A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

具有自动装卸搬运机器人的制造单元建模与分析

针对具有自动装卸搬运机器人的制造单元，着重考虑其双重资源约束的特点，提出了该系统的有限缓冲开排队网的建模方法。根据工件的加工、装卸与搬运等工艺流程，定义该系统的排队网模型的各种状态，基于连续时间马尔可夫链，采用精确解法对该模型进行分析求解，获得系统一系列的稳态性能指标。为了验证排队网建模方法的有效性，建立其对应的仿真模型，分别采用排队网模型的数值计算与仿真模型的仿真统计2种方法求出系统的性能指标进行对比，并基于排队网模型对该制造单元的系统性能进行分析，为该类制造单元的资源配置优化和设施布局优化等问题提供重要的理论支撑。     

An experimental study on the spray characteristics of pressure nozzle in a fluid bed granulation

The particles with a narrow size distribution are proper products in a fluid bed granulation and coating. As well, the process efficiency is one of the most important parameters, and the wall deposition of sprayed liquid reduces it. The modality of spray volume distribution is a key parameter in the definition of particle size distribution and process efficiency. This work is done in two steps: In the first step, an experimental study on spray characteristics including spray flux distribution, spray cone angle, and discharge coefficient is conducted. Based on the experimental data, the curves of liquid volume flux versus nozzle pressure and height are obtained. The results indicate that the flux distribution changes significantly with even small pressure and height changes. In the second step, the granulation process is carried out in a semi-industrial conical fluid bed, and the particles size distribution curves and the deposited mass on the bed wall are obtained. The results show that the precise determination of the spray flux distribution is an appropriate way to predict the well-advised nozzle pressure/height in order to achieve the narrow particle size distribution and high efficiency of the process.     

Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.     

桥梁检测机器人作业规划与位姿优化方法研究

针对桥梁底部病害人工检测的作业难题,介绍了桥梁检测机器人的工作原理,结合桥梁的结构化特征和视觉检测拍摄参数约束,研究了桥梁检测机器人作业规划与位姿优化方法。首先,提出一种以最佳拍摄模型约束的桥梁检测机器人拍摄作业位姿规划方法。在最佳拍摄规划方法的基础上,针对小箱梁桥梁和T型梁桥梁底部的褶皱结构,研究了以安全拍摄模型为约束的拍摄位姿优化方法,设计了结合拍摄偏角和拍摄距离的权重函数,推导了优化算法公式并给出了收敛证明。通过对不同拍摄参数的配置,进行了针对空心板桥梁的拍摄作业位姿规划方法仿真;针对小箱梁桥梁的结构,在位姿规划仿真结果基础上进行了位姿优化方法的仿真。最后以研制的桥梁检测机器人为对象,进行了现场测试与验证,仿真和实验结果均表明,该规划和优化方法符合桥梁拍摄检测的要求,具有很好的鲁棒性和实时性。     

表面织构形状对牙轮钻头轴承摩擦学性能影响的实验研究

牙轮钻头滑动轴承作为钻头破岩过程中传递载荷的关键部件,在低速重载的钻井过程中易发生黏着磨损失效。为改善牙轮钻头滑动轴承的耐磨性能,利用纳秒激光雕刻技术,在牙轮钻头滑动轴承轴颈表面加工了面积比为10%、深度为20μm的圆形、矩形、三角形及复合织构;基于赫兹相似理论,设计近似模拟钻头轴承工况的环-块配对副单元实验方案,并在UMT摩擦试验机上开展织构形状对钻头滑动轴承摩擦副摩擦因数、磨损量、温升变化和微观形貌影响规律的实验研究。结果表明:仿生织构的形状及布置方式对减摩和耐磨效果影响极大,其中圆形、矩形织构的减摩和耐磨性能最优,其次为三角形织构,而复合织构反而增大了摩擦因数及磨损量;单一织构对试件磨损量及温升的影响不大,而复合织构在增加摩擦的同时温度有明显升高,不利于井下高温环境下延长牙轮钻头寿命。     

深锥形槽体对细粒煤干扰沉降分级实验研究

本文重点研究了自制深锥形干扰沉降分级机在不同影响因素下对细粒煤的分级效率,结果表明:底部和中部同时给水形式的设计有利于细粒煤分级,底部与中部给水量比值对分级效率有一定影响但不明显;给料速度及浓度值越低,细粒煤分级效率越高。该装置的适宜分级条件为流量配比2∶1、给料速度0.50m/s、矿浆浓度40%时,粒级级配(0.25~0.125mm和1.0~0.5mm)分级效率接近65%,较常规分级旋流器分级效率50%~60%提高约5%~15%,实验结果表明深锥形干扰沉降分级机可有效实现细粒煤的分级。