Search Results (534)

Energy-efficient Train Control (EETC) strategy needs to meet safety, punctuality, and energy-saving requirements during train operation, and puts forward higher requirements for online use and adaptive ability. In order to meet the above requirements and reduce the dependence on an accurate mathematical model of train operation, this paper proposes a train-speed trajectory-optimization method combining data-driven energy consumption estimation and deep reinforcement learning. First of all, using real subway operation data, the key unit basic resistance coefficient in train operation is analyzed by regression. Then, based on the identified model, the energy consumption experiment data of train operation is generated, into which Gaussian noise is introduced to simulate real-world sensor measurement errors and environmental uncertainties. The energy consumption estimation model based on a Backpropagation (BP) neural network is constructed and trained. Finally, the energy consumption estimation model serves as a component within the Deep Deterministic Policy Gradient (DDPG) algorithm environment, and the action adjustment mechanism and reward are designed by integrating the expert experience to complete the optimization training of the strategy network. Experimental results demonstrate that the proposed method reduces energy consumption by approximately 4.4% compared to actual manual operation data. Furthermore, it achieves a solution deviation of less than 0.3% compared to the theoretical optimal baseline (Dynamic Programming), proving its ability to approximate global optimality. In addition, the proposed algorithm can adapt to the changes in train mass, initial set running time, and halfway running time while ensuring convergence performance and trajectory energy saving during online use. Full article

Fault diagnosis methods based on deep learning have made certain progress in recent years. However, in actual industrial scenarios, there are severe strong background noise and limited computing resources, which poses challenges to the practical application of fault diagnosis models. In response to the above issues, this paper proposes a novel noise-resistant and lightweight fault diagnosis framework with nonlinear timestep degenerative greedy strategy (NTDGS) and dual residual horizontal feature pyramid (DRHFPN) for fault diagnosis in strong noise scenarios. This method takes advantage of the strong fitting ability of deep learning methods to model the agent in reinforcement learning by the ways of parameterization, fully leveraging the advantages of both deep learning and reinforcement learning methods. NTDGS is further developed to adaptively adjust the action sampling strategy of the agent at different training stages, improving the convergence speed of the network. To enhance the noise resistance of the network, DRHFPN is constructed, which can filter out interference noise at the feature map level by fusing local feature details and global semantic information. Furthermore, the feature map weighting attention mechanism (FMWAM) is designed to enhance the weak feature extraction ability of the network through adaptive weighting of the feature maps. Finally, the performance of the proposed method is evaluated in different datasets and strong noise environments. Experiments show that in various fault diagnosis scenarios, the proposed method has better noise resistance, higher fault diagnosis accuracy, and fewer parameters compared to other methods. Full article

In light of ongoing advancements in smart manufacturing, there is a growing need for intelligent fault diagnosis methods that maintain reliability under noisy, high-variability operating conditions. Conventional feature selection strategies often struggle when data contain outliers or suboptimal feature subsets, limiting their diagnostic utility. This study introduces a density-based feature space optimization (DBFSO) framework that integrates feature selection with localized density estimation to enhance feature space separability and classifier efficiency. Using k-nearest neighbor density estimation, the method identifies and removes low-density feature vectors associated with noise or outlier behavior, thereby sharpening the feature space and improving class discriminability. Experiments using roll-to-roll (R2R) manufacturing data under mechanical disturbances demonstrate that DBFSO improves classification accuracy by up to 36–40% when suboptimal feature subsets are used and reduces training time by 60–71% due to reduced feature space volume. Even with already-optimized feature sets, DBFSO provides consistent performance gains and increased robustness against operational variability. Additional validation using a bearing fault dataset confirms that the framework generalizes across domains, yielding improved accuracy and significantly more compact, noise-resistant feature representations. These findings highlight DBFSO as an effective preprocessing strategy for intelligent fault diagnosis in intelligent manufacturing systems. Full article

