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With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists’ preferences and tourist attractions, and incorporate the attention mechanism to enhance the model’s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists’ transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists’ travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists’ interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model’s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model’s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development. [ABSTRACT FROM AUTHOR]

The promotion of electric vehicles brings notable environmental and economic advantages. Precisely estimating the state of health (SOH) of lithium-ion batteries is crucial for maintaining their efficiency and safety. This study introduces an SOH estimation approach for lithium-ion batteries that integrates multi-feature analysis with a convolutional neural network and kolmogorov-arnold network (CNN-KAN). Initially, we measure the charging time, current, and temperature during the constant voltage phase. These include charging duration, the integral of current over time, the chi-square value of current, and the integral of temperature over time, which are combined to create a comprehensive multi-feature set. The CNN's robust feature extraction is employed to identify crucial features from raw data, while KAN adeptly models the complex nonlinear interactions between these features and SOH, enabling accurate SOH estimation for lithium batteries. Experiments were carried out at four different charging current rates. The findings indicate that despite significant nonlinear declines in the SOH of lithium batteries, this method consistently provides accurate SOH estimations. The root mean square error (RMSE) is below 1%, with an average coefficient of determination (R 2 ) exceeding 98%. Compared to traditional methods, the proposed method demonstrates significant advantages in handling the nonlinear degradation trends in battery life prediction, enhancing the model's generalization ability as well as its reliability in practical applications. It holds significant promise for future research in SOH estimation of lithium batteries. [ABSTRACT FROM AUTHOR]

Accurate measurement is an important prerequisite for sound source localization. In the enclosed environments, noise and reverberation tend to cause localization errors. To address these issues, this paper proposes a compound model particle filter algorithm based on multi-feature. Based on a multi-feature observation, the likelihood function of speaker tracking is constructed for particle filter, and multi-hypothesis and frequency selection function are adopted to establish multi-feature optimization mechanism, including time delay selection and beam output energy fusion. It is found that they effectively solved the difficulty in the simultaneous suppression of noise and reverberation by single feature. Moreover, considering the randomness of speaker motion, a compound model for sound source tracking is developed, where the stability of the speaker tracking system is improved by integrating multi-feature observation into the compound model filtering. The experimental results with both simulated and real acoustic data indicate that the proposed method has better tracking performance, compared with the existing ones with low SNR and strong reverberation as well as highly mobile conditions. [ABSTRACT FROM AUTHOR]

Underwater bubble plume images contain a wealth of information on wave field and flow characteristics, which can provide valuable research data for marine development, environmental protection, and underwater surveys. However, based on fusing image features and wave field environment features, identifying accurately the underwater bubble plume is still very difficult. In order to improve the accuracy and robustness of bubble plume identification in complex underwater environments, an underwater bubble plume recognition algorithm based on multi-feature fusion understanding is proposed. In this paper, a weight-independent dual-channel residual convolutional neural network (CNN) for feature extraction of the original optical images and the nonsubsampled contourlet transform (NSCT) low-frequency images, and the multi-scale composite feature map groups are generated. Then adaptive fusion is performed based on the feature contribution of the target in different types of images. Next, logical region of interest (ROI) masks are generated by the attention mechanism and superimposed on the fused image to further highlight the target features. Finally, the multi-scale dual-channel fused feature maps containing ROI masks are used for underwater bubble plume target recognition. The experimental results show that the designed recognition network can effectively fuse the features of the original optical images and the NSCT low-frequency imagers, improve the depth of information fusion, and retain the target texture features and the morphological features while reducing the interference of the background information, and have good recognition accuracy and robustness for multi-scale bubble targets in the underwater environment. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively. [ABSTRACT FROM AUTHOR]

In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets. [ABSTRACT FROM AUTHOR]

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency–space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency–space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence–arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence–arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments. [ABSTRACT FROM AUTHOR]

In this work, a novel method for damage evolution analysis of concrete under uniaxial compression is proposed based on the multi-feature acoustic emission (AE) and the Gaussian mixture model (GMM) clustering. The hierarchical clustering algorithm is adopted to select optimal AE parameters, while multiple features of these parameters are generated through the principal component analysis (PCA). Then the concrete damage signals are separated by using the GMM clustering with multiple features. Meanwhile, the waveform signals associated with concrete damage in each cluster are validated using the Fast Fourier transform (FFT) and the continuous wavelet transform (CWT). Finally, the damage evolution process of concrete under uniaxial compression is divided based on the variation of AE characteristics. The results show that the frequency ranges of signals for micro-cracks, mixed cracks and friction of concrete under uniaxial compression are 20–30 kHz, 20–65 kHz, and 100–120 kHz, respectively. The damage process of concrete under uniaxial compression is divided into three stages according to the inflection points of the cumulative energy curve, namely the initial compaction stage, the crack formation stage and the failure stage. [ABSTRACT FROM AUTHOR]

