Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition. We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data. We represent a skeleton action sequence as a 3D skeleton cloud and colorize each point in the cloud according to its temporal and spatial orders in the original (unannotated) skeleton sequence. Leveraging the colorized skeleton point cloud, we design an auto-encoder framework that can learn spatial-temporal features from the artificial color labels of skeleton joints effectively. Specifically, we design a two-steam pretraining network that leverages fine-grained and coarse-grained colorization to learn multi-scale spatial-temporal features. In addition, we design a Masked Skeleton Cloud Repainting task that can pretrain the designed auto-encoder framework to learn informative representations. We evaluate our skeleton cloud colorization approach with linear classifiers trained under different configurations, including unsupervised, semi-supervised, fully-supervised, and transfer learning settings. Extensive experiments on NTU RGB+D, NTU RGB+D 120, PKU-MMD, NW-UCLA, and UWA3D datasets show that the proposed method outperforms existing unsupervised and semi-supervised 3D action recognition methods by large margins and achieves competitive performance in supervised 3D action recognition as well.Comment: This work is an extension of our ICCV 2021 paper [arXiv:2108.01959] https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Skeleton_Cloud_Colorization_for_Unsupervised_3D_Action_Representation_Learning_ICCV_2021_paper.htm

Effect of Mean Stress on the Fatigue Life Prediction of Notched Fiber-Reinforced 2060 Al-Li Alloy Laminates under Spectrum Loading

This paper presents a study on the fatigue life prediction of notched fiber-reinforced 2060 Al-Li alloy laminates under spectrum loading by applying the constant life diagram. Firstly, a review on the state of the art of constant life diagram models for the life prediction of composite materials is given, which highlights the effect on the forecast accuracy. Then, the fatigue life of notched fiber-reinforced Al-Li alloy laminates (2/1 laminates and 3/2 laminates) is tested under cyclic stress, which has different stress cycle characteristics (constant amplitude loading and Mini-Twist spectrum loading). The introduced models are successfully realized based on the available experimental data of examined laminates. In the case of Mini-Twist spectrum loading, the effect of the constant life diagram on the life prediction accuracy of examined laminates is studied based on the rainflow-counting method and Miner damage criteria. The results show that the simple Goodman model and piecewise linear model have certain advantages compared to other complex models for the life prediction of notched fiber metal laminates with different structures under Mini-Twist loading. From the engineering perspective, the S-N curve prediction based on the piecewise linear model is most applicable and accurate among all the models

Cumulative production curve method for the quantitative evaluation on the effect of oilfield development measures: A case study of the nitrogen injection pilot in Yanling oilfield, Bohai Bay Basin

To evaluate the effect of oilfield development measures quantitatively, based on the theory of Arps production decline, this study deduced a linear relation between the product of cumulative production with production time (Npt) and production time (t), and established the cumulative production curve method for quantitative evaluation on the effect of development measures. The nitrogen injection pilot in Yanling oilfield was taken as an example to calculate the recoverable reserves before and after the nitrogen injection, and through the variation of recoverable reserves, the effect of the nitrogen injection on actual production was quantitatively evaluated. Similarity analysis of decline curve shape in the late period shows that the method is not restricted by decline types and the relationship curve between Npt and t in the late development is always tending to a straight line. The cumulative production curve method is not only suitable for single wells but also not restricted by reservoir types. Combined with derivative curve in diagnosis, it reflects the microscopic variations of the slope in the straight line segment and the variations of recoverable reserves and the process of reserve producing. The single wells in the Yanbei nitrogen injection pilot were evaluated quantitatively using the cumulative production curve method, the results show that: the nitrogen injection causes obvious productivity increase of the oil wells in the hillside of the buried hill, productivity decrease of the oil wells at the top of buried hill, and little influence on the productivity of oil wells in the margins of burial hill. Key words: development effect, quantitative evaluation, cumulative production, recoverable reserves, Bohai Bay Basin, Yanling Oilfield, nitrogen-injection pilo

