On Dual-Mode Driving Control Method for a Novel Unmanned Tractor With High Safety and Reliability

Due to the non-standardization and complexity of the farmland environment, it is always a huge challenge for tractors to achieve fully autonomy (work at Self-driving mode) all the time in agricultural industry. Whereas, when tractors work in the Tele-driving (or Remote driving) mode, the operators are prone to fatigue because they need to concentrate for long periods of time. In response to these, a dual-mode control strategy was proposed to integrate the advantages of both approaches, i.e., by combing Self-driving at most of the time with Tele-driving under special (complex and hazardous) conditions through switching control method. First, the state switcher was proposed, which is used for smooth switching the driving modes according to different working states of a tractor. Then, the state switching control law and the corresponding subsystem tracking controllers were designed. Finally, the effectiveness and superiority of the dual-mode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly, stably, and efficiently between the two driving modes automatically. The average control accuracy has been improved by 20% and 15% respectively, compared to the conventional Tele-driving control and Self-driving control with low-precision navigation. In conclusion, the proposed dual-mode control method can not only satisfy the operation in the complex and changeable farmland environment, but also free drivers from high-intensity and fatiguing work. This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.      

Adaptive non-singular fast terminal sliding mode based on prescribed performance for robot manipulators

A fast convergent non-singular terminal sliding mode adaptive control law based on prescribed performance is formulated to solve the uncertainties and external disturbances of robot manipulators. First, the tracking error of robot manipulators is transformed by using the prescribed performance function, which improves the transient behaviors and steady-state accuracy of robot manipulators. Then, a novel fast convergent non-singular terminal sliding mode surface is brought up according to the transformed error, and the control law is derived to meet the stability requirements of robot manipulators. In practice, the upper boundary of the lumped disturbances cannot be accurately obtained. Therefore, an adaptive prescribed performance control (PPC) controller to lumped disturbances is brought up to ensure the stability and finite-time convergence of robot manipulators. Finally, the system stability of robot manipulators is proved by the Lyapunov theorem. Simulation results and comparative analysis demonstrate the superiority and robustness of the raised strategy.     

Digital twin-assisted fault diagnosis system for robot joints with insufficient data

The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning-based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. To solve the problem of insufficient data, a digital twin-assisted fault diagnosis system for robot joints is proposed. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X-domain data for the digital twin model. Second, a CycleGAN-based digital twin model is proposed to map the virtual entity (X-domain) data to the physical entity (Y-domain) utilizing only a small amount of measured data. In the end, a test-rig for the robot joint is built to simulate the robot's working conditions, and the CNN-ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data.     

基于平面电容的油水多界面检测方法研究

含水量是衡量原油质量的重要指标,在原油的生产和储运过程中油水混合液分界面不断变化,因此全过程都要用到高精度传感器对其进行界面检测。基于电容边缘效应设计了一种侵入式平面电容传感器,其主要结构由基板和平面电极阵列两部分组成。运用有限元软件建立8电极阵列传感器模型,对不同电极工作时的电场分布进行研究,分析了平面电容传感器的检测灵敏度和成像精度。并且,研究了电极宽度、长度和相邻间距对传感器灵敏场分布的影响。对介电分布进行图像重建,使设计的平面电容阵列传感器可以检测3个分界面的高度,且经过尺寸参数优化,提高了传感器的成像精度。实验证明,运用平面电容阵列检测油水界面的方法具有可行性和有效性。     

大型发电机定子线棒端部防晕结构温度和电场分布仿真研究

大型发电机定子线棒端部防晕层温度和电场分布是表征防晕结构性能的重要参数,关系着发电机的长期安全稳定运行。工频电气试验过程中,防晕材料的电导损耗是线棒端部绝缘温度梯度分布的主要原因,电场分布取决于防晕层电导和主绝缘电容,而电导率又是电场强度的函数,因此发电机定子线棒端部防晕结构温度场和电场的计算是一个相互耦合的过程。本文借助COMSOL Multiphysics有限元分析软件多物理场仿真功能,以某大型水氢冷发电机定子线棒端部防晕结构作为研究对象,仿真分析了其工频状况下的电场分布情况和时域条件下的温度场分布情况,以匹配线棒端部防晕结构设计开发过程。将仿真温度场结果与真机线棒试验结果对比,两者吻合较好,为进一步优化设计防晕结构和材料参数奠定了基础。     

新型电力负荷管理系统国内标准体系研究与建议

现阶段,电力负荷管理系统标准体系化程度需要进一步加强。国内相关标准主要分布在电力负荷管理、用电信息采集两个业务分支内,各项标准内容与当前信息通信技术、自动化控制技术、大数据分析技术的发展不匹配,标准的功能及性能指标缺乏先进性,很多关键内容难以适应当下负荷精准分类、实时监测、实时控制的需要。首先阐述了国内电力负荷管理系统标准体系架构,包括系统类、终端类、接口类和管理类4个方面内容;其次对电力负荷管理系统标准体系现状进行分类梳理和解读,每个方面选取典型标准进行介绍,探讨了各个环节标准发展现状及实际应用中存在的问题;最后结合电网发展对电力负荷管理系统的新要求以及实际运行中面临的新问题,提出新型电力负荷管理系统标准体系完善建议及重点行动计划,对今后新型电力负荷管理系统相关标准的修订具有一定参考价值。     

人工噪声辅助的多用户安全传输方法

针对传统人工噪声辅助的安全传输方法无法适用于发射天线数等于接收天线数的多用户(Multiuser, MU)多输入多输出(Multiple-Input Multiple-Output, MIMO)系统的问题,提出了一种人工噪声辅助的多用户安全传输新方法。通过合理设计人工噪声与预编码矩阵,该方法可在同时满足所有用户传输需求的基础上,显著提升发射天线数等于接收天线数的MU-MIMO系统的安全性。同时,针对提出的方法进行了检测算法与平均保密速率的推导与分析。理论分析与仿真结果表明,所提方法可有效提升无线通信系统的安全性,在信噪比为20 dB时,该方法较传统方法的平均保密速率提升达到120%。     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

Research advances on blockchain-as-a-service: architectures,applications and challenges

Due to the complexity of blockchain technology, it usually costs too much effort to build, maintain and monitor a blockchain system that supports a targeted application. To this end, the emerging “Blockchain as a Service” (BaaS) makes the blockchain and distributed ledgers more accessible, particularly for businesses, by reducing costs and overheads. BaaS combines the high computing power of cloud computing, the pervasiveness of IoT and the decentralization of blockchain, allowing people to build their own applications while ensuring the transparency and openness of the system. This paper surveys the research outputs of both academia and industry. First, it introduces the representative architectures of BaaS systems and then summarizes the research contributions of BaaS from the technologies for service provision, roles, container and virtualization, interfaces, customization and evaluation. The typical applications of BaaS in both academic and practical domains are also introduced. At present, the research on the blockchain is abundant, but research on BaaS is still in its infancy. Six challenges of BaaS are concluded in this paper for further study directions.