The promotion of electric vehicles brings notable environmental and economic advantages. Precisely estimating the state of health (SOH) of lithium-ion batteries is crucial for maintaining their efficiency and safety. This study introduces an SOH estimation approach for lithium-ion batteries that integrates multi-feature analysis with a convolutional neural network and kolmogorov-arnold network (CNN-KAN). Initially, we measure the charging time, current, and temperature during the constant voltage phase. These include charging duration, the integral of current over time, the chi-square value of current, and the integral of temperature over time, which are combined to create a comprehensive multi-feature set. The CNN’s robust feature extraction is employed to identify crucial features from raw data, while KAN adeptly models the complex nonlinear interactions between these features and SOH, enabling accurate SOH estimation for lithium batteries. Experiments were carried out at four different charging current rates. The findings indicate that despite significant nonlinear declines in the SOH of lithium batteries, this method consistently provides accurate SOH estimations. The root mean square error (RMSE) is below 1%, with an average coefficient of determination (R2) exceeding 98%. Compared to traditional methods, the proposed method demonstrates significant advantages in handling the nonlinear degradation trends in battery life prediction, enhancing the model’s generalization ability as well as its reliability in practical applications. It holds significant promise for future research in SOH estimation of lithium batteries.

Ancient buildings have strict standards for vibration control. Effectively identifying vibration types and controlling construction vibrations during construction activities is advantageous to the structural safety of ancient buildings. This study is based on an analysis of vibration data from the top, foundation, and bedrock of the White Pagoda in Hangzhou, Zhejiang province, which is an ancient building. Considering the surrounding construction and wind environment, this study focuses on analyzing the data features of tower vibrations under three types of structural vibration. We propose a support vector machine (SVM) vibration identification method that incorporates multi-feature parameters and multi-sensor signal correlation. This method effectively identifies the source of structural vibration by distinguishing between typical wind-induced vibrations, typical construction vibrations, and typical mixed vibrations. The application of this method could guide construction activities and mitigate the safety impacts of construction and mixed vibrations on historical building structures. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance. [ABSTRACT FROM AUTHOR]

Dynamic ensemble selection has emerged as a promising approach for hyperspectral image classification. However, selecting relevant features and informative samples remains a pressing challenge. To address this issue, we introduce two novel dynamic residual ensemble learning methods. The first proposed method is called multi-features driven dynamic weighted residuals ensemble learning (MF-DWRL). This method leverages various combinations of features to construct classifier pools that incorporate feature differences. The K-Nearest Neighbors algorithm is employed to establish the region of competence (RoC) in the dynamic ensemble selection process. By assessing the performance of the RoC, the feature sets that yield the highest classification accuracy are identified as the optimal feature combinations. Additionally, the classification accuracy is utilized as prior information to guide the residual adjustments of each classifier. The second method, known as features and samples double-driven dynamic weighted residual ensemble learning (FS-DWRL), further enhances the performance of the ensemble. This approach not only considers the selection of feature combinations but also takes into account the informative samples. By jointly optimizing the feature and sample selection processes, FS-DWRL achieves superior classification accuracy compared to existing state-of-the-art methods. To evaluate the effectiveness of the proposed methods, three hyperspectral datasets from China—WHU-Hi-HanChuan, WHU-Hi-LongKou, and WHU-Hi-HongHu—are used for classification experiments. For these datasets, the proposed methods achieve the highest classification accuracies of 90.57 %, 98.77 %, and 91.08 %, respectively. The MF-DWRL and FS-DWRL methods exhibit significant improvements in classification accuracy.

Objective: To address the challenges arising from the rapid growth of text data in the biomedical field, including the problems of irrelevant argument interference and deep semantic association neglect in existing event argument detection methods, as well as the difficulty of multiple event extraction. We aim to propose a new event argument detection method that can accurately mine valuable information from biomedical texts through multi-feature fusion and the question-and-answer paradigm, while also addressing the limitations of existing methods.Methods: We propose an event argument detection method based on multi-feature fusion and the question-answer paradigm. First, we split each event in the sentence into an independent question-and-answer format to reduce the complexity of detection. Then, in order to reduce the interference of irrelevant arguments, we use syntactic distance and external prior knowledge to find the corresponding argument prior template for each event type. Next, we introduce the multi-feature attention mechanism to fully explore the deep semantic features. Finally, we formulate post-processing methods for predefined event structures to form final biomedical events.Results: On the MLEE dataset, our model achieved 62.50% in event extraction of F1 scores, which is superior to other advanced event extraction methods.Conclusion: Our method achieves good performance in the event extraction task and provides strong support for the mining of valuable information in biomedical texts.

