Multi-dimensional Feature Fusion Research Articles

Dynamic liquid level prediction for multiple oil wells based on transfer learning and multidimensional feature fusion network

Abstract Accurate prediction of the dynamic liquid level (DLL) in oil wells is crucial for the intelligent optimization of pumping systems. It not only provides real-time insights into the operational conditions of the pumping system but also supports the optimization of operational parameters with data. However, due to the long-term operation of oil wells and their complex internal environments, direct measurement of the DLL is challenging, leading to low reliability of the obtained data. Therefore, this paper conducts an in-depth analysis of the parameters involved in the pumping process, identifies the model's input features, and develops a DLL prediction model for multiple wells based on multidimensional feature fusion (MFF). This model captures the characteristics of DLL changes and the diversity of input features. To address the issues of slow model training and low prediction accuracy caused by insufficient datasets in practical applications, this paper integrates transfer learning techniques and proposes a new model, the DLL model for multiple wells based on Transfer Learning and Multidimensional Feature Fusion (TMFF). Initially, the Euclidean distance and Maximum mean discrepancy methods are employed to verify the feature similarity between the source and target domains, using highly similar DLL data as experimental data. By combining transfer learning techniques with the MFF model, the TMFF model is established. The model's capabilities are validated using field-collected data with broad representativeness. Experimental results demonstrate that the proposed MFF model possesses high accuracy and generalization capability. Additionally, the TMFF model effectively resolves the issue of insufficient data during model training. In summary, the methods proposed in this paper can provide accurate DLL data for practical applications in intelligent oilfields.

Cavitation state recognition method of centrifugal pump based on multi-dimensional feature fusion and convolutional gate recurrent unit

The rapid and accurate recognition of cavitation in centrifugal pumps has become essential for improving production efficiency and ensuring machinery longevity. To address the limitations of existing methods in terms of applicability, accuracy, and efficiency, a new method based on multi-dimensional feature fusion and convolutional gate recurrent unit (MCGN) was proposed. Experimental monitoring of cavitation of centrifugal pumps was conducted. Five signals at different water temperatures and operating frequencies were collected. Key modulating features were extracted by time-frequency analysis and principal component analysis. The multi-dimensional features are fused by one and two dimensional convolutional neural networks. The cavitation state label was used to label the sample set by cavitation number, net positive suction head, and cavitation evolution images captured by high-speed cameras. Finally, the neural network based on the convolutional gate recurrent unit was used to classify the cavitation state of the centrifugal pump. The experimental results demonstrate that the proposed method achieves recognition accuracies exceeding 98% for vibration signals, noise signals, outlet pressure pulsation signals, and torque signals. Compared with the short-time Fourier transform-autoencoder model, MCGN model can improve the recognition accuracy by 4.03%, computation efficiency by 20%, and loss by 87%. These advances underscore the potential of the method in monitoring and maintenance practices for centrifugal pumps.

Fine-grained sentiment analysis using multidimensional feature fusion and GCN

ABSTRACT The demand for analysing sentiment information in social media data is increasing. However, current fine-grained sentiment analysis methods fail to consider both global and local semantic features simultaneously, leading to the oversight of grammatical information in sentences and an inability to address the issue of polysemy. To address these challenges, we propose a microblog fine-grained sentiment analysis model based on multidimensional feature fusion and graph convolutional neural networks (GCN). Built upon the ALBERT model, we utilize BiLSTM and capsule network models to extract global and local semantic features, thereby capturing bidirectional semantic dependencies and textual positional semantic information. Finally, we employ multi-head self-attention and GCN to select key features and sentence information, ensuring the integrity of fine-grained features. The experimental results indicate that the model outperforms several other models on fine-grained sentiment analysis datasets SMP2020-EWECT, NLPCC2013, NLPCC2014, and the binary classification dataset weibo_senti_100k, achieving accuracies of 80.64%, 67.19%, 71.37%, and 98.43%, respectively.

Realizing Small UAV Targets Recognition via Multi-Dimensional Feature Fusion of High-Resolution Radar

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets.

