This article proposes a four-dimensional brain mapping method, which can represent the continuous process of a person’s fatigue state in the form of image frames in a space-time range. This work couples 3-D-convolutional neural networks and long-short-term memory networks to form a cognitive detection model of brain fatigue dynamics, which can simulate the continuous process of a person’s brain fatigue dynamics and accurately identify different cognitive fatigue states. Our approach can be applied to any type of brain fatigue detection.

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.

Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

In order to help robots understand and perceive an object's properties during noncontact robot-object interaction, this article proposes a deeply supervised subspace learning method. In contrast to previous work, it takes the advantages of low noise and fast response of noncontact sensors and extracts novel contactless feature information to retrieve cross-modal information, so as to estimate and infer material properties of known as well as unknown objects. Specifically, a depth-supervised subspace cross-modal material retrieval model is trained to learn a common low-dimensional feature representation to capture the clustering structure among different modal features of the same class of objects. Meanwhile, all of unknown objects are accurately perceived by an energy-based model, which forces an unlabeled novel object's features to be mapped beyond the common low-dimensional features. The experimental results show that our approach is effective in comparison with other advanced methods.

In robotics and haptics, actuators that move at multiple degrees of freedom (DOFs) without the intermediate transmission mechanisms and have high force/torque output with compact size are widely expected to improve the stability and transparency of interactions. For this reason, utilizing the characteristics that the rheological properties of magnetorheological (MR) fluid can be continuously and reversibly changed by an external magnetic field within a few milliseconds, a multi-directional controlled 3-DOF spherical MR actuator is proposed in this paper. Through the special design of the stator part, the actuator can implement force feedback control in multiple directions. Then, based on the calculated torque model and analysis of the magnetic circuit, finite element analysis (FEA) is used to optimize the geometry and internal magnetic field distribution of the actuator. In order to achieve precise control and positioning of multi-DOF motion, a small inertial measurement unit (IMU) is integrated in the upper part of the joystick. We built a prototype of this actuator and tested its performance under various control conditions. The results show that the actuator can provide force feedback with reasonable magnitude and direction to users according to the change of interaction conditions, thus overcoming the disadvantage of the existing spherical MR actuators that limit the movement of the user in all directions after being activated.

This article presents a new aviation brain-computer interface, which includes the construction of a color brain power map and a cognitive detection network. The developed network, Bpmnet, can effectively detect the cognitive state of the brain. To improve the effectiveness of model parameter optimization algorithms, momentum and batch normalization are proposed during Bayesian posterior parameter inference. Bpmnet reduces the risk of model overfitting and increases the uncertainty of outlier prediction. Experimental results demonstrate that our approach significantly outperforms state-of-the-art techniques.