The detection and traceability research of massive log information has always been a focal point in Advanced Persistent Threats (APT) studies. Causality relationship technology analyzes attack paths and simplifies graph scale by mapping attack behaviors, but rule-based detection methods often introduce numerous false alarms. To address this issue, this paper proposes a multi-feature provenance graph for the Accurate Alert System (FPGAA). It constructs trace graphs by selecting feature graphs based on the attack types, effectively improving the compression ratio of trace graphs. The FPGAA system utilizes graph neural network technology to efficiently filter out erroneous attack paths generated by false alarm information. We evaluated the system‘s performance using widely recognized classic datasets, including the DARPA TC dataset and the ATLAS dataset, as well as a self-constructed dataset to ensure comprehensiveness and reliability. The results demonstrate that the FPGAA system shows significant improvements in trace graph optimization compared to similar systems, successfully reducing the provenance graph size by over 300 times. Additionally, the FPGAA system effectively overcomes the interference caused by false alarm alerts, successfully filtering out approximately 94% of false alarms. In summary, our system can accurately identify a variety of attack paths, expedite event investigation efficiency through concise contextual alarms, and enhance identification accuracy by increasing features.

Discovering potential drug-target interactions is crucial for advancing pharmacology. In recent years, the development of large-scale DTI datasets has propelled advancements in DTI prediction computational methods. Various deep learning approaches for interaction prediction often rely on sequence data or structural complexity, yet the synergistic integration of diverse bioinformatics and binding site data remains underexploited, constraining prediction precision. Therefore, a novel approach to integrate available data is required to enhance DTI prediction performance. In this paper, we present a novel aggregation prediction model named MDiDTI, designed to facilitate multi-attribute dual interaction learning. The multi-head self-attention interaction network extracts substructure information of drug molecules and pocket information of targets from biomedical data, enabling spatial-level learning of structural attributes. Meanwhile, the dual-weight mapping network aggregates the chemical semantic features of drug-target pairs, facilitating semantic attribute learning at the sequence level. Lastly, the model combines structural and semantic attributes to compute the interaction values for DTI tasks. Performance evaluation metrics were conducted on three mainstream datasets: BioSNAP, BindingDB, and Human. Experimental results indicate that MDiDTI outperforms existing methods and serves as a reliable and highly generalizable tool for DTI prediction.

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R2 of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed.

In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency–space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency–space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence–arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence–arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.

In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets.

User portrait has become a research hot spot in the field of knowledge graph in recent years and the rationality of tag extraction directly affects the quality of user portrait. However, most of the current tag extraction methods for portraits only consider the methods based on word frequency statistics and semantic clustering. These methods have some drawbacks: they cannot effectively discover the preferred themes of the enterprise, dynamically update the portrait tags, and adapt to the needs of the enterprise. In this paper, we propose an enterprise adaptive tag extraction method based on multi-feature dynamic portrait (ATEMDP). ATEMDP first uses K-means to measure the similarity between enterprise texts in preference division, and converts similar enterprise text clustering problems into tag feature clusters to obtain the point cluster structure containing the distribution of tag preference topics. In addition, in the multi-feature selection, the professional domain thesaurus is introduced for feature expansion, and the topic text is introduced into the Bert model as a sample set to discover the potential features of the enterprise text. In the end, in dynamic tag extraction, BiLSTM and CNN are used to extract features, and dynamic preference tags are obtained by updating enterprise text. THUCNews data set and Ente-pku data set are used for simulation, and seven other methods are considered in comparison. The experimental results indicate that ATEMDP is not only superior to other conventional methods in accuracy and F1-score, but also effectively solves the dynamic tagging problem of enterprise portrait. [ABSTRACT FROM AUTHOR]

Abstract User portrait has become a research hot spot in the field of knowledge graph in recent years and the rationality of tag extraction directly affects the quality of user portrait. However, most of the current tag extraction methods for portraits only consider the methods based on word frequency statistics and semantic clustering. These methods have some drawbacks: they cannot effectively discover the preferred themes of the enterprise, dynamically update the portrait tags, and adapt to the needs of the enterprise. In this paper, we propose an enterprise adaptive tag extraction method based on multi-feature dynamic portrait (ATEMDP). ATEMDP first uses K-means to measure the similarity between enterprise texts in preference division, and converts similar enterprise text clustering problems into tag feature clusters to obtain the point cluster structure containing the distribution of tag preference topics. In addition, in the multi-feature selection, the professional domain thesaurus is introduced for feature expansion, and the topic text is introduced into the Bert model as a sample set to discover the potential features of the enterprise text. In the end, in dynamic tag extraction, BiLSTM and CNN are used to extract features, and dynamic preference tags are obtained by updating enterprise text. THUCNews data set and Ente-pku data set are used for simulation, and seven other methods are considered in comparison. The experimental results indicate that ATEMDP is not only superior to other conventional methods in accuracy and F1-score, but also effectively solves the dynamic tagging problem of enterprise portrait.

