Disturbance observer-based tracking control with prescribed performance specifications for a class of nonlinear systems subject to mismatched disturbances

This work investigates simultaneous prescribed performance tracking control and mismatched disturbance rejection problems for a class of strict-feedback nonlinear systems. A novel control scheme combining prescribed performance control, disturbance observer technique, and backstepping method is proposed. The disturbance estimations are introduced into the design of virtual control law design in each step to compensate the mismatched disturbances. To further improve the control performance, a prescribed performance function characterizing the error convergence rate, maximum overshoot, and steady-state error is used to construct the composite controller. The proposed controller guarantees transient and steady-state performance specifications of tracking error and provides much better disturbance attenuation ability simultaneously. Rigorous stability analysis for the closed-loop system is established by direct Lyapunov function method. It is shown that all the states in the resulting closed-loop system are stable, and the tracking error evolves within the prescribed performance boundaries and asymptotically converges to zero even in the presence of mismatched external disturbances. Finally, theoretical results are illustrated and demonstrated by two simulation examples.     

Visual video evaluation association modeling based on chaotic pseudo-random multi-layer compressed sensing for visual privacy-protected keyframe extraction

In current society, artificial intelligence processing technology offers convenient video monitoring, but also raises the risk of privacy leakage. Theoretically, the data used in intelligent video processing methods may directly convey visual information containing private content. For the above problem, this paper uses a multi-layer visual privacy-protected (VPP) coding method to blur private content in the video at the visual level, while avoiding the loss of important visual features contained in the video as much as possible. And this provides a guarantee of the quality of the subsequent keyframe extraction step. Then a visual evaluation algorithm is proposed for assessing the quality of VPP-encoded video privacy protection. And the experiment shows that the results are consistent with those of subjective evaluation. In addition, for VPP-encoded video, we propose an unsupervised two-layer clustering keyframe extraction method with corresponding performance evaluation index. Finally, an association model is established to balance the privacy protection quality and the keyframe extraction performance.     

神经网络结构搜索方法综述

如今,深度学习广泛地应用于生活、工作中的各个方面,给我们带来了极大的便利.在此背景下,需要设计针对不同任务的神经网络结构,满足不同的需求.但是,人工设计神经网络结构需要专业的知识,进行大量的实验.因此,神经网络结构搜索算法的研究显得极为重要.神经网络结构搜索(NAS)是自动深度学习(AutoDL)过程中的一个基本步骤,对深度学习的发展与应用有着重要的影响.早期,一些神经网络结构搜索算法虽然搜索到了性能优越的神经网络结构,但是需要大量的计算资源且搜索效率低下.因此,研究人员探索了多种设计神经网络结构的算法,也提出了许多减少计算资源、提高搜索效率的方法.本文首先简要介绍了神经网络结构的搜索空间,其次对神经网络结构搜索算法进行了全面的分类汇总、分析,主要包括随机搜索算法、进化算法、强化学习、基于梯度下降的方法、基于顺序模型的优化算法,再其次探索并总结了提高神经网络结构搜索效率的方法,最后探讨了目前神经网络结构搜索工作中存在的问题以及未来的研究方向.     

一种针对TomcatFilter型的MemShell检测技术研究

近些年来,随着计算机技术的不断发展和应用,Web应用技术也在快速更迭,与其一起发展的还有木马后门技术,但传统的木马后门技术已经不能满足攻击者的需求,因而基于内存攻击的方式不断涌现,包括powershell内存载入攻击、.NET assembly托管代码注入攻击以及内存马(Memory WebShell,MemShell)攻击等,这些攻击方式为现有的安全防御检测机制带来了极大的挑战。因而业界对面向解决基于内存的攻击尤其是内存马的攻击展现出了强烈的需求。但当前业内针对内存马的检测能力较弱,学术界也缺乏对该领域的研究工作,所以本文提出了一种针对Tomcat Filter型的内存马检测方法。通过研究发现,内存马其最核心技术便是无文件(Fileless)及不落地(Living off the Land),但尽管如此,内存马最终会在内存中展现其功能并执行命令,所以内存是所有威胁的交汇点,因此本文将Java虚拟机(Java Virtual Machine,JVM)作为起始点,首先利用JVM内存扫描技术遍历出JVM内存中加载的所有Filter类型对象,但需要注意的是这些对象并非都是有威胁的,并且每一个对象都具有一定的特征,所以可以对这些特征通过人工经验进行分类并且筛选出具有代表性的特征向量,然后获取每一个Filter类型对象的所有代表特征向量,并根据特征向量的值梳理出异常表现序列;最后,利用朴素贝叶斯算法将大量正常和异常的Filter对象的异常表现序列作为训练样本,计算出对应项的条件概率并形成贝叶斯分类器。利用训练出的贝叶斯分类器就可以构建出一个内存马检测模型,该模型能够有效得针对该类型的内存马进行检测。实验结果表明,本文提出的方法针对Tomcat Filter型内存马的检测,实现了零误报率和94.07%的召回率。     

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.     

