The maritime environment is uniquely challenged by sparse scattering, sea wave movement, and ducting effects, which significantly impede communication efficiency. Anticipating channel dynamics proactively presents a solution to these challenges. Therefore, this paper investigates the problem of predicting wireless channel path loss in the complex maritime environment. First, a holistic framework that accounts for the synergistic effects of meteorological and radio frequency factors on maritime channels is introduced. Then, an evolutionary-enhanced ensemble neural networks approach is developed that dynamically tailors neural network-based models for the accurate prediction of the maritime channel’s path loss. Numerical results demonstrate that the proposed method outperforms existing state-of-the-art techniques in both prediction accuracy and reliability.

The super-refractive characteristics of evaporation ducts confine electromagnetic waves to a thin layer near the sea surface, significantly reducing energy attenuation and enabling beyond-line-of-sight transmission. Accurately predicting the evaporation duct height (EDH) is crucial for the deployment of maritime communication systems. In this paper, a meteorological factor-driven framework for predicting EDH is proposed. Specifically, a voting-based ensemble model is introduced to capture complex nonlinear relationships and characterize local abrupt changes in meteorological data. Moreover, an evolutionary strategy is devised to optimize the model’s hyperparameters, thereby enhancing convergence performance. Numerical results demonstrate that the proposed method outperforms state-of-the-art models in EDH prediction and exhibits superior cross-regional generalization capabilities under complex meteorological and oceanic conditions.

Satellite communication is widely recognized as a solution to achieve global coverage. However, due to the high mobility of the Low Earth Orbit (LEO) satellites, user equipment (UE) needs to handover frequently to maintain a stable connection. Furthermore, the complex topology of satellite networks exacerbates the challenge of user mobility management. This paper leverages artificial intelligence (AI) to optimize the satellite handover process, proposing a multi-objective optimization strategy to minimize handover frequency, balance satellite load, and maximize system throughput. A novel reward function is designed to enhance the stability of the user service, and a lightweight multilayer neural network is introduced to reduce computational complexity. The simulation results show that the proposed method outperforms state-of-the-art approaches in improving system throughput and reducing average handover times.

Path planning is an important step in ensuring the safety of unmanned surface vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: 1) the image processing module; 2) the meteorological analysis module; and 3) the path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed.

In the context of the large-scale deployment of 5G base stations, atmospheric ducts cause remote interference in time division duplex systems. Addressing the impact of remote interference on communication systems necessitates timely prediction and emergency mitigation of atmospheric ducts. In this paper, a genetic programming-based multiclass remote interference prediction network model is proposed. Firstly, the proposed model can directly learn to make predictions from extensive databases without relying on any assumptions. Secondly, it presents a genetic programming strategy capable of automatically adjusting the model's structure, thereby enhancing the prediction accuracy of various interference classes. Numerical results demonstrate that the multiclass remote interference prediction network (MRIPNet) outperforms state-of-the-art interference prediction models when tested on real-world datasets. Further-more, MRIPNet excels in accurately predicting a small number of severe interference, which help operators promptly execute interference avoidance measures.

Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions.

To respond to the rapid growth of shipping container throughput, terminals urgently need to improve the efficiency of thier operations and reduce operational costs through automation and intellectualization upgrades, thereby improving service levels and enhancing market competitiveness. Due to the advantages of reliable transportation, efficient operation, and environmental friendliness, AGV-based automated container terminal (ACT) has become the development trend of container terminals. To help ACT improve its operational management capabilities, plenty of scholars have explored the transportation system of ACT. Through the analysis of operational management issues, the paper defines the four main research topics in vehicle transportation of the ACT including equipment scheduling, path planning, exception handling, and vehicle management. Then, in each topic, the works in the recent 25 years are summarized and several research opportunities for possible follow-up research directions in different fields are proposed. We expect our survey could not only provide references for more scholars on the research of operation and management of terminals, but also provide guidance for system evaluation and improvement for terminal system engineers and operation managers.

This paper proposes solutions to channel prediction with atmospheric duct based on feedforward neural network (FNN) modeling. Specifically, FNN-based model is to produce accurate prediction by directly learning from large database rather than depending on any assumption. The prediction accuracy of the model applied to Sub-6 GHz and 28 GHz bands attain 88.72% and 94.77%, respectively. Besides, the paper also validates the difference of prediction performance of networks by comparisons of artificial neural network (ANN) and FNN-based networks. The results show that when the bands get higher, FNN-based framework would enhance the prediction accuracy while ANN-based framework would bring it down. It is demonstrated that the proposed FNN-based network obtain accuracy gain over 30% than ANN-based framework.