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method. [ABSTRACT FROM AUTHOR]

Unpaired image-to-image translation for the generation field has made much progress recently. However, these methods suffer from mode collapse because of the overfitting of the discriminator. To this end, we propose a straightforward method to construct a contrastive loss using the feature information of the discriminator output layer, which is named multi-feature contrastive learning (MCL). Our proposed method enhances the performance of the discriminator and solves the problem of model collapse by further leveraging contrastive learning. We perform extensive experiments on several open challenge datasets. Our method achieves state-of-the-art results compared with current methods. Finally, a series of ablation studies proved that our approach has better stability. In addition, our proposed method is also practical for single image translation tasks. Code is available at https://github.com/gouayao/MCL. [ABSTRACT FROM AUTHOR]

Safety issues are one of the main limitations for further application of lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods are mostly developed for a single feature and a single operating condition as well as a single battery material system, which consequently makes it difficult to guarantee robustness and generalization. This paper proposes a data-driven and multi-feature collaborative SOH estimation method based on equal voltage interval discharge time, incremental capacity (IC) and differential thermal voltammetry (DTV) analysis for feature extraction. The deep learning model is constructed based on bi-directional long short-term memory (Bi-LSTM) with the addition of attention mechanism (AM) to focus on the important parts of the features. The proposed method is validated based on a NASA dataset and Oxford University dataset, and the results show that the proposed method has high accuracy and strong robustness. The estimated root mean squared error (RMSE) are below 0.7% and 0.3%, respectively. Compared to single features, the collaboration between multiple features and AM resulted in a 25% error improvement, and the capacity rebound is well captured. The proposed method has the potential to be applied online in an end-cloud collaboration system. [ABSTRACT FROM AUTHOR]

Dynamic networks integrates time attributes on the basis of static networks, and it contains multiple connotations such as the complexity and dynamics of the network structure.It is a better thinking object for studying complex network link prediction problems in the real world.Its high application value has attracted much attention in recent years.However, most of the research objects of traditional methods are still limited to static networks, and there are problems such as insufficient utilization of network time-domain evolution information and high time complexity.Combining sociological theory, a novel temporal link prediction method was proposed based on community multi-feature fusion embedding representation.The core idea of this method was to analyze the dynamic evolution characteristics of the network, learn the embedded representation vector of nodes within the community, and effectively fuse multiple features to measure the generation probability of the connection between nodes.The network was divided into several subgraphs by using community detection with collective influence weights and the Similarity index was proposed based on the collective influence.Then, the biased random walk and the Skip-gram were used to get the embedded vectors for every node and the Similarity index was proposed based on the random walk within the community.Integrating the collective influence, multiple central features of the community, and the representation vector learned within the community, the Similarity index was proposed based on the multi-features fusion.Compared with classical temporal link prediction methods, including moving average methods, embedded representation methods, and graph neural network methods, experimental results on six real data sets show that the proposed methods based on the random walk within the community and the multi-features fusion both achieve better prediction performance under the evaluation criteria of AUC.

Abstract Unpaired image-to-image translation for the generation field has made much progress recently. However, these methods suffer from mode collapse because of the overfitting of the discriminator. To this end, we propose a straightforward method to construct a contrastive loss using the feature information of the discriminator output layer, which is named multi-feature contrastive learning (MCL). Our proposed method enhances the performance of the discriminator and solves the problem of model collapse by further leveraging contrastive learning. We perform extensive experiments on several open challenge datasets. Our method achieves state-of-the-art results compared with current methods. Finally, a series of ablation studies proved that our approach has better stability. In addition, our proposed method is also practical for single image translation tasks. Code is available at https://github.com/gouayao/MCL.

In view of the problem of insufficient feature utilization of low-resolution image in super-resolution reconstruction, a multi-feature gated feedback residual network is proposed based on feedback mechanism and attention mechanism. The network has a simple structure and realizes the reuse of network parameters with a circular way, which can save compute resource effectively. The output features of network iteration are retained to achieve multi-feature fusion. In addition, a further feature refine block is used to extract the reconstructed high-resolution image features to obtain better reconstruction result. Experimental results on five test data sets show that when the scale factor is 4, the peak signal-to-noise ratio of the proposed network is 32.50 dB, 28.83 dB, 27.75 dB, 26.65 dB and 31.12 dB, respectively. Compared with the comparison networks, the test results of the proposed algorithm are significantly improved. [ABSTRACT FROM AUTHOR]

