Search

Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our research comprises two main parts: the first involves preprocessing the large raw AIS dataset to extract features, and the second focuses on trajectory prediction. We emphasize a specialized preprocessing approach tailored for AIS data, including advanced filtering techniques to remove outliers and erroneous data points, and the incorporation of contextual information such as environmental conditions and ship-specific characteristics. Our deep learning models utilize trajectory data sourced from the Automatic Identification System (AIS) to train and learn regular patterns within ship trajectory data, enabling them to predict trajectories for the next hour. Experimental results reveal that CNN has substantially reduced the Mean Absolute Error (MAE) and Mean Square Error (MSE) of ship trajectory prediction, showcasing superior performance compared to other deep learning algorithms. Additionally, a comparative analysis with other models—Recurrent Neural Network (RNN), GRU, LSTM, and DBS-LSTM—using metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), and Non-Linear ADE (NL-ADE), demonstrates our method's robustness and accuracy. Our approach not only cleans the data but also enriches it, providing a robust foundation for subsequent deep learning applications in ship trajectory prediction. This improvement effectively enhances the accuracy of trajectory prediction, promising advancements in maritime traffic safety. [ABSTRACT FROM AUTHOR]

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters. Our approach involves the integration of neural networks within the filtering algorithms, enabling the dynamic augmentation of parameters in response to performance feedback. We present two modified filters: a neural network-based Kalman filter and an alpha-beta filter, each augmented to incorporate neural network-driven parameter tuning. The alpha-beta filter is enhanced with neural network outputs for its α and β parameters, while the Kalman filter employs a neural network to optimize its internal parameter R and noise factor F. We assess these advanced models using the root mean square error (RMSE) metric, where our neural network-based alpha-beta filter demonstrates a significant 38.2% improvement in prediction accuracy, and the neural network-based Kalman filter achieves a 53.4% enhancement. Hence, our novel approach of integrating neural networks into filtering algorithms stands out as an effective strategy to significantly enhance their performance in dynamic environments. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea syndrome (OSAS) is associated with cerebrovascular disease, which can lead to life-threatening outcomes. The purpose of the study was to investigate the relationship between OSAS and comorbid intracranial aneurysms. We retrospectively reviewed 564 patients who underwent a polysomnography and brain magnetic resonance angiography as part of their health checkup. We calculated the prevalence of an intracranial aneurysm and OSAS in patients and measured the size of the intracranial aneurysm if present. The mean patient age was 55.6 ± 8.5 years, and 82.3% of them were men. The prevalence of an intracranial aneurysm in patients with OSAS was 12.1%, which is significantly higher than patients with non-OSAS (5.9%, p = 0.031). Patients with OSAS had a much higher prevalence of intracranial aneurysms, after adjusting all possible confounding factors such as age, sex, smoking status, alcohol drinking, and body mass index (odds ratio: 2.32; 95% confidence interval: 1.07–5.04). Additionally, the OSAS group had noticeably larger aneurysms compared with those of the non-OSAS group (3.2 ± 2.0 mm vs. 2.0 ± 0.4 mm, p = 0.013). We found a significant association between OSAS and intracranial aneurysms. OSAS could be another risk factor for the development of intracranial aneurysms. [ABSTRACT FROM AUTHOR]

The alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the dynamic performance of the alpha–beta filter algorithm, a new prediction learning model is proposed in this study. The proposed model has two main components: (1) the alpha–beta filter algorithm is the main prediction module, and (2) the learning module is a feedforward artificial neural network (FF‐ANN). Furthermore, the model uses two inputs, temperature sensor and humidity sensor data, and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings. Using the novel proposed technique, prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network, and also reduces the root mean square error (RMSE) and mean absolute error (MAE). We carried out different experiments with different experimental setups. The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter. A higher prediction accuracy was achieved, and the MAE and RMSE were 35.1%–38.2% respectively. The final proposed model results show increased performance when compared to traditional methods. [ABSTRACT FROM AUTHOR]

Recent projections from marine transportation experts highlight an uptick in maritime traffic, attributed to the fourth industrial revolution's technological strides and global economic rebound. This trend underscores the need for enhanced systems for maritime accident prediction and traffic management. In this study, to analyze the flow of maritime traffic macroscopically, spatiality and continuity reflecting the output of ships are considered. The course–speed (CS) model used in this study involved analyzing COG, ROT, speed, and acceleration, which can be obtained from the ship's AIS data, and calculating the deviation from the standard plan. In addition, spatiality and continuity were quantitatively analyzed to evaluate the smoothness of maritime traffic flow. A notable finding is that, in the target sea area, the outbound and inbound CS indices are measured at 0.7613 and 0.7501, suggesting that the outbound ship flows are more affected than inbound ship flows to the liquidity of maritime traffic flow. Using the CS model, a detailed quantitative evaluation of the spatiality and continuity of maritime traffic is presented. This approach facilitates robust comparisons over diverse scales and periods. Moreover, the research advances our understanding of factors dictating maritime traffic flow based on ship attributes. The study insights can catalyze the development of a novel index for maritime traffic management, enhancing safety and efficiency. [ABSTRACT FROM AUTHOR]

Background: Anticoagulant therapy has been important for stroke prevention in patients with atrial fibrillation (AF). However, it was not recommended due to its relatively higher risk of bleeding than its lower risk of stroke in patients with a CHA2DS2‐VASc score of 0. Hypothesis: This study aimed to evaluate the predictors of stroke in AF patients with very low risk of stroke. Methods: Between 1990 and 2020, 542 patients with non‐valvular AF (NVAF) with a CHA2DS2‐VASc score of 0 followed up for at least 6 months were enrolled. Patients with only being woman as a risk factor were included as a CHA2DS2‐VASc score of 0 in this study. The primary outcome was stroke or systemic embolism. Results: The primary outcome rate was 0.78%/year. In Cox hazard model, age of ≥50 years at diagnosis (hazard ratio [HR] 6.710, 95% confidence interval [CI] 1.811–24.860, p =.004), LVEDD of ≥46 mm (HR 4.513, 95% CI 1.038–19.626, p =.045), and non‐paroxysmal AF (HR 5.575, 95% CI 1.621–19.175, p =.006) were identified as independent predictors of stroke or systemic embolism. Patients with all three independent predictors had a higher risk of stroke or systemic embolism (4.21%/year), whereas those without did not have a stroke or systemic embolism. Conclusion: The annual stroke or systemic embolism rate in NVAF patients with CHA2DS2‐VASc score of 0 was 0.78%/year, and age at AF diagnosis, LVEDD, and non‐paroxysmal AF were independent predictors of stroke or systemic embolism in patients considered to have a very low risk of stroke. [ABSTRACT FROM AUTHOR]