Search Results (8,543)

Rapid and accurate prediction of the maximum remaining life of lithium-ion batteries is a critical technical challenge for enhancing battery management system reliability and enabling the efficient secondary utilization of retired batteries. Traditional approaches that rely on full charge–discharge cycles or complex electrochemical models often suffer from long detection time and limited adaptability, making them unsuitable for fast testing scenarios. To address these limitations, this study proposes a novel capacity prediction method that integrates charging segment feature extraction with a back-propagation neural network (BPNN) co-optimized using the genetic algorithm (GA) and dung beetle optimizer (DBO). Leveraging the public CALCE datasets, key degradation-related features were extracted from partial charging segments to serve as inputs to the prediction framework. The hybrid GA_DBO algorithm is employed to jointly optimize the BPNN’s weights, learning rate, and activation thresholds. A comparative analysis is conducted across various charging durations (900 s, 1800 s, and 2700 s) to evaluate performance under different input lengths. Results reveal that the model using 1800 s charging segment features achieves the best overall accuracy, with a test set mean squared error (MSE) of 0.0001 Ah², mean absolute error (MAE) of 0.0092 Ah, root mean square error (RMSE) of 0.0122 Ah, and a coefficient of determination (R²) of 99.66%, demonstrating strong robustness and predictive capability. This research overcomes the traditional reliance on full cycles, demonstrating the effectiveness of short charging segments combined with intelligent optimization algorithms. The proposed method offers a high-precision, low-cost solution for online battery health monitoring and rapid sorting of retired batteries, highlighting its significant engineering application potential. Full article

Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable few-shot image-text representation abilities across a range of visual tasks, including anomaly detection. Despite their promise, real-world industrial anomaly datasets often contain noisy labels, which can degrade prompt learning and detection performance. In this paper, we propose AnomalyNLP, a new Noisy-Label Prompt Learning approach designed to tackle the challenge of few-shot anomaly detection. This framework offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of VLMs for industrial anomaly detection. First, we design a Noisy-Label Prompt Learning (NLPL) strategy. This strategy utilizes feature learning principles to suppress the influence of noisy samples via Mean Absolute Error (MAE) loss, thereby improving the signal-to-noise ratio and enhancing overall model robustness. Furthermore, we introduce a prompt-driven optimal transport feature purification method to accurately partition datasets into clean and noisy subsets. For both image-level and pixel-level anomaly detection, AnomalyNLP achieves state-of-the-art performance across various few-shot settings on the MVTecAD and VisA public datasets. Qualitative and quantitative results on two datasets demonstrate that our method achieves the largest average AUC improvement over baseline methods across 1-, 2-, and 4-shot settings, with gains of up to 10.60%, 10.11%, and 9.55% in practical anomaly detection scenarios. Full article

In the context of deep foundation pit excavation, the settlement of surrounding buildings is a critical indicator for safety assessment and early warning. Due to the non-stationary and nonlinear characteristics of settlement data, traditional prediction approaches often fail to achieve satisfactory accuracy. To address this challenge, this study proposes a hybrid prediction model integrating the Grey Wolf Optimizer (GWO), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks, referred to as the GWO-VMD-LSTM model. In the proposed framework, GWO is employed to optimize the key hyperparameters of VMD as well as LSTM, thereby ensuring robust decomposition and prediction performance. Experimental results based on settlement monitoring data from four typical points around the Yongning Hospital foundation pit in Taizhou, China, demonstrate that the proposed model achieves superior predictive accuracy compared with five benchmark models. Specifically, the GWO-VMD-LSTM model attained an average coefficient of determination (R²) of 0.951, mean squared error (MSE) of 0.002, root mean square error (RMSE) of 0.033 mm, mean absolute error (MAE) of 0.031 mm, and mean absolute percentage error (MAPE) of 1.324%, outperforming all alternatives. For instance, compared with the VMD-LSTM model, the proposed method improved R² by 26.56% and reduced MAPE by 45.87%. These findings confirm that the GWO-VMD-LSTM model not only enhances the accuracy and generalization of settlement prediction but also provides a reliable and practical tool for real-time monitoring and risk assessment of buildings adjacent to deep foundation pits in soft soil regions. Full article

