Search Results (1,433)

The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations. This article adopts a hybrid architecture of convolutional neural network and long short-term memory network (CNN-LSTM). In the 1D-CNN layer, the one-dimensional convolutional neural network can automatically learn and extract local transient features of time series data by sliding convolution operations on time series data through its convolution kernel, making these local transient features present a specific form in the local time window. In the double-layer LSTM layer, the neural network model captures the transient characteristics of switch current through the gating mechanism and state transfer. The hybrid architecture of the constructed model has significant advantages in accuracy, with metrics such as root mean square error (RMSE) and mean absolute error (MAE) significantly reduced, compared to traditional switch current models, solving the problem of insufficient accuracy in traditional models. The neural network model has good fitting performance at both room and high temperatures, with an average coefficient close to 1. The new neural network hybrid architecture has short running time and low computational resource consumption, meeting the needs of practical applications. Full article

Using Photoplethysmogram (PPG) signals for identity recognition has been proven effective in biometric authentication. However, in real-world applications, PPG signals are prone to interference from noise, physical activity, diseases, and other factors, making it challenging to ensure accurate user recognition and verification in complex environments. To address these issues, this paper proposes an improved MSF-SE ResNet50 (Multi-Scale Feature Squeeze-and-Excitation ResNet50) model based on 2D PPG signals. Unlike most existing methods that directly process one-dimensional PPG signals, this paper adopts a novel approach based on two-dimensional PPG signal processing. By applying Continuous Wavelet Transform (CWT), the preprocessed one-dimensional PPG signal is transformed into a two-dimensional time-frequency map, which not only preserves the time-frequency characteristics of the signal but also provides richer spatial information. During the feature extraction process, the SENet module is first introduced to enhance the ability to extract distinctive features. Next, a novel Lightweight Multi-Scale Feature Fusion (LMSFF) module is proposed, which addresses the limitation of single-scale feature extraction in existing methods by employing parallel multi-scale convolutional operations. Finally, cross-stage feature fusion is implemented, overcoming the limitations of traditional feature fusion methods. These techniques work synergistically to improve the model’s performance. On the BIDMC dataset, the MSF-SE ResNet50 model achieved accuracy, precision, recall, and F1 scores of 98.41%, 98.19%, 98.27%, and 98.23%, respectively. Compared to existing state-of-the-art methods, the proposed model demonstrates significant improvements across all evaluation metrics, highlighting its significance in terms of network architecture and performance. Full article

The Fit for 55 package introduced by the European Union aims to achieve a 55% reduction in greenhouse gas emissions by 2030. In parallel, increasingly stringent exhaust gas regulations have intensified research into alternative fuels. Ethanol presents a promising option due to its compatibility with gasoline, higher octane rating, and lower exhaust emissions compared to conventional gasoline. Additionally, ethanol can be derived from agricultural waste, further enhancing its sustainability. This study examines the impact of two ethanol–gasoline blends (E10, E20) on emissions and performance in a turbocharged gasoline direct injection (TGDI) spark-ignition (SI) engine. The investigation is conducted using three-dimensional computational fluid dynamics (3D CFD) simulations to minimize development time and costs. This paper details the model development process and presents the initial results. The boundary conditions for the simulations are derived from one-dimensional (1D) simulations, which have been validated against experimental data. Subsequently, the simulated performance and emissions results are compared with experimental measurements. The E10 simulations correlated well with experimental measurements, with the largest deviation in cylinder pressure being an RMSE of 1.42. In terms of emissions, HC was underpredicted, while CO was overpredicted compared to the experimental data. For E20, the IMEP was slightly higher at some operating points; however, the deviations were negligible. Regarding emissions, HC and CO emissions were higher with E20, whereas NOx and CO₂ emissions were lower. Full article