信息系统性能效率评估方法

针对当前信息系统性能效率评估模型简单、评估结果具有随机性和模糊性的问题,给出一种信息系统性能效率评估方法;分析并绘制信息系统性能效率执行框架图,构建评估指标,应用改进的功效系数法从多层级角度归一化评估指标的评分,基于权重间的对比性和冲突性,构建综合多位专家主观权重和客观权重的多权重组合赋权法,既克服单一专家的主观偏向性,又综合考量主客观权重优点,并采用云模型结合组合赋权权重以自然语言描述性能效率综合评估结果,克服综合评估的模糊性与随机性;最后,将该方法应用于某大型CRM信息系统,结果表明该CRM系统综合评估等级为适中性能,基本满足性能效率的要求,该结果与预案评审结果一致,验证了该方法在性能效率评估中的有效性和适用性。     

基于贝叶斯最优最劣和改进物元可拓的特高压输电工程综合效益评价

由于中国能源消费与能源分布格局的不一致性,建设具有远距离、大容量以及低损耗的特高压输电工程具有重要的意义,而对特高压输电工程效益的评估是判断该工程能否实现预期目标、满足工程建设要求的关键。从运行、财务、环境以及社会4个维度构建了特高压输电工程综合效益评估指标体系,提出一种基于贝叶斯最优最劣和改进物元可拓方法的特高压输电工程综合效益评价模型,前者结合贝叶斯理论和最优最劣模型确定各指标的权重,后者通过对传统物元可拓方法进行改进,从而实现对特高压输电工程综合效益的等级评定。通过对某±800 kV特高压直流输电工程实证研究,结果表明该工程的综合效益处于“较好”级别。算例结果证明了该模型用于特高压输电工程综合效益评价的科学性与有效性。     

同性极式感应子电机有限元分析及试验研究

同性极式感应子电机具有无刷励磁和结构简单等特点,在飞轮储能、脉冲功率电源等领域有着广阔的应用前景。首先给出同性极式感应子电机的拓扑结构和运行原理。采用有限元法分析了矩形槽、弧形槽和正弦形槽转子对电机气隙磁密、反电动势和转矩等电磁性能的影响,研究不同槽形的转子在高速旋转下的应力分布规律。然后,综合考虑电磁和机械特性,选择弧形槽转子结构的感应子电机作为对象,对其性能进行分析。最后,对一台感应子电机样机进行试验测试,验证了相关分析的合理性。研究结果表明：同性极式感应子电机采用正弦形槽转子虽能获得较为正弦的气隙磁密,但在相同槽深条件下其基波分量比矩形槽和弧形槽约少了25.2%和24.3%,并且输出转矩较低。此外,矩形槽和正弦形槽都出现了明显的应力集中,分别位于转子凸极根部和槽表面,而采用弧形槽具有更均匀的应力分布。     

Benchmarking and learning garbage collection delays for resource-restricted graphical user interfaces

Tablets, smartphones, and wearables have limited resources. Applications on these devices employ a graphical user interface (GUI) for interaction with users. Language runtimes for GUIs employ dynamic memory management using garbage collection (GC). However, GC policies and algorithms are designed for data centers and cloud computing, but they are not necessarily ideal for resource-constrained embedded devices. In this article, we present GUI GC, a JavaFX GUI benchmark, which we use to compare the performance of the four GC policies of the Eclipse OpenJ9 Java runtime on a resource-constrained environment. Overall, our experiments suggest that the default policy Gencon registered significantly lower execution times than its counterparts. The region-based policy, Balanced, did not fully utilize blocking times; thus, using GUI GC, we conducted experiments with explicit GC invocations that measured significant improvements of up to 13.22% when multiple CPUs were available. Furthermore, we created a second version of GUI GC that expands on the number of controllable load-stressing dimensions; we conducted a large number of randomly configured experiments to quantify the performance effect that each knob has. Finally, we analyzed our dataset to derive suitable knob configurations for desired runtime, GC, and hardware stress levels.     

Digital at the edge – antecedents and performance effects of boundary resource deployment

With business ecosystems digitalizing by the force of digital innovation, the deployment of boundary resources (such as application programming interfaces: APIs) becomes a strategic option across contexts. We distinguish between boundary resources that provide access openness and those that provide resource openness, and theorize the antecedents and consequences of their deployment. Employing panel data regressions to a longitudinal cross-industry dataset, we find that the digital knowledge base of the focal firm and the existence of potential digital complementors drive boundary resource deployment. Such deployment benefits firm performance depending on the firm’s market power. From our empirical analysis, we reveal a differentiated perspective on the quality of the confined openness provided by boundary resources as well as the embeddedness of their deployment in the rationales and motivations of the associated actors in digital business ecosystems. We complement the existent theoretical framework on boundary resources and provide valuable insights to managers reflecting about deploying boundary resources in a beneficial way.