Cardiovascular disease (CVD) remains the leading cause of global mortality, highlighting the need for continuous vital sign monitoring. Photoplethysmography (PPG) is well suited for wearable devices. Smart rings, benefiting from dense capillary distribution and minimal tissue interference, can capture high-quality PPG signals with comfort, making them a promising next-generation wearable. However, ring rotation relative to the finger alters the optical path, especially for multi-wavelength light, thus reducing accuracy. This paper proposes a rotation-robust PPG sensor for smart rings. Monte Carlo simulations analyze photon transmission under different LED–photodiode (PD) angles, showing that at ±60, green, red, and infrared light achieve optimal penetration into the microcirculation layer. Considering non-ideal conditions, the green-light angle is adjusted to ±30, and a symmetrical sensor design is adopted. A prototype smart ring is developed, capable of recording 4-channel PPG, 3-axis acceleration, and 4-channel temperature signals at 100, 25, and 0.2 Hz, respectively. The system achieves reliable PPG acquisition with only 0.59 mA average current consumption. In continuous testing, heart rate estimation reached mean absolute errors of 0.82, 0.79, and 0.78 bpm for green, red, and IR light. The results provide a reference for future smart ring development. Full article

Traditionally, the determination of the Denavit–Hartenberg (DH) parameters for serial robotic manipulators is a manual process that depends on manufacturer documentation or user-defined conventions, often leading to inefficiency and ambiguity in DH frame placement and parameters. This study introduces a universal and systematic methodology for automatically deriving DH parameters directly from a robot’s zero configuration, using only the geometric relationships between consecutive joint axes. The approach was implemented in a MATLAB-based kinematics toolbox capable of computing both the classical and modified DH parameters. In addition to parameter extraction, the toolbox integrates workspace visualization, manipulability and dexterity analysis, and a slicing and alpha-shape algorithm for accurate workspace volume computation. Validation was conducted on multiple industrial robots by comparing the extracted parameters with the manufacturer data and the RoboDK models. Benchmark studies confirmed the accuracy of the volume estimation, yielding an absolute percentage error of less than 4%. While the current implementation relies on RoboDK models for verification and requires the manual tuning of the alpha-shape parameter, the toolbox provides a reproducible and extensible framework for research, education, and robot design. Full article

Accurate and timely crop yield estimation is essential for effective agricultural management and global food security, particularly for winter wheat. This study aimed to assess the effectiveness of EnMAP hyperspectral imagery in combination with machine learning and deep learning models for winter wheat yield prediction in Hungary. Using EnMAP images from February and May 2023, along with ground truth yield data from four fields, we derived 10 distinct vegetation indices. Random Forest, Gradient Boosting, and Multilayer Perceptron algorithms were employed, and model performance was evaluated using Mean Absolute Error (MAE) and Coefficient of Determination (R²) values. The results consistently demonstrated that integrating multi-temporal data significantly enhanced predictive accuracy, with the MLP model achieving an R² of 0.79 and an MAE of 0.27, notably outperforming single-date predictions. Shortwave infrared (SWIR) indices were particularly critical for early-season yield estimations. This research highlights the substantial potential of hyperspectral data and advanced machine learning techniques in precision agriculture, emphasizing the promising role of future missions such as CHIME in further refining and expanding yield estimation capabilities. Full article

Distribution-level long-term load forecasting with hourly resolution is essential for modern power systems operation, yet it remains challenging due to complex temporal patterns and error accumulation over extended horizons. This study proposes a Multi-Resolution Residual LSTNet framework integrating Discrete Wavelet Transform (DWT), Long Short-Term Memory Networks (LSTNet), and Normalized Linear (NLinear) models for accurate one-year ahead hourly load forecasting. The methodology decomposes load time series into daily, weekly, and monthly components using multi-resolution DWT, applies direct forecasting with LSTNet to capture short-term and long-term dependencies, performs residual correction using NLinear models, and integrates predictions through dynamic weighting mechanisms. Validation using five years of Korean distribution feeder data (2015–2019) demonstrates significant performance improvements over benchmark methods including Autoformer, LSTM, and NLinear, achieving Mean Absolute Error of 0.5771, Mean Absolute Percentage Error of 17.29%, and Huber Loss of 0.2567. The approach effectively mitigates error accumulation common in long-term forecasting while maintaining hourly resolution, providing practical value for demand response, distributed resource control, and infrastructure planning without requiring external variables. Full article

