activation at optimal length (95% CI: 18.2–29.8%). The lumbar erector spinae and multifidus muscles exhibited the highest activation during asymmetric 20% loading in CLBP participants (0.282 and 0.263%MVIC, respectively), indicating compensatory neuromuscular strategies. Conclusion: Asymmetric load carriage creates disproportionately high mechanical and neuromuscular demands, effects that are greatly amplified in individuals with CLBP. These findings support rehabilitation strategies that improve load distribution and restore motor control, thereby reducing compensatory strain and enhancing trunk stability. Full article

The effective focal length is a critical determinant of optical performance and imaging quality, serving as a fundamental parameter for components ranging from ophthalmic lenses to precision microlens arrays. With the rapid advancement of complex optical systems in microscopy and smart manufacturing, there is an increasing demand for high-precision measurement techniques that can characterize these parameters with low uncertainty. In this paper, a quadri-wave lateral shearing interferometry (QWLSI) measurement system was developed. A novel precision focal length measurement method of optical lenses based on the principle of QWLSI is presented. A theoretical model for solving the focal length of the measured lens from the curvature radius of the wavefront was derived. We also proposed a novel algorithm and subsequently developed a dedicated hardware platform and a corresponding software package for its real-time implementation. Different sets of repeated measurement experiments were carried out for two convex lenses with symmetrical and asymmetrical structures, a large-scale concave lens, and a microlens array, to verify the measurement uncertainty and robustness of the QWLSI measurement system. The expanded uncertainty was also analyzed and determined as 0.31 mm (k = 1.96, normal distribution). The results show that the proposed QWLSI measuring system possesses good performance in measuring the focal lengths of different kinds of lenses and can be widely used in fields such as advanced optics manufacturing. Full article

Extended stiffened end-plate bolted connections represent one of the most utilised steel connection types in seismic-prone regions, and several studies have been dedicated to the improvement of their performance. Recently, a new stiffened joint configuration, with a non-symmetric octagonal bolt arrangement, was proposed, highlighting its excellent performance in seismic scenarios. Therefore, two new design procedures according to both the European and North American codes were developed. Within this framework, the present work aims to investigate the performance of this innovative joint under column loss scenarios. A total of sixteen beam-to-column steel assemblies, defined by varying the beam depth and the design procedure, were numerically investigated using advanced FE models validated against experimental results. The numerical results show that the innovative joints exhibit a ductile behaviour, even better than traditional joints designed according to the current versions of EU and US codes. Indeed, the new bolt arrangement allows us to reduce the damage in the connection thanks to a better stress distribution among the bolts. Full article

This article introduces a new flexible distribution, called the alpha power Topp–Leone Dagum (APTLDa) distribution, which extends the classical Dagum model by combining the Topp–Leone generator with the alpha power transformation (APT). The proposed distribution is capable of modeling data with symmetrical and asymmetrical shapes for the probability density and hazard rate functions. This makes it suitable for lifetime and reliability data analysis. Several important statistical properties of the new distribution are derived, including the quantile function, moments, entropy measures, order statistics, and reliability-related functions. Parameter estimation is carried out using the maximum likelihood method, and the performance of the estimators is examined through an extensive simulation study under different sample sizes and parameter settings. The simulation results demonstrate the consistency and good finite-sample behavior of the estimators. The practical usefulness of the proposed distribution is illustrated through applications to two real datasets, where its performance is compared with several competing models. The results show that the APTLDa distribution provides a flexible and effective alternative for modeling lifetime data. Full article

Offshore wind energy is a key enabler of the global net-zero transition. As nearshore fixed-bottom projects reach maturity, floating offshore wind turbines (FOWTs) are becoming the next major focus for large scale deployment. To accelerate this development and reduce construction costs, it is essential to optimize mooring systems through a systematic and performance driven framework. This study focuses on the mooring assessment of the Taiwan-developed DeltaFloat semi-submersible platform supporting a 15 MW turbine at a 70 m water depth offshore Hsinchu, Taiwan. A full-chain catenary mooring system was designed based on site specific metocean conditions. The proposed framework integrates ANSYS AQWA (version 2024 R1) and Orcina OrcaFlex (version 11.5) simulations with sensitivity analyses and performance-based fitness metrics including offset, inclination, and line tension to identify key parameters governing mooring behavior. Additionally, an analysis of variance (ANOVA) was conducted to quantitatively evaluate the statistical significance of each design parameter. Results indicate that mooring line length is the most influential factor affecting system performance, followed by line angle and diameter. Optimizing these parameters significantly improves platform stability and reduces tension loads without excessive material use. Building on the optimized symmetric configuration, an asymmetric mooring concept with unequal line lengths is proposed. The asymmetric layout achieves performance comparable to traditional 3 × 1 and 3 × 2 systems under extreme environmental conditions while demonstrating potential reductions in material use and overall cost. Nevertheless, the unbalanced load distribution highlights the need for multi-scenario validation and fatigue assessment to ensure long-term reliability. Overall, the study establishes a comprehensive and sensitivity-based evaluation framework for floating wind mooring systems. The findings provide a balanced and practical reference for the cost-efficient design of floating offshore wind farms in the Taiwan Strait and other shallow-water regions. Full article

With the rapid development of precision agriculture, the application of computer vision in the intelligent detection of crop pests has advanced significantly. Within the honeysuckle cultivation industry, accurate pest identification is crucial for ensuring the quality of the medicinal material. However, existing detection models often underperform in two key scenarios: firstly, when pests exhibit substantial scale variations in images due to different shooting distances, and secondly, when distinguishing between morphologically similar pests based on subtle features. To address these challenges, this paper proposes a symmetry-aware model termed the YOLO-Multi-dimensional Pyramid Attention Module (YOLO-MPAM), which incorporates structural symmetry principles as its fundamental design paradigm. The core component, a Multi-dimensional Pyramid Attention Module (MPAM), employs a symmetrical pyramid architecture to achieve balanced feature processing across different scales. This design ensures scale symmetry by treating features at different resolutions equivalently, while the attention mechanism establishes channel–spatial symmetry through coordinated processing of both dimensions. The symmetrical design enables the reliable capture of discriminative features across objects of varying sizes, effectively addressing the inherent asymmetries in pest distribution and scale variation. Full article

The escalating heat flux density and temperature in highly integrated microelectronic devices adversely affect their reliability and service life, making efficient thermal management crucial for stable operation. This study utilizes Galinstan liquid metal as the coolant to investigate the flow and heat transfer performance in microchannel heat sinks incorporating various turbulator configurations. It is revealed that for microchannels featuring expanded regions, turbulators that create highly symmetric flow fields are preferable due to improved flow distribution. The long teardrop-shaped turbulator provides the best heat transfer performance among all the investigated heat transfer enhancement structures. And this turbulator yields a 13.8–25.9% higher enhancement effectiveness compared to other configurations, at the expense of a 28–41% increase in pressure loss. However, the sudden cross-sectional expansion in the expanded region causes a significant reduction in fluid velocity. Consequently, microchannels with expanded regions and turbulators exhibit a higher bottom surface temperature than the original, straight microchannels, leading to an overall deterioration in heat transfer performance. Full article