Search Results (857)

With the widespread use of lithium-ion batteries in various application fields, accurate prediction of battery state of health (SOH) has become an important research topic to ensure battery performance and safety. To improve the accuracy of SOH prediction, this paper proposes a novel approach that combines multidimensional feature extraction and a transformer–LSTM fusion model. This method extracts time domain, frequency domain, and time dimension features from voltage, energy, and temperature curves. It evaluates feature importance, removes redundancy, and focuses on key features most relevant to SOH. Then, using the self-attention mechanism of transformer and the long-term dependency capture ability of LSTM, an efficient fusion model is constructed to further improve the accuracy and stability of SOH prediction. The proposed method is validated based on the cycling data from 124 commercial lithium iron phosphate/graphite batteries under fast-charging conditions. Compared with existing methods, the proposed approach effectively extracts key features closely related to SOH and builds models based on these features. It achieves a prediction accuracy exceeding 50% and demonstrates superior generalization performance relative to current methods. Full article

The safety helmet is the equipment that construction workers must wear, and it plays an important role in protecting their lives. However, there are still many construction workers who do not pay attention to the wearing of helmets. Therefore, the real-time high-precision intelligent detection of construction workers’ helmet wearing is crucial. To this end, this paper proposes a lightweight helmet-wearing detection algorithm based on StarNet-YOLOv10. Firstly, the StarNet network structure is used to replace the backbone network part of the original YOLOv10 model while retaining the original Spatial Pyramid Pooling Fast (SPPF) and Partial Self-attention (PSA) parts. Secondly, the C2f module in the neck network is optimised by combining the PSA attention module and the GhostBottleneckv2 module, which improves the extraction of feature information and the expression ability of the model. Finally, optimisation is performed in the head network by introducing the Large Separable Kernel Attention (LSKA) attention mechanism to improve the detection accuracy and detection efficiency of the detection head. The experimental results show that compared with the existing Faster R-CNN, YOLOv5s, YOLOv6, and the original YOLOv10 models, the StarNet-YOLOv10 model proposed in this paper has a greater degree of improvement in the accuracy, recall, average precision mean, computational volume, and frame rate, in which the accuracy is as high as 83.36%, the recall rate can be up to 81.17%, and the average precision mean can reach 78.66%. Meanwhile, compared with the original YOLOv10 model, this model improves 1.7% in accuracy, 1.62% in recall, and 4.43% in mAP. Therefore, the present model can well meet the detection requirements of helmet wearing and can effectively reduce the safety hazards caused by not wearing helmets on construction sites. Full article

Graphene film has excellent electrical conductivity and flexibility, with which it can be used as a versatile substrate to load active species to construct free-standing electrochemical sensors. In this work, Pd nanoparticle-decorated N-doped porous graphene film (Pd/NPGF) was prepared by a simple and mild strategy to enhance the electrochemical behavior of graphene film-based free-standing electrodes. The morphological structure and surface component of the Pd/NPGF were characterized by scanning electron microscopy, transmission electron microscopy, Raman spectra and X-ray photoelectron spectroscopy measurements. The results revealed that the Pd/NPGF contained abundant pores and uniformly dispersed Pd nanoparticles, which could bring a favorable electrochemical response. Due to the synergetic effects of abundant pores, uniform Pd nanoparticles and the substitutional doping of the graphene framework with N, the novel free-standing Pd/NPGF electrode provides a high active site exposure, a high specific area and fast electron/mass diffusion during electrochemical reactions. Considering the favorable flexibility and excellent electrical conductivity of Pd/NPGF, we selected hydrogen peroxide, a significant biomarker, as a model to investigate its electrochemical performance in neutral conditions. The electrochemical biosensor based on the Pd/NPGF electrode exhibited enhanced activity relative to the NPGF and porous graphene film (PGF) with different concentrations of H₂O₂. The Pd/NPGF electrode displayed a high sensitivity (176.7 μA·mM⁻¹·cm⁻²), a large linear range from 5 μM to 36.3 mM, a low limit of detection (LOD) of 2.3 μM, excellent stability and a short response time, all of which qualify the Pd/NPGF electrode for a promising sensor for H₂O₂ sensing. Full article

