The Analytic Representation of Band-Pass Signals

The analytic representation of band-pass signals, which is extremely useful in information transmission and radar, is usually obtained by first introducing the Hilbert transform of a signal. We consider this procedure artificial, and propose here to follow the opposite path, which leads, in a natural way, to a motivated definition of the Hilbert transform. The key to this procedure is to show how, as a simple application of the Fourier integral, every band-pass signal can be expressed as the real part of a low frequency signal which modulates an exponential. Such a representation, called the analytic signal, is then used to define the Hilbert transform in a natural manner. Finally, we show that the term analytic signal is motivated by the fact that this signal is the restriction to the real axis of a function defined and analytic (holomorphic) in the upper half of the complex plane.     

On robust detection of discrete-time stochastic signals

The problem of designing robust systems for the detection of stochastic signals in noise is considered for the large-sample-size, small-signal case. By applying two previously-established models for the detection of stochastic signals, known results for the robust detection of deterministic signals are extended on a limited basis to the stochastic- signal case. The proposed detectors are seen to be robust over a class of possible noise statistics, based on a Huber-Tukey mixture model, which contains noises characterized by heavy-tailed probability density functions. In addition, numerical results are presented which verify the robustness property of the proposed detectors over wider classes of noise mixtures.     

Measures of generalized magnitude-squared coherence: Differences and similarities

The magnitude-squared coherence (MSC) is a measure that estimates the extent to which one real- or complex-valued signal can be predicted from another real- or complex-valued signal using a linear model. It is also used as a measure of the similarities in the frequency content of two signals. The measure is widely used in signal analysis, especially in fields such as biomedical, where a large number of signals must be processed simultaneously. It is natural to wish to generalize this idea to compare two vector-valued signals, and a variety of approaches have been proposed. This paper reviews these generalizations, presents new relationships among the measures, and demonstrates a series of results that show the similarities and dissimilarities among these measures. Some of the measures have a clear link with total interdependence; some are related to the mutual information rate. Basic results such as the various Sandwich Theorems show how the measures relate, and understanding these properties is key to an informed use of any vector generalization of MSC.     

高校教师激励因素及其相互关系研究

将激励视作一个系统,从分析高等院校教师在生存、关系、权力等方面的需求入手,研究确定影响教师工作绩效的报酬、人力资本、能力、压力、权力等因素,构建了高校教师激励因素的作用模式和作用关系分析模型,并对模型中的激励要素,如经济性奖酬、非经济性奖酬、努力程度、绩效水平等因素之间的相互关系进行了分析,提出了所有激励方式都含有经济性奖酬与非经济性奖酬两种成分。     

Active fault diagnosis for a class of closed-loop systems via parameter estimation

This paper addresses an active fault diagnosis problem for a class of discrete-time closed-loop system with stochastic noise. By introducing the theories of system identification, a novel active fault diagnosis method is developed to detect and isolate the faults. An important advantage of the proposed method is that there is no need to cut off the original input signal, which is necessary in most active fault diagnosis methods. Firstly, due to the features of the faults, we transform the problem of fault diagnosis into a problem of model selection by estimating model parameters. Then, the sufficient condition for active fault diagnosability is analysed, and the property that auxiliary input signal can enhance the fault diagnosability is given. Finally, simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.     

Decentralized adaptive synchronization with bounded identification errors for discrete-time nonlinear multi-agent systems with unknown parameters and unknown high-frequency gains

This paper addresses the challenging problem of decentralized adaptive control for a class of coupled hidden leader-follower multi-agent systems, in which each agent is described by a nonlinearly parameterized uncertain model in discrete time and can interact with its neighbors via the history information from its neighbors. One of the agents is a leader, who knows the desired reference trajectory, while other agents cannot receive the desired reference signal or are unaware of existence of the leader. In order to tackle unknown internal parameters and unknown high-frequency gains, a projection-type parameter estimation algorithm is proposed. Based on the certainty equivalence principle and neighborhood history information, the decentralized adaptive control is designed, under which, the boundedness of identification error is guaranteed with the help of the Lyapunov theory. Under some conditions, it is shown that the multi-agent system eventually achieves synchronization in the presence of strong couplings. Finally, a simulation example is given to support the results of the proposed scheme.     

Stabilization of discrete-time switched singular systems with state,output and switching delays

This paper is concerned with state feedback stabilization of discrete-time switched singular systems with time-varying delays existing simultaneously in the state, the output and the switching signal of the switched controller. On the basis of equivalent dynamics decomposition and Lyapunov–Krasovskii method, exponential estimates for the response of slow states of the closed-loop subsystems running in asynchronous and synchronous periods are first given. Exponential estimates for the response of fast states are also provided by establishing an analytic equation to solve the fast states and using some algebraic techniques. Then, by employing the obtained exponential estimates and the piecewise Lyapunov function approach with average dwell time (ADT) switching, sufficient conditions for the existence of a class of stabilizing switching signals and state feedback gains are derived, which explicitly depend on upper bounds on the delays and a lower bound on the ADT. Finally, two numerical examples are provided to illustrate the effectiveness of the obtained theoretical results.     

