分组零膨胀泊松模型的半参数统计推断

泊松回归模型常常用于计数数据的研究中,然而在实际数据中零值的比例可能远远大于泊松分布中取零值的概率,而且这些零值通常都有其特殊含义.此外计数数据可能是分组数据,即观测到的数据不是确切值而只是已知其落在某一个区间范围之内;或者某些特定的数据,例如工资,要先对它进行人为的分组然后再进行分析.考虑一种零膨胀泊松半参数回归模型来处理上述分组计数数据.该模型中泊松分布的期望与协变量之间采用部分线性连接函数,而零值的概率与协变量之间采用线性连接函数.利用Sieve极大似然估计方法来估计该回归模型中参数和非参数函数,并提出了一种得分检验方法来检验是否存在零膨胀.在一定正则条件下,获得了Sieve极大似然估计的渐近性质,证明了参数部分的估计是强相合,渐近正态及渐近有效的;同时非参数函数的估计达到了最优收敛速度.模拟研究表明,估计和检验方法效果都比较好,最后将此模型和推断方法应用于一组公共卫生领域实际数据研究.     

Non-flocking and flocking for the Cucker-Smale model with distributed time delays

In this paper, we study a flocking behavior that may or not appear for Cucker - Smale model with distributed time delays. For the short range communicated Cucker - Smale model, the flocking condition has strong restrictions on initial data. For this case, we mainly consider the non - flocking behavior. By establishing and appropriately estimating an inequality of the position variance such that the second order space moment is unbounded, we drive a sufficient condition for the non - existence of the asymptotic flocking when the time delays satisfy a suitable smallness assumption. Furthermore, we also provide a sufficient condition of asymptotic flocking. Finally, we present numerical simulations to validate the theoretical results.     

基于地形梯度的土地利用类型分布特征分析——以湖南省浏阳市为例

地形因素是影响土地利用空间布局的重要因素之一。以湖南省浏阳市为例,应用TM遥感影像数据及DEM数据,在ArcGIS空间分析模块支持下,采用高程梯度、坡地梯度和地形梯度三种方法对比研究,通过分析不同土地利用类型在三种地形梯度上的分布指数,揭示研究区域的土地利用类型分布的模式及其与地形因素之间的对应关系。结果表明:耕地、园地、其他农用地和建设用地主要分布在较低的地形梯度上,分布指数随着地形梯度的增大而减少,逐渐趋向于0,以上同种土地利用类型在三种地形梯度上的分布具有一定的相似性;林地主要分布在中高等级高程、较高坡度和较高等级地形位上,而未利用地分布区域却相反;对三种研究方法的比较表明,地形位综合了高程和坡度的信息,能更方便地反映土地利用类型随地形变化的分布趋势;而地形位指数则可以更有效地反映土地利用类型的分布特征。本研究可为区域土地利用布局研究提供一种可行的定量分析方法。     

A new approach to the modeling of distributed structures for control

Building structures represent complex distributed-parameter systems. The motion of such systems is described by partial differential equations complemented by suitable boundary conditions. For control design purposes, distributed-parameter systems must be discretized in the spatial variables. But, if the discrete model is not sufficiently accurate, controls designed on the basis of the discrete model can destabilize the actual distributed structure, in which case the controls are said to be sensitive to discretization errors. This paper presents a new approach to the discretization of distributed structures yielding accurate discrete models of relatively low order. A numerical example illustrates how controls can be designed for a complex structure subjected to earthquake excitations.     

Robustly distributed diagnosis of discrete-event systems and its application to Alipay transaction systems

In most of existing literature, it is assumed that all of the sensors can work normally. However in some situations, several sensors occur abnormal behavior or stuck at faults such that prior diagnosable decisions may not hold. By this regard, we address the problem of robustly distributed failure diagnosis of discrete-event systems with observation losses in this paper. In order to ensure diagnosability, the notion of robustly diagnosability is proposed in the distributed framework. Motivated by earlier works, new communication models and dilation operators are constructed, based on which the robustly distributed diagnosis problem is converted to a distributed diagnosis problem. One algorithm for the verification of robustly distributed diagnosis is proposed. Followed by it, a necessary and sufficient condition for the robustly diagnosability is presented. Finally, a part of Alipay transaction systems as an application is used to illustrate the construction of some automata and the verification algorithm.     

Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction

Since the risk of loan defaulting in peer-to-peer (P2P) lending is notoriously difficult to evaluate, a deep neural network-based decision-making approach is proposed in this work for more effective assessment of P2P lending risks. Although normally a dozen features were used for neural network modeling in previous studies carried out by other researchers on similar topics, more comprehensive features including both numeric and categorical ones (e.g. home ownership and purpose of loan), are considered in this work for improved modeling. Since categorical data cannot be used directly as the input of neural networks, they are converted to numerical data using one-hot encoding function. The deep neural network (DNN) used in this work is a multilayer perceptron (MLP) with three hidden layers trained by the back-propagation algorithm. In empirical analysis, the loan data issued by the Lending Club through 2007–2015 are classified into three classes, i.e. safe loan, risky loan and bad loan using TensorFlow. The training and test data sets consist of 221,712 and 55,428 data observations, respectively. Since most of the data belong to the class of safe loan, Synthetic Minority Over-Sampling Technique (SMOTE) is used to improve the DNN prediction accuracy. It is shown that with the proposed approach the test data are classified at an accuracy of 93%, which is much higher than the predication accuracy of 75% obtained using MLP with only one hidden layer.     

Distributed event-triggered adaptive finite-time consensus control for second-order multi-agent systems with connectivity preservation

In this paper, we consider the robust finite-time consensus problem for second-order multi-agent systems (MASs) with limited sensing range and weak communication ability. As a stepping stone, a novel distributed finite-time sliding mode manifold is developed for MASs. Then, by combining artificial potential function technique with the presented sliding mode manifold, a robust distributed control scheme is proposed to enable the finite-time consensus of MASs while preserving the prescribed communication connectivity. Furthermore, the sampling frequency and implementation burden of the proposed controller can be reduced with resort to the event-triggered methodology. Finally, numerical examples are given to show the effectiveness of the proposed method.     

螺旋管簧可靠性分析的参数灵敏度

机械零部件的安全可靠是机械设计的主要目的之一，由于工程材料性的离散性以及制造、安装误差等因素的影响，使机械零部件的参数具有固有的随机性，因此研究这种机械系统的可靠性问题有十分重要的意义，这种研究可以帮助工程设计人员合理地建立机械系统的安全容限和控制随机参数对机械系统安全的影响．将可靠性设计理论与灵敏度分析方法相结合，讨论了螺旋管簧的可靠性的参数灵敏度分析问题，提出了可靠性灵敏度设计的计算方法，研究了正态分布设计参数的改变对螺旋管簧可靠性的影响，给出了可靠性灵敏度的变化规律，根据编制的相关的实用程序，可以迅速准确地得到螺旋管簧的可靠性灵敏度设计信息，为螺旋管簧的可靠性设计提供了理论依据。     

Distributed Kalman filtering over sensor networks with fading measurements and random link failures

This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method.     

Resilient sampled-data control design for singular networked systems with random missing data

This paper proposes an active resilient control strategy for singular networked control systems with external disturbances and missing data scenario based on sampled-data scheme. To characterize the missing data scenario, a stochastic variable satisfying Bernoulli distributed white sequence is introduced. Based on this scenario, in this paper, two different models are proposed. For both the models, by using Lyapunov–Krasovskii functional approach, which fully uses the available information about the actual sampling pattern, some sufficient conditions in terms of linear matrix inequalities (LMIs) are separately obtained to guarantee that the resulting closed-loop system is admissible and strictly dissipative with a prescribed performance index. In particular, Jensen’s and Wirtinger based integral inequalities are employed to simplify the integral terms which appeared in the derivation of stabilization results. Then, if the obtained LMIs are feasible, the corresponding parameters of the designed resilient sampled-data controller are determined. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed control design technique.     

Analysis and design for continuous-time string-connected Takagi–Sugeno systems

Distributed systems consist of interconnected, lower-dimensional subsystems. For such systems, distributed analysis and design present several advantages, such as modularity, easier analysis and design, and reduced computational complexity. A special case of distributed systems is when the subsystems are connected in a string. Applications include distributed process control, traffic and communication networks, irrigation systems, hydropower valleys, etc. By exploiting such a structure, in this paper, we propose conditions for the distributed stability analysis of Takagi–Sugeno fuzzy systems connected in a string. These conditions are also extended to observer and controller design and illustrated on numerical examples.     

三维裂隙网络渗流路径识别算法及其优化

裂隙化岩体广泛存在于地表浅层,为主要的地下水渗透介质之一,而裂隙网络构成地下水及溶质在裂隙化岩体中运移的主要甚至唯一导水通道.根据图论知识将裂隙网络概化为非连通图,采用图这种数据结构表示三维裂隙网络数据.编写了计算机程序识别裂隙化岩体中沿水力梯度方向的渗流路径,剔除对渗流无贡献的裂隙,可明显缩短裂隙网络地下水渗流与溶质运移数值模拟计算时间,使大尺度裂隙网络渗流与溶质运移模拟研究与工程应用的可行性显著提高.     

Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes

Fault detection and diagnosis is crucial in recent industry sector to ensure safety and reliability, and improve the overall equipment efficiency. Moreover, fault detection and diagnosis based on k-nearest neighbor rule (FDD-kNN) has been effectively applied in industrial processes with characteristics such as multi-mode, non-linearity, and non-Gaussian distributed data. The main challenge associated with FDD-kNN is the on-line computational complexity and storage space that are needed for searching neighbors. To deal with these issues, this paper proposes a monitoring approach where the Fuzzy C-Means clustering technique is used to decrease the overall on-line computations and required storage by measuring the neighbors of the clusters’ centres rather than the raw data. After building the monitoring model off-line based on the data clusters’ centres, the faults are detected by comparing the average squared Euclidean distance between the on-line data sample and the clusters’ centres with a predefined threshold. Then, the detected faults can be diagnosed by calculating the contribution of each variable in the fault detection index. Furthermore, for easily analysing the fault diagnosis results, the relative contribution for each sample data vector is considered. A numerical example and the Tennessee Eastman chemical process are used to demonstrate the performance of the proposed FCM-kNN for fault detection and diagnosis.     

Recursive distributed fusion estimation for nonlinear stochastic systems with event-triggered feedback

This paper focus on the distributed fusion estimation problem for a multi-sensor nonlinear stochastic system by considering feedback fusion estimation with its variance. For any of the feedback channels, an event-triggered scheduling mechanism is developed to decide whether the fusion estimation is needed to broadcast to local sensors. Then event-triggered unscented Kalman filters are designed to provide local estimations for fusion. Further, a recursive distributed fusion estimation algorithm related with the trigger threshold is proposed, and sufficient conditions are builded for boundedness of the fusion estimation error covariance. Moreover, an ideal compromise between fusion center-to-sensors communication rate and estimation performance is achieved. Finally, validity of the proposed method is confirmed by a numerical simulation.     

Event-triggered fully distributed asymptotic consensus for leaderless multiple Euler-Lagrange systems

In this paper, the fully distributed consensus for a class of multiple Euler-Lagrange systems is investigated, where the protocol is designed under the event-triggered control framework and the dynamics of Euler-Lagrange systems are heterogeneous. Since only local information interactions at triggered instants can be used and the Euler-Lagrange systems are of relatively complex dynamics, it is challenging to achieve asymptotic consensus without using any global information (such as the Laplacian matrix information). By skillfully integrating the adaptive control, distributed control and event-triggered control techniques, a novel protocol is proposed for the investigated multiple Euler-Lagrange systems. It is proven that the asymptotic consensus can be achieved by the developed protocol. By a numerical example, the effectiveness of the developed protocol is illustrated.     