Drawing on formal parallels between scalar diffraction theory and quantum mechanics, it is demonstrated that quantum wavefunction propagation requires a holographic model of time. Measurable time manifests between interactions as a duration which is encoded in the frequency domain. It is thus a unified entity, and attempts to subdivide these intervals introduce oscillatory artifacts or spectral broadening, altering the system’s physical characteristics. Analogous to spatial holograms, where information is distributed across interference patterns, temporal intervals encode information as a discrete whole. This framework challenges the concept of continuous time evolution, suggesting instead that discrete trajectories define a frequency spectrum which holographically constructs the associated time interval, giving rise to the experimentally observed energy spread of particles in applications such as time-bin entanglement, ultra-fast light pulses, and the temporal double slit. A generalized model of quantum wavefunction propagation based on recursive Fourier transforms is discussed, and novel applications are proposed, including starlight analysis and quantum cryptography. Full article

In the field of nondestructive evaluation (NDE) using ultrasonic guided waves, accurately assessing the aging failure of insulation coatings remains a challenging and prominent research topic. While the application of ultrasonic guided waves in material testing has been extensively explored in the existing literature, there is still a significant gap in quantitatively evaluating the aging failure of insulation coatings. This study innovatively proposes an NDE method for assessing insulation coating aging failure by integrating signal processing and machine learning technologies, thereby effectively addressing both theoretical and practical gaps in this domain. The proposed method not only enhances the accuracy of detecting insulation coating aging failure but also introduces new approaches to non-destructive testing technology in related fields. To achieve this, an accelerated aging experiment was conducted to construct a cable database encompassing various degrees of damage. The effects of aging time, temperature, mechanical stress, and preset defects on coating degradation were systematically investigated. Experimental results indicate that aging time exhibits a three-stage nonlinear evolution pattern, with 50 days marking the critical inflection point for damage accumulation. Temperature significantly influences coating damage, with 130 °C identified as the critical threshold for performance mutation. Aging at 160 °C for 100 days conforms to the time-temperature superposition principle. Additionally, mechanical stress concentration accelerates coating failure when the bending angle is ≥90°. Among preset defects, cut defects were most destructive, increasing crack density by 5.8 times compared to defect-free samples and reducing cable life to 40% of its original value. This study employs Hilbert–Huang Transform (HHT) for noise reduction in ultrasonic guided wave signals. Compared to Fast Fourier Transform (FFT), HHT demonstrates superior performance in feature extraction from ultrasonic guided wave signals. By combining HHT with machine learning techniques, we developed a hybrid prediction model—HHT-LightGBM-PSO-SVM. The model achieved prediction accuracies of 94.05% on the training set and 88.36% on the test set, significantly outperforming models constructed with unclassified data. The LightGBM classification model exhibited the highest classification accuracy and AUC value (0.94), highlighting its effectiveness in predicting coating aging damage. This research not only improves the accuracy of detecting insulation coating aging failure but also provides a novel technical means for aviation cable health monitoring. Furthermore, it offers theoretical support and practical references for nondestructive testing and life prediction of complex systems. Future studies will focus on optimizing model parameters, incorporating additional environmental factors such as humidity and vibration to enhance prediction accuracy, and exploring lightweight algorithms for real-time monitoring. Full article

This paper proposes a task planning method that integrates deep Q-learning network (DQN) with matrix sorting for Earth-oriented static multi-target cooperative observation tasks. The approach addresses emergent satellite failures in imaging constellations by eliminating the need for network model retraining during satellite malfunctions. It enables real-time generation of optimal task allocation schemes in contingency scenarios, ensuring efficient and adaptive task planning. Firstly, a mission scenario model is established by formulating task constraints and defining optimization objectives; secondly, a deep reinforcement learning framework is constructed to output the observation target sequence; then, the observation target sequence is transformed into a target sequence matrix, and a matrix-sorting planning method is proposed to carry out the optimal assignment of the task; lastly, a replanning method is designed for sudden satellite failure and insertion of urgent tasks. The experimental results show that the method has fast task planning speed, high task completion, and immediate task replanning capability. Full article

