A novel MP-LSTM method for ship trajectory prediction based on AIS data

The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

The influence of vegetation cover on the spectroscopic estimation of soil properties

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.

In the context of global warming and the dramatic increase in greenhouse gas emissions, the power industry is the largest source of carbon emissions. Adjusting and optimizing the carbon dioxide emissions from the power industry will help China achieve its “dual carbon” goals and is of great significance for mitigating global carbon dioxide emissions. This paper takes six power plants in Jilin Province as the research objects, and firstly accounts for the carbon emission production data between January 2020 and December 2023 according to the "Accounting Methods and Reporting Guidelines for Greenhouse Gas Emissions from Enterprises - Power Generation Facilities". Then, LSTM was used to establish carbon emission prediction models for six different power plants in Jilin Province, and the analysis of each model showed that that the single-step prediction RMSEs are all less than one, with higher prediction accuracy, but only can used in short-term prediction, the multi-step prediction RMSEs are bigger than one, with lower prediction accuracy, but can used in long-term carbon emission trend prediction can be achieved. The carbon emission trend prediction of six power plants in Jilin province between January 2024 and August 2031 confirms that the carbon emissions of power plants will be affected by seasons and shown cyclical changes. Finally, reasonable policy recommendations are provided for the successful realisation of the "double carbon" target for electricity in Jilin Province.

In marine transportation, many ships are equipped with AIS devices. The AIS data sent by AIS devices can help the maritime authorities and other ships obtain the navigation condition of the ship, thereby ensuring the safety of ships during navigation. However, when a ship is involved in illegal activities, the crew may close the AIS device or tamper with Maritime Mobile Service Identity (MMSI) in the AIS data. To detect these two kinds of behaviors, this paper designs the AIS closing detection algorithm and MMSI spoofing detection algorithm based on the spatiotemporal data provided by AIS and radar. As the radar data does not include the ship’s identification, the associated relationship between radar data and AIS data is difficult to determine in the multi-ship scenario. To solve this problem, the D-TRAP is defined in this paper, it is applied in the process of searching for the associated AIS points of radar trajectory points, when the number of effective AIS points is reduced caused by the above two behaviors, the association method used in the paper has better performance. In addition, real data and simulation data are used to verify the two algorithms. The verification results show that when the ship is simultaneously monitored by radar and AIS, and the monitoring process continues for a period of time, the AIS closing detection algorithm has good performance. When the ship is monitored by AIS and radar before and after MMSI spoofing, and both monitoring processes continue for a period of time, the MMSI spoofing algorithm has good performance.

Ship collision avoidance behaviour recognition and analysis based on AIS data

There are nearly 50 million AIS data generated every day in the Yangtze River Basin. Efficient storage and query of AIS data become the key problem of smart maritime administration. To overcome this problem, a distributed storage and query scheme based on Hadoop platform is proposed. Firstly, the original AIS message is parsed, the abnormal data is removed and the parsed data is pushed to the Kafka message queue. Secondly, the messages in the queue are pushed to different servers to complete the storage of real-time cache, historical trajectory and regional information. Finally, the query interface is provided to realize the real-time query of ship status, trajectory and regional ship in a certain period of time. The real applications show that compared with the traditional relational database, the solution can meet the requirements of real-time query and has high scalability.

Advanced prediction of the sinking speed of open caissons based on the spatial-temporal characteristics of multivariate structural stress data

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

In this paper, we describe a case study in developing an access cost model for WebSources in the context of a wrapper mediator architecture. We document our experiences in validating this model, and note successes and lessons learned. Using experimental data of query feedback from several WebSources, we develop a Catalog and Access Cost model. We identify WebSource characteristics of the query feedback that are reflective of the particular WebSource behavior and identify groupings of WebSources based on these characteristics. We also characterize the Access Cost model as having High or Low Prediction Accuracy, with respect to its ability to predict access costs for the WebSources. We then correlate WebSource characteristics and groupings of WebSources with High or Low prediction accuracy of the model.

Human mobility prediction is of great importance in a wide range of modern applications in different fields such as personalized recommendation systems, the fifth-generation (5G) mobile communication systems, and so on. Generally, the prediction goal varies from different application scenarios. For the applications of 5G network including resource allocation and mobility management, it is essential to predict the positions of mobile users in the near future from dozens of seconds to a few minutes so as to make preparation in advance, which is actually a trajectory prediction problem. In this paper, with the particular focus on multi-user multi-step trajectory prediction, we first design a basic deep learning-based prediction framework, where the long short-term memory (LSTM) network is directly applied as the most critical component to learn user-specific mobility pattern from the user's historical trajectories and predict his/her movement trends in the future. Motivated by the related findings after testifying and analyzing this basic framework on a model-based dataset, we extend it to a region-oriented prediction scheme and propose a multi-user multi-step trajectory prediction framework by further incorporating the sequence-to-sequence (Seq2Seq) learning. The experimental results on a realistic dataset demonstrate that the proposed framework has significant improvements on generalization ability and reduces error-accumulation effect for multi-step prediction.

