The article addresses the three-dimensional (3D) underwater path planning problem of an autonomous underwater vehicle (AUV) in a time-varying current. A modified artificial potential field algorithm combining the velocity vector synthesis method is proposed to search for the optimal path. The modified potential field (MPF) algorithm is designed to dynamically plan the non-collision path. Meanwhile, this modified method is also proved to be an effective solution to the goals not reachable with obstacles nearby (GNRON), U-shaped trap, and rotation unreachable problems. To offset the influence of time-varying current, the velocity synthesis approach is designed to adjust the AUV movement direction. Besides, considering path planning in the complex underwater environment, the multi-beam forward-looking sonar (FLS) model is used. Finally, simulation studies substantiate that the designed algorithm can implement the AUV path planning effectively and successfully in a 3D underwater environment.

Multi-culture facial expression recognition remains challenging due to cross cultural variations in facial expressions representation, caused by facial structure variations and culture specific facial characteristics. In this research, a joint deep learning approach called racial identity aware deep convolution neural network is developed to recognize the multicultural facial expressions. In the proposed model, a pre-trained racial identity network learns the racial features. Then, the racial identity aware network and racial identity network jointly learn the racial identity aware facial expressions. By enforcing the marginal independence of facial expression and racial identity, the proposed joint learning approach is expected to be purer for the expression and be robust to facial structure and culture specific facial characteristics variations. For the reliability of the proposed joint learning technique, extensive experiments were performed with racial identity features and without racial identity features. Moreover, culture wise facial expression recognition was performed to analyze the effect of inter-culture variations in facial expression representation. A large scale multi-culture dataset is developed by combining the four facial expression datasets including JAFFE, TFEID, CK+ and RaFD. It contains facial expression images of Japanese, Taiwanese, American, Caucasian and Moroccan cultures. We achieved 96% accuracy with racial identity features and 93% accuracy without racial identity features.