Search Results (1,138)

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

Ultra-short-term wind power forecasting is challenged by high volatility and complex temporal patterns, with traditional single-model approaches often failing to provide stable and accurate predictions under diverse operational scenarios. To address this issue, a framework based on the TCN-ELM hybrid model with temporal alignment clustering and feature refinement is proposed for ultra-short-term wind power forecasting. First, dynamic time warping (DTW)–K-means is applied to cluster historical power curves in the temporal alignment space, identifying consistent operational patterns and providing prior information for subsequent predictions. Then, a correlation-driven feature refinement method is introduced to weight and select the most representative meteorological and power sequence features within each cluster, optimizing the feature set for improved prediction accuracy. Next, a TCN-ELM hybrid model is constructed, combining the advantages of temporal convolutional networks (TCNs) in capturing sequential features and an extreme learning machine (ELM) in efficient nonlinear modelling. This hybrid approach enhances forecasting performance through their synergistic capabilities. Traditional ultra-short-term forecasting often focuses solely on historical power as input, especially with a 15 min resolution, but this study emphasizes reducing the time scale of meteorological forecasts and power samples to within one hour, aiming to improve the reliability of the forecasting model in handling sudden meteorological changes within the ultra-short-term time horizon. To validate the proposed framework, comparisons are made with several benchmark models, including traditional TCN, ELM, and long short-term memory (LSTM) networks. Experimental results demonstrate that the proposed framework achieves higher prediction accuracy and better robustness across various operational modes, particularly under high-variability scenarios, out-performing conventional models like TCN and ELM. The method provides a reliable technical solution for ultra-short-term wind power forecasting, grid scheduling, and power system stability. Full article

Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization–Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry. Full article

The rapid advancement of Artificial Intelligence of Things (AIoT) has driven an urgent demand for intelligent video anomaly detection (VAD) to ensure industrial safety. However, traditional approaches struggle to detect unknown anomalies in complex and dynamic environments due to the scarcity of abnormal samples and limited generalization capabilities. To address these challenges, this paper presents an adaptive VAD framework powered by edge intelligence tailored for resource-constrained industrial settings. Specifically, a lightweight feature extractor is developed by integrating residual networks with channel attention mechanisms, achieving a 58% reduction in model parameters through dense connectivity and output pruning. A multidimensional evaluation strategy is introduced to dynamically select optimal models for deployment on heterogeneous edge devices. To enhance cross-scene adaptability, we propose a multilayer adversarial domain adaptation mechanism that effectively aligns feature distributions across diverse industrial environments. Extensive experiments on a real-world coal mine surveillance dataset demonstrate that the proposed framework achieves an accuracy of 86.7% with an inference latency of 23 ms per frame on edge hardware, improving both detection efficiency and transferability. Full article

When a transformer operates under three-phase unbalanced conditions, the location of the winding hot-spot temperature (HST) is no longer fixed on a certain phase. Taking an S13-M-100 kVA/10 kV transformer as the research object, this paper proposes a streamline inversion method for inverting the winding HST based on the analysis of oil flow morphology. The study employs the finite volume method for coupled calculations of a transformer’s thermal fluid field and combines a support vector regression (SVR) model for the HST inversion. An orthogonal experimental method is used to construct the training and testing sample sets, and the grid search method is utilized to optimize the parameters of the SVR model. In response to variations in hot-spot locations under three-phase unbalanced conditions, representative streamlines are reasonably selected, and a genetic algorithm-based dimensionality reduction optimization is performed on the feature quantities. The research results indicate that the established inversion model exhibits high inversion accuracy under three-phase unbalanced conditions, with a maximum temperature difference of 3.71 K, and the robustness check verifies the stability of the model. Full article

Sweetpotato virus disease (SPVD) poses a significant threat to global sweetpotato production; therefore, early, accurate field-scale detection is necessary. To address the limitations of the currently utilized assays, we propose PLCNet (Plant-Level Classification Network), a rapid, non-destructive SPVD identification framework using UAV-acquired hyperspectral imagery. High-resolution data from early sweetpotato growth stages were processed via three feature selection methods—Random Forest (RF), Minimum Redundancy Maximum Relevance (mRMR), and Local Covariance Matrix (LCM)—in combination with 24 vegetation indices. Variance Inflation Factor (VIF) analysis reduced multicollinearity, yielding an optimized SPVD-sensitive feature set. First, using the RF-selected bands and vegetation indices, we benchmarked four classifiers—Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Residual Network (ResNet), and 3D Convolutional Neural Network (3D-CNN). Under identical inputs, the 3D-CNN achieved superior performance (OA = 96.55%, Macro F1 = 95.36%, UA_mean = 0.9498, PA_mean = 0.9504), outperforming SVM, GBDT, and ResNet. Second, with the same spectral–spatial features and 3D-CNN backbone, we compared a pixel-level baseline (CropdocNet) against our plant-level PLCNet. CropdocNet exhibited spatial fragmentation and isolated errors, whereas PLCNet’s two-stage pipeline—deep feature extraction followed by connected-component analysis and majority voting—aggregated voxel predictions into coherent whole-plant labels, substantially reducing noise and enhancing biological interpretability. By integrating optimized feature selection, deep learning, and plant-level post-processing, PLCNet delivers a scalable, high-throughput solution for precise SPVD monitoring in agricultural fields. Full article

