Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

教学型智能制造生产线的设计

以智能制造主要技术元素为切入点,设计了教学型智能制造生产线。介绍了企业资源计划系统、制造执行系统、立体仓储单元、工业机器人、数控加工设备、激光打标设备、视觉检测设备、自动装配设备、物料传输设备等教学型智能制造生产线的组成结构,分析了工作流程。这一教学型智能制造生产线具有良好的柔性,可以根据客户需求增减工作单元、调整零件类型、定制商标等,具有较高的实用价值。     

基于DSP的智能电源控制单元设计与实现

本文利用TI公司TMS320F28335芯片高效的浮点运算能力,结合片上丰富的外设,设计并实现了一种具有高可靠性的智能电源控制单元。该控制单元周期性地对各片上外设进行自检维护,完成多路负载通道控制、电压、电流的实时监控,并对故障进行指示、处理和上报,同时提供人机交互界面更新状态信息。经过验证,该控制单元工作稳定,具备良好的工程应用价值。     

Utilizing sensor data to model students’ creativity in a digital environment

While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.     

综放高瓦斯矿井煤层瓦斯涌出量综合预测

为了成功预测竹林山煤矿综放高瓦斯矿井大采高工作面煤层瓦斯涌出量,以主采3号煤层为主要研究对象,针对3号煤层以往开采情况,通过布设测点测量其煤层瓦斯含量和了解相邻矿井瓦斯含量,采用分源预测法、回归法及统计法等预测方法得到了3号煤层瓦斯含量的分布规律,并绘制了3号煤层的瓦斯含量等值线图。对矿井不同生产时期的瓦斯含量进行预测,得到了生产前期、中期及后期采区的最大绝对瓦斯涌出量和最大相对瓦斯涌出量,说明了竹林山煤矿各个时期均属于高瓦斯矿井。     

矿山地质环境恢复治理技术方法研究

自工业革命以来人类社会的发展和经济的建设就离不开对矿产资源的使用。我国自改革开放以来,经济得到了前所未有的飞速发展,人民的生活水平日益提高,但经济高速发展的背后,存在着对矿产资源过度开采和使用的问题。矿产资源的不合理开采和使用对原有的自然环境造成了严重的破坏,为了改善此现状,对矿山地质环境的恢复治理技术的研究就至关重要。本文就矿山地质恢复治理的意义进行阐述,进一步对其方法进行探究,最终提出有关的措施和方法。     

煤矿下山巷道水与空气不耦合装药爆破技术对比研究

为了提高三级煤矿许用炸药能量利用率,改善爆破破岩效果，保证开采区的工作效率，对不耦合装药结构进行优化，以理论推导的方式分别得出空气不耦合与水不耦合装药系数对爆生气体准静压力的影响，并进行了对比分析；通过实验室爆破模型试验研究水和空气径向不耦合分别作用下爆炸应力波的传播分布特性，分析峰值应力与测点到炮孔比例距离、不耦合系数之间的关系，并与理论分析对比验证。结果表明：当不耦合系数相同时，炮孔处水不耦合装药爆炸应力波峰值高于空气不耦合装药，随着不耦合系数的增加，炮孔处爆炸应力波的峰值应力减小；模型试验测得同一测点的水不耦合装药峰值应力是空气不耦合装药峰值应力的1.15~1.52倍，峰值应力作用时间长于空气不耦合装药，同一测点水和空气不耦合装药结构测得的应力波及其峰值应力均随不耦合系数的增加呈负幂函数衰减，且空气不耦合装药时衰减得更快，与理论分析一致；贵州某煤矿下山巷道，于梯形掘进断面实施传统空气不耦合装药结构掏槽爆破，效果不甚理想，运用水不耦合装药爆破技术，提高了炮孔利用率，减小了岩石破碎块度，使爆堆集中，显著提高了岩石破碎效果与出渣效率。     

A three-dimensional surface measurement system implemented with Gaussian process based adaptive sampling

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.     

Biologically Inspired Design of Context-Aware Smart Products

The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware.