Search Results (2,304)

With the rapid development of large model technology, data storage as well as collection is very important to improve the accuracy of model training, and Feature Selection (FS) methods can greatly eliminate redundant features in the data warehouse and improve the interpretability of the model, which makes it particularly important in the field of large model training. In order to better reduce redundant features in data warehouses, this paper proposes an enhanced Secretarial Bird Optimization Algorithm (SBOA), called BSFSBOA, by combining three learning strategies. First, for the problem of insufficient algorithmic population diversity in SBOA, the best-rand exploration strategy is proposed, which utilizes the randomness and optimality of random individuals as well as optimal individuals to effectively improve the population diversity of the algorithm. Second, to address the imbalance in the exploration/exploitation phase of SBOA, the segmented balance strategy is proposed to improve the balance by segmenting the individuals in the population, targeting individuals of different natures with different degrees of exploration and exploitation performance, and improving the quality of the FS subset when the algorithm is solved. Finally, for the problem of insufficient exploitation performance of SBOA, a four-role exploitation strategy is proposed, which strengthens the effective exploitation ability of the algorithm and enhances the classification accuracy of the FS subset by different degrees of guidance through the four natures of individuals in the population. Subsequently, the proposed BSFSBOA-based FS method is applied to solve 36 FS problems involving low, medium, and high dimensions, and the experimental results show that, compared to SBOA, BSFSBOA improves the performance of classification accuracy by more than 60%, also ranks first in feature subset size, obtains the least runtime, and confirms that the BSFSBOA-based FS method is a robust FS method with efficient solution performance, high stability, and high practicality. Full article

Tool wear monitoring is crucial for optimizing cutting performance, reducing costs, and improving production efficiency. Existing tool wear prediction models usually design integrated models based on a convolutional neural network (CNN) and recurrent neural network (RNN) to extract spatial and temporal features separately. However, the topological structures between multi-sensor networks are ignored, and the ability to extract spatial features is limited. To overcome these limitations, a novel spatial–temporal adaptive graph convolutional network (STAGCN) is proposed to capture spatial–temporal dependencies with multi-sensor signals. First, a simple linear model is used to capture temporal patterns in individual time-series data. Second, a spatial–temporal layer composed of a bidirectional Mamba and an adaptive graph convolution is established to extract degradation features and reflect the dynamic degradation trend using an adaptive graph. Third, multi-scale triple linear attention (MTLA) is used to fuse the extracted multi-scale features across spatial, temporal, and channel dimensions, which can assign different weights adaptively to retain important information and weaken the influence of redundant features. Finally, the fused features are fed into a linear regression layer to estimate the tool wear. Experimental results conducted on the PHM2010 dataset demonstrate the effectiveness of the proposed STAGCN model, achieving a mean absolute error (MAE) of 3.40 μm and a root mean square error (RMSE) of 4.32 μm in the average results across three datasets. Full article

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research’s significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future. Full article

The precise prediction of natural gas pipeline corrosion rates holds great significance for pipeline maintenance and corrosion control. Existing prediction methods, especially traditional models, often fail to adequately consider noise interference and the strong nonlinear characteristics of corrosion data, resulting in insufficient prediction accuracy. To enhance predictive performance, a hybrid prediction model based on machine learning is been proposed. This model consists of three main components: data processing, model optimization, and prediction performance evaluation. In this model, data decomposition algorithms and principal component analysis are employed to eliminate redundant noise from the original data and capture their primary features. A stratified sampling method is utilized to divide the data into a training set and test set, avoiding biases caused by random sampling. A modified particle swarm optimization algorithm is applied to optimize the parameters of a back propagation neural network. The model’s predictive performance is assessed using various indicators, including R², MAPE, RMSE, MAE, U1, U2, RE, forecasting effectiveness, comparing the results with existing literature, Grey Relational Analysis, and interpretability research. The proposed prediction model is compared with eight advanced prediction models using data from a natural gas pipeline in western China. This study reveals that the developed model outperforms the others, demonstrating excellent prediction accuracy and effectively guiding the formulation of corrosion control measures. Full article

Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation. Full article

This study investigated the relative contributions of natural and anthropogenic factors to the nutrient status of 33 representative lakes and reservoirs in the Yangtze River Basin. Using national water quality monitoring data, remote sensing imagery, Geographic Information System, (GIS), Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and Redundancy Analysis (RDA), we analyzed the Spatiotemporal differences of total nitrogen (TN), total phosphorus (TP), the ratio of TN to TP (TN/TP), trophic level index (TLI), and habitat quality (HQ). Results revealed significant spatial heterogeneity in lake nutrient status, with upstream reservoirs exhibiting better water quality than their midstream and downstream counterparts. Over time, there is a decreasing trend in nutrient loads in lakes and reservoirs, yet the risk of eutrophication remains high. The middle and lower reaches of lakes and reservoirs face more severe eutrophication pressure. The contribution rates of natural factors and human activities to TN and TP in lakes and reservoirs are 19.1% and 35.0%, respectively. The main driving factors are livestock and poultry breeding volume, habitat quality, and urbanization, with contribution rates of 13.0%, 9.8%, and 0.2%, respectively. The contribution rates of natural factors and human activities to TN/TP and TLI of lakes and reservoirs are 19.8% and 15.5%, respectively. Actual Evapotranspiration (7.8%), habitat quality (7.3%), and hydraulic retention time (3.1%) were key drivers for the shifts of TN/TP and TLI. Management strategies should therefore control agricultural nitrogen fertilizer inputs upstream, industrial and agricultural non-point source pollution in the midstream, and enhanced wastewater treatment alongside population density and economic development control in the downstream areas. This research provides a crucial scientific basis for the ecological environment protection and sustainable utilization of water resources in the Yangtze River Basin. Full article

Background: Jeju Island is a world-renowned tourist destination. However, with the increasing intensity of anthropogenic activities, the coastal marine environment of Jeju Island has been negatively affected. Methods and Results: Our study used five benthic indices to assess the ecological quality of the subtidal zone in northern Jeju Island. Our analysis indicated that apart from the benthic polychaetes amphipods index (BPA), other benthic indices assessed the ecological quality of the subtidal zone in northern Jeju Island as high or good. Distance-based redundancy analysis (dbRDA) indicated that pH and salinity primarily influenced macrobenthic communities in 2011 and 2012. According to the Spearman rank correlation analysis, the multivariate AZTI marine biotic Index (M-AMBI) showed stronger correlations with environmental data than other benthic indices. Conclusions: Overall, the averages of the five benthic indices suggested that the benthic ecological quality of the subtidal zone in the northern part of Jeju Island was high or good. Although immediate marine environmental management is not required, long-term monitoring remains essential. M-AMBI outperformed other benthic indices; however, we recommend using multiple benthic indices to assess the benthic ecological quality of Jeju Island due to the complexity of marine ecosystems. Effective support has been provided by our study for the marine environmental conservation along the coast of Jeju Island. Full article

The rapid growth of Internet of Things (IoT) devices has brought about significant advancements in automation, data collection, and connectivity across various domains. However, this increased interconnectedness also poses substantial security challenges, making IoT networks attractive targets for malicious actors. Intrusion detection systems (IDSs) play a vital role in protecting IoT environments from cyber threats, necessitating the development of sophisticated and effective NIDS solutions. This paper proposes an IDS that addresses the curse of dimensionality by eliminating redundant and highly correlated features, followed by a wrapper-based feature ranking to determine their importance. Additionally, the IDS incorporates cutting-edge image processing techniques to reconstruct data into images, which are further enhanced through a filtering process. Finally, a meta classifier, consisting of three base models, is employed for efficient and accurate intrusion detection. Simulation results using industry-standard datasets demonstrate that the hybrid parameter selection approach significantly reduces computational costs while maintaining reliability. Furthermore, the combination of image transformation and ensemble learning techniques achieves higher detection accuracy, further enhancing the effectiveness of the proposed IDS. Full article

Numerous studies leverage pose estimation to extract human keypoint data and then classify sitting postures. However, employing neural networks for direct keypoint classification often yields suboptimal results. Alternatively, modeling keypoints into other data representations before classification introduces redundant information and substantially increases inference time. In addition, most existing methods perform well only under a single fixed viewpoint, limiting their applicability in complex real-world scenarios involving unseen viewpoints. To better address the first limitation, we propose KeypointNet, which employs a decoupled feature extraction strategy consisting of a Keypoint Feature Extraction module and a Multi-Scale Feature Extraction module. In addition, to enhance multi-view recognition capability, we propose the Multi-View Simulation (MVS) algorithm, which augments the viewpoint information by first rotating keypoints and then repositioning the camera. Simultaneously, we propose the multi-view sitting posture (MVSP) dataset, designed to simulate diverse real-world viewpoints. The experimental results demonstrate that KeypointNet outperforms the other state-of-the-art methods on both the proposed MVSP dataset and the other public datasets, while maintaining a lightweight and efficient design. Ablation studies demonstrate the effectiveness of MVS and all KeypointNet modules. Furthermore, additional experiments highlight the superior generalization, small-sample learning capability, and robustness to unseen viewpoints of KeypointNet. Full article