Accurate spatiotemporal forecasting of electric vehicle (EV) charging load is essential for smart grid management and efficient charging service operation. This paper introduced a novel spatiotemporal graph convolutional network with cross-attention (STGCN-Attention) for multi-factor charging load prediction. The model demonstrated a strong capability to capture cross-scale spatiotemporal correlations by adaptively integrating historical charging load, charging pile occupancy, dynamic electricity prices, and meteorological data. Evaluations in real-world charging scenarios showed that the proposed model achieved superior performance in hour forecasting, reducing Mean Absolute Error (MAE) by 9% and 16% compared to traditional STGCN and LSTM models, respectively. It also attained approximately 30% higher accuracy than 24 h prediction. Furthermore, the study identified an optimal 1-2-1 multi-scale temporal window strategy (hour–day–week) and revealed key driver factors. The combined input of load, occupancy, and electricity price yielded the best results (RMSE = 37.21, MAE = 27.34), while introducing temperature and precipitation raised errors by 2–8%, highlighting challenges in fine-grained weather integration. These findings provided actionable insights for real-time and intraday charging scheduling. Full article

This study aims to enhance the accuracy of human lower limb motion intention recognition based on surface electromyography (sEMG) signals and proposes a signal denoising method based on Sequential Variational Mode Decomposition (SVMD) optimized by the Parrot Optimization (PO) algorithm and a joint motion angle prediction model combining Residual Network (ResNet) with Gated Recurrent Unit (GRU) for the two aspects of signal processing and predictive modeling, respectively. First, for the two motion conditions of level walking and stair climbing, sEMG signals from the rectus femoris, vastus lateralis, semitendinosus, and biceps femoris, as well as the motion angles of the hip and knee joints, were simultaneously collected from five healthy subjects, yielding a total of 400 gait cycle data points. The sEMG signals were denoised using the method combining PO-SVMD with wavelet thresholding. Compared with denoising methods such as Empirical Mode Decomposition, Partial Ensemble Empirical Mode Decomposition, Independent Component Analysis, and wavelet thresholding alone, the signal-to-noise ratio (SNR) of the proposed method was increased to a maximum of 23.42 dB. Then, the gait cycle information was divided into training and testing sets at a 4:1 ratio, and five models—ResNet-GRU, Transformer-LSTM, CNN-GRU, ResNet, and GRU—were trained and tested individually using the processed sEMG signals as input and the hip and knee joint movement angles as output. Finally, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) were used as evaluation metrics for the test results. The results show that for both motion conditions, the evaluation metrics of the ResNet-GRU model in the test results are superior to those of the other four models. The optimal evaluation metrics for level walking are 2.512 ± 0.415°, 1.863 ± 0.265°, and 0.979 ± 0.007, respectively, while the optimal evaluation metrics for stair climbing are 2.475 ± 0.442°, 2.012 ± 0.336°, and 0.98 ± 0.009, respectively. The method proposed in this study achieves improvements in both signal processing and predictive modeling, providing a new method for research on lower limb motion intention recognition. Full article

Accurate prediction of dam deformation is crucial for structural safety monitoring. For enhancing the prediction accuracy of concrete dam deformation and addressing the issues of insufficient precision and poor stability in existing methods when modeling complex nonlinear time series, a concrete dam deformation prediction method based on mode decomposition and Self-Attention-Gated Recurrent Unit (SAGRU) was proposed. First, Variational Mode Decomposition (VMD) was employed to decompose the raw deformation data into several Intrinsic Mode Functions (IMFs). These IMFs were then classified by K-means algorithm into regular signals strongly correlated with water level, temperature, and aging factors and weakly correlated random signals. For the random signals, an Improved Wavelet Threshold Denoising (IWTD) method was specifically applied for noise suppression. Based on this, a Deep Learning (DL) model based on SAGRU was constructed to train and predict the decomposed regular signals and the denoised random signals, respectively. And finally, the sum of the calculation results of each signal can be output as the predicted deformation. Experimental results demonstrate that the proposed method outperforms existing models in both prediction accuracy and stability. Compared to LSTM, this method reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by approximately 30.9% and 27.2%, respectively. This provides an effective tool for analyzing concrete dam deformation and offers valuable reference directions for future time series prediction research. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

