Durable Integrated K-Metal Anode with Enhanced Mass Transport through Potassiphilic Porous Interconnected Mediator

K-metal batteries have become one of the promising candidates for the large-scale energy storage owing to the virtually inexhaustible and widely potassium resources. The uneven K⁺ deposition and dendrite growth on the anode causes the batteries prematurely failure to limit the further application. An integrated K-metal anode is constructed by cold-rolling K metal with a potassiphilic porous interconnected mediator. Based on the experimental results and theoretical calculations, it demonstrates that the potassiphilic porous interconnected mediator boosts the mass transportation of K-metal anode by the K affinity enhancement, which decreases the concentration polarization and makes a dendrite-free K-metal anode interface. The interconnected porous structure mitigates the internal stress generated during repetitive deposition/stripping, enabling minimized the generation of electrode collapse. As a result, a durable K-metal anode with excellent cycling ability of exceed 1, 000 h at 1 mA cm⁻²/1 mAh cm⁻² and lower polarization voltage in carbonate electrolyte is obtained. This proposed integrated anode with fast K⁺ kinetics fabricated by a repeated cold rolling and folding process provides a new avenue for constructing a high-performance dendrites-free anode for K-metal batteries.     

基于多变量形态学特征的健康老年人认知发展预测算法

由于体积、表面积等常规形态学指标对于皮层下核团而言过于笼统,因此传统的形态特征获取手段难以检测到其表面形态的细微变化。为解决这一问题,本文提出了一种针对皮层下核团的精细特征提取算法,并将其应用到老年人认知状态预测任务上。通过表面共形参数化、表面共形表示和基于互信息的表面流配准,提取了46名被试双侧海马和杏仁核各15 000×2个顶点上的形态学特征;通过斑块选择、稀疏编码与字典学习,和最大池化的降维流程,避免了维度诅咒的同时充分保留了核团的纹理信息;最后,以树为弱学习器,采用GentleBoost算法集成了最终的强分类器做认知预测。结果显示,仅纳入海马和杏仁核两个皮层下结构的新颖特征,即可达到85%的预测准确率,为皮层下结构的精细特征发掘提供了新思路。     

Adaptive fuzzy leader-follower consensus control using sliding mode mechanism for a class of high-order unknown nonlinear dynamic multi-agent systems

In this article, a distributed leader-follower consensus approach is developed for a class of high-order unknown nonlinear dynamic multi-agent systems (MASs). Because every agent of the MAS contains multiple state variables, the existing consensus methods are not completely applicable for it. In order to find the qualified consensus protocol for this high-order MAS, sliding mode mechanism can be naturally considered for designing the consensus control because it can manage multiple state variables with the help of a constructed hyperplane. To this consensus control design, the sliding mode term is composed of all tracking error variables. Since the method does not require the switching control term around sliding surface, it can avoid the chattering phenomenon, which exits in most of the published sliding mode controls (SMCs). Furthermore, to handle the unknown nonlinear dynamic problem, the adaptive approximation strategy is implemented by employing fuzzy logic system (FLS). In the light of Lyapunov stability analysis, it is demonstrated that the proposed control approach can accomplish the consensus tasks. Finally, a numerical example is implemented to further show the desired results.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

接触网检测大数据的应用与发展

接触网是保障电气化铁路可靠运营的重要基础设施,对接触网设备进行定期检测并依据结果进行维修,能有效保障铁路供电的可靠性。文中通过对铁路行业大数据技术的应用研究,分析了接触网大数据专业运用现状与目标之间的差距,梳理了为实现大数据应用而进行的数据规范、数据清理等有效检测数据管理手段,然后介绍了对设备状态微小变化识别和接触网质量状态变化趋势分析等智能分析技术,并对其在接触网设备状态预测预警方面的应用进行了阐述,最后介绍了人工智能和云计算等在接触网检测大数据中的应用。     

交叉口混合交通流微观控制模型

为构建智能网联汽车(CAV)和有人驾驶汽车(HDV)混合通行情况下的交叉口通行机制与控制方法, 本文提出CAV专用道条件下交叉口协同通行模型. 首先, 设计CAV专用道条件下的交叉口布置, 对交叉口进行网格化处理,将CAV通行时隙和HDV绿灯相位对交叉口某部分网格某时段的占用统一到交叉口时空资源描述框架下; 其次, 建立兼顾CAV与HDV的交叉口时空网格资源分配模型, 构建自适应信号灯控制算法和CAV轨迹规划算法; 再次, 以车辆最小延误为目标进行自适应信号灯配时优化和CAV轨迹优化; 最后, 选取广州某典型交叉口建立仿真实验对所提方法的有效性进行了验证.     