In this work, we show the probability density functions (PDFs) and cumulative density functions (CDFs) of the nonnormalized field components and the associated powers received inside coupled reverberation chambers (CRCs), considering two canonical cases of single electrically small coupling apertures (ESCAs). These two cases involve one-dimensional (1D) and two-dimensional (2D) single electrically small CAs, respectively. We achieve normalized statistics from the nonnormalized ones for both field components and associated powers. We show that the comparison of the mean square values (MSVs) of the nonnormalized PDFs of the field components to the mean values (MVs) of the related nonnormalized PDFs of the powers is a proper method to corroborate the accuracy of the same achieved theoretical distributions, when they are achieved in an independent way. The achieved theoretical results are also validated by measurements. Moreover, for the sake of completeness and rigor of published results, we show two useful cases of the results from the measurements using two electrically large CAs. Full article

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method. Full article

The study on soil consolidation and settlement is of great importance in geotechnical engineering practice. Nowadays, physics-informed neural networks (PINN) are becoming more and more popular in solving geotechnical engineering problems thanks to their meshless, physically constrained, and data-driven nature. Although there have been some successful applications in one-dimensional (1D) consolidation problems in saturated soils, the ability and stability to deal with more complex boundary conditions remain to be tested. In this paper, the effects of activation function and random state on the PINN are investigated for solving two 1D consolidation problems in saturated soils, and the proposed method for inverse modeling of the two 1D consolidation problems. The results show that PINN with layer-wise locally adaptive activation functions improves the convergence speed and prediction accuracy of the PINN for solving the 1D nonlinear soil consolidation problems, and at the same time the robustness of the model to random states. Moreover, the proposed method still converges faster in the inverse modeling of 1D consolidation problems. Full article

The controllable loading of a cocatalyst on a semiconductor is the key to further improving the efficiency and stability of visible-light photocatalytic hydrogen production. It is of great practical significance to load a cocatalyst onto a semiconductor spatially separated to realize space charge separation for efficient photocatalytic hydrogen evolution. The inherent anisotropic morphology of one-dimensional nanorods can provide two spatially separated locations at the tip and side surfaces of the nanorods. In this review, we systematically summarize non-centrosymmetric and centrosymmetric cocatalyst-tipped one-dimensional (1D) photocatalysts, including their preparation method, catalytic hydrogen production performance, and catalytic mechanism. This review will bring new vitality to the design, preparation, and application of cocatalyst-tipped one-dimensional nanorods. Full article

This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is important to ensure a stable output and to counteract degradation and thermal runaway. To design the optimal system, it is most effective to use a 1D (one-dimensional) vehicle system simulation model, which connects each unit model inside the vehicle, due to the system’s complexity. In order to create a long-term degradation simulation in a vehicle system model, it is important to reduce computational load. Therefore, in this paper, we studied a suitable battery degradation calculation for the vehicle system model based on an equivalent circuit model (ECM) and degradation approximation formulas. After implementing these models, we analyzed long-term degradation behavior through the real-world operation of an electric vehicle driver. We first implemented a high-accuracy ECM using transient charge–discharge tests and Bayesian Optimization. Next, we formulated approximation formulas for degradation prediction based on calendar and cycle degradation tests. Finally, we simulated real-world degradation behavior using these models. The simulation results revealed that even for users who frequently use electric vehicles, degradation under storage conditions is the dominant factor in overall degradation. Full article