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm significantly outperforms traditional methods in both single-fault and mixed-fault identification, achieving a fault identification accuracy rate of 99.4% on the test set. It maintains high accuracy even in real-world scenarios with superimposed noise and environmental sounds, fully validating its generalization capability and interference resistance, and providing effective technical support for the intelligent maintenance of hydroelectric generator units. Full article

Decision-making plays a pivotal role in data-driven optimization, aiming to achieve optimal results by identifying the most effective combination of input variables. Traditionally, in multi-objective data-driven optimization problems, decision-making relies solely on the Pareto front derived from the training data, as provided by the optimizer. This approach limits consideration to a subset of solutions and often overlooks potentially superior solutions on test set within the optimizer’s final population. What if we include the entire final population in the decision-making process? This paper is the first to systematically explore the potential of utilizing the entire final population, rather than relying solely on the optimization Pareto front, for decision-making in data-driven multi-objective optimization. This novel perspective reveals overlooked yet potentially superior solutions that generalize better to unseen data and help mitigate issues such as overfitting and training-data bias. This paper highlights the use of the entire final population of the optimizer for final decision-making in multi-objective optimization. Using feature selection as a case study, this method is evaluated on two key objectives: minimizing classification error rate and reducing the number of selected features. We compare the proposed test Pareto front, derived from the final population, with traditional test Pareto fronts based on training data. Experiments conducted on fifteen large-scale datasets reveal that some optimal solutions within the entire population are overlooked when focusing solely on the optimization Pareto front. This indicates that the solutions on the optimization Pareto front are not necessarily the optimal solutions for real-world unseen data. There may be additional solutions in the final population yet to be utilized for decision-making. Full article

Fashion products are typically sold through both online and offline channels during two distinct phases: the launch and markdown period. Pricing strategies present significant challenges for manufacturers, particularly as consumers increasingly adopt strategic purchasing behaviors. Key factors, including product fashion utility, purchase timing, and consumer characteristics, complicate manufacturers’ channel selection, pricing decisions, and service strategy formulation—necessitating deeper investigation. This paper establishes a two-echelon supply chain model featuring a fashion manufacturer and a retailer to determine optimal channel, pricing, and service strategies across both selling periods amid strategic consumer behavior. We examine four channel strategies: (1) the MM strategy: the manufacturer operates both channels (online and offline channels) during both periods (launch and markdown period); (2) the MR strategy: the manufacturer operates both channels during the launch stage, and the retailer sells online during the markdown period; (3) the RR strategy: the manufacturer sells offline, and the retailer operates the online channel during both stages; (4) the RM strategy: the manufacturer sells online during both stages, and the retailer sells through the offline channel. Our analysis yields critical insights: When off-season discounts are limited, the manufacturer should maintain direct control of both channels. However, when the off-season discount is significant, the manufacturer needs to set the channel strategy according to the fashion utility. If the fashion utility is small, direct sales through offline channels during the launch period, while entrusting the retailer to distribute in online channels during both periods, should be adopted. If the fashion utility is large, a dual-channel, two-stage, entirely direct sales strategy should be adopted. This study elucidates the optimal manufacturer channel and pricing strategy options and provides some theoretical contributions and practical implications. Full article

Phytoecdysteroids represent a class of naturally occurring substances known for their diverse biological functions, particularly their strong ability to stimulate protein anabolism. In this study, a computational machine learning-driven quantitative structure–activity relationship (QSAR) approach was applied to analyze the anabolic potential of 23 ecdysteroid compounds. The ML-based QSAR modeling was conducted using a combined approach that integrates Genetic Algorithm-based feature selection with Multiple Linear Regression Analysis (GA-MLRA). Additionally, structure optimization by semi-empirical quantum-chemical method was employed to determine the most stable molecular conformations and to calculate an additional set of structural and electronic descriptors. The most effective QSAR models for describing the anabolic activity of the investigated ecdysteroids were developed and validated. The proposed best model demonstrates both strong statistical relevance and high predictive performance. The predictive performance of the resulting models was confirmed by an external test set based on R²_test values, which were within the range of 0.89 to 0.97. Full article