Current multimodal imaging–based source localization (SoL) methods often rely on synchronously recorded data, and many neural network–driven approaches require large training datasets, conditions rarely met in clinical neuroimaging. To address these limitations, we introduce MieSoL (Multimodal Mutual Information Extraction and Source Localization), a unified framework that fuses EEG and MRI, whether acquired synchronously or asynchronously, to achieve robust cross-modal information extraction and high-accuracy SoL. Targeting neuroimaging applications, MieSoL combines Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), leveraging their complementary strengths—MRI’s high spatial resolution and EEG’s superior temporal resolution. MieSoL addresses key limitations of existing SoL methods, including poor localization accuracy and an unreliable estimation of the true source number. The framework combines two existing components—Unified Left Eigenvectors (ULeV) and Efficient High-Resolution sLORETA (EHR-sLORETA)—but integrates them in a novel way: ULeV is adapted to extract a noise-resistant shared latent representation across modalities, enabling cross-modal denoising and an improved estimation of the true source number (TSN), while EHR-sLORETA subsequently performs anatomically constrained high-resolution inverse mapping on the purified subspace. While EHR-sLORETA already demonstrates superior localization precision relative to sLORETA, replacing conventional PCA/ICA preprocessing with ULeV provides substantial advantages, particularly when data are scarce or asynchronously recorded. Unlike PCA/ICA approaches, which perform denoising and source selection separately and are limited in capturing shared information, ULeV jointly processes EEG and MRI to perform denoising, dimension reduction, and mutual-information-based feature extraction in a unified step. This coupling directly addresses longstanding challenges in multimodal SoL, including inconsistent noise levels, temporal misalignment, and the inefficiency of traditional PCA-based preprocessing. Consequently, on synthetic datasets, MieSoL achieves 40% improvement in Average Correlation Coefficient (ACC) and 56% reduction in Average Error Estimation (AEE) compared to conventional techniques. Clinical validation involving 26 epilepsy patients further demonstrates the method’s robustness, with automated results aligning closely with expert epileptologist assessments. Overall, MieSoL offers a principled and interpretable multimodal fusion paradigm that enhances the fidelity of EEG source localization, holding significant promise for both clinical and cognitive neuroscience applications. Full article

To achieve a substantial enhancement in thermodynamic efficiency, Generation IV nuclear reactors are designed to operate at significantly elevated temperatures compared to conventional reactors. Moreover, they typically employ a fast neutron spectrum, characterized by higher neutron energy and flux. This combination results in a considerably more intense radiation environment within the core relative to traditional thermal neutron reactors. Therefore, the measurement of neutron flux in the core of Generation IV nuclear reactors faces the challenge of a high-temperature and high-radiation environment. Conventional neutron flux monitoring equipment—including fission chambers, gas ionization chambers, scintillator detectors, and silicon or germanium semiconductor detectors—faces considerable challenges in Generation IV reactor conditions. Under high temperatures and intense radiation, these sensors often experience severe performance degradation, significant signal distortion, or complete obliteration of the output signal by noise. This inherent limitation renders them unsuitable for the aforementioned applications. Consequently, significant global research efforts are focused on developing neutron detectors capable of withstanding high-temperature and high-irradiation environments. The objective is to enable accurate neutron flux measurements both inside and outside the reactor core, which are essential for obtaining key operational parameters. In summary, the four different types of neutron detectors have different performance characteristics and are suitable for different operating environments. This review focuses on 4H-SiC, diamond detectors, high-temperature fission chambers, and self-powered neutron detectors. It surveys recent research progress in high-temperature neutron flux monitoring, analyzing key technological aspects such as their high-temperature and radiation resistance, compact size, and high sensitivity. The article also examines their application areas, current development status, and offers perspectives on future research directions. Full article

The Loop-Back Quantum Key Distribution (LB-QKD) protocol establishes a bidirectional architecture in which a single photon travels forth and back through the same optical channel. Unlike conventional one-way schemes such as BB84, Alice performs both state preparation and measurement, while Bob acts as a passive polarization modulator and reflector. This design eliminates detectors at Bob’s side, minimizes synchronization requirements, and enables compact, low-power implementations suitable for quantum-mobile and IoT platforms. An extended three-basis configuration $\{X,Y,Z\}$ is introduced, preserving the simplicity of the two-basis scheme while improving noise tolerance through enhanced orthogonality-based filtering. Analytical modeling shows that the effective protocol error decreases from $E_{\mathrm{protocol}}^{\left(2\right)}=e/2$ to $E_{\mathrm{protocol}}^{\left(3\right)}=e/3$, achieving a 33% improvement in noise resilience. Despite its slightly lower sifting efficiency ($\eta =1/6$), the total information gain reaches $G=0.26$ bits per pulse, maintaining post-sifting throughput comparable to BB84. The protocol doubles the tolerable QBER of conventional QKD, sustaining secure operation up to $22\%$ for two bases and approximately $47.58\%$ for three bases. Its passive, self-verifying architecture enhances resistance to man-in-the-middle, photon-number-splitting, and side-channel attacks, providing a scalable and energy-efficient framework for secure key distribution and authentication in next-generation mobile and distributed quantum networks. Full article

