Search Results (993)

Accurate and consistent self-localization is essential for multi-UAV aerial missions in complex dynamic environments. However, communication constraints and heterogeneous sensor reliability variations often lead to cumulative localization errors and degraded robustness in conventional fusion frameworks. To address these challenges, this paper proposes a distributed cooperative localization framework integrating deep temporal feature learning, heterogeneous multi-sensor fusion, and consistency-aware distributed state estimation. First, an LSTM-based staged fusion strategy is designed to integrate VIO, GPS, and UWB measurements for accurate single-UAV localization. Second, a Squeeze-and-Excitation LSTM Self-Attention (SE-LSTM-SA) network is developed to adaptively recalibrate heterogeneous sensor channels and enhance temporal feature extraction under dynamic sensing conditions. Finally, a consistency-aware distributed fusion mechanism based on the Labeled Multi-Bernoulli (LMB) framework is introduced to improve inter-UAV state consistency through iterative local-neighbor information exchange. Experiments conducted on the XTDrone platform demonstrate that the proposed framework achieves superior localization accuracy compared with traditional EKF and conventional LSTM-based methods. Specifically, the proposed method achieves lower RMSE, MAE, and Maximum Prediction Error (MaxPE), while significantly improving global consistency performance. Experimental results demonstrate that the proposed framework provides accurate and consistent localization performance for multi-UAV systems in complex dynamic environments. Full article

Wound-rotor synchronous machines (WRSMs) offer a promising, magnet-free alternative for safety-critical transportation sectors like electric vehicles (EVs) and marine propulsion. While multiphase structures enhance fault tolerance in these applications, conventional WRSMs still suffer from reliance on maintenance-prone slip rings and brushes. Brushless multiphase self-excitation presents a compelling solution, but it introduces a critical design challenge: ensuring decoupled control between the torque-producing (αβ) and magnetizing () subspaces to prevent severe performance degradation. To address this cross-coupling issue, this paper proposes a 20-slot/18-pole five-phase architecture. By exploiting distinct spatial harmonics, the stator generates two independently controlled magnetic fields with a dedicated rotor harmonic winding. An integrated diode rectifier then seamlessly converts the induced AC voltages into the required DC field excitation. Extensive finite-element analysis (FEA) using ANSYS Maxwell is conducted to validate the design and rigorously evaluate subspace cross-coupling. Simulation results confirm that the proposed machine meets design specifications, demonstrating stable self-excited operation, acceptable efficiency, and representative fault-tolerant operation under a single open-phase condition, thereby confirming the electromagnetic feasibility of the proposed topology as a promising magnet-free candidate for future alternatives to PMSM-based traction solutions. Full article

We present a review of theoretical studies of the structure and reactions of N = 7 and N = 8 nuclei in the vicinity of ¹¹Li, carried out within a framework based on Nuclear Field Theory. The coupling of valence nucleons to low-lying surface vibrations of the spherical core plays a central role, giving rise to self-energy processes that renormalize single-particle states and transfer form factors, as well as to an induced pairing interaction arising from the exchange of collective vibrations, which renormalizes the bare pairing force. Excitation spectra and cross sections for one- and two-nucleon transfer reactions populating states in the quasi-continuum are calculated and compared with available experimental data. Collective excitations in the particle-particle channel are investigated, with particular emphasis on Giant Pairing Vibrations and on their damping mechanisms arising from coupling to more complex configurations and continuum states. Comparisons with other theoretical schemes are also presented. We conclude that a coherent understanding of experimental data requires the detailed consideration of particle-vibration coupling effects. Full article

The composition of the surface, optical response, and size of the carbon dots synthesized from Valencia orange peels (Citrus sinensis L. Osbeck) were studied. The peels used in the hydrothermal synthesis were at three ripeness stages, and the synthesis was carried out at 220 °C and 3 MPa. Infrared spectroscopy results showed that carbon dots synthesized from the peels of unripe oranges are functionalized with oxygenated groups, and the carbonization process was effective. Instead, carbon dots obtained from peels of ripe oranges exhibit a nitrogen-functionalized surface. These results were confirmed by the bond-breakdown analysis in photoelectron spectroscopy. Additionally, the self-doped surface modified the optical response of the carbon dots, exhibiting an enhancement of the absorption band located at 283 nm corresponding to the contribution from n-π* transitions in nitrogen. Also, the excitation and emission wavelengths present a red shift for the ripe peels. Based on the above and the transmission electron microscopy results, it is concluded that the emission mechanism is associated with surface states and not particle size. Statistical analysis yielded an average size of less than 10 nm, regardless of the orange peels’ ripeness stage. It was observed that the CDs-N3 sample has more crystalline nuclei, which is justified because ripe peels follow a shorter carbonization pathway. Full article

