Temporal Features Research Articles

EEG-based patient-specific seizure prediction based on Spatial–Temporal Hypergraph Attention Transformer

Background and Objectives:Patient-specific seizure prediction based on Electroencephalogram (EEG) is a challenging and significant neuromedicine study, which could potentially safeguard drug-resistant patients from unforeseen risks in their daily lives. However, prevalent seizure prediction frameworks based on convolutional neural networks (CNNs) prioritize local information while overlooking long-range global dependencies and high-order spatial correlations prevalent among brain leads. Methods:To address the limitations above, we propose a novel Spatial–Temporal Hypergraph Attention Transformer (STHAT) framework for extracting the key features in EEG signals. Aimed at the characteristics of epilepsy EEG data, our framework comprises two primary branches: (1) Long-range Temporal Feature Dependencies: This branch aims to capture temporal dependencies within EEG signals by leveraging Swin Transformer and 2D-CNN. Utilizing shifting windows and convolution operations, we encode local and global content dependencies within segmented EEG samples. (2) High-Order Spatial Information Correlations: We construct a raw graph structure using the Pearson correlation coefficient, enabling the extraction of initial node embeddings through graph convolution operations (GCO). Subsequently, a dynamic hypergraph is formed using K-Nearest Neighbors (KNN) and K-means clustering, allowing us to employ a Hypergraph Attention Network (HAN) to capture structural context relationships among brain regions. By integrating information from both branches, we utilize a linear layer to derive the final seizure prediction outcome. Experiment and Results:Extensive evaluations are conducted on the widely used CHB-MIT dataset. Our comprehensive experimental results demonstrate the effectiveness of the proposed framework in achieving superior performance in automatic seizure data prediction, outperforming state-of-the-art algorithms. The STHAT framework effectively exploits both temporal and spatial information within EEG signals, enhancing seizure prediction accuracy.

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder's experience, leading to feed wastage and environmental pollution. To achieve precision in feeding, it is crucial to adjust the feed according to the fish's feeding state. Existing computer vision-based methods for assessing feeding intensity are limited by their dependence on a single spatial feature and manual threshold setting for determining feeding status constraints. These models lack practicality due to their specificity to certain scenarios and objectives. To address these limitations, we propose an integrated approach that combines computer vision technology with a Convolutional Neural Net-work (CNN) to assess the feeding intensity of farmed fish. Our method incorporates temporal, spatial, and data statistical features to provide a comprehensive evaluation of feeding intensity. Using computer vision techniques, we preprocessed feeding images of pearl gentian grouper, extracting temporal features through optical flow, spatial features via binarization, and statistical features using the gray-level co-occurrence matrix. These features are input into their respective specific feature discrimination networks, and the classification results of the three networks are fused to construct a three-stream network for feeding intensity discrimination. The results of our proposed three-stream network achieved an impressive accuracy of 99.3% in distinguishing feeding intensity. The model accurately categorizes feeding states into none, weak, and strong, providing a scientific basis for intelligent fish feeding in aquaculture. This advancement holds promise for promoting sustainable industry development by minimizing feed wastage and optimizing environmental impact.

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error (MAPE) of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

An Efficient Fusion Network for Fake News Classification

Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF-IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification.

A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions.

Bi-TAM-Net framework: fault diagnosis for insulated bearing based on new noise-resistant time-series framework

Abstract Insulated bearings are extensively employed in wind turbines and other applications as essential core parts of high-power frequency control motors. However, the influence of the wind turbine structure makes it difficult to define the wind turbine insulated bearing fault signal extraction, and there is no distinctive index or qualitative formula to describe the bearing fault. In order to solve the above challenges, Bi-TAM-Net framework is developed to diagnose the insulated bearing fault signals and achieve accurate identification of bearing faults. Firstly, the temporal information feature fusion model is created by the Bi-TAM-Net framework using the time-series bearing dataset as the model data input with recursive and chain linking rules in the direction of time-series evolution. By virtue of its parallel approach and deep stacking structure, it comprehensively portrays the bearing fault data and temporally fuses the global feature information, which can effectively improve the classification performance of insulated bearing fault data. Then the self-attention mechanism is introduced into the structure of the designed temporal information fusion model for optimization, which can be modeled in sequences of arbitrary length, extracting the correlation between the features, isolating the unimportant noise information, and strengthening the extraction ability of the proposed framework for important information. Finally, in order to verify the accuracy and superiority of the developed Bi-TAM-Net framework for bearing faults, based on the same dataset, the Bi-TAM-Net framework is compared and analyzed with seven methods such as the advanced TAM-Net model, and the results show that the Bi-TAM-Net framework has better superiority. This study offers the analytical approach with engineering application value for the design and maintenance of insulated bearings used in wind turbines.

TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data.

Solar irradiation forecast enhancement using clustering based CNN-BiLSTM-attention hybrid architecture with PSO

Accurate solar irradiation forecasting is essential for optimising solar energy use. This paper presents a novel forecasting approach: the ‘Clustering-based CNN-BiLSTM-Attention Hybrid Architecture with PSO’. It combines clustering, attention mechanisms, Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (BiLSTM) networks, and Particle Swarm Optimisation (PSO) into a unified framework. Clustering categorises days into groups, improving predictive capabilities. The CNN-BiLSTM model captures spatial and temporal features, identifying complex patterns. PSO optimises the hybrid model’s hyperparameters, while an attention mechanism assigns probability weights to relevant information, enhancing performance. By leveraging spatial and temporal patterns in solar data, the proposed model improves forecasting accuracy in univariate and multivariate analyses with multi-step predictions. Extensive tests on real-world datasets from various locations show the model’s effectiveness. For example, with NASA power data, the model achieves a Mean Absolute Error (MAE) of 24.028 W/m2, Root Mean Square Error (RMSE) of 43.025 W/m2, and an R 2 score of 0.984 for 1-hour ahead forecasting. The results show significant improvements over conventional methods.