Research on interference suppression technology for enhanced long-range navigation (eLoran) signals is crucial for enhancing receiver performance. To address the zero-point drift phenomenon in eLoran signals during adaptive filtering, we propose a segmented inaction algorithm based on normal time–frequency transform (NTFT), which is designed for challenging environments, such as low signal-to-noise ratio (SNR) and complex noise conditions. The algorithm splits the 20 kHz frequency band of the eLoran signal into 200 equal sub-bands, then applies the inaction algorithm sequentially to each sub-band, which exhibits strong noise resistance and high robustness. It is regarded as a pre-filter of the adaptive filter, ensuring a cleaner input signal for subsequent processing. Simulation results indicate that, when processing low-SNR eLoran signals affected by multi-frequency narrow-band interference and band-limited Gaussian noise, the combined algorithm significantly improves root mean square error (RMSE) by 33.3% and relative root mean square error (R-RMSE) by 39.1% compared to the single VSS-LMS method. Additionally, it compensates for zero-point drift (the deviation observed in the time series between the positive zero-crossing point of the third period of the reconstructed signal and that of the original signal) by 79.3% and maintains third-week forward over-zero error at a very low level. The effectiveness of the combined algorithm was further validated through actual measurement experiments. Full article

Bowhead whales (Balaena mysticetus) are an important species in the Arctic ecosystem, but their conservation is challenged by environmental noise from shipping and climate change. Effective monitoring of Bowhead whale vocalizations is essential for their conservation, yet traditional acoustic methods face limitations in detecting weak and non-stationary signals amidst complex background noise. In this study, we propose a novel method, Local Maximum Simultaneous Extraction of Generalized S-Transforms (LMSEGST), to enhance the feature extraction of Bowhead whale calls. The LMSEGST method integrates generalized S-transforms with local maximum extraction, improving time-frequency resolution and noise immunity. We compare the performance of LMSEGST with traditional methods (STFT, GST, and LMSST) using synthetic and real-world datasets. The results show that LMSEGST outperforms the other methods, with a 28.32% reduction in Rayleigh entropy compared to STFT at 5 dB SNR and a 28.24% reduction compared to LMSST at 10 dB SNR. Additionally, LMSEGST maintained higher SNR values, demonstrating superior noise resistance. These findings suggest that LMSEGST offers a more robust solution for acoustic monitoring of Bowhead whales, particularly in noisy, Arctic environments, contributing to more effective conservation strategies for this species. Full article

This study presents a novel softsign-function-based fractional-order proportional–integral–derivative (softsign-FOPID) controller optimized using the fungal growth optimizer (FGO) for the stabilization and precise position control of an unstable magnetic ball suspension system. The proposed controller introduces a smooth nonlinear softsign function into the conventional FOPID structure to limit abrupt control actions and improve transient smoothness while preserving the flexibility of fractional dynamics. The FGO, a recently developed bio-inspired metaheuristic, is employed to tune the seven controller parameters by minimizing a composite objective function that simultaneously penalizes overshoot and tracking error. This optimization ensures balanced transient and steady-state performance with enhanced convergence reliability. The performance of the proposed approach was extensively benchmarked against four modern metaheuristic algorithms (greater cane rat algorithm, catch fish optimization algorithm, RIME algorithm and artificial hummingbird algorithm) under identical conditions. Statistical analyses, including boxplot comparisons and the nonparametric Wilcoxon rank-sum test, demonstrated that the FGO consistently achieved the lowest objective function value with superior convergence stability and significantly better (p < 0.05) performance across multiple independent runs. In time-domain evaluations, the FGO-tuned softsign-FOPID exhibited the fastest rise time (0.0089 s), shortest settling time (0.0163 s), lowest overshoot (4.13%), and negligible steady-state error (0.0015%), surpassing the best-reported controllers in the literature, including the sine cosine algorithm-tuned PID, logarithmic spiral opposition-based learning augmented hunger games search algorithm-tuned FOPID, and manta ray foraging optimization-tuned real PIDD². Robustness assessments under fluctuating reference trajectories, actuator saturation, sensor noise, external disturbances, and parametric uncertainties (±10% variation in resistance and inductance) further confirmed the controller’s adaptability and stability under practical non-idealities. The smooth nonlinearity of the softsign function effectively prevented control signal saturation, while the fractional-order dynamics enhanced disturbance rejection and memory-based adaptability. Overall, the proposed FGO-optimized softsign-FOPID controller establishes a new benchmark in nonlinear magnetic levitation control by integrating smooth nonlinear mapping, fractional calculus, and adaptive metaheuristic optimization. Full article

