Recently， the leading human pose estimation algorithms are heatmap-based algorithms. Heatmap decoding （i.e. transforming heatmaps to coordinates of human joint points） is a basic step of these algorithms. The existing heatmap decoding algorithms neglect the effect of systematic errors. Therefore， an error compensation based heatmap decoding algorithm was proposed. Firstly， an error compensation factor of the system was estimated during training. Then， the error compensation factor was used to compensate the prediction errors including both systematic error and random error of human joint points in the inference stage. Extensive experiments were carried out on different network architectures， input resolutions， evaluation metrics and datasets. The results show that compared with the existing optimal algorithm， the proposed algorithm achieves significant accuracy gain. Specifically， by using the proposed algorithm， the Average Precision （AP） of the HRNet-W48-256×192 model is improved by 2.86 percentage points on Common Objects in COntext （COCO）dataset， and the Percentage of Correct Keypoints with respect to head （PCKh） of the ResNet-152-256×256 model is improved by 7.8 percentage points on Max Planck Institute for Informatics （MPII）dataset. Besides， unlike the existing algorithms， the proposed algorithm did not need Gaussian smoothing preprocessing and derivation operation， so that it is 2 times faster than the existing optimal algorithm. It can be seen that the proposed algorithm has applicable values to performing fast and accurate human pose estimation.

The problem of multiple Autonomous Underwater Vehicles (AUV) collaborative task allocation for information acquisition in the underwater detection network was researched. Firstly, a comprehensive model of underwater acoustic monitoring network system was constructed considering the influence of network system sensor nodes status and communication channel status synthetically. Secondly, because of the multi-interference factors under water, with the inaccuracy of the model generation considered, and the multi-AUV task allocation system was modeled as a robust Restless Bandits Problem (RBP) based on the theory of reinforce learning. Lastly, the robust Whittle algorithm was proposed to solve the RBP problem to get the task allocation policy of multi-AUV. Simulation results show that when the system selected 1, 2 and 3 targets, the system cumulative return performance of the robust allocation policy improves by 5.5%, 12.3% and 9.6% respectively compared with that of the allocation strategy without interference factors considered, proving the effectiveness of the proposed approaches.

Focused on the issue that energy consumption is excessively high in the underwater wireless sensor cooperative communication networks, to balance the energy consumption between the nodes and increase the channel capacity of the system, a distributed power allocation game-theoretic algorithm based on the node residual energy was proposed. The trading model between the user node and the relay node was constructed as a two-tier Stackelberg game, so that the node with less residual energy could provide less power for forwarding service, otherwise the node with more residual energy could provide more power to service, so as to balance the energy consumption between nodes. Compared with the algorithm without considering the residual energy, the channel capacity increases by 9.4%, 23.1% and 16.7% when there are 2, 3, 4 relay nodes respectively. The simulation results show that the algorithm not only improves the total channel capacity of the system, but also prolongs the lifetime of the underwater sensor cooperative communication network.

Variety bias is a prevalent problem existing in decision tree algorithms.For solving this problem,a new decision tree algorithm,AF algorithm,was proposed.The mechanism how it avoided this default was analysed.Experiments compared to ID3 algorithm show that AF algorithm can avoid the variety bias of ID3 algorithm, and has no worse performance in classifying instances then ID3 algorithm.