Photoplethysmography Features Correlated with Blood Pressure Changes.

Blood pressure measurement is a key indicator of vascular health and a routine part of medical examinations. Given the ability of photoplethysmography (PPG) signals to provide insights into the microvascular bed and their compatibility with wearable devices, significant research has focused on using PPG signals for blood pressure estimation. This study aimed to identify specific clinical PPG features that vary with different blood pressure levels. Through a literature review of 297 publications, we selected 16 relevant studies and identified key time-dependent PPG features associated with blood pressure prediction. Our analysis highlighted the second derivative of PPG signals, particularly the b/a and d/a ratios, as the most frequently reported and significant predictors of systolic blood pressure. Additionally, features from the velocity and acceleration photoplethysmograms were also notable. In total, 29 features were analyzed, revealing novel temporal domain features that show promise for further research and application in blood pressure estimation.

A New Scene Sensing Model Based on Multi-Source Data from Smartphones

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm.

Dynamic Spatial-Temporal Memory Augmentation Network for Traffic Prediction.

Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial-temporal embeddings to capture dynamic spatial-temporal contexts and encode the unique characteristics of time units and spatial states. Secondly, these three spatial-temporal components are integrated to form a multi-scale spatial-temporal block, which effectively extracts hierarchical spatial-temporal dependencies. Finally, we introduce a meta-memory node bank to construct an adaptive neighborhood graph, implicitly representing spatial relationships and enhancing the learning of spatial heterogeneity through a secondary memory mechanism. Evaluation on four public datasets, including METR-LA and PEMS-BAY, demonstrates that the proposed model outperforms benchmark models such as MTGNN, DCRNN, and AGCRN. On the METR-LA dataset, our model reduces the MAE by 4% compared to MTGNN, 6.9% compared to DCRNN, and 5.8% compared to AGCRN, confirming its efficacy in traffic flow prediction.

Transient Stability Assessment in Power Systems: A Spatiotemporal Graph Convolutional Network Approach with Graph Simplification

Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable.

A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion Detection

Network traffic is a crucial indicator of network performance and network intrusions typically result in traffic anomalies. Capturing the differences and commonalities between different input features is challenging due to high-dimensional traffic data. To address this, we propose a multi-scale feature extraction method based on global additive attention (MSFE-GAA), which integrates time position information encoded by trigonometric functions to capture multi-scale temporal features. An improved Transformer with a similarity matrix captures the commonalities and differences, enhanced by global additive attention for long-term dependencies. Experiments on two public datasets show that the MSFE-GAA model outperforms other baseline models.

HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.

EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.

Temporally enhanced abnormal behavior detection based on multi-channel coupling

Abnormal behavior detection in surveillance videos based on deep learning has becomea hot topic in current research. However, due to the complexity and variability of crowd movement and external environment, detecting abnormal behavior faces great challenges. Current abnormal behavior recognition models have limitations in feature extraction and pay insufficient attention to dynamic temporal features. To address these problems, a spatiotemporal enhancement anomaly detection method based on multi-channel coupling is proposed. Based on the SlowFast network, a multi-channel coupled temporal enhancement module and a spatial enhancement module are introduced respectively to extract more discriminative static and dynamic feature information. A large number of experiments are carried out on three benchmark datasets (Violent Flow, Hockey Fight, and Real-life Violence Situations). The results show that the prediction accuracy of the proposed abnormal behavior recognition method reaches 95.3%、97.3%and respectively 94%, thus verifying the effectiveness of the method.

Efficient Method for Photovoltaic Power Generation Forecasting Based on State Space Modeling and BiTCN

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Multi‐function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network

AbstractIn modern electronic warfare, multi‐function radar work mode recognition is increasingly crucial. However, the challenges posed by complex electromagnetic environments, such as lost pulses, spurious pulses, and measurement errors, along with the reliance of traditional multi‐task learning strategies on clean samples, make it difficult for existing algorithms to achieve satisfactory recognition performance in real‐world scenarios. To address these issues, this paper introduces a novel residual shrinkage reconstruction recurrent neural network (RS‐RRNN). The network uses a Gated Recurrent Unit as its backbone to extract temporal features and enhances feature extraction by reconstructing the GRU's input, while also reducing dependence on clean samples. These features are then processed through a residual shrinkage structure to reduce noise, which significantly improves the model's robustness in non‐ideal scenarios. Simulations demonstrate that RS‐RNN has better performances in accuracy and robustness than existing networks.

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data

ABSTRACT Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from topological data analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of topological data analysis, Mapper results are highly impacted by parameter selection. Given that noninvasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding “true” state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper to any dataset.

Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data

China is advancing urban gas pipeline safety through the strategic deployment of methane detectors. This study introduces a systematic approach for identifying underground gas leaks, and overcoming challenges such as biogas interference and environmental factors. Abnormal methane sequences, defined by expert experience, undergo data preprocessing, including cleansing, compressing, and extracting rising segments. Feature engineering is performed on these rising segments across three dimensions: temporal, volatility, and temperature features. Different classification algorithms, including K-nearest neighbor, decision tree, and support vector machine, are employed for gas leakage recognition. The decision tree proves most effective, achieving a 92.86% recall rate for methane leakage segments. Additionally, feature importance ranking from the decision tree model highlights the maximum value of preprocessed methane concentration time series and the fitting slope of the rising segment as crucial features for methane leakage detection.