煤矿掘进机电气系统的发展

介绍了煤矿掘进机电气系统的发展及各阶段的技术特点,并针对现代化矿井生产和信息化管理的要求,探讨了近期掘进机电气系统的发展趋势。     

聚类与信息共享的多智能体深度强化学习协同控制交通灯

该文提出一种适用于多路口交通灯实时控制的多智能体深度循环Q-网络(MADRQN),目的是提高多个路口的联合控制效果。该方法将交通灯控制建模成马尔可夫决策过程,将每个路口的控制器作为智能体,根据位置和观测信息对智能体聚类,然后在聚类内部进行信息共享和中心化训练,并在每个训练过程结束时将评价值最高的值函数网络参数分享给其它智能体。在城市交通仿真软件(SUMO)下的仿真实验结果表明,所提方法能够减少通信的数据量,使得智能体之间的信息共享和中心化训练更加可行和高效,车辆平均等待时长少于当前最优的基于多智能体深度强化学习的交通灯控制方法,能够有效地缓解交通拥堵。     

深度学习在生物序列分析领域的应用进展

随着生物技术的不断发展和生物学数据的大量产出,传统生物学数据分析方式不足以应对日益复杂庞大的生物序列数据. 面对这种情况,国内外学者逐步将深度学习应用到生物学分析中,利用其处理高维数据的优势,取得了一系列进展,并成为生物序列数据分析中的研究热门. 为了更好地了解深度学习在生物序列数据分析领域中的新进展,对该领域研究现状进行了综述. 首先,介绍深度学习应用到生物序列数据分析中的重要意义;其次,对目前应用领域中具有代表性的深度学习模型进行阐述;然后,分析深度学习在生物学领域的应用研究现状;最后,说明目前深度学习在生物学领域中的局限性,并进一步提出未来发展应考虑的因素.

     

可寄生式双极电感绝对角度传感器研究

角度传感器作为电机的核心定位部件,对电机的定位精度有重要影响。该文设计了一种双极电感式绝对角度传感器,该传感器通过周期性改变线圈中感应电压的大小来测量角度,敏感结构主要包括转子和定子,可实现与电机主轴的一体化。其中转子由内外单周期、多周期扇形铜箔组成,形成双极布局,定子由激励线圈、接收线圈以及后续处理电路组成。定子中的两组接收线圈,一组线圈由8个回路组成,对应为外沿多周期扇形铜箔,另一组由2个回路组成,对应中心180°扇形(半圆形)铜箔,两组线圈相互独立,互不影响。当转子在接收线圈上方转动时,转子中产生的涡流会使相邻两个接收线圈感应电压呈周期性正余弦形式变化。8回路线圈测量精度高,但360°内会出现多个周期信号,无法实现绝对位置测量。而2回路线圈在360°范围感应出1个周期信号,通过2回路线圈为8回路线圈提供周期数鉴别,进而解决了绝对位置测量的问题。通过算法对正余弦信号进行识别解算,以高精度转台为基准对样机进行测试,结果表明,传感器测量误差可以达到0.04°,满足电机位置控制精度要求,验证了该方案的可行性。     

利用自适应光照初始化的弱光图像增强方法

由于光照分量分解估计的高度不确定性,如何准确估计图像的光照分量一直是基于Retinex模型的图像增强方法需要解决的难题。该文提出一个简单有效的方法,准确估计图像的初始光照分量,进而实现弱光图像增强。具体地,首先根据输入图像得到其对应的光照权重矩阵,以指导光照分量的自适应初始化估计;随后在光照结构约束下,对初始光照分量优化估计,并进一步执行非线性光照调整;最终结合Retinex模型得到增强结果。实验表明,该方法不仅能够实现准确的图像分解估计,而且与现有的弱光图像增强方法相比,该文所提方法在多个数据集上的主观视觉效果和客观评价指标都有更好的表现,同时也保持着良好的运行效率。     

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output (MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control (ILC) scheme based on the zeroing neural networks (ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer (IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise, an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

     

Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning

Goal-conditioned reinforcement learning (RL) is an interesting extension of the traditional RL framework, where the dynamic environment and reward sparsity can cause conventional learning algorithms to fail. Reward shaping is a practical approach to improving sample efficiency by embedding human domain knowledge into the learning process. Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity. This paper proposes a novel magnetic field-based reward shaping (MFRS) method for goal-conditioned RL tasks with dynamic target and obstacles. Inspired by the physical properties of magnets, we consider the target and obstacles as permanent magnets and establish the reward function according to the intensity values of the magnetic field generated by these magnets. The nonlinear and anisotropic distribution of the magnetic field intensity can provide more accessible and conducive information about the optimization landscape, thus introducing a more sophisticated magnetic reward compared to the distance-based setting. Further, we transform our magnetic reward to the form of potential-based reward shaping by learning a secondary potential function concurrently to ensure the optimal policy invariance of our method. Experiments results in both simulated and real-world robotic manipulation tasks demonstrate that MFRS outperforms relevant existing methods and effectively improves the sample efficiency of RL algorithms in goal-conditioned tasks with various dynamics of the target and obstacles.

     

Local-to-Global Causal Reasoning for Cross-Document Relation Extraction

Cross-document relation extraction (RE), as an extension of information extraction, requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts. Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity. However, similarity-based methods cannot distinguish valid information from highly similar retrieved documents well. How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue. To address this problem, we design a novel local-to-global causal reasoning (LGCR) network for cross-document RE, which enables efficient distinguishing, filtering and global reasoning on complex information from a causal perspective. Specifically, we propose a local causal estimation algorithm to estimate the causal effect, which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE. Furthermore, based on the causal effect, we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning. Experimental results under the closed and the open settings of the large-scale dataset CodRED demonstrate our LGCR network significantly outperforms the state-of-the-art methods and validate the effectiveness of causal reasoning in confusing information processing.

     

基于分岔理论的磁阀式可控电抗器匝数比研究

匝数比取值对磁阀式可控电抗器(MVCR)的工作性能有较大影响.匝数比取值合理.能够保证MVCR的端电压瞬间变化时,其工作电流迅速稳定在某一个当前值;若匝数比取值不当.会使MVCR在运行过程中表现出很强的非线性特性,对系统的运行特性造成极大的影响.甚至无法工作.利用非线性动力学的分岔理论,阐述了一种固定周期离散映射的建模方法.首先建立MVCR的离散模型.然后利用该模型计算得到匝数比不同取值时等效工作电流的稳态值.在分析稳态值分岔现象的基础上,给出了匝数比的合理取值范围.仿真结果验证了所提方法的有效性和合理性.