Background: Hospital autopsies, once considered fundamental to evidence-based medicine, have declined in many Western countries due to legislative changes, faith-based objections, and inadequate funding in public healthcare. However, the COVID-19 pandemic has revitalized interest in their importance for determining the cause of death and generating public health statistics. This study analyzes the factors contributing to this decline, with a focus on the Italian context and the new medicolegal perspectives arising in the post-pandemic era. Methods: The research was conducted through an analysis of 91 relevant scientific sources, including government documents, policy briefs, and academic studies. Legislative and regulatory factors influencing the reduction in hospital autopsies were examined, along with new technological and medicolegal perspectives that have emerged following the COVID-19 pandemic. Special attention was given to the Italian context and the pandemic’s impact on autopsy practices. Results: The analysis revealed that the perceived “low value” of autopsies often stems from technological advancements, which have led to the view of these procedures as redundant. However, in the post-COVID-19 era, there has been a renewed awareness of the medicolegal value of autopsies, with significant implications for public health statistics and the evolution of forensic medicine. Conclusions: The COVID-19 pandemic highlighted the vital role of autopsies in delivering accurate medical information, prompting renewed attention to these procedures in the post-pandemic era. Moving forward, hospital autopsies will need to balance technological innovations with traditional medicolegal practices, paving the way for new perspectives in forensic medicine and public health data management. Full article

With the rapid development of the semiconductor industry, the demand for high-speed testing in the large-scale production of semiconductor devices and integrated circuit production lines continues to grow. As one of the key tools in semiconductor device performance testing and integrated circuit testing, source measure unit (SMU) plays a crucial role in high-precision transient response testing scenarios. In high-precision measurement scenarios, multiple measurements are often required and averaged to improve measurement accuracy, but this can slow down the measurement speed. This article proposes a measurement acceleration algorithm based on BA-Informer time series prediction to solve the problem of decreased measurement speed in high-precision measurement. On the one hand, this algorithm improves the encoder structure. Traditional time series prediction models may have limitations in handling long-term dependencies and trend extraction. BiRNN is an extended version of recurrent neural network (RNN), which consists of two directional RNN. One forward RNN processes data from the beginning to the end of the sequence, while the other reverse RNN processes data from the end to the beginning of the sequence. In the end, the outputs from both directions are merged at each time step. Compared to traditional one-way RNN, BiRNN can more effectively handle data with before and after dependencies. Based on its characteristics, this article integrates BiRNN into the encoder structure. This algorithm can simultaneously process input sequences from both positive and negative directions, effectively limiting the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. In this paper, BiRNN is integrated into the encoder structure, and the algorithm can simultaneously process input sequences from both positive and negative directions, more effectively capturing the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. This improvement enables the model to more accurately grasp the overall trend of data changes during prediction, thereby improving prediction accuracy. On the other hand, an attention discrete cosine transform (ADCT) module is introduced between the encoder and decoder to convert time-domain signals into frequency-domain representations. This not only reveals the spectral characteristics of the signal but also reduces data redundancy and improves the efficiency of subsequent processing by combining attention mechanisms. Finally, the algorithm performance is analyzed by analyzing the output characteristic curves of loads with different properties. The experiment shows that the prediction algorithm and the combination of measurement and prediction method proposed in this article save half of the measurement time by combining measurement and prediction while ensuring the same amount of data obtained, verifying the effectiveness of the proposed method. Full article

