Search Results (76)

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

Graph-based multi-view clustering has gained significant attention in recent years due to its superior ability to reveal clustering structures. However, existing methods often incur high computational costs when capturing local information and overlook the higher-order correlations between multiple views. To address these issues, we propose Tensorized Consensus Graph Learning for Incomplete Multi-View Clustering with Confidence Integration (TCGL). This approach constructs adjacency and local heat kernel graphs by filtering missing samples to better capture local structures while leveraging a t-SVD-based weighted tensor nuclear norm sparsification method to reduce noise. Additionally, we introduce a matrix energy-based adjacency graph normalization strategy that utilizes common nearest neighbors to generate probability matrices, enhancing noise resistance and improving structural exploration. Experimental results demonstrate that TCGL effectively handles incomplete data and significantly outperforms state-of-the-art approaches across multiple datasets. Full article

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

With the increasing complexity of power systems, the monitoring data of UPFC submodules suffers from high missing rates due to sensor failures and environmental interference, significantly limiting equipment condition assessment and fault warning capabilities. To overcome the computational complexity, poor real-time performance, and limited generalization of existing methods like GRU-GAN and SOM-LSTM, this study proposes a hybrid framework combining Bayesian multiple imputation with a Support Vector Machine (SVM) for data repair. The framework first employs an adaptive Kalman filter to denoise raw data and remove outliers, followed by Bayesian multiple imputation that constructs posterior distributions using normal linear correlations between historical and operational data, generating optimized imputed values through arithmetic averaging. A kernel-based SVM with RBF and soft margin optimization is then applied for nonlinear calibration to enhance robustness and consistency in high-dimensional scenarios. Experimental validation focusing on capacitor voltage, current, and temperature parameters of UPFC submodules under a 50% missing data scenario demonstrates that the proposed method achieves an 18.7% average error reduction and approximately 30% computational efficiency improvement compared to single imputation and traditional multiple imputation approaches, significantly outperforming neural network models. This study confirms the effectiveness of integrating Bayesian statistics with machine learning for power data restoration, providing a high-precision and low-complexity solution for equipment condition monitoring in complex operational environments. Future research will explore dynamic weight optimization and extend the framework to multi-source heterogeneous data applications. Full article

In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or leaves the view field, it may result in tracking loss of the target. To address these limitations, this work proposes an improved CF tracking algorithm based on some existent ones. Firstly, as for the scale changing of tracking target, an adaptive scale box is proposed to adjustably change the scale of the target box. Secondly, to address boundary effects caused by fast maneuvering, a spatio-temporal search strategy is presented, utilizing spatial context from the target region in the current frame and temporal information from preceding frames. Thirdly, aiming at the problem of tracking loss due to occlusion or out-of-view situations, this work proposes a fusion strategy based on the YOLOv5s_MSES target detection algorithm. Finally, the experimental results show that, compared to the baseline algorithm on the UAV123 dataset, our DP and AUC increased by 14.07% and 14.39%, respectively, and the frames per second (FPS) amounts to $37.5$. Additionally, on the OTB100 dataset, the proposed algorithm demonstrates significant improvements in distance precision (DP) metrics across four challenging attributes compared to the baseline algorithm, showing a 12.85% increase for scale variation (SV), 16.45% for fast motion (FM), 18.66% for occlusion (OCC), and 17.09% for out-of-view (OV) scenarios. To sum up, the proposed algorithm not only achieves the ideal tracking effect, but also meets the real-time requirement with higher precision, which means that the comprehensive performance is superior to some existing methods. Full article

Video Object Tracking (VOT) is a critical task in computer vision. While Siamese-based and Transformer-based trackers are widely used in VOT, they struggle to perform well on the OTB100 benchmark due to the lack of dedicated training sets. This challenge highlights the difficulty of effectively generalizing to unknown data. To address this issue, this paper proposes an innovative method that utilizes tensor decomposition, an underexplored concept in object-tracking research. By applying L1-norm tensor decomposition, video sequences are represented as four-mode tensors, and a real-time background subtraction algorithm is introduced, allowing for effective modeling of the target–background relationship and adaptation to environmental changes, leading to accurate and robust tracking. Additionally, the paper integrates an improved multi-kernel correlation filter into a single frame, locating and tracking the target by comparing the correlation between the target template and the input image. To further enhance localization precision and robustness, the paper also incorporates Tucker2 decomposition to integrate appearance and motion patterns, generating composite heatmaps. The method is evaluated on the OTB100 benchmark dataset, showing significant improvements in both performance and speed compared to traditional methods. Experimental results demonstrate that the proposed method achieves a 15.8% improvement in AUC and a ten-fold increase in speed compared to typical deep learning-based methods, providing an efficient and accurate real-time tracking solution, particularly in scenarios with similar target–background characteristics, high-speed motion, and limited target movement. Full article

A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model’s hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy. Full article

Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation. Building on this, we present DSiam-CnK, a Siamese neural network with dynamic template updating capabilities, facilitating adaptive adjustments in tracking strategy. The proposed algorithm is tailored to elevate the Siamese neural network’s accuracy and robustness for prolonged tracking, all the while preserving its tracking velocity. In our research, we assessed the performance on the OTB2015, VOT2018, and LaSOT datasets. Our method, when benchmarked against established trackers, including SiamRPN on OTB2015, achieved a success rate of 92.1% and a precision rate of 90.9%. On the VOT2018 dataset, it excelled, with a VOT-A (accuracy) of 46.7%, a VOT-R (robustness) of 135.3%, and a VOT-EAO (expected average overlap) of 26.4%, leading in all categories. On the LaSOT dataset, it achieved a precision of 35.3%, a normalized precision of 34.4%, and a success rate of 39%. The findings demonstrate enhanced precision in tracking performance and a notable increase in robustness with our method. Full article