Dynamic networks integrates time attributes on the basis of static networks, and it contains multiple connotations such as the complexity and dynamics of the network structure. It is a better thinking object for studying complex network link prediction problems in the real world. Its high application value has attracted much attention in recent years. However, most of the research objects of traditional methods are still limited to static networks, and there are problems such as insufficient utilization of network time-domain evolution information and high time complexity. Combining sociological theory, a novel temporal link prediction method was proposed based on community multi-feature fusion embedding representation. The core idea of this method was to analyze the dynamic evolution characteristics of the network, learn the embedded representation vector of nodes within the community, and effectively fuse multiple features to measure the generation probability of the connection between nodes. The network was divided into several subgraphs by using community detection with collective influence weights and the Similarity index was proposed based on the collective influence. Then, the biased random walk and the Skip-gram were used to get the embedded vectors for every node and the Similarity index was proposed based on the random walk within the community. Integrating the collective influence, multiple central features of the community, and the representation vector learned within the community, the Similarity index was proposed based on the multi-features fusion. Compared with classical temporal link prediction methods, including moving average methods, embedded representation methods, and graph neural network methods, experimental results on six real data sets show that the proposed methods based on the random walk within the community and the multi-features fusion both achieve better prediction performance under the evaluation criteria of AUC. [ABSTRACT FROM AUTHOR]

Webshell is the most common malicious backdoor program for persistent control of Web application systems, which poses a huge threat to the safe operation of Web servers.For most Webshell detection method based on the request packet data for training, the method for web-based Webshell recognition effect is poorer, and the model of training efficiency is low.In response to the above problems, a Webshell malicious traffic detection method based on multi-feature fusion was proposed.The method was characterized by the three dimensions of Webshell packet meta information, packet payload content and traffic access behavior.Combining domain knowledge, feature extraction of request and response packets in the data stream.Transformed into feature extraction information for information fusion, forming a discriminant model that could detect different types of attacks.Compared with the previous research method, the accuracy rate of the method here in the two classification of normal and malicious traffic has been improved to 99.25%.The training efficiency and detection efficiency have also been significantly improved, and the training time and detection time have been reduced by 95.73% and 86.14%.

Webshell is the most common malicious backdoor program for persistent control of Web application systems, which poses a huge threat to the safe operation of Web servers. For most Webshell detection method based on the request packet data for training, the method for web-based Webshell recognition effect is poorer, and the model of training efficiency is low. In response to the above problems, a Webshell malicious traffic detection method based on multi-feature fusion was proposed. The method was characterized by the three dimensions of Webshell packet meta information, packet payload content and traffic access behavior. Combining domain knowledge, feature extraction of request and response packets in the data stream. Transformed into feature extraction information for information fusion, forming a discriminant model that could detect different types of attacks. Compared with the previous research method, the accuracy rate of the method here in the two classification of normal and malicious traffic has been improved to 99.25%. The training efficiency and detection efficiency have also been significantly improved, and the training time and detection time have been reduced by 95.73% and 86.14%.

Doppler-radar-based continuous human motion recognition recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of continuous human motion recognition (CHMR) using mmWave FMCW Radar are not considered the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS) problem, called as CHMR-HS, by using the modified Transformer-based learning model. As far as we know, this is the first result in the literature to investigate the continuous HMR with the high similarity. To obtain the clear FMCW radar images, the background and target signals of the detected human are separated through the background denoising and the target extraction algorithms. To investigate the effects of the spectral-temporal multi-features with different dimensions, Doppler, range, and angle signatures are extracted as the 2D features and range-Doppler-time and range-angle-time signatures are extracted as the 3D features. The 2D/3D features are trained into the adjusted Transformer-encoder model to distinguish the difference of the high-similarity human motions. The conventional Transformer-decoder model is also re-designed to be Transformer-sequential-decoder model such that Transformer-sequential-decoder model can successfully recognize the continuous human motions with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme are 95.2% and 94.5% if using 3D and 2D features, the simulation results also illustrates that our CHMR-HS scheme has advantages over existing CHMR schemes. [ABSTRACT FROM AUTHOR]

Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames. Moreover, exploiting all frames in a video for the CSLR task could lead to suboptimal performance since each frame contains a different level of information, including main features in the inferencing of noise. Therefore, we propose novel spatio-temporal continuous sign language recognition using the attentive multi-feature network to enhance CSLR by providing extra keypoint features. In addition, we exploit the attention layer in the spatial and temporal modules to simultaneously emphasize multiple important features. Experimental results from both CSLR datasets demonstrate that the proposed method achieves superior performance in comparison with current state-of-the-art methods by 0.76 and 20.56 for the WER score on CSL and PHOENIX datasets, respectively. [ABSTRACT FROM AUTHOR]

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.