The core idea of prediction-based anomaly detection is to identify anomalies by constructing a prediction model and comparing predicted and observed values. However, most existing traffic flow prediction models primarily focus on spatio-temporal features, neglecting comprehensive frequency-domain feature learning. Additionally, anomaly detection accuracy is often limited by insufficient prediction error analysis. To address this limitation, this paper proposes a prediction-based anomaly detection method for traffic flow data with multi-domain feature extraction. The prediction model is built as follows: first, Bidirectional Long Short-Term Memory network (Bi-LSTM) and a Graph Attention Network (GAT) extract temporal and spatial features, respectively. Then, Fast Fourier Transform (FFT) converts time-domain signals into the frequency domain, where Transformer learns magnitude and phase features. Finally, a prediction model is constructed using the extracted time-domain and frequency-domain features. For error analysis, this paper innovatively applies Chebyshev’s inequality to determine the error threshold, identifying anomalies based on whether errors exceed this threshold. Experimental results show that integrating multi-domain features can more comprehensively capture data characteristics and improve model prediction accuracy. In the anomaly detection experiment, it was verified that constructing a high-accuracy prediction model and conducting reasonable error analysis can effectively enable anomaly detection in the data. Full article

Accurately predicting road traffic flows is a primary challenge in the development of smart cities, providing a scientific basis and reference for urban planning, construction, and traffic management. Road traffic flow is influenced by various complex features, including temporal and weather conditions, which introduce challenges to traffic flow prediction. To enhance the accuracy of traffic flow prediction and improve the adaptability across different weather conditions, this study introduced a traffic flow prediction model with explicit consideration of weather factors including temperature, rainfall, air quality index, and wind speed. The proposed model utilized grey relational analysis (GRA) to transform weather data into weighted traffic flow data, expanded input variables into a new data matrix, and employed one-dimensional convolutional neural networks (CNNs) to extract valuable feature information from these input variables, as well as bidirectional long short-term memory (BiLSTM) to capture temporal dependencies within the time-series data. Bayesian optimization was employed to fine-tune the hyperparameters of the model, offering advantages such as fewer iterations, high efficiency, and fast speed. The performance of the proposed prediction model was validated using the traffic flow data collected at an intersection in China and on the M25 motorway in the United Kingdom. The results demonstrated the effectiveness of the proposed model, achieving improvements of at least 9.0% in MAE, 2.8% in RMSE, 2.3% in MAPE, and 0.06% in R² compared to five baseline models. Full article

Small nuclear power reactors have small core dimensions, frequent power changes, and more severe power distortion compared to nuclear power stations. However, their core has fewer measurement points, making it difficult to observe their core power distribution. High-precision physical calculation programs can accurately calculate the core power distribution, but the real-time performance of the calculation is poor, which is not conducive to online use. In this study, based on physical computing programs, the power distribution spectrum library of small nuclear power reactors under different operating conditions is calculated, and artificial intelligence algorithms are designed. A data-driven model for the proxy relationship between operating state parameters and core power distribution is trained and constructed to achieve rapid calculation and online support of core power distribution, which improves the level of online safety supervision of small power reactors. Numerical experiments show that this method has high accuracy and good robustness, and can meet the requirements of small nuclear power reactor operation safety support. This research is based on a data-driven proxy model and has achieved fast computation of power distribution in the fuel cores of small modular reactors. It addresses the issue of insufficient real-time performance of high-precision physical programs and has important significance for the safe operation of reactors. Full article