One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching

One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge of collecting and annotating large-scale skeleton action data. However, most existing studies match skeleton sequences by comparing their feature vectors directly which neglects spatial structures and temporal orders of skeleton data. This paper presents a novel one-shot skeleton action recognition technique that handles skeleton action recognition via multi-scale spatial-temporal feature matching. We represent skeleton data at multiple spatial and temporal scales and achieve optimal feature matching from two perspectives. The first is multi-scale matching which captures the scale-wise semantic relevance of skeleton data at multiple spatial and temporal scales simultaneously. The second is cross-scale matching which handles different motion magnitudes and speeds by capturing sample-wise relevance across multiple scales. Extensive experiments over three large-scale datasets (NTU RGB+D, NTU RGB+D 120, and PKU-MMD) show that our method achieves superior one-shot skeleton action recognition, and it outperforms the state-of-the-art consistently by large margins.Comment: 8 pages, 4 figures, 6 tables. Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

Parallel vacuum arc discharge with microhollow array dielectric and anode

An electrode configuration with microhollow array dielectric and anode was developed to obtain parallel vacuum arc discharge. Compared with the conventional electrodes, more than 10 parallel microhollow discharges were ignited for the new configuration, which increased the discharge area significantly and made the cathode eroded more uniformly. The vacuum discharge channel number could be increased effectively by decreasing the distances between holes or increasing the arc current. Experimental results revealed that plasmas ejected from the adjacent hollow and the relatively high arc voltage were two key factors leading to the parallel discharge. The characteristics of plasmas in the microhollow were investigated as well. The spectral line intensity and electron density of plasmas in microhollow increased obviously with the decease of the microhollow diameter

An NMR-Based Method for Multiphase Methane Characterization in Coals

Discriminating multiphase methane (adsorbed and free phases) in coals is crucial for evaluating the optimal gas recovery strategies of coalbed methane (CBM) reservoirs. However, the existing volumetric-based adsorption isotherm method only provides the final methane adsorption result, limiting real-time dynamic characterization of multiphase methane in the methane adsorption process. In this study, via self-designed nuclear magnetic resonance (NMR) isotherm adsorption experiments, we present a new method to evaluate the dynamic multiphase methane changes in coals. The results indicate that the T2 distributions of methane in coals involve three different peaks, labeled as P1 (T2 T2 = 20–300 ms), and P3 (T2 > 300 ms) peaks, corresponding to the adsorbed phase methane, free phase methane between particles, and free phase methane in the sample cell, respectively. The methane adsorption Langmuir volumes calculated from the conventional volumetric-based method qualitatively agree with those obtained from the NMR method, within an allowable limit of approximately ~6.0%. Real-time dynamic characterizations of adsorbed methane show two different adsorption rates: an initial rapid adsorption of methane followed by a long stable state. It can be concluded that the NMR technique can be applied not only for methane adsorption capacity determination, but also for dynamic monitoring of multiphase methane in different experimental situations, such as methane adsorption/desorption and CO2-enhanced CBM

An NMR-Based Method for Multiphase Methane Characterization in Coals

Discriminating multiphase methane (adsorbed and free phases) in coals is crucial for evaluating the optimal gas recovery strategies of coalbed methane (CBM) reservoirs. However, the existing volumetric-based adsorption isotherm method only provides the final methane adsorption result, limiting real-time dynamic characterization of multiphase methane in the methane adsorption process. In this study, via self-designed nuclear magnetic resonance (NMR) isotherm adsorption experiments, we present a new method to evaluate the dynamic multiphase methane changes in coals. The results indicate that the T2 distributions of methane in coals involve three different peaks, labeled as P1 (T2 < 8 ms), P2 (T2 = 20–300 ms), and P3 (T2 > 300 ms) peaks, corresponding to the adsorbed phase methane, free phase methane between particles, and free phase methane in the sample cell, respectively. The methane adsorption Langmuir volumes calculated from the conventional volumetric-based method qualitatively agree with those obtained from the NMR method, within an allowable limit of approximately ~6.0%. Real-time dynamic characterizations of adsorbed methane show two different adsorption rates: an initial rapid adsorption of methane followed by a long stable state. It can be concluded that the NMR technique can be applied not only for methane adsorption capacity determination, but also for dynamic monitoring of multiphase methane in different experimental situations, such as methane adsorption/desorption and CO2-enhanced CBM