Approximate Piecewise Constant Curvature Equivalent Model and Their Application to Continuum Robot Configuration Estimation

The continuum robot has attracted more attention for its flexibility. Continuum robot kinematics models are the basis for further perception, planning, and control. The design and research of continuum robots are usually based on the assumption of piecewise constant curvature (PCC). However, due to the influence of friction, etc., the actual motion of the continuum robot is approximate piecewise constant curvature (APCC). To address this, we present a kinematic equivalent model for continuum robots, i.e. APCC 2L-5R. Using classical rigid linkages to replace the original model in kinematic, the APCC 2L-5R model effectively reduces complexity and improves numerical stability. Furthermore, based on the model, the configuration self-estimation of the continuum robot is realized by monocular cameras installed at the end of each approximate constant curvature segment. The potential of APCC 2L-5R in perception, planning, and control of continuum robots remains to be explored

Differentiable Frank-Wolfe Optimization Layer

Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks. The existing methods leverages the optimality conditions and implicit function theorem to obtain the Jacobian matrix of the output, which increases the computational cost and limits the application of differentiable optimization. In addition, some non-differentiable constraints lead to more challenges when using prior differentiable optimization layers. This paper proposes a differentiable layer, named Differentiable Frank-Wolfe Layer (DFWLayer), by rolling out the Frank-Wolfe method, a well-known optimization algorithm which can solve constrained optimization problems without projections and Hessian matrix computations, thus leading to a efficient way of dealing with large-scale problems. Theoretically, we establish a bound on the suboptimality gap of the DFWLayer in the context of l1-norm constraints. Experimental assessments demonstrate that the DFWLayer not only attains competitive accuracy in solutions and gradients but also consistently adheres to constraints. Moreover, it surpasses the baselines in both forward and backward computational speeds

Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics

Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation. Most previous works address this problem in a goal-conditioned way and adapt goal images to specify different tasks, which limits the multi-task learning performance and can not generalize to new tasks. Thus, we adapt language instruction to specify deformable object manipulation tasks and propose a learning framework. We first design a unified Transformer-based architecture to understand multi-modal data and output picking and placing action. Besides, we have introduced the visible connectivity graph to tackle nonlinear dynamics and complex configuration of the deformable object. Both simulated and real experiments have demonstrated that the proposed method is effective and can generalize to unseen instructions and tasks. Compared with the state-of-the-art method, our method achieves higher success rates (87.2% on average) and has a 75.6% shorter inference time. We also demonstrate that our method performs well in real-world experiments.Comment: submitted to ICRA 202

MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information are exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods

Chinese Medicine Injection Qingkailing for Treatment of Acute Ischemia Stroke: A Systematic Review of Randomized Controlled Trials

Qingkailing (QKL) injection was a famous traditional Chinese patent medicine, which was extensively used to treat the acute stages of cerebrovascular disease. The aim of this study was to assess the quantity, quality and overall strength of the evidence on QKL in the treatment of acute ischemic stroke. Methods. An extensive search was performed within MEDLINE, Cochrane, CNKI, Vip and Wan-Fang up to November 2011. Randomized controlled trails (RCTs) on QKL for treatment of acute stroke were collected, irrespective of languages. Study selection, data extraction, quality assessment, and data analyses were conducted according to the Cochrane standards, and RevMan5 was used for data analysis. Results. 7 RCTs (545 patients) were included and the methodological quality was evaluated as generally low. The pooled results showed that QKL combined with conventional treatment was more effective in effect rate, and the score of MESSS and TNF-α level compared with conventional treatment alone, but there was no significant difference in mortality of two groups. Only one trial reported routine life status. There were four trials reported adverse events, and no obvious adverse event occurred in three trials while one reported adverse events described as eruption and dizziness

Capacity Expansion of High Renewable Penetrated Energy Systems Considering Concentrating Solar Power for Seasonal Energy Balance

With the increasing proportion of variable renewable energy which owns fluctuation characteristics and the promotion of the Clean Heating policy, the seasonal energy imbalance of the system has been more and more challenging. There is a lack of effective means to mitigate this challenge under the background of gradual compression of the traditional thermal unit construction. Concentrating solar power (CSP) is a promising technology to replace thermal units by integrating emergency boilers to cope with extreme weather, and can meet long-time energy balance as a seasonal peak regulation source. In this paper, we propose a long-term high-resolution expansion planning model of the energy system under high renewable penetration which integrates CSP technology for seasonal energy balance. With the projection to 2050, by taking the energy system in Xinjiang province which is a typical area of the Clean Heating project with rich irradiance as a case study, it shows that the optimal deployment of CSP and electric boiler (EB) can reduce the cost, peak-valley difference of net load and renewable curtailment by 8.73%, 19.72% and 58.24% respectively at 65% renewable penetration compared to the base scenario.Comment: 17 pages, 13 figure

Determination of Optimal Cell and Plasmid Concentration for Transfection of I-SceI by DR-GFP Reporter

https://openworks.mdanderson.org/sumexp21/1195/thumbnail.jp