Convection-radiation from a continuously moving fin of variable thermal conductivity

The homotopy analysis method (HAM) is applied to generate an analytic solution for heat transfer in a moving fin of variable thermal conductivity which is losing heat by simultaneous convection and radiation to its surroundings. The accuracy of the analytic solution is validated by comparing it with the direct numerical solution of the problem. The analytic solution is found to be accurate to at least three places of decimal for a wide range of values of the parameters that are commonly encountered in thermal processing application. Graphs displaying the results are interpreted in physical terms.     

Event-triggered adaptive NN control for MIMO switched nonlinear systems with non-ISpS unmodeled dynamics

This paper investigates the problem of event-triggered adaptive neural network (NN) control for multi-input multi-output (MIMO) switched nonlinear systems with output and state constraints and non-input-to-state practically stable (ISpS) unmodeled dynamics. A nonlinear mapping is firstly utilized to deal with output and state constraints. Also, by developing a new switching signal with persistent dwell-time (PDT) and a switching dependent dynamic signal, the difficulty caused by some non-ISpS unmodeled dynamics is overcome. Then, a type of switching event-triggering mechanisms (ETMs) and event-triggered adaptive NN controllers of subsystems are designed, which handle the issue of asynchronous switching without requiring any known restriction on maximum asynchronous time. A piecewise constant introduced into this ETM effectively ensures a strict positive lower bound of inter-event times. Zeno behavior is thus ruled out. Finally, by proposing a novel class of switching signals with reset PDT, it is ensured that all output and state constrains are never violated and all signals of the switched closed-loop system are semi-global uniform ultimate boundedness (SGUUB). A two inverted pendulum system and a numerical example are provided for illustrating the applicability and validity of the proposed method.     

快速独立分量变换与去噪初探

独立分量分析 (ICA)通过对非高斯分布数据进行有效表示 ,获得在统计学上独立的各个分量。这种表示可以获取数据的基本结构 ,包括特征提取和信号分离。简述ICA基本理论和快速算法 ,对照主分量分析 (PCA)的Karhunen Loeve(K L)变换 ,提出独立分量变换 (ICT)概念。在分析地震信号特点的基础上 ,对模拟和实际含噪地震道进行独立分量变换、信噪分离和去噪处理初步探索 ,重建获得令人满意的去噪结果。研究表明 ,ICA在勘探地震信号处理中具有应用前景     

Zero dynamics and funnel control for linear electrical circuits

We consider electrical circuits containing linear resistances, capacitances and inductances. The circuits can be described by differential-algebraic input–output systems, where the input consists of voltages of voltage sources and currents of current sources and the output consists of currents of voltage sources and voltages of current sources. We generalize a characterization of asymptotic stability of the circuit and give sufficient topological criteria for its invariant zeros being located in the open left half-plane. We show that asymptotic stability of the zero dynamics can be characterized by means of the interconnectivity of the circuit and that it implies that the circuit is high-gain stabilizable with any positive high-gain factor. Thereafter we consider the output regulation problem for electrical circuits by funnel control. We show that for circuits with asymptotically stable zero dynamics, the funnel controller achieves tracking of a class of reference signals within a pre-specified funnel; this means in particular that the transient behavior of the output error can be prescribed and the funnel controller does neither incorporate any internal model for the reference signals nor any identification mechanism, it is simple in its design. The results are illustrated by a simulation of a discretized transmission line.     

Stabilization of switched delay systems with polytopic uncertainties under asynchronous switching

In this paper, the problem of stabilization for a class of switched delay systems with polytopic type uncertainties under asynchronous switching is investigated. When the switching of the controllers has a lag to the switching of subsystems, i.e. the switching signal of the switched controller involves delay, parameter-dependent Lyapunov functionals are constructed, which are allowed to increase during the running time of active subsystems with the mismatched controller. Based on the average dwell time method, sufficient conditions for exponential stability are developed for a class of switching signals. Finally, a river pollution control problem is given to demonstrate the feasibility and effectiveness of the proposed design techniques.     

Event-triggered sliding mode control of uncertain switched systems under denial-of-service attacks

This study is concerned with the event-triggered sliding mode control problem for a class of cyber-physical switched systems, in which the Denial-of-Service (DoS) attacks may randomly occur according to the Bernoulli distribution. A key issue is how to design the output feedback sliding mode control (SMC) law for guaranteeing the dynamical performance of the closed-loop system under DoS attacks. To this end, an event-triggered mechanism is firstly introduced to reduce the communication load, under which the measurement signal is transmitted only when a certain triggering condition is satisfied. An usable output signal for the controller is constructed to compensate the effect of unmeasured states and DoS attacks. And then, a dynamic output feedback sliding mode controller is designed by means of the attack probability and the compensated output signals. Both the reachability and the mean-square exponential stability of sliding mode dynamics are investigated and the corresponding sufficient conditions are obtained. Finally, some numerical simulation results are provided.     