This paper proposes a passive in-orbit calibration method for phased array antennas using GNSS carrier-phase measurements. By performing synchronous observation and exploiting the short-baseline property between the positioning antenna and array elements, the first differencing operation eliminates space propagation errors and clock biases. By further utilizing receiver channel consistency, the second differencing operation cancels out the receiver channel errors, thereby extracting the relative receive-chain phase error of the element under test. Under typical operating conditions, the calibration accuracy can reach an RMS error of approximately $3.02\mathrm{mm}$, corresponding to a phase accuracy of $5.72°$ in the GPS L1 band. This accuracy is close to the $5.625°$ minimum phase step of a 6-bit digital phase shifter, and can be further improved under higher $C/N_0$ and well-controlled residual error conditions. Without requiring a dedicated GNSS band excitation signal, this method avoids co-frequency self-interference with the positioning antenna, which provides an auxiliary approach for in-orbit calibration of phased array receive chains. Full article

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening. Full article

To reveal the nonlinear instability mechanism by which the three-degree-of-freedom rotor system of a magnetic-liquid double suspension bearing transforms from stable suspension to periodic vibration, a nonlinear dynamic model considering electromagnetic suspension force, hydrostatic supporting force, rotor unbalance force, and liquid film resistance is established. The equilibrium point of the system is linearized, and the Hopf bifurcation boundary is determined using the Routh–Hurwitz criterion. Numerical simulations are then carried out to investigate the effects of the initial current i₀, supply flow rate q₀, and different initial disturbances on the displacement time histories, phase trajectories, and spatial phase trajectories of the rotor. The results show that, under the given system parameters, the Hopf bifurcation boundary is 0.61 A for the initial current and 9.62 × 10⁻⁵ m³/s for the supply flow rate. Current variation mainly affects electromagnetic stiffness and nonlinear electromagnetic force, whereas flow rate variation primarily changes the hydrostatic load capacity and oil film damping characteristics. Under different initial disturbances, the system may exhibit amplitude attenuation, recovery to stable suspension, or finite amplitude periodic vibration. Experimental results show good agreement with numerical simulations in terms of frequency spectra, displacement time histories, and phase trajectories, thereby verifying the effectiveness of the proposed three-degree-of-freedom dynamic model and Hopf bifurcation analysis method. The results can provide theoretical guidance for parameter matching, stability evaluation, and self-excited vibration suppression of magnetic-liquid double suspension bearings. Full article

The linear and third-order nonlinear optical (NLO) properties of a water-soluble perylenediimide derivative, N,N′-di(2-(trimethylammonium iodide) ethylene) perylenediimide (TAIPDI), doped with semiconductor nanoparticles (NPs), were systematically investigated under femtosecond laser excitation. ZnO and TiO₂ NPs were synthesized using a pulsed laser ablation technique. Nanocomposite systems were prepared by incorporating different concentrations of ZnO and TiO₂ NPs into the TAIPDI dye solution. The optical properties were characterized using UV–visible absorption spectroscopy together with open- and closed-aperture Z-scan measurements at 800 nm. Linear absorption measurements revealed concentration-dependent modifications in the optical band gap, indicating electronic interaction between the dye molecules and the semiconductor NPs. Open-aperture Z-scan results demonstrated strong nonlinear absorption (NLA) behavior dominated by two-photon absorption and excited-state absorption processes. Closed-aperture measurements showed a negative nonlinear refractive (NLR) index, corresponding to self-defocusing behavior. Both the NLA coefficient and the NLR index increased with increasing NP concentration, resulting in a significant enhancement of the third-order nonlinear susceptibility of the nanocomposite systems. In addition, optical limiting measurements revealed a pronounced reduction in the limiting threshold with increasing nanoparticle concentration, demonstrating improved laser attenuation capability. These findings indicate that ZnO@TAIPDI and TiO₂@TAIPDI nanocomposites are promising candidates for applications in optical limiting, all-optical switching, and advanced photonic devices. Full article

Flow-induced self-excited vibration may exhibit high-frequency numerical oscillations and chaotic-like responses in long-duration simulations due to strong nonlinearity and multimodal coupling. In this study, a two-node cable finite element model incorporating torsional degrees of freedom, nonlinear aerodynamic forces, and geometric nonlinearity is developed to evaluate the long-term computational performance of the Newmark average acceleration method and the Bathe composite integration scheme. Simulations are conducted for weakly nonlinear, transitional nonlinear, and near 1:1 internal resonance regimes. The results show that, as the degree of nonlinearity increases, the Newmark method produces more pronounced non-principal high-frequency components, a more scattered distribution of Poincaré points, and larger deviations from the expected principal-mode-dominated beating response. These observations indicate that, under the present model and discretization conditions, the chaotic-like response obtained by the Newmark method is strongly affected by non-principal high-frequency contamination. In contrast, the response computed by the Bathe method remains stably governed by the two dominant frequencies associated with the near-resonant beating mechanism. The results indicate that, for the long-duration nonlinear galloping problems considered in this study, appropriate algorithmic dissipation can reduce non-principal high-frequency disturbances and improve the interpretability of the numerical results. Full article

