Tool-Center-Point control of the KAI manipulator using constrained QP optimization

The KAI manipulator is a four joint mobile manipulator, which will be used within the German road clearance package to investigate improvised explosive devices and ordnance from within an armored vehicle. To improve handling of the manipulator, a Tool-Center-Point (TCP) control is implemented. By using constrained quadratic optimization (cQP) it is possible to allow for the control of the manipulator within three different operating spaces. The QP is formulated to account for constraints in the joint angular rates and TCP velocities, as well as additional velocity constraints, e.g. on the movement of the center of mass of the manipulator. The proposed algorithm is able to handle redundant as well as non redundant manipulator kinematics. By using an efficient QP solver the algorithm can be used within a real-time trajectory generation scheme. The performance of the algorithm is demonstrated using simulation results and validated by measurements of the TCP control.     

Preparation and electrocatalytic properties of spinel CoxFe3-xO4 nanoparticles

The catalysts are often used in fuel cells and metal-air batteries to speed up electrochemical reactions. In this study, we prepared CoFe₂O₄ nanoparticles with mainly inverse spinel structure and FeCo₂O₄ nanoparticles with mainly spinel structure as bifunctional catalysts by hydrothermal method. After annealing at 350 °C, pure CoFe₂O₄ and FeCo₂O₄ nanoparticles with uniform size distribution have been obtained. The CoFe₂O₄ nanoparticles showed high current density of 5.5 mA/cm² at −0.8 V in the ORR test. It's low Tafel slope of 83.0 mV/dec further confirmed the excellent ORR catalytic properties of CoFe₂O₄ nanoparticles. Furthermore, the CoFe₂O₄ nanoparticles also showed good OER properties with satisfied current density of 35.7 mV/cm² at l.0 V and low OER Tafel slope of 71.0 mV/dec. Both the ORR and OER properties of CoFe₂O₄ nanoparticles showed good time stability which were compared with FeCo₂O₄ nanoparticles. These results indicated that CoFe₂O₄ nanoparticles with mainly inverse spinel structure had better electrocatalytic performance than FeCo₂O₄ nanoparticles with mainly spinel structure. The CoFe₂O₄ nanoparticles with mainly inverse spinel structure show a significant potential application in rechargeable battery.     

Preparation of nanocrystalline nickel oxide from nickel hydroxide using spark plasma sintering and inverse Hall-Petch related densification

Nanocrystalline nickel oxide (NiO) was prepared from nickel hydroxide by Spark plasma sintering (SPS) and the mechanisms involved in the densification of NiO were studied. Reverse precipitated nickel hydroxide powders were SPS processed at 400, 600 and 700?°C with 70?MPa pressure. Pure NiO with 12?nm crystallite size formed after 400?°C sintering process. However NiO grains had grown to 18 and 38?nm after 600 and 700?°C sintering respectively. NiO pellets prepared using 600 and 700?°C SPS sintering schedules had relative densities of 83% and 94% respectively. Two displacement rate regimes were observed during densification of NiO in both 600 and 700?°C sintering processes. Decomposition of nickel hydroxide and particle sliding of NiO led to first displacement rate maximum while inverse Hall-Petch based plastic deformation facilitated densification during the constant second displacement rate regime. No densification occurred during sintering holding times indicating the limited role that diffusion played during densification.     

Physics-informed deep learning for one-dimensional consolidation

Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research.In this context,a review of related research is first presented and discussed.The potential offered by such physics-informed deep learning models for computations in geomechanics is demonstrated by application to one-dimensional(1D)consolidation.The governing equation for 1D problems is applied as a constraint in the deep learning model.The deep learning model relies on automatic differentiation for applying the governing equation as a constraint,based on the mathematical approximations established by the neural network.The total loss is measured as a combination of the training loss(based on analytical and model predicted solutions)and the constraint loss(a requirement to satisfy the governing equation).Two classes of problems are considered:forward and inverse problems.The forward problems demonstrate the performance of a physically constrained neural network model in predicting solutions for 1D consolidation problems.Inverse problems show prediction of the coefficient of consolidation.Terzaghi’s problem,with varying boundary conditions,is used as a numerical example and the deep learning model shows a remarkable performance in both the forward and inverse problems.While the application demonstrated here is a simple 1D consolidation problem,such a deep learning model integrated with a physical law has significant implications for use in,such as,faster realtime numerical prediction for digital twins,numerical model reproducibility and constitutive model parameter optimization.     

岩土工程逆反分析的进化神经网络研究

逆反分析实质为系统的逆辩识过程，对此采用神经网络描述比较合理有效。基于提出的网络权值及其结构同时进化的新型进化神经网络模型，进行了岩土工程逆反分析的研究，并用一个算例进行了效果验证。结果证明反分析精度较高，其效果良好。由研究得到以下结论：网络训练样本应由正分析得到，且应采用正交实验设计进行样本选取；神经网络逆反分析时多参数反演的精度低于单参数反演。     

烷醇酰胺在胶磷矿和白云石浮选分离中的作用

探讨了烷醇酰胺影响胶磷矿和白云石分离的机理。结果表明 :烷醇酰胺只能作为增效剂 ,且在矿物表面以物理吸附形式存在     

反渗透滤筒对脉冲布袋除尘器清灰的影响研究

通过在滤袋内部安装特定的反渗透装置，阻碍诱导气流进入滤袋上部侧壁，减弱负压的影响，试验结果表明加入反渗透滤筒后，滤袋上部负压峰值可以降低50％，而清灰的正峰压基本没有变化，从而提高了滤袋整体的清灰能力。     

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling

Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. PRACTICAL IMPLICATIONS: The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.     

逆作法施工技术浅析

逆作法施工技术是深基础高层建筑施工的有效方法。它主要特点是可节约工期，最大限度地减少工程基础施工对相邻建筑物的影响。本文简要介绍厦门九州大厦地下室逆作法施工方法以及为适应逆作法施工的结构构造处理措施。