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Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles (UAVs) and has attracted extensive attention from scholars. Knowledge-based approaches rely on prior knowledge, while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems (UASs). Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models, they often lack parameter selection and are limited by the cost of labeling anomalous data. Furthermore, flight data with random noise pose a significant challenge for anomaly detection. This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder (STC-LSTM-AE) neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data. First, UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model. Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge. Then, the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner. Finally, the method’s effectiveness is validated on real UAV flight data.

ABSTRACT Background: The value of prenatal identification of morphology of ductus arteriosus in fetuses with congenital heart defects (CHD) with pulmonary atresia and duct-dependent pulmonary circulation (DDPC) in planning neonatal ductal stenting procedure is untested. The aim of the study is to analyze the utility of three-dimensional/four-dimensional (3D/4D) spatiotemporal image correlation (STIC) fetal echocardiography in delineating the morphology of ductus arteriosus in fetuses with DDPC undergoing neonatal ductal stenting. Methods: In this retrospective study (2017-22), prenatal imaging of pulmonary artery (PA) anatomy, aortic arch sidedness, and morphology of ductus arteriosus (ductal origin was classified as vertical/horizontal and ductal course as tortuous/straight) was done using 3D/4D STIC imaging and volume datasets. Prenatal findings were correlated with angiographic findings during stenting and the degree of agreement was calculated. Results: We included 27 fetuses with a prenatal diagnosis of CHD with DDPC who underwent neonatal ductal stenting. The accuracy of prenatal assessment of PA anatomy, branch PA stenosis, and arch sidedness was 100%, 92.6%, and 88.9%, respectively. The accuracy of prenatal assessment of ductal origin and course, compared with angiography, was 85.2% and 88.9%, respectively. Prenatal imaging had a diagnostic accuracy of 100% for vertical straight and horizontal tortuous ducts, 84.6% for vertical tortuous, and 67% for horizontal straight ducts. Duct stenting was successful in 25 (92.6%) babies; two died after the procedure from stent occlusion. Conclusion: Fetal echocardiography using 3D/4D STIC imaging enables accurate delineation of the morphology of ductus arteriosus in fetuses with DDPC, thereby aiding parental counseling and planning neonatal ductal stenting.

The thermal error of a machine tool is one of the main factors affecting the machining accuracy. By establishing the error model and compensating the error, the accuracy can be improved effectively. This paper presents a novel static thermal deformation modeling method based on a hybrid CNN-LSTM model with spatiotemporal correlation (ST-CLSTM). Firstly, by organizing the temperature data into a specific matrix, a sample set with spatiotemporal characteristics is constructed. Secondly, using convolutional neural network (CNN) to extract spatiotemporal features in the sample set, the problem of selecting temperature-sensitive points in thermal error modeling can be solved. Thirdly, the long short-term memory (LSTM) network is used to capture the characteristics of temperature change abstractly from the perspective of the time series of temperature data. Finally, the ST-CLSTM model is verified at different working conditions and compared with other traditional methods, such as the multiple linear regression (MLR) model, the back propagation neural network (BPNN) model, the CNN model, and the LSTM model. The experimental results show that the ST-CLSTM model obtains higher prediction accuracy in X, Y, and Z directions, which guarantees the stability of prediction performance. The proposed model possesses strong robustness and shows a preliminary industrial application prospect.

As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.

The accurate prediction of sea surface temperature (SST) is essential for studying marine phenomena, understanding climate dynamics, and forecasting environmental changes. However, developing a general SST prediction model is challenging due to significant regional variations and the impacts of diverse climate phenomena. To improve the performance of SST predictions, we propose a hybrid framework that effectively models the spatial and temporal dependencies of SST data with a Gaussian process-enhanced Long Short-Term Memory network. The LSTM module adaptively captures both long and short-term temporal trends in SST variation, while the Gaussian process incorporates the spatial dependency of neighboring data to further refine the predictions. Furthermore, our proposed framework estimates the uncertainty associated with SST predictions, providing crucial information for practical applications. Comprehensive experiments are conducted on the OISST dataset, with a focus on the Bohai Sea and the South China Sea. The results of our framework outperform state-of-the-art methods, validating its superiority in SST prediction.

To investigate how chromatin architecture is spatiotemporally organized at a double-strand break (DSB) repair locus, we established a biophysical method to quantify chromatin compaction at the nucleosome level during the DNA damage response (DDR). The method is based on phasor image-correlation spectroscopy of histone fluorescence lifetime imaging microscopy (FLIM)-Förster resonance energy transfer (FRET) microscopy data acquired in live cells coexpressing H2B-eGFP and H2B-mCherry. This multiplexed approach generates spatiotemporal maps of nuclear-wide chromatin compaction that, when coupled with laser microirradiation-induced DSBs, quantify the size, stability, and spacing between compact chromatin foci throughout the DDR. Using this technology, we identify that ataxia–telangiectasia mutated (ATM) and RNF8 regulate rapid chromatin decompaction at DSBs and formation of compact chromatin foci surrounding the repair locus. This chromatin architecture serves to demarcate the repair locus from the surrounding nuclear environment and modulate 53BP1 mobility.

Short-term origin–destination (OD) flow prediction is vital for operations planning, control, and management in urban railway systems. While the entry and exit passenger demand prediction problem has been studied in various studies, the OD passenger flow prediction problem receives much less attention. One key challenge for short-term OD flow prediction is the partial observability of the OD flow information due to trips having not been completed at a certain time interval. This paper develops a novel deep learning architecture for the OD flow prediction in urban railway systems and examines various mechanisms for data representation and for dealing with partial information. The deep learning framework consists of three main components, including multiple LSTM networks with an attention mechanism capturing short/long-term temporal dependencies, a temporally shifted graph matrix for spatiotemporal correlations, and a reconstruction mechanism for partial OD flow observations. The model is validated using smart card data from Hong Kong’s Mass Transit Railway (MTR) system and compared with state-of-the-art prediction models. Experiments are designed to examine the characteristics of the proposed approach and its various components. The results show the superior performance (accuracy and robustness) of the proposed model and also the importance of partial observations of OD flow information in improving prediction performance. In terms of data representation, predicting the deviation of OD flows performs consistently better than predicting OD flows directly.

Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning.

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction.

There are significant spatiotemporal correlations among the traffic flows of neighboring road sections in the road network. Correctly identifying such correlations makes an essential contribution for improving the accuracy of traffic flow prediction. Many efforts have been made by several researchers to solve this issue, but they assume that the spatiotemporal correlations among traffic flows are stationary in both time and space, i.e., the degrees to which traffic flows affect each other are fixed. In this study, we propose a clustering based traffic flow prediction method that considers the dynamic nature of spatiotemporal correlations. In order to express the short-term dependence between the target road section and neighboring ones, the spatiotemporal correlation matrices are introduced. The historical traffic data are divided into several clusters according to the similarity between spatiotemporal correlation matrices. The spatiotemporal correlation analysis and the predictor selection based on the mutual information are performed in each cluster, and the multiple prediction models are trained separately. A prediction model corresponding to the cluster to which the current traffic pattern belongs is selected to output the prediction result. Experimental results on real traffic data show that the proposed method achieves good prediction accuracy by distinguishing the heterogeneity of spatiotemporal correlations among the traffic flows.