Safety issues are one of the main limitations for further application of lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods are mostly developed for a single feature and a single operating condition as well as a single battery material system, which consequently makes it difficult to guarantee robustness and generalization. This paper proposes a data-driven and multi-feature collaborative SOH estimation method based on equal voltage interval discharge time, incremental capacity (IC) and differential thermal voltammetry (DTV) analysis for feature extraction. The deep learning model is constructed based on bi-directional long short-term memory (Bi-LSTM) with the addition of attention mechanism (AM) to focus on the important parts of the features. The proposed method is validated based on a NASA dataset and Oxford University dataset, and the results show that the proposed method has high accuracy and strong robustness. The estimated root mean squared error (RMSE) are below 0.7% and 0.3%, respectively. Compared to single features, the collaboration between multiple features and AM resulted in a 25% error improvement, and the capacity rebound is well captured. The proposed method has the potential to be applied online in an end-cloud collaboration system.

Quantifying the information content of remote sensing images is considered to be a fundamental task in quantitative remote sensing. Traditionally, the grayscale entropy designed by Shannon's information theory cannot capture the spatial structure of images, which has prompted successive proposals of a series of neighborhood-based improvement schemes. However, grayscale or neighborhood-based spatial structure is only a basic feature of the image, and the spatial structure should be divided into the overall structure and the local structure and separately characterized. For this purpose, a multi-feature quantification framework for image information content is proposed. Firstly, the information content of optical remote sensing images is measured based on grayscale, contrast, neighborhood-based topology, and spatial distribution features instead of simple grayscale or spatial structure. Secondly, the entropy metrics of the different features are designed to quantify the uncertainty of images in terms of both pixel and spatial structure. Finally, a weighted model is used to calculate the comprehensive information content of the image. The experimental results confirm that the proposed method can effectively measure the multi-feature information content, including the overall and local spatial structure. Compared with state-of-the-art entropy models, our approach is the first study to systematically consider the multiple features of image information content based on Shannon entropy. It is comparable to existing models in terms of thermodynamic consistency. This work demonstrates the effectiveness of information theory methods in measuring the information content of optical remote sensing images. [ABSTRACT FROM AUTHOR]

Tree species classification is crucial for forest resource investigation and management. Remote sensing images can provide monitoring information on the spatial distribution of tree species and multi-feature fusion can improve the classification accuracy of tree species. However, different features will play their own unique role. Therefore, considering various related factors about the growth of tree species such as spectrum information, texture structure, vegetation phenology, and topography environment, we fused multi-feature and multi-temporal Sentinel-2 data, which combines spectral features with three other types of features. We combined different feature-combinations with the random forest method to classify Changbai Mountain tree species. Results indicate that topographic features participate in tree species classification with higher accuracy and more efficiency than phenological features and texture features, and the elevation factor possesses the highest importance through the Mean Decrease in Gini (MDG) method. Finally, we estimated the area of the target tree species and analyzed the spatial distribution characteristics by overlay analysis of the Classification 3 result and topographic features (elevation, slope, and aspect). Our findings emphasize that topographic factors have a great influence on the distribution of forest resources and provide the basis for forest resource investigation. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) is a crucial topic in human–computer interaction. However, there are still many challenges to extracting emotional embeddings. Emotional embeddings extracted by network models often contain noise and incomplete emotional information. To meet these challenges, this study developed an innovative model (MVIB-DVA) composed of a multi-feature variational information bottleneck (MVIB) based on the information bottleneck (IB) principle and a dual-view aware module (DVAM) with an attention mechanism. MVIB employs the IB principle as the driving model and utilizes learned minimal sufficient single-feature emotional embeddings as auxiliary information. The aims are to capture unique emotional information in individual features and complementary information between different types of features as well as reduce noise and represent rich emotional information with fewer parameters. DVAM proposes (1) a frequency-domain statistical aware module (FDSAM) in the frequency domain that emphasizes the frequency that best reflects emotional information and (2) a frame aware module (FAM) in the time domain that focuses on the frames that contribute to the most to the final emotion recognition results. A separate channel extracts details ignored in the frequency and time domain views, extracting more comprehensive emotional information. The experimental results show that our method performs excellently in recognizing speech emotions. MVIB-DVA achieved weighted accuracy (WA) of 74.05% and unweighted accuracy (UA) of 75.67% on the IEMOCAP dataset. Similarly, on the RAVDESS dataset, MVIB-DVA attained WA of 86.66% and UA of 86.51%. [ABSTRACT FROM AUTHOR]

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid. [ABSTRACT FROM AUTHOR]