A Q-enhanced notch filter for interference-rejection LNAs is proposed in this brief. The active capacitance is introduced into the notch filter to improve the quality factor by the negative resistance effect. The designed notch filter achieves excellent performance with a narrow attenuation bandwidth from 5.75 GHz to 5.95 GHz, which can be applied to suppress interference from the IEEE 802.11a. To validate the feasibility of the proposed trap filter in both GaAs process technology and principle, a 3–15 GHz ultra-wideband low-noise amplifier was designed and fabricated using a 0.15-micron gallium arsenide pseudomorphs field-effect transistor process. The frequency-dependent feedback loops are employed between gate and drain stages for wideband input matching and gain flatness. The notch filter is inserted between two stages of the LNA. The measurement results show that the interference-rejection LNA achieves a maximum gain of 24.5 dB and a minimum noise figure of 1.8 dB in the operating band. The notch filter has a maximum interference-rejection ratio of 35.2 dB at 5.8 GHz with almost no effect on the desired gain of the LNA. The LNA has a power consumption of 168 mW, including the notch filter with a size of 1.93 × 0.72 mm². Full article

The wheel–rail friction coefficient is a critical factor influencing train traction and braking performance. Low-adhesion conditions not only limit the enhancement of railway transport capacity but are also the primary cause of surface damage such as scratches, delamination, and flat spots. This study employs femtosecond laser technology to fabricate wavy groove textures on U75V rail surfaces, systematically investigating the effects of the wavy angle and texture area ratio on friction enhancement under various medium conditions. Findings indicate that parameter-optimized textured surfaces not only significantly increase the coefficient of friction but also exhibit superior wear resistance, vibration damping, and noise reduction properties. The optimally designed wavy textured surface achieves significant friction enhancement under water conditions. Among the tested configurations, the surface with parameters θ = 150°@η = 30% demonstrated the most pronounced friction enhancement, achieving a coefficient of friction as high as 0.57—a 42.5% increase compared to the non-textured surface (NTS). This enhancement is attributed to the unique hydrophilic and anisotropic characteristics of the textured surface, where droplets tend to spread perpendicular to the sliding direction, thereby hindering the formation of a continuous lubricating film as a third body. Analysis of friction vibration signals reveals that textured surfaces exhibit lower vibration signal amplitudes and richer frequency components. Furthermore, comparison of Stribeck curves under different lubrication regimes for the θ = 150°@η = 30% specimen and NTS indicated an overall upward shift in the curve for the textured sample. The amplitude, energy, and wear extent of the textured surface consistently decreased across boundary lubrication, hydrodynamic lubrication, and mixed lubrication regimes. These findings provide crucial theoretical insights and technical guidance for addressing low-adhesion issues at the wheel–rail interface, offering significant potential to enhance wheel–rail adhesion characteristics in engineering applications. Full article

Network covert channels embed secret data into legitimate traffic, but existing methods struggle to balance undetectability, robustness, and throughput. Application-independent channels at lower protocol layers are easily normalized or disrupted by network noise, while application-dependent streaming schemes rely on handcrafted traffic manipulations that fail to preserve the spatio-temporal dynamics of real encrypted flows and thus remain detectable by modern machine learning (ML)-based classifiers. Meanwhile, with the rapid adoption of HTTP/3, Quick UDP Internet Connections (QUIC) has become the dominant transport for streaming services, offering stable long-lived flows with rich spatio-temporal structure that create new opportunities for constructing resilient covert channels. In this paper, a QUIC streaming-based Covert Channel framework, QuicCC-SMD, is proposed that dynamically Shapes Multi-Dimensional traffic features to identify and exploit redundancy spaces for secret data embedding. QuicCC-SMD models the statistical and temporal dependencies of QUIC flows via Markov chain-based state representations and employs convex optimization to derive an optimal deformation matrix that maps source traffic to legitimate target distributions. Guided by this matrix, a packet-level modulation performs through packet padding, insertion, and delay operations under a periodic online optimization strategy. Evaluations on a real-world HTTP/3 over QUIC (HTTP/3-QUIC) dataset containing 18,000 samples across four video resolutions demonstrate that QuicCC-SMD achieves an average F1 score of 56% at a 1.5% embedding rate, improving detection resistance by at least 7% compared with three representative baselines. Full article

