无线ILC系统测控信号延时效应补偿方法

针对通过无线信道进行测控信号传输的远程迭代学习控制( ILC)系统,研究信号传输延时对控制系统性能的影响.分析了测控信号延时情况下ILC系统的收敛性问题,通过引入预控制向量,提出了ILC系统信道延时效应补偿的向量更新方法.数值仿真结果表明了所提方法的有效性.     

一种新的码片内多径时延估计方法

根据信道冲激响应的稀疏特性,提出了一种频域的时延估计压缩感知模型,将时延估计问题转化为基于欠采样数据的稀疏向量估计问题.利用离散傅里叶变换(Discrete Fourier transform,DFT)矩阵的子矩阵所满足的受限等距性(Restricted isometry property,RIP)以及信道冲激响应的稀疏特性充分降低了时延估计所需数据量的要求.分析了本文模型具有码片内多径分辨能力以及良好抗噪性能的原因,并与多信号分类(Multiple signal classification,MUSIC)和旋转不变技术的信号参数估计(Estimation of signal parameters via rotational invariance technique,ESPRIT)算法的时延估计性能进行仿真比较.仿真结果表明,本文提出的方法不需要预知多径的条数,对码片内多径时延具有较高的估计精度,其时延估计性能在特定条件下优于MUSIC和ESPRIT算法.     

一种基于高阶统计信息的信噪比估计改进算法

针对多进制正交幅度调制(Multiple quadrature amplitude modulation,MQAM)信号在低信噪比条件下估计精度不高的问题,提出了一种基于综合利用高阶统计信息的信噪比估计改进算法。根据所选高阶统计量最高阶数的不同,建立了3种信噪比与多种高阶统计量运算式之间的线性关系,利用全回归线性分析方法将3种线性关系转化为3种全回归模型,并求解模型系数。该算法充分利用了多种高阶统计量的有用信息,提高了信噪比估计精度。MQAM的仿真结果表明:在低信噪比条件下,该算法减小了信噪比估计误差,其估计性能明显优于传统的其他算法,且3种模型估计性能依次增加,可依据不同的信噪比要求对3种模型进行选取。     

最小支持度在教学质量评价系统中的数据挖掘应用

以当前的高职高专院校教育教学为平台,研究分析最小支持度关联规则挖掘技术与教学质量评价相结合的问题,针对教学质量评价结果中的盲目性、不易把握等问题,提出利用最小支持度来分析质量评价体系,获取以影响因素为条件,以评价分析为结论的关联规则来解决目前教学质量评价中的不合理性,使教学质量评价公平、公正、合理、高效,并给教学管理人员提供决策分析资料。     

3D human pose estimation in motion based on multi-stage regression

3D human pose estimation in motion is a hot research direction in the field of computer vision. However, the performance of the algorithm is affected by the complexity of 3D spatial information, self-occlusion of human body, mapping uncertainty and other problems. In this paper, we propose a 3D human joint localization method based on multi-stage regression depth network and 2D to 3D point mapping algorithm. First of all, we use a single RGB image as the input, through the introduction of heatmap and multi-stage regression to constantly optimize the coordinates of human joint points. Then we input the 2D joint points into the mapping network for calculation, and get the coordinates of 3D human body joint points, and then to complete the 3D human body pose estimation task. The MPJPE of the algorithm in Human3.6 M dataset is 40.7. The evaluation of dataset shows that our method has obvious advantages.     

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.     

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.     

避雷器接地电阻的测量与预测方法

介绍了避雷器三极法测量接地电阻的接线、测量原理及测量电压的选取,根据测得的历史数据建立了基于回归分析的接地电阻变化趋势预测模型,并通过标准离差检验证明了该预测模型可用于实际接地电阻的预测。实践表明,该测量与预测方法切实可行,能在一定程度上避免因接地电阻超标而引发的雷击事故。     

液压支架工作状态模糊识别系统研究

针对目前综采工作面液压支架压力监测系统只能监测综采工作面压力,不能对液压支架工作状态进行识别的问题,设计了一种液压支架工作状态模糊识别系统。该系统可对现有的综采工作面液压支架压力监测系统监测到的压力数据进行模糊识别,根据模糊识别输出值即可判断液压支架的5种工作状态,即降架、移架、升架、增阻、卸压保持;当液压支架处于增阻工作状态且增阻时间过长时,该系统可及时通知液压支架操作工增大液压支架压力,辅助实现顶煤破碎。测试结果验证了该系统的可行性。     

基于超声波的围岩松动圈实时监测系统设计

实时地掌握围岩松动圈的范围对矿藏的安全生产具有重要的意义。利用超声波探测的灵活性和准确性,本文提出了一种基于超声波的围岩松动圈监测系统设计方法。此方案利用超声波在岩体中的传播速度与岩体的受力状态和裂隙程度的关系来实时地监测围岩松动圈的情况,具有方便、灵活、易于操作等特点。