Ultra-short-term wind power exhibits significant minute-level fluctuation characteristics, leading to substantial instantaneous prediction errors. To mitigate the impact of instantaneous wind power prediction errors, the following steps are taken: First, the correlation between instantaneous prediction errors and meteorological factors is determined, and strongly associated variables are selected as model inputs. Next, the particle swarm optimization algorithm is employed to optimize the initial weights and threshold parameters of the spatiotemporal backpropagation neural network prediction model to enhance its performance. Subsequently, based on the nonlinear relationship between wind speed/direction data and instantaneous prediction errors, a wind speed matrix gradient correction method and a deep learning correction method with physical constraints on prediction errors are constructed to address errors caused by declining model generalization under strong disturbances. To validate the effectiveness of the proposed prediction algorithm integrating parameter optimization and the error correction method, it is compared with typical convolutional neural networks, long short-term memory networks, and backpropagation neural algorithms. The results demonstrate that compared to other wind power prediction strategies, this method reduces the mean absolute percentage error, root mean square error, and mean absolute error by 48.49%, 45.51%, and 50.8%, respectively. These results confirm that combining error correction strategies with prediction model parameter optimization effectively enhances the ability to reduce instantaneous wind power prediction errors, providing a practical technical solution for optimizing ultra-short-term wind power prediction accuracy and offering valuable insights for ensuring the stability of wind power grid integration. Full article

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. Full article

Accurate prediction of resource utilization is essential for efficient cloud resource management and Quality-of-Service (QoS) assurance in online services. However, most existing methods neglect to explicitly model inherent periodic patterns in resource usage—particularly those characterized by extended period lengths, consistent trend shapes with significant magnitude variations across periods—which limits their predictive accuracy. To address this gap, we propose PRPOS (Periodicity-aware Resource Prediction for Online Services), a novel periodicity-aware prediction framework specifically designed for online service workloads. PRPOS operates in two cohesive phases: It first employs a robust period detection mechanism that effectively handles magnitude variations and noise to identify dominant periods; then, a dual scale based on a Gated Recurrent Units (GRU) predictor explicitly incorporates the identified periodicity to concurrently model fine-grained in-period dynamics and coarse-grained cross-period trends. Extensive evaluation on the Alibaba Cluster Trace v2018 demonstrates that PRPOS consistently outperforms state-of-the-art approaches, achieving average improvements of 45.3% in Mean Absolute Percentage Error (MAPE) and 44.3% in Root Mean Squared Error (RMSE). The demonstrated performance enables the application of PRPOS to cloud resource orchestration for online services, allowing for proactive resource provisioning that enhances both efficiency and reliability. Full article

During the operation of steel cable-driven radiation-resistant robots in nuclear industrial environments, the tensile force of a steel cable is influenced by temperature variations, which can cause significant detection errors. To address this problem, this study proposes a temperature-compensated axial force characterization method for steel cables based on the magnetoelastic effect, aiming to ensure the measurement accuracy of magnetoelastic sensors. The principle of the magnetoelastic measurement method involves magnetizing the steel cable. When subjected to tensile forces, the magnetization characteristics of the steel cable change, thereby altering the detection signal of the magnetoelastic sensor. By analyzing the relationship between steel cable tension and variations in the detection signal, effective force measurement can be achieved. First, experiments are conducted to investigate the influence of temperature on the detection signals of a magnetoelastic sensor under zero-load conditions. Then, additional tests are performed to examine the combined effects of a tensile force and temperature on the sensor’s signals. Finally, based on the experimental data, axial force prediction models are constructed using both surface fitting and a backpropagation neural network (BPNN). The results demonstrate that, compared to the resistance values, inductance exhibits superior stability under temperature variations. In the temperature range of 20–50 °C, the inductance variation is approximately 0.15 μH, which indicates improved suitability for characterizing the axial force of steel cables. It is also shown that under isothermal conditions, the inductance increases linearly with the applied tensile force, exhibiting a slope of approximately 0.025 μH/kN. Both the surface fitting-based and BPNN-based axial force prediction models demonstrate high accuracy, with absolute prediction errors consistently below 5% compared to actual data. Full article