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to classify 19-channel EEG entropy features into three classes (dementia, stroke, healthy control; N = 45; 15 per class). The architecture suggests four new designs. First, the Channel Importance Attention (CIA) block, which adaptively learns to re-weight the importance of electrodes via squeeze-excitation. Second, the dual-branch encoder, which combines the global multi-head self-attention with the local depthwise-separable convolution. Third, the gated sigmoid fusion mechanism. Fourth, the bottleneck residual classification head, to solve overfitting. Eight entropy feature sets: Amplitude-Aware Permutation Entropy (AAPE), Attention Entropy (AttEn), Dispersion Entropy (DisEn), Distribution Entropy (DistrEn), Fluctuation-based Dispersion Entropy (FDispEn), Fuzzy Entropy (FuzEn), Linear Gaussian Estimation of the Conditional Entropy (LinEn), and Symbolic Dynamics (SyDy) were evaluated individually with stratified 5-fold cross-validation on within-fold SMOTE augmentation. Results: EEG-ChTABNet consistently outperformed the baseline Transformer on all 8 feature sets. DisEn and SyDy features yielded peak classification accuracy of 73.3% (AUC: 0.823 and 0.857, respectively) compared to the corresponding baseline of 57.8% and 55.6%. SyDy achieved the best overall AUC of 0.857 and the dementia detection sensitivity was up to 86.7% over multiple feature sets. Conclusions: EEG-ChTABNet shows the effectiveness of channel-adaptive, dual-branch Transformer Designs for EEG-based neurological classification from Small-Sample Entropy Feature Data, and Identifying SyDy and DisEn as the Most Discriminative Feature Representations for Three-Class Neurological EEG Classification. Full article

In industrial applications such as in situ leaching and uranium mining, permanent magnet synchronous motors (PMSMs) for submersible pumps are frequently connected to frequency converters via long cables. During this long-distance transmission, traveling wave reflections induced by high-frequency pulse width modulation (PWM) generate severe transient overvoltages that threaten motor insulation. Because installation space at deep-well motor terminals is severely restricted, overvoltage suppression must be implemented at the inverter output. Here, the parameter design and optimization of a passive LC filter specifically developed for 250 Hz high-frequency PMSMs are presented. The optimal inductance and capacitance parameters were determined by balancing multiple operational constraints, including fundamental voltage drop, high-frequency harmonic attenuation, and the avoidance of low-order harmonic resonance. Furthermore, the anti-saturation performance of the magnetic core material, evaluated thermal characteristics through electromagnetic-thermal co-simulation, and analyzed the risk of self-excited oscillation between the filter capacitors and the motor was analyzed. Finally, hardware experiments conducted on a 20 m cable test bench validate that the designed LC filter effectively mitigates terminal overvoltage. The peak terminal voltage was reduced from 900 V to 505 V, and total harmonic distortion (THD) was limited to below 5%. This design provides a highly reliable, space-efficient solution for overvoltage suppression in high-speed, long-cable motor drive systems. Full article

As critical air-dropped acoustic sensors for underwater target detection, sonobuoys are frequently compromised by severe hydrodynamic self-noise induced by sea-surface wave excitation, which masks target signals and degrades detection performance. While structural optimizations have traditionally been employed, effective signal-processing-based noise suppression remains challenging because the noise is non-stationary and physically coupled with buoy motion. To address the limited physical interpretability of conventional decomposition methods, this study proposes a physically guided self-noise suppression framework: VMD Constrained by DCCA Correlation (VMD-DCCA). The main contribution is the incorporation of the Detrended Cross-Correlation Analysis (DCCA) coefficient between the sonobuoy’s vertical velocity and the acoustic data as a correlation-dependent constraint within the Variational Mode Decomposition (VMD) optimization process. This motion prior allows more targeted isolation of motion-induced components than standard data-driven decomposition. Simulation and controlled water-tank results show that VMD-DCCA outperforms EEMD and standard VMD, achieving an SNR improvement of approximately 15 dB at an input SNR of −9 dB. The reconstructed signal also preserves visible narrowband spectral lines in the time-frequency representation. These results demonstrate the potential of the proposed method for controlled or post-processing sonobuoy self-noise reduction, while validation under irregular open-ocean conditions remains necessary. Full article

