Three-Dimensional Distribution of Turbulent Mixing in the South China Sea*

A three-dimensional distribution of turbulent mixing in the South China Sea (SCS) is obtained for the first time, using the Gregg–Henyey–Polzin parameterization and hydrographic observations from 2005 to 2012. Results indicate that turbulent mixing generally increases with depth in the SCS, reaching the order of 10[superscript −2] m[superscript 2] s[superscript −1] at depth. In the horizontal direction, turbulence is more active in the northern SCS than in the south and is more active in the east than the west. Two mixing “hotspots” are identified in the bottom water of the Luzon Strait and Zhongsha Island Chain area, where diapycnal diffusivity values are around 3 × 10[superscript −2] m[superscript 2] s[superscript −1]. Potential mechanisms responsible for these spatial patterns are discussed, which include internal tide, bottom bathymetry, and near-inertial energy

Research on Construction Method of Operational Reliability Control Model for Space Manipulator Based on Particle Filter

The operational reliability of the space manipulator is closely related to the control method. However the existing control methods seldom consider the operational reliability from the system level. A method to construct the operational reliability system control model based on particle filter for the space manipulator is presented in this paper. Firstly, the definition of operational reliability and the degree of operational reliability are given and the state space equations of the control system are established as well. Secondly, based on the particle filter algorithm, a method to estimate the distribution of the end position error and calculate the degree of operational reliability with any form of noise distribution in real time is established. Furthermore, a performance model based on quality loss theory is built and a performance function is obtained to evaluate the quality of the control process. The adjustment value of the end position of the space manipulator can be calculated by using the performance function. Finally, a large number of simulation results show that the control method proposed in this paper can improve the task success rate effectively compared to the simulation results using traditional control methods and control methods based on Bayesian estimation

CTC-based Non-autoregressive Speech Translation

Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency. In this paper, we investigate the potential of connectionist temporal classification (CTC) for non-autoregressive speech translation (NAST). In particular, we develop a model consisting of two encoders that are guided by CTC to predict the source and target texts, respectively. Introducing CTC into NAST on both language sides has obvious challenges: 1) the conditional independent generation somewhat breaks the interdependency among tokens, and 2) the monotonic alignment assumption in standard CTC does not hold in translation tasks. In response, we develop a prediction-aware encoding approach and a cross-layer attention approach to address these issues. We also use curriculum learning to improve convergence of training. Experiments on the MuST-C ST benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67$\times$, which is comparable to the autoregressive counterpart and even outperforms the previous best result of 0.9 BLEU points.Comment: ACL 2023 Main Conferenc

A Hierarchical Reliability Control Method for a Space Manipulator Based on the Strategy of Autonomous Decision-Making

In order to maintain and enhance the operational reliability of a robotic manipulator deployed in space, an operational reliability system control method is presented in this paper. First, a method to divide factors affecting the operational reliability is proposed, which divides the operational reliability factors into task-related factors and cost-related factors. Then the models describing the relationships between the two kinds of factors and control variables are established. Based on this, a multivariable and multiconstraint optimization model is constructed. Second, a hierarchical system control model which incorporates the operational reliability factors is constructed. The control process of the space manipulator is divided into three layers: task planning, path planning, and motion control. Operational reliability related performance parameters are measured and used as the system’s feedback. Taking the factors affecting the operational reliability into consideration, the system can autonomously decide which control layer of the system should be optimized and how to optimize it using a control level adjustment decision module. The operational reliability factors affect these three control levels in the form of control variable constraints. Simulation results demonstrate that the proposed method can achieve a greater probability of meeting the task accuracy requirements, while extending the expected lifetime of the space manipulator

Ocean internal tides suppress tropical cyclones in the South China Sea

Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s−1 day−1) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present

Enhanced Context Learning with Transformer for Human Parsing

Human parsing is a fine-grained human semantic segmentation task in the field of computer vision. Due to the challenges of occlusion, diverse poses and a similar appearance of different body parts and clothing, human parsing requires more attention to learn context information. Based on this observation, we enhance the learning of global and local information to obtain more accurate human parsing results. In this paper, we introduce a Global Transformer Module (GTM) via a self-attention mechanism to capture long-range dependencies for effectively extracting context information. Moreover, we design a Detailed Feature Enhancement (DFE) architecture to exploit spatial semantics for small targets. The low-level visual features from CNN intermediate layers are enhanced by using channel and spatial attention. In addition, we adopt an edge detection module to refine the prediction. We conducted extensive experiments on three datasets (i.e., LIP, ATR, and Fashion Clothing) to show the effectiveness of our method, which achieves 54.55% mIoU on the LIP dataset, 80.26% on the average F-1 score on the ATR dataset and 55.19% on the average F-1 score on the Fashion Clothing dataset

Enhanced Context Learning with Transformer for Human Parsing

Human parsing is a fine-grained human semantic segmentation task in the field of computer vision. Due to the challenges of occlusion, diverse poses and a similar appearance of different body parts and clothing, human parsing requires more attention to learn context information. Based on this observation, we enhance the learning of global and local information to obtain more accurate human parsing results. In this paper, we introduce a Global Transformer Module (GTM) via a self-attention mechanism to capture long-range dependencies for effectively extracting context information. Moreover, we design a Detailed Feature Enhancement (DFE) architecture to exploit spatial semantics for small targets. The low-level visual features from CNN intermediate layers are enhanced by using channel and spatial attention. In addition, we adopt an edge detection module to refine the prediction. We conducted extensive experiments on three datasets (i.e., LIP, ATR, and Fashion Clothing) to show the effectiveness of our method, which achieves 54.55% mIoU on the LIP dataset, 80.26% on the average F-1 score on the ATR dataset and 55.19% on the average F-1 score on the Fashion Clothing dataset

Temporal Variability of Diapycnal Mixing in the Northern South China Sea

Temporal variability of diapycnal mixing over 7 months in the northern South China Sea was examined based on McLane Moored Profiler observations from 850 to 2200 m by employing a finescale parameterization. Intensified diffusivity exceeding the order of 10−3 m2/s in magnitude was found over the first half of October 2014, and from 2 December 2014 to 21 January 2015 (a typical wintertime). Strong internal tides and winds in winter were the likely candidates for the high‐level diapycnal mixing in winter. As for the enhanced mixing during October 2014, we suspect the generation of near‐bottom near‐inertial waves through the interaction of mesoscale eddies and unique bottom topography was the cause