基于算术傅里叶变换的离散Hartley变换的快速算法

离散Hartley变换是一种有用的实值正交变换。文中对其快速算法进行研究,首先介绍利用算术傅里叶变换（AFT）计算离散傅里叶变换（DFT）可使其乘法计算量仅为O（N）,然后文章根据这一特点,分析离散Hartley变换（DHT）的结构特征,通过DFT将AFT和DHT建立了直接联系,提出了一种新的快速DHT算法。算法的计算复杂度能够达到线性O（N）,且算法结构简单,公式统一且易于实现,并与其他快速算法进行了比较,分析可知在数据长度不是2的幂次方时,文中提出的算法的计算时间明显比其他算法的计算时间要小。实验结果也验证了文中算法的有效性,从而为DHT的快速计算开辟了新的思路和途径。     

Protection switching schemes and mapping strategies for fail-operational hard real-time NoCs

Communication infrastructures designed for mixed-critical MPSoCs must provide isolation of traffic, hard real-time guarantees, and fault-tolerance. In previous work, we proposed the combination of protection-switching with a hybrid Time-Division-Multiplexed (TDM) and packet-switched Network-on-Chip (NoC) to achieve all three goals. In this paper, we present an FPGA implementation of such a NoC with all its features. We give synthesis results for the hybrid NoC, including the network interface, and show that our router uses over 32% fewer LUTs and registers than a competitive state-of-the-art router for mixed-critical MPSoC. We then explore different channel and task mapping strategies for critical applications which use protection switching and evaluate the effect these mappings have on the best-effort (BE) traffic in the system. Results show, that spreading out the critical traffic rather than naively dividing the system in critical and non-critical application domains is advantageous or even necessary in many cases and can allow for up to 13% more BE traffic. We give a comprehensive trade-off analysis of three protection switching schemes—1:n, 1:1, and 1+1—and show that 1+1 protection has less than half the worst case latency for critical traffic that 1:n and 1:1 protection have. At the same time, 1+1 protection, on average, only causes a 1.18% earlier saturation rate for BE traffic, which we consider to be affordable. We conclude that 1+1 protection is ideally suited for use in mixed-critical systems with high safety requirements.     

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.     

Hybrid function projective synchronization of chaotic systems with uncertain time-varying parameters via Fourier series expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

基于群智能混合算法的物流配送路径研究

针对物流车辆路径优化问题,考虑到基本蚁群算法有收敛速度慢、易陷入局部最优的缺点,采用了一种双种群蚁群算法,在蚁群的基础上引入差分进化(DE)和粒子群算法(PSO)。通过在PSOAS种群和DEAS种群之间建立一种信息交流机制,使信息能够在两个种群中传递,以免某一方因错误的信息判断而陷入局部最优点。通过matlab仿真实验测试,表明该群智能混合算法可以较好地解决TSP的问题。     

SPI总线扩展PLC输出点数的设计与实现

针对PLC主模块可用输出点数少的缺点,提出了采用SPI总线和移位寄存器HC595相结合扩展PLC输出点数,并设计了输出扩展电路;根据SPI总线及HC595的时序特点,确定了SPI的时钟模式,解决了时序的配合问题,并确定了输出点数的逻辑顺序;UDTY端接一下拉电阻,保证其处于高电平,SPI2CLK信号取反后输入到HC595,保证其采样到稳定的SPI输出数据,滤波电容,提高了电路抗干扰性能;编程实现对扩展输出点数的控制。实际应用表明,该设计方案经济、可靠。     

基于离散单元法的锥形双螺杆输送及混合机理分析

锥形双螺杆挤出机结构模型与其他普通螺杆挤出机相差很大且构成独特,近年来在聚合物加工行业中发挥了很大的作用。设计螺槽深度与螺杆半径比为1∶2、2∶5、1∶3的三种锥形双螺杆挤出机,通过离散单元法模拟了锥形双螺杆挤出机挤出LDPE、HDPE两种颗粒的过程,分析了锥形双螺杆挤出机螺槽内颗粒速度的变化,同时在不同参数下对三种锥形双螺杆挤出机的输送效率进行了比较。利用EDEM软件对LDPE和HDPE颗粒进行离散元分析,得出不同时刻两种颗粒在三种锥形双螺杆挤出机内的混合情况。结果表明,螺槽深度与螺杆半径比为1∶2的锥形双螺杆挤出机的混合效率优于螺槽深度与螺杆半径比为2∶5、1∶3的锥形双螺杆挤出机。     

基于时序特征的发电机转子故障诊断方法

提出了一种利用时序特征进行发电机转子的故障诊断方法.利用时间序列法中的AR或ARMA模型得到的脉冲传递函数的特点,并根据不同故障类别振动信号的格林函数变化的规律性,提出了利用格林函数的统计量特征作为故障诊断的特征参数进行故障诊断的方法.     

基于时间序列模式表示的异常检测算法*

提出了一种基于时间序列的模式表示提取时间序列异常值的异常检测算法(PREOV).时间序列的模式表示本身就具有压缩数据、保持时间序列基本形态的功能,并且具有一定的除噪能力.在时间序列模式表示的基础上提取异常值,可以大大提高算法的效率和准确性,达到事半功倍的效果.在本算法中,还使用了一定的剪枝策略,使得算法的时间复杂度进一步降低.该算法计算简单、实现方便、无须训练,可以支持时间序列的动态增长.