四旋翼无人机姿态系统复合连续快速非奇异终端滑模控制EI北大核心CSCD

本文针对受多源干扰影响的四旋翼无人机姿态系统,基于复合连续快速非奇异终端滑模算法,研究了姿态指令变化率未知情况下的连续有限时间姿态跟踪控制问题.首先,基于四旋翼无人机姿态回路动力学模型,通过引入虚拟控制量实现姿态跟踪误差动态的三通道解耦;其次,分别针对各通道跟踪误差动态设计高阶滑模观测器,实现跟踪误差变化率和集总干扰的有限时间估计;最后,结合姿态跟踪误差变化率的估计信息,构建动态快速非奇异终端滑模面,并在控制设计中用指数幂函数代替符号函数以保证控制量连续.并且基于Lyapunov分析方法给出了姿态跟踪误差有限时间收敛的严格证明,仿真结果验证了所提方法的有效性.     

RTDMiner: 基于数据挖掘的引用计数更新缺陷检测方法

在Linux内核等大型底层系统中广泛采用引用计数来管理共享资源.引用计数需要与引用资源的对象个数保持一致,否则可能导致不恰当引用计数更新缺陷,使得资源永远无法释放或者被提前释放.为检测不恰当引用计数更新缺陷,现有静态检测方法通常需要知道哪些函数增加引用计数,哪些函数减少引用计数.而手动获取这些关于引用计数的先验知识过于费时且可能有遗漏.基于挖掘的缺陷检测方法虽然可以减少对先验知识的依赖,但难以有效检测像不恰当引用计数更新缺陷这类路径敏感的缺陷.为此,提出一个将数据挖掘技术和静态分析技术深度融合的不恰当引用计数更新缺陷检测方法 RTDMiner.首先,根据引用计数的通用规律,利用数据挖掘技术从大规模代码中自动识别增加或减少引用计数的函数.然后,采用路径敏感的静态分析方法检测增加了引用计数但没有减少引用计数的缺陷路径.为了降低误报,在检测阶段再次利用数据挖掘技术来识别例外模式.在Linux内核上的实验结果表明,所提方法能够以将近90%的准确率自动识别增加或减少引用计数的函数.而且RTDMiner检测到的排行靠前的50个疑似缺陷中已经有24个被内核维护人员确认为真实缺陷.     

MAML2: meta reinforcement learning via meta-learning for task categories

Meta-learning has been widely applied to solving few-shot reinforcement learning problems, where we hope to obtain an agent that can learn quickly in a new task. However, these algorithms often ignore some isolated tasks in pursuit of the average performance, which may result in negative adaptation in these isolated tasks, and they usually need sufficient learning in a stationary task distribution. In this paper, our algorithm presents a hierarchical framework of double meta-learning, and the whole framework includes classification, meta-learning, and re-adaptation. Firstly, in the classification process, we classify tasks into several task subsets, considered as some categories of tasks, by learned parameters of each task, which can separate out some isolated tasks thereafter. Secondly, in the meta-learning process, we learn category parameters in all subsets via meta-learning. Simultaneously, based on the gradient of each category parameter in each subset, we use meta-learning again to learn a new meta-parameter related to the whole task set, which can be used as an initial parameter for the new task. Finally, in the re-adaption process, we adapt the parameter of the new task with two steps, by the meta-parameter and the appropriate category parameter successively. Experimentally, we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task set. Additionally, our algorithm presents a more rapid adaptation process within re-adaptation. Moreover, we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.     

Stabilization of uncertain networked control systems with actuator saturation and probabilistic cyberattacks

This paper investigates the stabilization of uncertain networked control systems (NCSs) with actuator saturation and cyberattacks. The cyberattacks are governed by a set of independent random variables satisfying Bernoulli distribution. To relieve the network bandwidth load effectively, an event-triggered communication strategy is proposed. By employing Lyapunov stability theory and stochastic analysis techniques, a stability criterion is obtained for the system with actuator saturation and cyberattacks. Moreover, the desired controller gain is derived by solving some matrix inequalities. Finally, the validity and applicability of the criteria are verified through numerical examples.