Various nature-based solutions (NbS)—such as constructed wetlands, drainage ditches, and vegetated buffer strips—have recently demonstrated strong potential for mitigating pollutant transport in open channels and river systems. Numerical modeling is a widely adopted and effective approach for assessing the performance of these interventions. This study presents the first development of a two-dimensional (2D) meshless advection–diffusion model based on an Eulerian smoothed particle hydrodynamics (SPH) framework, specifically designed to simulate passive pollutant transport in open channel flows. The proposed model marks a pioneering application of the ESPH technique to environmental pollutant transport problems. It couples the 2D depth-averaged shallow water equations with an advection–diffusion equation to represent both fluid motion and pollutant concentration dynamics. A uniform particle arrangement ensures that each fluid particle interacts symmetrically with eight neighboring particles for flux computation. To represent the pollutant transport process, the dispersion coefficient is defined as the sum of molecular and turbulent diffusion components. The turbulent diffusion coefficient is calculated using a prescribed turbulent Schmidt number and the eddy viscosity obtained from a Smagorinsky-type mixing-length turbulence model. Three analytical case studies, including one-dimensional transcritical open channel flow, 2D isotropic and anisotropic diffusion in still water, and advection–diffusion in a 2D uniform flow, are employed to verify the model’s accuracy and convergence. The model demonstrates first-order convergence, with relative root mean square errors (RRMSEs) of approximately 0.2% for water depth and velocity, and 0.1–0.5% for concentration. Additionally, the model is applied to a laboratory experiment involving 2D pollutant dispersion in a 90° junction channel. The simulated results show good agreement with measured velocity and concentration distributions. These findings indicate that the developed model is a reliable and effective tool for evaluating the performance of NbS in mitigating pollutant transport in open channels and river systems. Full article

Digital images have been widely applied in fields such as mobile devices, the Internet of Things, and medical imaging. Although significant progress has been made in image encryption technology, it still faces many challenges, such as attackers using powerful computing resources and advanced algorithms to crack encryption systems. To address these challenges, this paper proposes a novel image encryption algorithm based on one-dimensional sawtooth wave chaotic system (1D-SAW) and the three-strand structure of DNA. Firstly, a new 1D-SAW chaotic system was designed. By introducing nonlinear terms and periodic disturbances, this system is capable of generating chaotic sequences with high randomness and initial value sensitivity. Secondly, a new diffusion rule based on the three-strand structure of DNA is proposed. Compared with the traditional DNA encoding and XOR operation, this rule further enhances the complexity and anti-attack ability of the encryption process. Finally, the security and randomness of the 1D-SAW and image encryption algorithms were verified through various tests. Results show that this method exhibits better performance in resisting statistical attacks and differential attacks. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

The time-dependent creep behavior of coal is essential for assessing long-term structural stability and operational safety in deep coal mining. Therefore, this work develops a full-stage creep constitutive model. By integrating fractional calculus theory with statistical damage mechanics, a nonlinear fractional-order (FO) damage creep model is constructed through serial connection of elastic, viscous, viscoelastic, and viscoelastic–plastic components. Based on this model, both one-dimensional and three-dimensional (3D) fractional creep damage constitutive equations are acquired. Model parameters are identified using experimental data from deep coal samples in the mining area. The result curves of the improved model coincide with experimental data points, accurately describing the deceleration creep stage (DCS), steady-state creep stage (SCS), and accelerated creep stage (ACS). Furthermore, a sensitivity analysis elucidates the impact of model parameters on coal creep behavior, thereby confirming the model’s robustness and applicability. Consequently, the proposed model offers a solid theoretical basis for evaluating the sustained stability of deep coal mining and has great application potential in deep underground engineering. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

This research presents a novel method for detecting building roof types using deep learning models based on height profiles from high-resolution digital surface models. While deep learning has proven effective in digit, handwritten, and time series classification, this study focuses on the emerging and crucial area of height profile detection for building roof type classification. We propose an innovative approach to automatically generate, classify, and detect building roof types using height profiles derived from normalized digital surface models. We present three distinct methods to detect seven roof types from two height profiles of the building cross-section. The first two methods detect the building roof type from two-dimensional (2D) height profiles: two binary images and a two-band spectral image. The third method, vector-based, detects the building roof type from two one-dimensional (1D) height profiles represented as two 1D vectors. We trained various one- and two-dimensional convolutional neural networks on these 1D and 2D height profiles. The DenseNet201 network could directly detect the roof type of a building from two height profiles stored as a two-band spectral image with an average accuracy of 97%, even in the presence of consecutive chimneys, dormers, and noise. The strengths of this approach include the generation of a large, detailed, and storage-efficient labeled height profile dataset, the development of a robust classification method using both 1D and 2D height profiles, and an automated workflow that enhances building roof type detection. Full article