小波包分解在迎角传感器信号预处理中的应用

针对基于四象限压力传感器阵列的迎角测量方法中传感器信号受到干扰的问题,讨论了在其预处理中应用小波包变换进行信号分离提取的方法。根据小波包分解理论,对信号进行小波包分解,信号中频率不同的部分落在不同的尺度上,剔除反映干扰的变换尺度,提取出有用信息。结果表明,该方法可很好地提取信号的主要特征信息,也有效地去除了确定性干扰和随机噪声,与传统的信号滤波方法相比较具有明显的优点,同时为后续的信号处理奠定了基础。     

Quantized adaptive tracking control for nonlinear systems with actuator backlash compensation

In this paper, the problem of adaptive tracking control is investigated for nonlinear systems with asymmetric actuator backlash. We assume that the nonlinearities of the systems are unknown and the external disturbances are bounded. First, the control input will be quantized by a hysteresis-type quantizer, which can reduce the communication rate of the control signal. Then, the asymmetric actuator backlash is approximated to a new model, and a novel adaptive controller with the quantizer is designed via an adaptive backstepping technique to guarantee all the signals of the closed-loop tracking error system are uniform ultimate boundedness. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed algorithm.     

Time-optimal control problem for a linear parameter varying system with nonlinear item

In this paper, we considered a time-optimal control problem for a new type of linear parameter varying (LPV) system which is obtained through data identification in the process of dealing with actual problems. The addition of non-linear terms is compensation for the method that does not require linear expansion at the equilibrium point. Since the objective function is the terminal time which is an implicit function concerning decision variables, it is a non-standard optimal control problem with uncertain terminal time. To find the global optimal solution to this problem, firstly, the control parameterization method is used to transform it into a nonlinear optimization problem of parameter selection, and then the modifed particle swarm optimization (PSO) algorithm is combined to solve the equivalent nonlinear programming problem. Numerical examples are used to illustrate the effectiveness of the proposed algorithm.     

Entrepreneurial opportunity and related specialization in economic ecosystems

Entrepreneurial ecosystems are most often defined as locally bounded networks of institutions and individuals that help entrepreneurs to identify and exploit market opportunities. However, a prerequisite of the ability to exploit an entrepreneurial opportunity is the existence of the opportunity to begin with. In this paper, we propose an approach to representing how entrepreneurial opportunities are embedded in a regional economy. The central methodological contribution of our paper is to introduce the concept of related specialization, which we define as the concentration of high interdependencies between specialized industries within a metropolitan region, relative to the national level of concentration. We find that related specialization is positively correlated with entrepreneurial outcomes across U.S. metropolitan areas. We use related specialization to make a first attempt at representing the trophic structure of an economic ecosystem. In a biological ecosystem, the trophic structure refers to energy-transfer relationships between different species. As introduced in this paper, we propose the analogy that, in an economic ecosystem, the trophic structure may be mapped in terms of the supply-chain relationships between industries within a region. We argue that these analytic tools can help policymakers more readily understand the opportunities that are imminently available to entrepreneurs in their local environments.     

Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results are more valuable from a physician's perspective. Based on previous studies on English literature section identification, we experiment with the effective features to use with classic machine learning algorithms to tackle the problem. It is found that Conditional Random Fields, which consider sentence interdependency, is more effective in combining different feature sets, such as bag-of-words, part-of-speech, and headings, for Chinese literature section identification. Moreover, we find that classic machine learning algorithms are more effective than generic deep learning models for this problem. Based on these observations, we design a novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, which models word and sentence interdependency together with the contextual information. Experiments on a human-curated asthma literature dataset show that our approach outperforms the traditional machine learning methods and other deep learning methods and achieves close to 90% precision and recall in the task. The model shows good potential for use in other text mining tasks. The research has significant methodological and practical implications.     

Event-triggered adaptive tracking control of uncertain nonlinear systems with prescribed performance and actuator failures

This article studies adaptive prescribed performance tracking control problem for a class of strict-feedback nonlinear systems with parametric uncertainties and actuator failures. Firstly, in order to compensate the multiple uncertainties and eliminate the influence of actuator failure, a new adaptive tracking controller based on first-order filter technology will be proposed, which simplifies the algorithm design process. Then, by introducing an asymmetric state transition function, the transient and steady performances of the output tracking error are both constrained such that the predetermined performance control goal is achieved. Moreover, to reduce the communication burden from the controller to the actuator, the event-triggered mechanism is designed, and there will be no Zeno phenomenon. Based on Lyapunov stability theory, it is strictly proved that output signal can track the reference signal and all the signals of the closed-loop system are bounded. Finally, a simulation example is performed and the results demonstrate effectiveness of the proposed strategy.