Named entity recognition of forest diseases plays a key role in knowledge extraction in the field of forestry. The aim of this paper is to propose a named entity recognition method based on multi-feature embedding, a transformer encoder, a bi-gated recurrent unit (BiGRU), and conditional random fields (CRF). According to the characteristics of the forest disease corpus, several features are introduced here to improve the method's accuracy. In this paper, we analyze the characteristics of forest disease texts; carry out pre-processing, labeling, and extraction of multiple features; and construct forest disease texts. In the input representation layer, the method integrates multi-features, such as characters, radicals, word boundaries, and parts of speech. Then, implicit features (e.g., sentence context features) are captured through the transformer's encoding layer. The obtained features are transmitted to the BiGRU layer for further deep feature extraction. Finally, the CRF model is used to learn constraints and output the optimal annotation of disease names, damage sites, and drug entities in the forest disease texts. The experimental results on the self-built data set of forest disease texts show that the precision of the proposed method for entity recognition reached more than 93%, indicating that it can effectively solve the task of named entity recognition in forest disease texts. [ABSTRACT FROM AUTHOR]

This paper focuses on how to represent the testing face images for multi-feature face recognition. The choice of feature is critical for face recognition. The different features of the sample contribute differently to face recognition. The joint similar and specific learning (JSSL) has been effectively applied in multi-feature face recognition. In the JSSL, although the representation coefficient is divided into the similar coefficient and the specific coefficient, there is the disadvantage that the training images cannot represent the testing images well, because there are probable expressions, illuminations and disguises in the testing images. We think that the intra-class variations of one person can be linearly represented by those of other people. In order to solve well the disadvantage of JSSL, in the paper, we extend JSSL and propose the extended joint similar and specific learning (EJSSL) for multi-feature face recognition. EJSSL constructs the intra-class variant dictionary to represent the probable variation between the training images and the testing images. EJSSL uses the training images and the intra-class variant dictionary to effectively represent the testing images. The proposed EJSSL method is perfectly experimented on some available face databases, and its performance is superior to many current face recognition methods.

To investigate an effective representation model for 3D facial expression recognition, this paper proposes a multi-feature tensor neighborhood preserving embedding (MFTNPE) method, which seeks various attribute features from raw textured shape scan models of 3D facial expression to construct a multi-feature tensor model. With MFTNPE method, we can project the model into a tensor subspace, explore the interactions and correlations among multimodal data and then extract discriminative low-dimensional tensor features for 3D facial expression recognition. In the study, we first introduce a tensor model based on multi-feature integration for 3D facial expression recognition. Via exploring the textural and geometric features of 3D facial expression data, an original multi-feature tensor model is constructed by stacking various attribute maps orderly. Second, we utilize the tensor completion approach as the scheme of data preprocessing to correct the original multi-feature tensor model. Due to the presence of imperfect data in the projected attribute maps, the tensor completion can predict the missing data in tensor by the trace norm of multi-feature tensor model along each mode. Third, we redefine a weighted matrix for use in the orthogonal-based tensor neighborhood preserving embedding algorithm. In the weighted matrix, the class information is used to guarantee the correct neighborhood relationships for multi-feature tensor samples. This effectively avoids the disturbances and confusions between similar samples while preserving spatial structure and inter-class information. Finally, we conduct extensive experiments on two 3D facial expression recognition datasets. The experimental results show that the proposed method has much better efficiency and performance compared with the state-of-the-art methods.

Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition rate of handwritten formula symbols. The system proposed in this paper integrates the eight-directional feature of the original sequence in the convolutional layer, which significantly compensates for the lost dynamic trajectory information in the handwritten formula symbol. Meanwhile, the joint loss is constructed to improve the discriminability of features in the way of supervised learning, which enlarges the inter-class difference and decreases inner-class similarity. The standard mathematical formula symbol library provided by the Competition Organization on Recognition of Online Handwritten Mathematical Expression (CROHME) is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed SE-MCNN approach outperforms the state-of-the-art methods even at the condition of without using the data augmentation.

As the Software Define Network (SDN) adopts centralized control logic, it is vulnerable to various types of Distributed Denial of Service (DDoS) attacks. At present, almost all the research work focuses on high-rate DDoS attack against the SDN control layer. Moreover, most of the existing detection methods are effective for high-rate DDoS attack detection of the control layer, while a low-rate DDoS attack against the SDN data layer is highly concealed, and the detection accuracy against this kind of attack is low. In order to improve the detection accuracy of the low-rate DDoS attack against the SDN data layer, this paper studies the mechanism of such attacks, and then proposes a multi-feature DDoS attack detection method based on Factorization Machine (FM). The features extracted from the flow rules are used to detect low-rate DDoS attacks, and the detection of low-rate DDoS attacks based on FM machine learning algorithms is implemented. The experimental results show that the method can effectively detect the low-rate DDoS attack against the SDN data layer, and the detection accuracy reaches 95.80 percent. Because FM algorithm can achieve fine-grained detection for low-rate DDoS attack, which provides a reliable condition for defending against such attacks. Finally, this paper proposes a defense method based on dynamic deletion of flow rules, and carries out experimental simulation and analysis to prove the effectiveness of the defense method, and the success rate of forwarding normal packets reached 97.85 percent.

In the last 70 years, the automatic text summarization work has become more and more important because the amount of data on the Internet is increasing so fast, and automatic text summarization work can extract useful information and knowledge what user's need that could be easily handled by humans and used for many purposes. Especially in people's daily life, news text is the type of text most people are exposed to. In this study, a new automatic summarzation model for news text which based on fuzzy logic rules, multi-feature and Genetic algorithm (GA) is introduced. Firstly, the most important feature is word features, we score each word and extracted words that exceeded the preset score as keywords and because news text is a special kind of text, it contains many specific elements, such as time, place and characters, so sometimes these special news elements can be extracted directly as keywords. Second is sentence features, a linear combination of these features shows the importance of each sentence and each feature is weighted by Genetic algorithm. At last, we use fuzzy logic system to calculate the final score in order to get automatic summarization. The results of the proposed method was compared with other methods including Msword, System19, System21, System 31, SDS-NNGA, GCD, SOM and Ranking SVM by using ROUGE assessment method on DUC2002 dataset show that proposed method outperforms the aforementioned methods.

We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

Abstract Background Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Methods A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. Results Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. Conclusions The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

Dynamic ensemble selection has emerged as a promising approach for hyperspectral image classification. However, selecting relevant features and informative samples remains a pressing challenge. To address this issue, we introduce two novel dynamic residual ensemble learning methods. The first proposed method is called multi-features driven dynamic weighted residuals ensemble learning (MF-DWRL). This method leverages various combinations of features to construct classifier pools that incorporate feature differences. The K-Nearest Neighbors algorithm is employed to establish the region of competence (RoC) in the dynamic ensemble selection process. By assessing the performance of the RoC, the feature sets that yield the highest classification accuracy are identified as the optimal feature combinations. Additionally, the classification accuracy is utilized as prior information to guide the residual adjustments of each classifier. The second method, known as features and samples double-driven dynamic weighted residual ensemble learning (FS-DWRL), further enhances the performance of the ensemble. This approach not only considers the selection of feature combinations but also takes into account the informative samples. By jointly optimizing the feature and sample selection processes, FS-DWRL achieves superior classification accuracy compared to existing state-of-the-art methods. To evaluate the effectiveness of the proposed methods, three hyperspectral datasets from China-WHU-Hi-HanChuan, WHU-Hi-LongKou, and WHU-Hi-HongHu-are used for classification experiments. For these datasets, the proposed methods achieve the highest classification accuracies of 90.57 %, 98.77 %, and 91.08 %, respectively. The MF-DWRL and FS-DWRL methods exhibit significant improvements in classification accuracy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objective: To address the challenges arising from the rapid growth of text data in the biomedical field, including the problems of irrelevant argument interference and deep semantic association neglect in existing event argument detection methods, as well as the difficulty of multiple event extraction. We aim to propose a new event argument detection method that can accurately mine valuable information from biomedical texts through multi-feature fusion and the question-and-answer paradigm, while also addressing the limitations of existing methods., Methods: We propose an event argument detection method based on multi-feature fusion and the question-answer paradigm. First, we split each event in the sentence into an independent question-and-answer format to reduce the complexity of detection. Then, in order to reduce the interference of irrelevant arguments, we use syntactic distance and external prior knowledge to find the corresponding argument prior template for each event type. Next, we introduce the multi-feature attention mechanism to fully explore the deep semantic features. Finally, we formulate post-processing methods for predefined event structures to form final biomedical events., Results: On the MLEE dataset, our model achieved 62.50% in event extraction of F1 scores, which is superior to other advanced event extraction methods., Conclusion: Our method achieves good performance in the event extraction task and provides strong support for the mining of valuable information in biomedical texts., Competing Interests: The authors declare that there are no conflict of interests, we do not have any possible conflicts of interest., (© 2024 The Authors.)

Doppler-radar-based continuous human motion recognition recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of continuous human motion recognition (CHMR) using mmWave FMCW Radar are not considered the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS) problem, called as CHMR-HS, by using the modified Transformer-based learning model. As far as we know, this is the first result in the literature to investigate the continuous HMR with the high similarity. To obtain the clear FMCW radar images, the background and target signals of the detected human are separated through the background denoising and the target extraction algorithms. To investigate the effects of the spectral-temporal multi-features with different dimensions, Doppler, range, and angle signatures are extracted as the 2D features and range-Doppler-time and range-angle-time signatures are extracted as the 3D features. The 2D/3D features are trained into the adjusted Transformer-encoder model to distinguish the difference of the high-similarity human motions. The conventional Transformer-decoder model is also re-designed to be Transformer-sequential-decoder model such that Transformer-sequential-decoder model can successfully recognize the continuous human motions with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme are 95.2% and 94.5% if using 3D and 2D features, the simulation results also illustrates that our CHMR-HS scheme has advantages over existing CHMR schemes.