With the rapid development of high-speed imaging, aerospace, and telecommunications, high-performance photodetectors across a broadband spectrum are urgently demanded. Due to abundant surface configurations and exceptional electronic properties, two-dimensional (2D) materials are considered as ideal candidates for broadband photodetection applications. However, broadband photodetectors with both high responsivity and fast response time remain a challenging issue for all the researchers. This review paper is organized as follows. Introduction introduces the fundamental properties and broadband photodetection performances of transition metal dichalcogenides (TMDCs), perovskites, topological insulators, graphene, and black phosphorus (BP). This section provides an in-depth analysis of their unique optoelectronic properties and probes the intrinsic physical mechanism of broadband detection. In Two-Dimensional Material-Based Broadband Photodetectors, some innovative strategies are given to expand the detection wavelength range of 2D material-based photodetectors and enhance their overall performances. Among them, chemical doping, defect engineering, constructing heterostructures, and strain engineering methods are found to be more effective for improving their photodetection performances. The last section addresses the challenges and future prospects of 2D material-based broadband photodetectors. Furthermore, to meet the practical requirements for very large-scale integration (VLSI) applications, their work reliability, production cost and compatibility with planar technology should be paid much attention. Full article

Numerous strategies have been demonstrated to enhance the mechanical stretchability of electromechanical sensors for widespread applications in wearable electronics. However, ranging from composite to microstructural materials, their electromechanical sensing performances are usually vulnerable to large stretching deformations due to the low-ductility of the infilled conductive components and the modulus mismatch between the flexible polymer substrate and conductive fillers. Here, a novel design strategy is proposed to fabricate ultra-stretchable electromechanical composites constructed by a triple-level interaction conductive network (Tri-LICN) in buckled-TPU microfibers for strain sensors. The Tri-LICN is established by bridging one-dimensional cellulose nanocrystals (CNC) with zero-dimensional gold-nanoparticles (AuNPs) and two-dimensional MXene sheets using interface self-assembly and ultrasound-assisted anchoring to eliminate the modulus mismatching between the conductive material and polymer substrate. The buckled-TPU microfibers are introduced to improve the mechanical stretchability of composites by the external-stimuli-induced imbalance of the stretching conformation of TPU macromolecules. The Tri-LICN MXene/CNC/AuNPs@TPU composite sensor displays an enhanced strain sensitivity (GF~2514) with a fast response time (~150 ms) over a wide operational strain up to 200% and excellent durability over 1000 tensile cycles. Our finding offers a promising approach to enhancing the performance of stretchable sensors based on polymer materials, providing new opportunities for the development of next-generation electronics. Full article

Modern radar systems commonly utilize monopulse angle estimation techniques for target angle estimation, with the phase comparison method being one of the most widely adopted approaches. While the phase comparison method achieves high estimation precision, it is highly susceptible to noise and exhibits a suboptimal performance under low Signal-to-Noise Ratio (SNR) conditions, leading to a high SNR threshold. Moreover, conventional monopulse angle estimation methods provide limited target information, as a single measurement cannot reveal the target’s motion direction. To address these shortcomings, a novel approach based on the phase comparison method is proposed in this study, with the variation in the wave path difference modeled as a first-order motion model. By accumulating the conjugate-multiplied signals over multiple time steps, the Wave Path Difference Rate of Change Ambiguity Function (WPD-ROC AF) is constructed. A fast algorithm employing the 2D Chirp-Z Transform (2D-CZT) is proposed, enabling multi-pulse angle estimation through the identification of frequency and phase values corresponding to spectral peaks. Simulation results validate that the proposed method outperforms traditional monopulse angle estimation techniques under low-SNR conditions and effectively suppresses static clutter interference. Furthermore, the sign of the WPD-ROC AF is shown to be correlated with the target’s motion direction, providing practical utility for determining the direction of movement in remote sensing scenarios. Full article

