32 research outputs found
Modeling and adaptive tracking for stochastic nonholonomic constrained mechanical systems
This paper is devoted to the problem of modeling and trajectory tracking for stochastic nonholonomic dynamic systems in the presence of unknown parameters. Prior to tracking controller design, the rigorous derivation of stochastic nonholonomic dynamic model is given. By reasonably introducing so-called internal state vector, a reduced dynamic model, which is suitable for control design, is proposed. Based on the backstepping technique in vector form, an adaptive tracking controller is then derived, guaranteeing that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The efficiency of the controller is demonstrated by a mechanics system: a vertical mobile wheel in random vibration environment
Immunoregulatory effects of Huaier (Trametes robiniophila Murr) and relevant clinical applications
Huaier (Trametes robiniophila Murr) is a medicinal fungus of traditional Chinese medicine with more than 1000 years of history of clinical application. Its remarkable anticancer activities has led to its application in treating diverse malignancies. In recent years, the immunomodulatory effects of Huaier have been uncovered and proved to be beneficial in a plethora of immune-related diseases including cancer, nephropathy, asthma, etc. In this review, we comprehensively summarized the active components of Huaier, its regulatory activities on multifaceted aspects of the immune system, its application in various clinical settings as well as toxicologic evidence. Based on currently available literature, Huaier possesses broad-spectrum regulatory activities on various components of the innate and adaptive immune system, including macrophages, dendritic cells, natural killer cells, T and B lymphocytes, etc. Versatile immunologic reactions are under the regulation of Huaier from expression of damage-associated molecular patterns, immune cell activation and maturation to cell proliferation, differentiation, antibody production, expression of cytokines and chemokines and terminal intracellular signal transduction. Moreover, some modulatory activities of Huaier might be context-dependent, typically promoting the restoration toward normal physiological status. With excellent efficacy and minimal side effects, we foresee more extensive application of Huaier for treating immune-related disorders
NDDepth: Normal-Distance Assisted Monocular Depth Estimation
Monocular depth estimation has drawn widespread attention from the vision
community due to its broad applications. In this paper, we propose a novel
physics (geometry)-driven deep learning framework for monocular depth
estimation by assuming that 3D scenes are constituted by piece-wise planes.
Particularly, we introduce a new normal-distance head that outputs pixel-level
surface normal and plane-to-origin distance for deriving depth at each
position. Meanwhile, the normal and distance are regularized by a developed
plane-aware consistency constraint. We further integrate an additional depth
head to improve the robustness of the proposed framework. To fully exploit the
strengths of these two heads, we develop an effective contrastive iterative
refinement module that refines depth in a complementary manner according to the
depth uncertainty. Extensive experiments indicate that the proposed method
exceeds previous state-of-the-art competitors on the NYU-Depth-v2, KITTI and
SUN RGB-D datasets. Notably, it ranks 1st among all submissions on the KITTI
depth prediction online benchmark at the submission time.Comment: Accepted by ICCV 2023 (Oral
IEBins: Iterative Elastic Bins for Monocular Depth Estimation
Monocular depth estimation (MDE) is a fundamental topic of geometric computer
vision and a core technique for many downstream applications. Recently, several
methods reframe the MDE as a classification-regression problem where a linear
combination of probabilistic distribution and bin centers is used to predict
depth. In this paper, we propose a novel concept of iterative elastic bins
(IEBins) for the classification-regression-based MDE. The proposed IEBins aims
to search for high-quality depth by progressively optimizing the search range,
which involves multiple stages and each stage performs a finer-grained depth
search in the target bin on top of its previous stage. To alleviate the
possible error accumulation during the iterative process, we utilize a novel
elastic target bin to replace the original target bin, the width of which is
adjusted elastically based on the depth uncertainty. Furthermore, we develop a
dedicated framework composed of a feature extractor and an iterative optimizer
that has powerful temporal context modeling capabilities benefiting from the
GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and
SUN RGB-D datasets demonstrate that the proposed method surpasses prior
state-of-the-art competitors. The source code is publicly available at
https://github.com/ShuweiShao/IEBins.Comment: Accepted by NeurIPS 202
NENet: Monocular Depth Estimation via Neural Ensembles
Depth estimation is getting a widespread popularity in the computer vision
community, and it is still quite difficult to recover an accurate depth map
using only one single RGB image. In this work, we observe a phenomenon that
existing methods tend to exhibit asymmetric errors, which might open up a new
direction for accurate and robust depth estimation. We carefully investigate
into the phenomenon, and construct a two-level ensemble scheme, NENet, to
integrate multiple predictions from diverse base predictors. The NENet forms a
more reliable depth estimator, which substantially boosts the performance over
base predictors. Notably, this is the first attempt to introduce ensemble
learning and evaluate its utility for monocular depth estimation to the best of
our knowledge. Extensive experiments demonstrate that the proposed NENet
achieves better results than previous state-of-the-art approaches on the
NYU-Depth-v2 and KITTI datasets. In particular, our method improves previous
state-of-the-art methods from 0.365 to 0.349 on the metric RMSE on the NYU
dataset. To validate the generalizability across cameras, we directly apply the
models trained on the NYU dataset to the SUN RGB-D dataset without any
fine-tuning, and achieve the superior results, which indicate its strong
generalizability. The source code and trained models will be publicly available
upon the acceptance
Influencing factors of phase angle and prediction of clinical outcome in patients with non-dialysis chronic kidney disease
Objective To investigate the influencing factors of phase angle (PhA) and its predictive ability of clinical outcome in patients with non-dialysis chronic kidney disease (CKD). Methods Sixty patients with non-dialysis CKD admitted to Department of Nephrology of Tongji Hospital from January to November 2019 were recruited in this study. All of them received hematuria biochemical test, nutritional risk screening and assessment, SF-36 quality of life survey, anthropometry, grip strength test and body composition detection. According to their PhA, they were divided into group A (PhA>4.5°) and group B (PhA≤4.5°), with 30 cases in each group. The correlation of phase angle with nutrition, inflammation, disease, moisture and other related indicators was analyzed, and the main influencing factors of PhA were analyzed with stepwise multiple linear regression analysis. Follow-up was conducted for 3 to 4 years to compare the survival of patients between the 2 groups. Results The blood total protein (TP), albumin (ALB), prealbumin (PAB), life quality score and grip strength were significantly better in group A than group B (P < 0.05). The high-sensitive C-reactive protein (CRP), total cholesterol (TC), low density lipoprotein (LDL), total 24h urinary protein (UTP), total 24h urinary albumin (UMA), urinary albumin creatinine ratio (ACR), modified SGA score and extracellular water (ECW/TBW) ratio were obviously lower in group A than group B (P < 0.05). Correlation analysis showed that TP, ALB and PAB were positively correlated with PhA (P < 0.05), and CRP, TC, LDL, UMA, modified SGA score and ECW/TBW ratio were negatively correlated with PhA (P < 0.05). Stepwise multiple linear regression analysis indicated that that ECW/TBW ratio was an independent factor affecting PhA. The mortality rate was high in group B (20.00%, 6/30) than group A (6.67%, 2/30) during follow-up. Cox regression survival analysis suggested that age and PhA were the main factors affecting the survival of non-dialysis CKD patients. The older the age, the higher the risk of death, with the risk of death being increased to 2.605 times for every 1° reduction of PhA. Conclusion In non-dialysis CKD patients, ECW/TBW ratio is an independent factor affecting PhA, and PhA is negatively correlated to ECW/TBW ratio. PhA can well predict the survival of these patients. And the smaller the phase angle is, the higher the mortality rate