The method based on convolution neural networks (CNNs) has been widely developed and applied to residual life prediction, and many excellent results have been achieved. However, CNN models can only learn feature information relative to size, and it is difficult to extract complex time series features from data of long time series. In addition, the existing models still have some problems, such as capturing the correlation of each time series and generating a large amount of redundant information. In order to alleviate the above problems, this study proposes a residual life prediction method of rolling bearings based on a multi-scale improved temporal convolutional network (MITCN) model. It is used to solve problems such as the low accuracy of bearing life prediction and the difficulty of the temporal convolutional network (TCN) model to capture the correlation of each time series. The model adopts the framework of a time convolution network and has good ability to extract time series information. By introducing a multi-scale expanded causal convolution residual structure, improved temporal convolutional network (ITCN) modules with different expansion factors capture information on different time scales and combine soft threshold functions and channel attention mechanisms to adaptively generate thresholds and eliminate redundant information. Finally, the carbon border adjustment mechanism (CBAM) is an attention mechanism used to enhance useful features and suppress useless features, so as to realize the effective fusion of multi-scale features. The IEEE PHM 2012 challenge data set is hereby used to verify the proposed method, which can effectively solve the problem of the low prediction accuracy of the remaining life of bearings. Full article

Space-based gravitational wave detection uses an equilateral triangular satellite constellation with inter-satellite laser heterodyne interferometry to measure displacement variations caused by gravitational waves. Inter-satellite laser communication is critical for data transmission, redundancy and clock synchronization, which suppresses clock noise and enhances detection sensitivity. This integrated approach ensures precise gravitational wave information extraction, supporting the high-accuracy requirements of space-based observatories. This study focuses on the modeling and simulation of inter-satellite laser communication for space-based gravitational wave detection. Based on the data-transmission requirements of such systems, the principles of inter-satellite laser communication are analyzed. The research includes the selection of pseudo-random noise (PRN) codes, the signal scheme design and the development of the mathematical models for signal transmission. A simulation model is subsequently constructed in Simulink to evaluate the system. The simulation results confirm the accuracy of the model’s functionalities, including spreading, phase modulation, noise addition, phase demodulation and despreading. Additionally, the model achieves a data-transmission rate of 62.5 kbps with a bit error rate (BER) better than ${10}^{-6}$ when the modulation index exceeds $3.4\times {10}^{-3}$, meeting the requirements for inter-satellite laser communication in space-based gravitational wave detection. Full article

In high-speed railway signaling systems, the network structure of the Train Control Center and Inter-locking Integrated System (TIS) is highly complex, with a large number of interfaces, numerous redundant channels, and forwarding components such as switches. These factors result in challenges such as insufficient accuracy, low efficiency, and poor real-time performance in terms of network monitoring and fault diagnosis. As the scale of railway yards continues to expand, these issues are becoming increasingly prominent. To address these challenges, this paper proposes a network fault propagation model based on the Fuzzy Broad Learning System (FBLS). By leveraging nonlinear transformations and feature mapping techniques, FBLS can efficiently extract and analyze network fault features, even with a relatively small amount of data. Experimental results show that the FBLS-based diagnostic model achieves higher accuracy and faster response speed in fault identification and propagation path analysis compared to traditional graph theory and fuzzy reasoning methods. Further comparisons with existing methods validate the advantages of FBLS in handling multi-source data, improving noise tolerance, and adapting to large-scale railway yard network systems, demonstrating its broad application prospects in railway signaling systems. Full article

Hyperspectral images are high-dimensional data that capture detailed spectral information across a wide range of wavelengths, enabling the precise identification and analysis of different materials or objects. However, the high dimensionality of the data also introduces information redundancy and increases the computational overhead, making it necessary to perform band selection to retain the most discriminative and informative bands for the target task. Traditional band selection methods, such as ranking-based, searching-based, and clustering-based approaches, often rely on handcrafted features and heuristic rules, which fail to fully exploit the latent information and complex spatial–spectral relationships in hyperspectral images. To address this issue, this paper proposes a two-stage unsupervised band selection method based on deep reinforcement learning. First, we performed noise estimation preprocessing to filter out bands with high noise levels to reduce the interference in the agent’s learning process. Then, the band selection problem was formulated as a Markov Decision Process (MDP), where the agent learned an optimal band selection strategy through interactions with the environment. In the design of the reward function, the Optimal Index Factor (OIF) was introduced as the evaluation metric to encourage the agent to select bands with high information content and low redundancy, and thereby improve the efficiency and quality of the selection process. Experimental results on three hyperspectral datasets demonstrated that the proposed method could effectively improve the performance of the hyperspectral image band selection. Full article