Dynamic gesture recognition model based on FMCW radar and multidimensional feature fusion with dual-channel CNN and GRU

With the development of computers, gestures have also become a commonly used human-computer interaction, so it becomes important to recognize gestures correctly. The objective of this paper is to propose a deep learning model to improve the accuracy of low performance radar in dynamic gesture recognition.We use a dataset acquired by CAL60S244-AB radar to classify ten gestures. The model consists of a dual-channel convolutional neural network(CNN), Gate Recurrent Unit(GRU) and a classification module. The inputs to the model are feature vectors fused in four dimensions of distance, speed, receiving antenna channel and time, from which the dual-channel CNN can extract shallow and deep spatial information, and GRU can extract temporal information, and the combination of these two parts makes the model's classification accuracy improve. Through experiments, the accuracy of the model on the dataset reaches 95.1%, and the results show that the model proposed in this paper can improve the accuracy of dynamic gesture classification for low-precision radar, which verifies the effectiveness of the model.

Application of multi-source data fusion on intelligent prediction of photovoltaic power

Accurate photovoltaic power prediction is crucial for reducing the uncertainty of photovoltaic systems in grid operation. Variations in cloud cover are one of the primary factors leading to fluctuations in photovoltaic power generation. However, clouds possess highly complex three-dimensional structures. Existing photovoltaic power prediction methods typically rely on two-dimensional cloud images, which are insufficient for fully capturing the impact of clouds on photovoltaic power generation. To address these challenges, this paper proposes a Multi-source data Photovoltaic power Prediction Model (MPPM) based on satellite cloud images and meteorological data. MPPM mainly includes SpatioTemporal feature Conditional Diffusion Model (STCDM), Attention Stacked LSTM network (ASLSTM) and Multidimensional Feature Fusion Module (MFFM). STCDM model utilizes a diffusion model to accurately forecast two-dimensional satellite cloud imagery. ASLSTM extracts the features of three-dimensional meteorological elements. MFFM module integrates the two-dimensional satellite cloud imagery features with the three-dimensional meteorological element features. The two-dimensional satellite cloud imagery reflects the visible aspect of cloud layers, while the three-dimensional meteorological data, which includes cloud water and ice content correlated with altitude, mainly captures the invisible aspect of cloud layers. These two sets of information complement each other, enabling a more comprehensive capture of the three-dimensional characteristics of clouds. Satellite cloud image prediction experiment and photovoltaic power prediction experiment are carried out on STCDM and MPPM model respectively. The experimental results demonstrate that the STCDM model achieved a Structural Similarity index (SSIM) of 0.909 for predicting satellite cloud imagery within 1 h and 0.789 within 24 h. Meanwhile, the MPPM model attained a pearson Correlation coefficient (CORR) of 0.945 for predicting PV power within 1 h and 0.856 within 24 h. These findings indicate that both the STCDM and MPPM models outperform other comparative algorithms in forecasting satellite cloud imagery and PV power.

Cross-modal retrieval based on multi-dimensional feature fusion hashing

Along with the continuous breakthrough and popularization of information network technology, multi-modal data, including texts, images, videos, and audio, is growing rapidly. We can retrieve different modal data to meet our needs, so cross-modal retrieval has important theoretical significance and application value. In addition, because the data of different modalities can be mutually retrieved by mapping them to a unified Hamming space, hash codes have been extensively used in the cross-modal retrieval field. However, existing cross-modal hashing models generate hash codes based on single-dimension data features, ignoring the semantic correlation between data features in different dimensions. Therefore, an innovative cross-modal retrieval method using Multi-Dimensional Feature Fusion Hashing (MDFFH) is proposed. To better get the image’s multi-dimensional semantic features, a convolutional neural network, and Vision Transformer are combined to construct an image multi-dimensional fusion module. Similarly, we apply the multi-dimensional text fusion module to the text modality to obtain the text’s multi-dimensional semantic features. These two modules can effectively integrate the semantic features of data in different dimensions through feature fusion, making the generated hash code more representative and semantic. Extensive experiments and corresponding analysis results on two datasets indicate that MDFFH’s performance outdoes other baseline models.