Offline constraint-based causal feature selection (OC-CFS) algorithms are essential for identifying causal relationships from observational data. However, existing methods often suffer from limitations such as low prediction accuracy or high computational cost, particularly when sample sizes vary. To address these limitations, we propose Triplet, a novel framework that leverages the HITON-MB Parents and Children (PC) strategy to identify strongly relevant PC nodes while eliminating irrelevant and redundant features. It concurrently employs the BAMB strategy to detect relevant spouses and discard irrelevant ones, and applies the STMB non-Markov Blanket (non-MB) strategy to identify and exclude non-MB descendants. Through this integration, the proposed T-OCD[Formula: see text] overcomes these limitations, accurately identifying the true MB with high prediction accuracy and reduced runtime. To validate its effectiveness, we evaluated T-OCD[Formula: see text] on benchmark Bayesian networks (BNs) and real-world datasets. Extensive experimental results demonstrate that T-OCD[Formula: see text] achieves significant improvements in both prediction accuracy and computational efficiency compared to existing methods. On small sample sizes (n=500), T-OCD[Formula: see text] achieved the highest recall in 5 out of 7 datasets, with an average improvement of over 20% compared to rivals. On large sample sizes (n=5000), it excelled in precision, achieving the top score in 4 out of 7 datasets with an average precision of 94%. Computationally, T-OCD[Formula: see text] is highly efficient, operating as the second-fastest method overall. It ran over 55% faster than half of the benchmarks and a remarkable 35% faster than the average competitor on large datasets. The source code for this research is available at the following repository: https://github.com/vickykhan89/T-OCDmb.

The current prediction model for the system efficiency of pumping units primarily relies on a mechanistic approach. However, this approach incorporates numerous unnecessary factors, thereby, increasing the cost associated with predictions. With the improvement of the oil field database, the available information is increasing. Some scholars propose a prediction model based on a single neural network, however, such models face challenges in effectively capturing complex data, resulting in lower prediction accuracy and limited resistance to interference. To solve the above problems, the study proposes a novel stacking integrated learning prediction model, which incorporates fivefold cross‐validation. First, the magnitude of the correlation coefficient was quantified using the Pearson correlation coefficient. Second, the impact and predictive features were normalized. Final, convolutional neural network (CNN), recurrent neural network (RNN), Long Short‐Term Memory network (LSTM), gated recurrent unit (GRU), and transformer are used as the base models, and fully connected neural network (FNN) is used as the metamodel. Each base model was trained by fivefold cross‐validation, and the predicted values of each fold were stacked by rows. Next, the predicted values of each base model are stacked by columns as input variables to the metamodel and metamodel learning is performed, and the stacking integrated learning prediction model based on fivefold crossover validation is established. To validate the accuracy of the model, we selected 5,000 actual well parameters, including 26 impact features and one predictive feature, for comparative analysis. This analysis presents the maximum percentage reduction in mean square error (MSE), mean absolute error (MAE), and root‐mean‐square error (RMSE) of our proposed integrated learning model concerning a single neural network prediction model as 28.26%, 24.40%, and 15.66%, respectively. The maximum percentage improvement in R2 is 17.74%. It shows that our proposed integrated learning prediction model has high prediction accuracy.

In order to solve the problems of high battery capacity detection error and low life prediction accuracy existing in traditional lithium-ion battery cycle life prediction methods, based on the battery capacity detection results, the cycle life prediction of lithium-ion batteries for new energy electric vehicles was carried out. Firstly, the principle of charge and discharge of lithium-ion battery is analyzed. Based on this, the idea of differential equation is introduced to detect the capacity of lithium-ion battery in real time. Secondly, according to the battery capacity test results, the exponential function of lithium-ion battery cycle life decline is constructed, and the calculation result of life influence factor is obtained. Finally, the lithium-ion battery cycle life prediction model is constructed, and the final prediction results are obtained. The experimental results show that the proposed method can always keep a low battery capacity detection error in multiple charging and discharging cycles, and the battery cycle life prediction accuracy can reach 97%.

In the non-medical model physiological parameter monitoring system, learning the monitoring parameters can improve the diagnostic and prediction accuracy. Aiming at the problems of insufficient information mining and low prediction accuracy in multi-task time series, the supervised and semi-supervised learning methods in machine learning are combined to predict the physiological status of remote health monitoring objects. This method uses the K-means algorithm to cluster the same type of data and use the Multitasking Least Squares Support Vector Machine (MTLS-SVM) to train historical data for trend prediction. In order to evaluate the effectiveness of the method, the MTLS-SVM method is compared with the K-means and MTLS-SVM methods. It can be seen from the experimental results that the body temperature data measured by the GY-MCU90615 is close to that of the digital thermometer. Moreover, the body temperature speed collected by the GY-MCU90615 can reach the millisecond level, which can well meet the needs of the system. The research shows that the method has higher prediction accuracy and has a breakthrough significance for the monitoring of athletes’ physiological parameters.