北京永定河河岸带生态修复对河流水质的影响

为探讨河岸带生态修复措施对河流水质的直接影响,以永定河已修复的门城湖—莲石湖区段为研究对象,于2013年7—10月沿西岸选取8个采样点,测定溶解氧(DO)、氨氮(NH₄⁺—N)、硝氮(NO₃^-—N)和总磷(TP)的质量浓度.结果表明,河流DO浓度和各营养物质浓度均有明显的季节变化.大部分采样点的DO、NH₄⁺—N和TP浓度未达到地表水IV类水体标准.水生植物较多的河岸带处,河流NH₄⁺—N浓度较低.在生长旺季,长势好的水生植物越多,河流DO和NO₃^-—N浓度越高,TP浓度越低;在生长末期,枯落物较多的河岸带处,河流DO浓度较低,TP浓度较高.凸岸处的河流DO和NO₃^-—N浓度较高,而NH₄⁺—N浓度较低.因此,改变护岸类型、水生植物的多度及河岸的弯曲程度等河岸带结构特征可引起河流水质的变化.     

Event-triggered non-fragile H∞ fault detection for discrete time-delayed nonlinear systems with channel fadings

In this paper, the event-triggered non-fragile H_∞ fault detection filter is designed for a class of discrete-time nonlinear systems subject to time-varying delays and channel fadings. The Lth Rice fading model is utilized to reflect the actual received measurement signals, and its channel coefficients own arbitrary probability density functions on interval [0,1]. The event-based filter is constructed to reduce unnecessary data transmissions in the communication channel, which only updates the measurement signal to the filter when the prespecified “event” is triggered. Multiplicative gain variations are utilized to describe the phenomenon of parameter variations in actual implementation of the filter. Based on Lyapunov stability theory, stochastic analysis technology along with linear matrix inequalities (LMIs) skills, sufficient conditions for the existence of the non-fragile fault detection filter are obtained which make the filtering error system stochastically stable and satisfy the H_∞ constraint. The gains of the filter can be calculated out by solving the feasible solution to a certain LMI. A simulation example is given to show the effectiveness of the proposed method.     

Soft sensor modeling with a selective updating strategy for Gaussian process regression based on probabilistic principle component analysis

Considering the deviation of the working condition and the high updating frequency of the traditional moving window methods, this paper proposes a selective strategy of moving window for the Gaussian process regression in the latent probabilistic component space. First, the probabilistic principle component analysis (PPCA) is employed to deal with the multi-dimensional issue and extract essential information of the process data. Because the latent probabilistic components are more sensitive to the deviation of the working condition in the industrial process than the original data, the regression performance is improved under the PPCA framework. Under the proposed strategy, the soft sensor is able to detect the change of the working condition, and the updating is activated only when the predicted error exceeds the preset threshold, otherwise the model is kept unchanged. Furthermore, the promotion of both predicted accuracy and efficiency can be obtained by regulating the threshold. To test the effectiveness of the proposed method, a wastewater case study is provided, and the result shows that the proposed strategy works better under the probabilistic than other conventional methods.     

Event-based weighted residual generator design via non-PDC scheme for fault diagnosis in T–S fuzzy systems

This paper is concerned with the event-based weighted residual generator design via non-parallel distribution compensation (PDC) scheme for fault diagnosis in discrete-time T–S fuzzy systems, under consideration of the imperfect premise matching membership functions. An event-triggered mechanism is firstly introduced to save communication resources, which leads to the premise variables of the system and observer to be asynchronous. Then, a fuzzy diagnostic observer with mismatched premise variables is designed to estimate the unmeasurable states of the system. Moreover, by using non-PDC method, a diagnostic observer-based weighted residual generator is established to improve the fault detection (FD) performance by using the information provided by each local system, in which the membership functions structure of the diagnostic observer and residual generator need not to be the same as the systems, and the L_∞/L₂ and L_∞ FD scheme is used to optimize the FD performance. Finally, two simulation results are provided to show the efficiency of the proposed non-PDC method.     

Fractional-order follower observer design for tracking consensus in second-order leader multi-agent systems: Periodic sampled-based event-triggered control

This paper investigates the tracking consensus problem for the second-order leader systems by designing fractional-order observer, where a periodic sampled-based data event-triggered control is employed. In order to track the position information of leader, observers for followers are designed by fractional-order system, where only the relative position information is available. Furthermore, in the process of observers design, a sampled-based event-triggered strategy is proposed so that observers use the event-triggered sampled-data, to reduce the overall load of the network. In our proposed event-triggered strategy, the event detection only works at every sampling time instant which determines whether the sampled-data should be discarded or used. Under this control strategy, the Zeno-behavior is absolutely excluded since the minimum of inter-event times is inherently lower bounded by one sampling period. It is found that the followers can track state of the leader if fractional-order observers are appropriately designed and relevant parameters are properly selected. By using the generalized Nyquist stability criterion, a necessary and sufficient condition for the observer tracking consensus of the second-order leader systems is derived. The results show that the real and imaginary parts of the eigenvalues of the augmented Laplacian matrix, and fractional-order α of observer play a vital role in reaching consensus.     