MDSTF: a multi-dimensional spatio-temporal feature fusion trajectory prediction model for autonomous driving

In the field of autonomous driving, trajectory prediction of traffic agents is an important and challenging problem. Fully capturing the complex spatio-temporal features in trajectory data is crucial for accurate trajectory prediction. This paper proposes a trajectory prediction model called multi-dimensional spatio-temporal feature fusion (MDSTF), which integrates multi-dimensional spatio-temporal features to model the trajectory information of traffic agents. In the spatial dimension, we employ graph convolutional networks (GCN) to capture the local spatial features of traffic agents, spatial attention mechanism to capture the global spatial features, and LSTM combined with spatial attention to capture the full-process spatial features of traffic agents. Subsequently, these three spatial features are fused using a gate fusion mechanism. Moreover, during the modeling of the full-process spatial features, LSTM is capable of capturing short-term temporal dependencies in the trajectory information of traffic agents. In the temporal dimension, we utilize a Transformer-based encoder to extract long-term temporal dependencies in the trajectory information of traffic agents, which are then fused with the short-term temporal dependencies captured by LSTM. Finally, we employ two temporal convolutional networks (TCN) to predict trajectories based on the fused spatio-temporal features. Experimental results on the ApolloScape trajectory dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of weighted sum of average displacement error (WSADE) and weighted sum of final displacement error (WSFDE) metrics. Compared to the best baseline model (S2TNet), our method achieves reductions of 4.37% and 6.23% respectively in these metrics.

A study on the application of multidimensional feature fusion attention mechanism based on sight detection and emotion recognition in online teaching

Abstract Online teaching is not limited by time, but the problem of low learning efficiency is common. To address this problem, the study proposes an attention mechanism for multidimensional feature fusion, which first detects faces, uses a supervised gradient descent algorithm for face feature point detection, and improves the least-squares ellipse-fitting algorithm to detect the open/closed state of human eyes. The sight detection method is also improved, and the fuzzy inference method is used to identify students’ emotions, and the modules are fused to achieve multidimensional feature fusion attention detection for online teaching. The study found that the average accuracy rate was 84.5% with glasses and 92.0% without glasses. The research method with glasses had an average time consumption of 17 ms, while the method without glasses took 15 ms, indicating higher detection accuracy and faster real-time performance. The improved approach led to higher recognition accuracy and accuracy rate. The detection accuracy of a single feature and the research method was 74.1 and 91.9%, respectively. It shows that the research method helps in the detection of students’ attention in online teaching.

SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8.

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

A scholars’ personality traits augmented multi-dimensional feature fusion scholarly journal recommendation model

Journal recommendation is a popular research topic in academic resource recommendation. However, the reliability of the current model depends on rich features in the dataset, and ignores the issue of model performance being degraded by sparse sample features. To tackle this issue, inspired by personality trait-based recommendation techniques, we propose a Personality Trait-augmented Multi-dimensional Feature Fusion Journal Recommendation (PTMFFJRec) model that integrates scholars’ personality traits and multi-dimensional deep semantics, and utilize linguistic features and the big-5 personality model to estimate the personality of the scholars. This is the first multi-dimensional feature model that incorporates Transfer Learning, BERT, and GCN techniques to recommend academic journals based solely on the abstracts and titles of submitted manuscripts. Experimental results on the real-world Scopus’s dataset demonstrate that PTMFFJRec outperforms advanced benchmark models, specifically, surpassing the baseline models in metrics of MAP, MRR, Recall@20 and Diversity.