This brief tackles the predefined-time attitude tracking problem with guaranteed performance for rigid spacecraft subject to uncertain inertia, external disturbances, and actuator partial failure. Firstly, a nonlinear prescribed performance function (NPPF) is constructed, and a non-singular predefined-time terminal sliding mode (NPTSM) surface integrating with the NPPF is introduced. Secondly, adaptive non-singular predefined-time guaranteed performance control (ANPTGPC) is designed to tackle the robust attitude tracking problem of rigid spacecraft with predefined-time stability. It is proven that attitude tracking errors can be constrained in the preset tracking performance bound within predefined time. They tend to a small region centered around zero in predefined time and then converge to zero asymptotically. Features of the proposed ANPTGPC include an absence of a model, nonsingularity, predefined-time stability with performance quantified, fast transience, and high steady-state accuracy. Numerical simulation results validate the effectiveness and improved performance of the proposed approach. Full article

Synthesis of uniform and stable electrochromic films on a conductive layer is one of the effective ways to construct high-performance electrochromic devices. The development of more convenient and feasible polymer film preparation technology is important and necessary. Herein, we demonstrated the development of a continuous in situ polymerization method to prepare electrochromic film on ITO glass through Schiff base condensation of a tetraamine Fe-based complex and organic di-/tri-aldehyde precursors. The electrochromic film was successfully coated on the surface of the ITO conductive layer and exhibited uniform morphology and excellent stability. Film P1 exhibited two reversible redox processes allowing two steps of electrochromic processes, including the oxidation of Fe(II) to Fe(III) at 1.05 V and oxidation of triphenylamine moieties to cation radicals at 1.4 V, which induced three stable color states from initial yellow to orange red and blue. The utilization of the so-formed polymer film for the fabrication of electrochromic devices gave rise to excellent electrochromic performance of fast response time of 0.4−1.2 s and high coloration efficiencies of 241.5−352.9 cm²/C at 1.9 V (at 535 nm) and 2.5 V (at 755 nm). The present work provides a new feasible strategy for constructing polymer films for high-performance electrochromic devices. Full article

In order to study an algorithm that recognizes the state of health (SOH) of a battery rapidly and can be easily integrated into the micro-controller unit (MCU), it is proposed that signal temporal logic (STL) language is employed to describe the discharge curves, because the STL language is a formal language with strict mathematical definitions and the syntax is composed of simple logic, “and”, “or”, and “not”, under the constraints of time and parameter variation ranges, which is realizable and interpretable. Firstly, the drop voltage amplitude, drop time, voltage rebound amplitude, voltage rebound time, starting voltage, and ending voltage of the discharge curve are selected as the features of the STL formula, so the first-level and second-level primitive formulas are constructed to express the voltage of a battery in good health and poor health clearly. Secondly, the impurity measures of the information gain, misclassification gain, Gini gain, and robust extended gain are presented as the objective functions. Thirdly, the interpreter embedded in the MCU can interpret and execute each STL sentence. The voltage of a battery in good health rises slowly and falls slowly, while the voltage of a battery in poor health rises quickly and falls quickly. When the STL describes the discharge curve as “slow down slow up”, the battery is in good health. When the STL describes the discharge curve as “fast down, fast up”, the battery is in poor health. Among the different objective functions, the highest mean accuracy of the STL reaches 87.5%. In terms of the mean runtime, the extended misclassification gain and the extended Gini gain of the first-level primitives are 00851s and 0.0993, respectively. Under the same mean accuracy of 87%, the information gain and Gini gain of the second-level primitives are 0.2593 s and 0.2341 s. Compared with the existing machine learning algorithms, in terms of the mean runtime, the STL algorithm is superior to the CNN-BiLSTM-MHA model, RNN-LSTM-GRU model, and EC-MKRVM model. In terms of the mean accuracy, compared with the highest correct rate of the CNN-BiLSTM-MHA model, that is, 91.7%, the difference is 4%. As a means of quickly detecting whether the battery is in a healthy state, the accuracy difference is negligible, so the STL algorithm is apparently superior in terms of performance and realizability. Full article