Nonlinear optimal control with disturbance rejection for asteroid landing

This paper presents an optimal solution to asteroid soft landing problem, on the base of $\theta ?D$ control technique and disturbance rejection mechanism. The control objective is to drive a probe to reach the surface of an asteroid with a desirable line-of-sight angle and zero velocity, eliminating the influence of external disturbance. Firstly, elementary $\theta ?D$ technique is applied in the absence of disturbance to tackle the nonlinear optimal control problem. Secondly, the disturbance is estimated in the fast-estimation framework with explicitly bounded estimation error. Afterwards, an integrated control protocol is presented in a feed-forward structure by the aid of an additional variable-structure term to ensure stability under time-varying disturbance. Simulation results of the proposed approach compared with the results of elementary $\theta ?D$ method and robust $\theta ?D$ method are presented at the end of this paper, demonstrating the effectiveness of the proposed control protocol.     

Optimal jamming attack schedule for remote state estimation with two sensors

In cyber-physical systems (CPS), cyber threats emerge in many ways which can cause significant destruction to the system operation. In wireless CPS, adversaries can block the communications of useful information by channel jamming, incurring the so-called denial of service (DoS) attacks. In this paper, we investigate the problem of optimal jamming attack scheduling against remote state estimation wireless network. Specifically, we consider that two wireless sensors report data to a remote estimator through two wireless communication channels lying in two unoverlapping frequency bands, respectively. Meanwhile, an adversary can select one and only one channel at a time to execute jamming attack. We prove that the optimal attack schedule is continuously launching attack on one channel determined based on the system dynamics matrix. The theoretical results are validated by numerical simulations.     

A cognitive TV white space-broadband power line MIMO system for indoor communication networks

Broadband power line communication (BPLC) is a promising solution to satisfy the growing data rate demands for broadband indoor communication networks. However, the BPLC transmission power spectral density (PSD) is restricted in the very high frequency (VHF) band to avoid harmful interference to the existing wireless services. In this paper, a new hybrid system is proposed utilizing BPLC and cognitive radio over TV white space (TVWS) to enhance the system capacity over BPLC in VHF, forming a VHF TVWS BPLC multiple-input multiple-output (MIMO) system. An iterative precoding algorithm is proposed to satisfy the interference limit at the TV primary user (PU) receiver (Rx) and enhance the ergodic capacity. Moreover, a power allocation algorithm is developed for the MIMO system to achieve the maximum ergodic capacity subject to the average total power constraint and limit of interference to TV PU. Simulation results demonstrate the significant enhancement in the achieved capacity by our proposed system in the VHF band compared to both previous cognitive and hybrid BPLC systems.     

A new sensor selection scheme for Bayesian learning based sparse signal recovery in WSNs

In this paper, we address the issue of sparse signal recovery in wireless sensor networks (WSNs) based on Bayesian learning. We first formulate a compressed sensing (CS)-based signal recovery problem for the detection of sparse event in WSNs. Then, from the perspective of energy saving and communication overhead reduction of the WSNs, we develop an optimal sensor selection algorithm by employing a lower-bound of the mean square error (MSE) for the MMSE estimator. To tackle the nonconvex difficulty of the optimum sensor selection problem, a convex relaxation is introduced to achieve a suboptimal solution. Both uncorrelated and correlated noises are considered and a low-complexity realization of the sensor selection algorithm is also suggested. Based on the selected subset of sensors, the sparse Bayesian learning (SBL) is utilized to reconstruct the sparse signal. Simulation results illustrate that our proposed approaches lead to a superior performance over the reference methods in comparison.     

New insights in stability analysis of delayed Lotka–Volterra systems

Most of the reported Lotka–Volterra examples have at most one stability interval for the delay parameters. Furthermore, the existing methods fall short in treating more general case studies. Inspired by some recent results for analyzing the stability of time-delay systems, this paper focuses on a deeper characterization of the stability of Lotka–Volterra systems w.r.t. the delay parameters. More precisely, we will introduce the recently-proposed frequency-sweeping approach to study the complete stability problem for a broad class of linearized Lotka–Volterra systems. As a result, the whole stability delay-set can be analytically determined. Moreover, as a significant byproduct of the proposed approach, some Lotka–Volterra examples are found to have multiple stability delay-intervals. To the best of the authors’ knowledge, such a characterization represents a novelty for having some insights in the population dynamics: in some situations, a longer maturation period of species is helpful for the stability of a population system.