Super-resolution reconstruction of turbulent flows with a hybrid framework of attention

In a plethora of research endeavors concerning flow fields, acquiring high-resolution data is paramount. However, obtaining high-resolution turbulence data invariably requires substantial computational resources. Although super-resolution reconstruction of turbulent fields has emerged as a salient technique for detail extraction, conventional interpolation methods pose a significant challenge in reconstructing small-scale structures, often resulting in overly smooth outcomes. In this study, we propose a novel hybrid framework of spatially-adaptive feature attention (HSAFA) for the high-quality reconstruction of turbulent fields. This framework is characterized by the implementation of multidimensional feature fusion, which enhances the model's ability to capture details of turbulence. We rigorously applied the proposed model to datasets comprising laminar flow around a square cylinder and turbulent channel flows, with the reconstructed instantaneous velocity fields and statistics subjected to exhaustive and comparative analysis. Our findings demonstrate that HSAFA is capable of effectively reconstructing high-resolution turbulence fields from significantly low-resolution data, covering the range from laminar to turbulent flows.

CSF-GTNet: A Novel Multi-Dimensional Feature Fusion Network Based on Convnext-GeLU- BiLSTM for EEG-Signals-Enabled Fatigue Driving Detection.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field.

SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival.

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targets and decoys. However, most existing methods ignore the fuzziness of multi-dimensional features, and their performance mainly depends on the accuracy of feature extraction, with certain limitations in handling uncertainty and noise. This article proposes a space IR dim target fusion recognition method, which is based on fuzzy comprehensive of spatio-temporal correlation. First, we obtained multi-dimensional IR features of the target through multi-time and multi-spectral detectors, then we established and calculated the adaptive fuzzy-membership function of the features. Next, we applied the entropy weight method to ascertain the objective fusion weights of each feature and computed the spatially fuzzified fusion judgments for the targets. Finally, the fuzzy comprehensive function was used to perform temporal recursive judgment, and the ultimate fusion recognition result was obtained by integrating the results of each temporal recursive judgment. The simulation and comparative experimental results indicate that the proposed method improved the accuracy and robustness of IR dim target recognition in complex environments. Under ideal conditions, it can achieve an accuracy of 88.0% and a recall of 97.5% for the real target. In addition, this article also analyzes the impact of fusion feature combinations, fusion frame counts, different feature extraction errors, and feature database size on recognition performance. The research in this article can enable space-based IR detection systems to make more accurate and stable decisions, promoting defense capabilities and ensuring space security.

Mechanisms of Scientist Spirit Communication for Building Scientific Culture in the Context of Deep Learning

Abstract With science and technology as the first productive forces, the construction of science and culture has gradually become an important indicator of national strength, and the competition around the construction of science and culture has intensified. In this study, we construct an LSTM behavior recognition model with multidimensional feature fusion from a deep learning perspective, aiming to identify the propagation of the scientist’s spirit through speech behavior. We deeply delve into the role of scientists’ spirits and utilize association rules to explore the relationship between the dissemination of scientists’ spirits and the construction of scientific culture. This paper’s recognition model achieves a recognition accuracy of 95.84% for innovative spirit communication behaviors and a recognition rate of 73.56% for dedication spirit communication behaviors. The recognition accuracy for the six spiritual communication behaviors of patriotism, innovation, truth-seeking, dedication, collaboration, and education is average, with an effective recognition rate of 86.05%. In summary, this study suggests a development strategy for scientists’ spiritual communication aimed at fostering the advancement of scientific culture. This strategy serves as a scientific benchmark for enhancing the development of scientific culture.

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Research on educational applications based on diagnostic learning analytics in the context of big data analytics

Abstract In the context of the significant data era, this paper explores the educational applications based on diagnostic learning analytics technology to improve personalized learning and teaching effects in the educational process. The study adopts a multidimensional feature fusion approach to construct a cognitive diagnostic model to predict learners’ knowledge status and future learning performance. Through actual data testing, the model can effectively predict the students’ knowledge mastery state and analyze the students’ learning process in depth. The experimental results show that the diagnostic model exhibits high efficiency and accuracy in predicting students’ knowledge mastery status, with an accuracy rate of 92.97%, significantly better than traditional teaching methods. In addition, the study explores the encoding method of learners’ multidimensional features and constructs a dynamic diagnostic model of test factors and student factors based on graph attention network. The study provides a new learning analysis and diagnostic method in the education field, which helps improve the effect of personalized learning.

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception