Stability analysis for discrete linear systems with state saturation by a saturation-dependent Lyapunov functional

This paper concerns the stability analysis problem of discrete linear systems with state saturation using a saturation-dependent Lyapunov functional.We introduce a free matrix characterized by the sum of the absolute value of each elements for each row less than 1,which makes the state with saturation constraint reside in a convex polyhedron.A saturation-dependent Lyapunov functional is then designed to obtain a sufficient condition for such systems to be globally asymptotically stable.Based on this stability criterion,the state feedback control law synthesis problem is also studied.The obtained results are formulated in terms of bilinear matrix inequalities that can be solved by the presented iterative linear matrix inequality algorithm.Two numerical examples are used to demonstrate the effectiveness of the proposed method.     

Stability of impulsive stochastic functional differential systems in terms of two measures via comparison approach

Comparison principles for general impulsive stochastic functional differential systems are established.Employing the comparison principles and the theory of differential inequalities,stability and instability,involving two measures,of impulsive stochastic functional differential systems are investigated.Several stability and instability criteria are obtained,and two examples are also given to illustrate our results.     

工业机器人解释器语义分析研究

介绍了语义分析的发展现状。针对目前解释器语义分析效率较低的问题,并结合工业机器人控制系统的特点,研究了利用语法分析栈来实现工业机器人解释器语义分析的方法,并实现了工业机器人编程语言IRL的语义分析。基于该方法的解释器在Visual Studio2005平台上用C＋＋实现,生成的目标代码运行于WinCE嵌入式系统。实验结果证明了该方法的正确性和高效性。     

用于智能组卷的自适应小生境复合遗传算法

传统遗传算法的选择策略缺乏多样性保护机制,易出现早熟收敛。为解决智能组卷问题,采取小生境技术完成遗传操作中的种群进化机制。利用个体浓度的大小,设置自适应变异算子,保证种群多样性,防止种群陷入局部收敛;增加阈值以保证算法在接近最优解时回归到自适应遗传算法,简化算法计算量,加快算法的收敛速度。本文提出一种自适应与小生境技术复合遗传算法,来均衡算法的全局搜索和局部快速开发能力。最后,实例验证了所提算法的有效性。     

火焰动画快速模拟的新方法

针对火焰动画难以快速建模和绘制的问题,提出了完全利用GPU过程化生成火焰动画的高效算法。先用建模工具在预处理阶段构建一个由平面正方形组成的三维模型,在绘制阶段通过GPU编程对这些方形进行Billboard方式的变换,并用一些简单公式对他们的运动方式、大小和颜色来用过程化方式进行控制,最后通过alpha混合得到最终结果。算法完全在GPU里实现,能满足实时的绘制要求。     

Design of composite data acquisition gateway based on 5G network

The integrated data collection system hasn't changed much over the years. Their growth is dominated by digital filtering, and they get a new avionics bus. Time division multiplexing is still used to communicate with the system's data collector. These schemes make use of commands and data buses. While this method works, it has many drawbacks. These shortcomings are overwhelming the strict system architecture, system bandwidth limits, and expertise to only obtain an avionics bus, otherwise system bandwidth and one-way flow of data and control. This lead for high-end video recorders. To quietly monitor our actions and valuable information provides important warnings. However, it is usually completed by a device with this small processing power, which tracks the device, stores the data of the user's data, and is limited in processing. Therefore, it is important to store and process user data in the cloud to track 5 G network activity. This article proposes a simple 5G network gateway solution for custom user monitoring equipment. Collects charge data on Network 5G and Charging-aware multi-mode based routing protocol (CMRP). The CMRP routing mechanism is not yet stable, but the changes depend on the state of the energy charge sensor. This cannot be complemented by energy-efficient sensors and routing protocols that consume less energy.     

Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm

Managing sports performance is very important in the sports industry. Performance, the executives, centers on boosting competitor execution and decreasing the danger of injury. Several factors contribute to these goals, including player health, emotional status, exercise load and physical intensity requirements. Generally speaking, injury prediction is an essential component of injury prevention, and successful identification of injury prediction is a primary indicator for effective prevention. The proposed Artificial Neural Network (ANN) objective is to develop and use early-doing ability and exercise load data to validate a hierarchical machine learning prediction system with accurate detection of player injuries. The physical and workload that requires detection of this early personalized damage can be avoided with specific help. The framework is used to test 21 soccer players’ sports information from various sources, including gathered and inside burden information, outside burden information, and review information. The entirety of this information is fused into the proposed framework to improve the exactness of harm expectation. This calculation distinguishes competitors in danger of injury, with their early intervention available.     

The application of the methodology for secure cyber–physical systems design to improve the semi-natural model of the railway infrastructure

The paper suggests a new methodology for secure cyber–physical systems design. The proposed methodology consists of two main cycles. The main goal of the first cycle is in design of the system model, while the second one is about development of the system prototype. The key idea of the methodology is in providing of the most rational solutions that are improving the security of cyber–physical systems. Such solutions are called alternatives and built according to functional requirements and non-functional limitations to the system. Each cycle of the methodology consists of the verification process and seven stages that are associated with the used cyber–physical system model. The objective of the verification process is in checking of constructed models and prototypes in terms of their correctness and compatibility. The model represents cyber–physical systems as sets of building blocks with network between them, takes elements internal structure into account and allows direct and reverse transformations. The novelty of the suggested methodology is in the combination of design, development and verification techniques within a single approach. To provide an example of the design methodology application, in this paper it is used to improve the semi-natural model of the railway infrastructure.     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.