Volatility is a fundamental indicator for assessing the risk of financial assets. By integrating unstructured data, such as earnings call transcripts, the limitations of traditional time series data can be transcended, enabling collaborative forecasting from multiple data sources, enhancing the robustness of volatility prediction, and improving the efficiency of risk management. Although current research has effectively utilized earnings call data to predict asset volatility, price trends, and stock correlations, it often overlooks the inherent challenges of integrating textual and time series data, as well as the self-exciting and clustering characteristics of financial events. While conventional Long Short-Term Memory (LSTM) networks excel in processing fused data, they lack the structural capacity to explicitly model event-driven temporal decay, often failing to differentiate the varying influence of historical shocks over time. To surmount this limitation, we have significantly enhanced the predictive model by focusing on extracting salient information and integrating temporal dependency modeling with dynamic state adjustment mechanisms. The core innovation is introducing the Hawkes process to explicitly capture the self-exciting effect of financial events, which is the key to modeling volatility clustering around earnings releases. The proposed Hawkes LSTM model introduces a decay gating module and a textual information knowledge enhancement module. The decay gating module is specifically designed to more effectively capture the temporal dependencies between events within an event sequence. This allows the model to focus more on recent significant events, with the influence of an event on subsequent events typically diminishing as the temporal interval between them increases. By integrating temporal dependency modeling, the model is enabled to utilize historical data in a more flexible manner. The dynamic state adjustment mechanism further enhances its capacity to capture dynamically changing characteristics. Together, these features provide a more robust and precise solution for volatility prediction. Experimental results on two real-world earnings call datasets show that this approach significantly outperforms existing benchmark models on most prediction horizons, achieving competitive and superior performance and verifying its effectiveness and robustness. Full article

To support the seismic optimization of long-span bridges in regions of high seismicity, this study evaluates the seismic performance of continuous rigid-frame box-girder bridges with different web configurations. A continuous box-girder bridge with corrugated steel webs (CSWBGB) having a main span of 105 m was analyzed and compared with two control models: a continuous box-girder bridge with flat steel webs (FSWBGB) and a conventional prestressed concrete box-girder bridge (PCBGB). Finite element models of the three web types were developed using MIDAS/Civil, and seismic responses were evaluated using the response spectrum method with geometric nonlinearity incorporated; the analyses were conducted under E1 and E2 ground motion intensities (corresponding to a 63% probability of exceedance in 100 years and a 2% probability in 50 years, respectively, as specified in the Chinese seismic design code). Displacement, axial force, and shear force responses were systematically compared among the three configurations. The results show markedly different seismic responses despite the bridges having similar fundamental frequencies. In the longitudinal direction under seismic excitation, the CSWBGB exhibited larger axial displacement than the FSWBGB, yet its peak axial force and shear force decreased by 13% and 18%, respectively, indicating that the greater axial deformation helps relieve internal force demands. Under transverse E1 seismic action, the CSWBGB displayed smaller lateral displacements than both the FSWBGB and the PCBGB. Compared with the CSWBGB, the PCBGB experienced an 11% larger longitudinal displacement and a 43% higher peak axial force, reflecting its relatively limited seismic performance. These findings demonstrate that the CSWBGB not only provides lighter self-weight than the PCBGB but also offers enhanced transverse stiffness, which results in smaller lateral displacements and lower peak shear forces—thus achieving an optimal balance between lightweight design and structural strength. Although the CSWBGB shows strong potential for practical application, its longitudinal displacement response should be carefully controlled in design. Full article

This study presents a novel analytical–numerical approach to reveal bifurcation structures of an excited hybrid van der Pol–Rayleigh oscillator (HVR), driven by the necessity of enhanced understanding and prediction of intricate nonlinear behaviour of a hybrid self-excited system. The study used the non-perturbative approach (NPA)to analyze the existing scheme and estimate its efficiency. The non-perturbative approach serves as primary basis of He’s frequency formula (HFF). This procedure aims to produce a linear representation of a weakly nonlinear oscillator categorized by a nonlinear ordinary differential equation (ODE). The study aims to diverge from conventional perturbation techniques. The parametric equation is corroborated via Mathematica Software (MS), demonstrating substantial concordance with the original problem. Therefore, the study of current oscillator is considered as a new possibility in using established approaches. The stability enactment is assessed under various parameters. The current approach is categorized by specific concepts, exceptional numerical accuracy, suitability, and user-friendliness. Furthermore, Arnold tongues as well as Floquet multipliers are also examined. The dynamic of the nonlinear model is examined through Poincaré maps, phase portraits, and bifurcation diagrams, which are essential analytical elements affecting system behaviour. The lack of chaos and periodic oscillations are explained by the greatest Lyapunov exponent, which also sheds light on long-term stability. Full article