Porous concrete is widely recognized as an eco-friendly pavement material; however, existing studies mainly focus on its use as a base course, and systematic investigations on porous concrete specifically designed for heavy-traffic pavements and multifunctional surface performance remain limited. In this study, a novel multifunctional porous concrete with integrated noise reduction and drainage performance (PCNRD) was developed as a top-layer pavement material, addressing the performance gap in current applications. A comprehensive evaluation of the surface properties of porous concrete was performed based on tests of the sound absorption, void ratio, permeability, and wear resistance. The results demonstrate that the porous concrete exhibits excellent sound absorption (sound absorption coefficient 0.22–0.35) and high permeability (permeability coefficient 0.63–1.13 cm/s), and superior abrasion resistance (abrasion loss ≤ 20%) within an optimized porosity range of 17–23%. Furthermore, an optimized pavement thickness (8–10 cm) was proposed, and functional correlations among key surface performance indicators were revealed for the first time. Based on a uniform experimental design, four key mix parameters (water–cement ratio, cement content, silica fume content, and cement strength grade) were examined using strength and effective porosity as dual control indices, leading to the development of a novel mix design method tailored for PCNRD. This study not only fills the technical gap in high-performance porous concrete for heavy-traffic pavement surfaces but also provides a practical scientific framework for its broader engineering application. Full article

To address the limitations of traditional image encryption algorithms in key optimization and encryption quality assessment, in this paper we propose a framework for image encryption based on surrogate-assisted differential evolution. First, we construct a novel fitness function based on pixel correlation, which quantitatively evaluates and optimizes encryption quality by minimizing the pixel correlation coefficient. Second, we propose an adaptive hierarchical surrogate-assisted differential evolution algorithm (HSADE-IQUA), which combines global and local phases. In the global optimization phase, HSADE-IQUA significantly improves the convergence speed and solution quality in constrained optimization through adaptive parameter control. In the local optimization phase, the population size is dynamically adjusted using the exponential moving average (EMA), achieving a balance between exploration and exploitation. The performance of HSADE-IQUA has been validated on a commonly used expensive optimization benchmark suite, achieving excellent experimental results. Third, a Chen hyperchaotic-DNA coding fusion encryption framework optimized by HSADE-IQUA (HSADE-IQUA-DNA) was constructed and tested on standard computer vision images, labeled datasets, and remote sensing images, proving that HSADE-IQUA-DNA can significantly reduce pixel correlation, effectively resist exhaustive attacks, noise attacks, and shearing attacks, and accurately recover the original image. Compared with traditional chaotic image encryption, HSADE-IQUA-DNA not only has a bottleneck in parameter optimization but also alleviates the single-key issue, further improving encryption security. Full article

With the advancement of precision agriculture, improving the operational accuracy of agricultural machinery has received increasing attention. The rice transplanter is crucial in this context, as its performance directly affects rice yield. During operation, both the magnitude and stability of the driving wheel slip ratio affect the accuracy of plant spacing, thereby influencing rice yield. However, to date, no control method that can simultaneously stabilize the speed, reduce the slip ratio, and improve the stability of the slip ratio has been proposed for transplanters. To address this issue, this paper proposes a joint speed–slip ratio control method based on an adaptive Student t-kernel maximum correntropy Kalman filter (ASMCKF) and sliding mode control (SMC). First, a Student t-kernel maximum correntropy Kalman filter (SMCKF) is designed to identify the transplanter’s speed, wheel speed, traction force, and rolling resistance in real time, thereby enhancing control system robustness against non-Gaussian heavy-tailed noise in paddy fields. An adaptive kernel bandwidth adjustment method is also introduced for the SMCKF to increase the sensitivity of the cost function to variations in the system state, thereby further improving parameter identification accuracy. Building on this, a joint speed–slip ratio control method is designed based on SMC. Simulation results confirm that the ASMCKF achieves higher identification accuracy than conventional methods when facing non-Gaussian heavy-tailed noise. Field experiment results show that the proposed method can effectively stabilize the transplanter’s speed while significantly reducing the slip ratio and improving the stability of the slip ratio. Full article