Output feedback control of discrete‐time systems with self‐triggered controllers

This paper is concerned with the self‐triggered output feedback control for discrete‐time systems, where an updating instants scheduler is implemented to determine when the controller is updated. For both the full‐order and reduced‐order observer cases, the updating instants are determined, respectively, where only the information of the estimated state at the current updating instant is required to obtain the next updating instant. It is shown that, with the proposed self‐triggered control schemes, not only the updating frequency is significantly reduced, but also the uniform ultimate boundedness of the closed‐loop system is guaranteed. Finally, a numerical example is used to verify the effectiveness and the merits of the proposed approaches. Copyright © 2014 John Wiley & Sons, Ltd.     

Novel neural network modeling method and applications

Neural networks play an important role for designing the parametric model of electromagnetic structures. The current neural network methods are unfit for a circuit model with many input variables because it is costly to extract a large number of the training data and test data to complete the highly nonlinear mapping approximation. This article proposes a new neural network modeling method—the multidimensional neural network model, which can be used to solve the issue of multivariable radiofrequency and microwave passive device modeling. The entire multidimensional neural network modeling problem is simplified into a set of neural network submodels through decomposition method. Then the submodels are combined into an equivalent model, and the final entire model is produced through the neural‐network mapping model developed with the submodels and equivalent model. A microstrip hairpin filter model is developed using the proposed method. The simulation results show the correctness and the effectivity of the proposed method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:769–779, 2015.     

Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays在大规模系统上优化 TPC-C 评测程序

Transaction processing performance council benchmark C (TPC-C) is the de facto standard for evaluating the performance of high-end computers running on-line transaction processing applications. Differing from other standard benchmarks, the transaction processing performance council only defines specifications for the TPC-C benchmark, but does not provide any standard implementation for end-users. Due to the complexity of the TPC-C workload, it is a challenging task to obtain optimal performance for TPC-C evaluation on a large-scale high-end computer. In this paper, we designed and implemented a large-scale TPC-C evaluation system based on the latest TPC-C specification using solid-state drive (SSD) storage devices. By analyzing the characteristics of the TPC-C workload, we propose a series of system-level optimization methods to improve the TPC-C performance. First, we propose an approach based on SmallFile table space to organize the test data in a round-robin method on all of the disk array partitions; this can make full use of the underlying disk arrays. Second, we propose using a NOOP-based disk scheduling algorithm to reduce the utilization rate of processors and improve the average input/output service time. Third, to improve the system translation lookaside buffer hit rate and reduce the processor overhead, we take advantage of the huge page technique to manage a large amount of memory resources. Lastly, we propose a locality-aware interrupt mapping strategy based on the asymmetry characteristic of non-uniform memory access systems to improve the system performance. Using these optimization methods, we performed the TPC-C test on two large-scale high-end computers using SSD arrays. The experimental results show that our methods can effectively improve the TPC-C performance. For example, the performance of the TPC-C test on an Intel Westmere server reached 1.018 million transactions per minute.     

Active disturbance rejection control: between the formulation in time and the understanding in frequency

With the rapid deployments of the active disturbance rejection control (ADRC) as a bonafide industrial technology in the background, this paper summarizes some recent results in the analysis of linear ADRC and offers explanations in the frequency response language with which practicing engineers are familiar. Critical to this endeavor is the concept of bandwidth, which has been used in a more general sense. It is this concept that can serve as the link between the otherwise opaque state space formulation of the ADRC and the command design considerations and concerns shared by practicing engineers. The remarkable characteristics of a simple linear ADRC was first shown in the frequency domain, followed by the corresponding analysis in time domain, where the relationship between the tracking error and the ADRC bandwidth is established. It is shown that such insight is only possible by using the method of solving linear differential equations, instead of the more traditional techniques such as the Lyapunov methods, which tend to be more conservative and difficult to grasp by engineers. The insight obtained from such analysis is further demonstrated in the simulation validation.     

Morlet 小波在地表物候遥感识别中尺度的选取与应用

地表物候是人类了解地球生态系统的必要参数,也是动植物保护、农耕等活动的重要依据。使用遥感数据进行地表物候提取为大尺度的地表物候变化监测提供了一种有效途径。研究发现目前应用最广泛的非对称性高斯函数拟合法和双 Logistic 函数拟合法存在一定的缺陷,尤其是在提取 NDVI 峰值谷值和物候周期不明显区域 (如干旱地区、沙地) 的物候参数时存在严重的误差,而 morlet 小波在分析地表年际变化的周期特征方面表现良好。本文使用 morlet 小波对青海湖流域 2003-2014 年的 MODIS 数据进行分析,得到地表物候在该时间段内的年度变化与趋势,进行不同区域、不同时间的差异性分析,发现青海湖流域的地表物候期整体都略有提前,但生长季的长度呈延长趋势,认为青海湖流域的地表物候整体上响应全球变暖的趋势。中部地区的生长季长度大于高海拔、高纬度区域和沙地区域,认为青海湖流域的中部地区是最适合动植物生长、活动的范围。     

Attribute reduction in interval-valued information systems based on information entropies

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.     

Maximizing power saving with state transition overhead for multiple mobile subscriber stations in WiMAX

In the IEEE 802.16e/m standard, three power saving classes (PSCs) are defined to save the energy of a mobile sub-scriber station (MSS). However, how to set the parameters of PSCs to maximize the power saving and guarantee the quality of service is not specified in the standard. Thus, many algorithms were proposed to set the PSCs in IEEE 802.16 networks. However, most of the proposed algorithms consider only the power saving for a single MSS. In the algorithms designed for multiple MSSs, the sleep state, which is set for activation of state transition overhead power, is not considered. The PSC setting for real-time connections in multiple MSSs with consideration of the state transition overhead is studied. The problem is non-deterministic polynomial time hard (NP-hard), and a suboptimal algorithm for the problem is proposed. Simulation results demonstrate that the energy saving of the proposed algorithm is higher than that of state-of-the-art algorithms and approaches the optimum limit.     

An Intents-based approach for dynamic service discovery

Service discovery has been a very active research field attracting many researchers around the world. Most of the previous research efforts on service discovery have been based on the ternary classification of participant roles in web services, including service providers, brokers, and consumers. This classification is insufficient because it fails to make a difference between application developers and end users who both belong to service consumers. As a result, application developers usually take the responsibility of seeking services while end users who ultimately use the application are ignored. In order to address this issue, this paper presents an Intents-based approach for dynamic service discovery. The proposed approach is motivated from two industrial protocols: Android Intents and web Intents. It improves the two protocols by a generic model from the aspects of architecture, intent messages and their resolving process, Intents services, and user agents. It classifies the implicit intents into authoritative and naïve intents and is compatible with the current web service triangular architecture. This paper also illustrates a way of creating Intents services from SOAP and REST web services and presents two cases to demonstrate some benefits of the proposed approach.