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and receiver, accurate tracking essential for seamless 6G services is achievable. However, accurate three-dimensional (3D) outdoor positioning has been challenging to achieve in NLOS environments where positioning accuracy may be severely degraded. In this paper, a novel 3D outdoor positioning method considering both LOS and NLOS environments is proposed. Considering the practical positioning systems, the data received from satellites often contain null values and outliers. Thus, a kernel density estimation (KDE)-based outlier removal method is used for effectively detecting the null values and outliers through temporal correlation analysis. A dilution of precision-based adaptive Kalman filter (DOP-AKF) is proposed to mitigate the effects of an NLOS environment. In the proposed method, the DOP-AKF can optimize the performance of the 3D positioning system that dynamically adapts to complex environments. Experimental results show that the proposed method can improve 3D positioning accuracy by up to 18.84% compared to conventional methods. Therefore, the proposed approach can be suggested as a promising solution for 3D outdoor positioning in 6G wireless systems. Full article

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method. Full article

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target’s two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience. Full article

The target is often obstructed by obstacles with the dynamic tracking scene, leading to a loss of target information and a decrease in tracking accuracy or even complete failure. To address these challenges, we leverage the capabilities of Geiger-mode Avalanche Photodiode (GM-APD) LiDAR to acquire both intensity images and point cloud data for researching a target tracking method that combines the fusion of intensity images and point cloud data. Building upon Kernelized correlation filtering (KCF), we introduce Fourier descriptors based on intensity images to enhance the representational capacity of target features, thereby achieving precise target tracking using intensity images. Additionally, an adaptive factor is designed based on peak sidelobe ratio and intrinsic shape signature to accurately detect occlusions. Finally, by fusing the tracking results from Kalman filter and KCF with adaptive factors following occlusion detection, we obtain location information for the central point of the target. The proposed method is validated through simulations using the KITTI tracking dataset, yielding an average position error of 0.1182m for the central point of the target. Moreover, our approach achieves an average tracking accuracy that is 21.67% higher than that obtained by Kalman filtering algorithm and 7.94% higher than extended Kalman filtering algorithm on average. Full article

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections. Full article

Joint detection and tracking of weak underwater targets are challenging problems whose complexity is intensified when the target is disturbed by reverberation. In the low signal-to-reverberation ratio (SRR) environment, the traditional detection and tracking methods perform poorly in tracking robustness because they only consider the target motion characteristics. Recently, the kernel correlation filter (KCF) based on target features has received lots of attention and gained great success in visual tracking. We propose an improved multi-kernel correlation filter (IMKCF) tracking-by-detection algorithm by introducing the KCF into the field of underwater weak target detection and tracking. It is composed of the tracking-by-detection, the adaptive reliability check, and the re-detection modules. Specifically, the tracking-by-detection part is built on the multi-kernel correlation filter (MKCF), and it uses multi-frame data weighted averaging to update. The reliability check helps keep the tracker from corruption. The re-detection module, integrated with a Kalman filter, identifies target positions when the tracking is unreliable. Finally, the experimental data processing and analysis show that the proposed method outperforms the single-kernel methods and some traditional tracking methods. Full article

Purpose: In 2021, the WHO central nervous system (CNS) tumor classification criteria added the diagnosis of diffuse astrocytic glioma, IDH wild-type, with molecular features of glioblastoma, WHO grade 4 (DAG-G). DAG-G may exhibit the aggressiveness and malignancy of glioblastoma (GBM) despite the lower histological grade, and thus a precise preoperative diagnosis can help neurosurgeons develop more refined individualized treatment plans. This study aimed to establish a predictive model for the non-invasive identification of DAG-G based on preoperative MRI radiomics. Patients and Methods: Patients with pathologically confirmed glioma in Huashan Hospital, Fudan University, between September 2019 and July 2021 were retrospectively analyzed. Furthermore, two external validation datasets from Wuhan Union Hospital and Xuzhou Cancer Hospital were also utilized to verify the reliability and accuracy of the prediction model. Two regions of interest (ROI) were delineated on the preoperative MRI images of the patients using the semi-automatic tool ITK-SNAP (version 4.0.0), which were named the maximum anomaly region (ROI1) and the tumor region (ROI2), and Pyradiomics 3.0 was applied for feature extraction. Feature selection was performed using a least absolute shrinkage and selection operator (LASSO) filter and a Spearman correlation coefficient. Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. Moreover, the performance of prediction models was evaluated using area under the curve (AUC), precision (PRE), and other metrics. Results: According to the inclusion and exclusion criteria, 172 patients with grade 2–3 astrocytoma were finally included in the study, and a total of 44 patients met the diagnosis of DAG-G. In the prediction task of DAG-G, the average AUC of GNB classifier was 0.74 ± 0.07, that of KNN classifier was 0.89 ± 0.04, that of RF classifier was 0.96 ± 0.03, that of AB classifier was 0.97 ± 0.02, that of SVM classifier was 0.88 ± 0.05, and that of MLP classifier was 0.91 ± 0.03, among which, AB classifier achieved the best prediction performance. In addition, the AB classifier achieved AUCs of 0.91 and 0.89 in two external validation datasets obtained from Wuhan Union Hospital and Xuzhou Cancer Hospital, respectively. Conclusions: The prediction model constructed based on preoperative MRI radiomics established in this study can basically realize the prospective, non-invasive, and accurate diagnosis of DAG-G, which is of great significance to help further optimize treatment plans for such patients, including expanding the extent of surgery and actively administering radiotherapy, targeted therapy, or other treatments after surgery, to fundamentally maximize the prognosis of patients. Full article