Sandy beaches are vital geomorphic units with ecological, social, and economic significance, playing a key role in coastal protection and ecosystem regulation. However, they are increasingly threatened by climate change and human activities, highlighting the need for large-scale, high-precision monitoring to support sustainable management. Existing remote-sensing-based sandy beach extraction methods face challenges such as suboptimal feature selection and reliance on single data sources, limiting their generalization and accuracy. This study proposes a novel sandy beach extraction framework that integrates multi-source data, feature optimization, and collaborative modeling, with Fujian Province, China, as the study area. The framework combines Sentinel-1/2 imagery, nighttime light data, and terrain data to construct a comprehensive feature set containing 44 spectrum, index, polarization, texture, and terrain variables. The optimal feature subsets are selected using the Recursive Feature Elimination (RFE) algorithm. Six machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost)—along with an ensemble learning model, are employed for comparative analysis and performance optimization. The results indicate the following. (1) All models achieved the best performance when integrating all five types of features, with the average overall F1-score and accuracy reaching 0.9714 and 0.9733, respectively. (2) The number of optimal features selected by RFE varied by model, ranging from 19 to 36. The ten most important features across models were Band 2 (B2), Elevation, Band 3 (B3), VVVH_SUM, Spatial Average (SAVG), VH, Enhanced Water Index (EWI), Slope, Variance (VAR), and Normalized Difference Vegetation Index (NDVI). (3) The ensemble learning model outperformed all others, achieving an average overall accuracy, precision, recall, and F1-score of 0.9750, 0.9733, 0.9725, and 0.9734, respectively, under the optimal feature subset. A total of 555 sandy beaches were extracted in Fujian Province, covering an area of 43.60 km² with a total perimeter of 1263.59 km. This framework demonstrates strong adaptability and robustness in complex coastal environments, providing a scalable solution for intelligent sandy beach monitoring and refined resource management. Full article

In order to solve the problems of difficulty in extracting effective features from dissolved gases in transformer oil and limited recognition accuracy of the fault diagnosis model, a feature selection and improved black-winged kite algorithm (IBKA) optimized support vector machine (SVM) transformer fault diagnosis method based on dissolved gas analysis (DGA) in oil is proposed. Firstly, a hybrid feature selection method is used to perform quantitative analysis on the constructed 20-dimensional fault candidate feature set, thereby achieving the selection of feature variables. Then, the Tent chaotic mapping, the Gompertz growth model, and the Morlet wavelet variation strategy are introduced to improve the Black-Winged Kite Algorithm (BKA) to enhance its optimization searching performance; then, the IBKA is used to optimize the hyperparameters such as kernel function and penalty factor of SVM to improve the accuracy of model diagnosis results. Finally, case analysis based on 410 sets of IEC TC10 transformer fault data shows that the fault diagnosis accuracy of the proposed method reaches 98.37%, which verifies the effectiveness of the proposed method for classifying faults according to the IEC TC10 method. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

Under the current context of the large-scale integration of wind and solar power, the coupling of hydropower with wind and solar energy brings significant impacts on grid stability. To fully leverage the regulatory capacity of hydropower, this paper develops a multi-objective optimization scheduling model for hydropower, wind, and solar that balances generation-side power generation benefit and grid-side peak-regulation requirements, with the latter quantified by the mean square error of the residual load. To efficiently solve this model, Latin hypercube initialization, hybrid distance framework, and adaptive mutation mechanism are introduced into the Strength Pareto Evolutionary Algorithm II (SPEAII), yielding an improved algorithm named LHS-Mutate Strength Pareto Evolutionary Algorithm II (LMSPEAII). Its efficiency is validated on benchmark test functions and a reservoir model. Typical extreme scenarios—months with strong wind and solar in the dry season and months with weak wind and solar in the flood season—are selected to derive scheduling strategies and to further verify the effectiveness of the proposed model and algorithm. Finally, K-medoids clustering is applied to the Pareto front solutions; from the perspective of representative solutions, this reveals the evolutionary trends of different objective trade-off schemes and overall distribution characteristics, providing deeper insight into the solution set’s distribution features. Full article

Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. Additionally, Machine Learning (ML) approaches show significant promise for detecting intrusions in IoT environments. However, the high dimensionality, class imbalance, and complexity of network traffic—combined with the dynamic nature of sensor networks—pose substantial challenges to the development of efficient and effective detection algorithms. In this study, a multi-objective metaheuristic optimization approach, referred to as MOOIDS-IoT, is integrated with ML techniques to develop an intelligent cybersecurity system for IoT environments. MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. PSO optimizes convergence speed, model complexity, and classification accuracy by dynamically adjusting the weights and thresholds of the deployed classifiers. Furthermore, PSO integrates Pareto-based multi-objective optimization directly into the particle swarm framework, extending conventional swarm intelligence while preserving a diverse set of non-dominated solutions. In addition, the GA reduces training time and eliminates redundancy by identifying the most significant input characteristics. The MOOIDS-IoT framework is evaluated using two lightweight models—MOO-PSO-XGBoost and MOO-PSO-RF—across two benchmark datasets, namely the NSL-KDD and CICIoT2023 datasets. On CICIoT2023, MOO-PSO-RF obtains 91.42% accuracy, whereas MOO-PSO-XGBoost obtains 98.38% accuracy. In addition, both models perform well on NSL-KDD (MOO-PSO-RF: 99.66% accuracy, MOO-PSO-XGBoost: 98.46% accuracy). The proposed approach is particularly appropriate for IoT applications with limited resources, where scalability and model efficiency are crucial considerations. Full article