Application of iterated Laplace transformation to tracer transients in heterogeneous porous media

The Laplace transformation technique has been widely applied to modeling of tracer transport in oil and geothermal reservoirs, and in groundwater aquifers. However, mathematical models of many flow and transport problems could only be obtained as Laplace space solutions, and hence, their computations had to involve a numerical inversion technique.In this work, we employ the iterated Laplace transformation technique to develop novel closed form solutions to the tracer transport models in heterogeneous media. Two types of configurations have been considered: tracer transport in single fracture located in low-permeability matrix and tracer transport in a double porosity medium consisting of flowing and dead-end pore systems. In addition, both linear and radial flow geometries have been considered for both configurations. Applications of iterated Laplace transform technique to these four types of models are presented as fundamental examples and their numerical results were used as benchmarking for the numerical inversion results from Stehfest and Dubner and Abate algorithms.As the technique is quite versatile, we expect that the method should gain widespread acceptance to develop solutions to a wide range of problems in flow and transport in porous media and improve the application of nonlinear regression technique to these solutions.This work has achieved four important objectives: first, two novel Laplace transform relations that are useful in tracer studies are presented. Second, the present work serves to verify/invalidate the results of numerical inversion algorithms. In addition, it provides better insight into tracer transport mechanisms. Finally, it serves as a powerful tool of design and interpretation of tracer tests. All four objectives are illustrated in this work.     

Resilient distributed MPC for systems under synchronous round-robin scheduling

This paper is concerned with the resilient dynamic output-feedback (DOF) distributed model predictive control (DMPC) problem for discrete-time polytopic uncertain systems under synchronous Round-Robin (RR) scheduling. In order to alleviate the computation burden and improve the system robustness against uncertainties, the global system is decomposed into several subsystems, where each subsystem under synchronous RR scheduling communicates with each other via a network. The RR scheduling is adopted to avoid data collisions, however the updating information at each time instant is unfortunately reduced, and the underlying RR scheduling of subsystems are deeply coupled. The main purpose of this paper is to design a set of resilient DOF-based DMPC controllers for systems under the consideration of polytopic uncertainties and synchronous RR scheduling, such that the desirable performance can be obtained at a low cost of computational time. A novel distributed performance index dependent of the synchronous RR scheduling is constructed, where the last iteration information from the neighbor subsystems is used to deal with various couplings. Then, by resorting to the distributed RR-dependent Lyapunov-like approach and inequality analysis technique, a certain upper bound of the objective is put forward to establish a solvable auxiliary optimization problem (AOP). Moreover, by using the Jacobi iteration algorithm to solve such a problem online, the distributed feedback gains are directly obtained to guarantee the convergence of system states. Finally, two examples including a distillation process example and a numerical example are employed to show the effectiveness of the proposed resilient DMPC strategy.     

基于SE-DEA-NT的高校协同创新效率分异研究

基于2012—2016年教育部直属高校协同创新数据,运用超效率DEA模型测度高校协同创新效率和资源冗余程度,并借助非参数统计方法对效率进行分析。研究发现,样本高校中有16.9%的高校在分析期内保持持续的协同创新高效率,有28.8%的样本高校其协同创新活动在分析期内持续表现为无效状态;高校协同创新平均效率在分析期内的变化在统计上不显著;样本高校协同创新效率表现为10类不同的分布状况;仅有4所高校的经费和人力投入冗余率均较小,超过50%的样本高校在经费与人力投入冗余率上相当,其余高校则在经费和人力冗余率指标上差异显著。     

Distributed disturbance observer based consensus control for linear multi-agent systems with mismatched disturbances

This paper proposes two kinds of distributed disturbance observer (DO) based consensus control laws for linear multi-agent systems (MAS) with mismatched disturbances. For a linear MAS with mismatched disturbances generated by exosystems, we design relative information based distributed DOs for each agent to obtain information of disturbances. The first method is to utilise the information of disturbances obtained by the distributed DO as a feedforward term to reject influence of exogenous disturbances for consensus results, where the gain matrix of the feedforward term is obtained via solving a matrix equation. The second method is to design an internal model based dynamic compensator to reject influence of exogenous disturbances, where the dynamic compensator is also updated by the distributed DO. The leaderless and leader-follower consensus are both considered in this paper, and rigorous proof of consensus results is also given. Finally, some numerical simulations verify effectiveness of the proposed consensus control laws.     

Consensus of multi-agent systems with both input amplitude and input rate constraints

In this paper, the consensus problem is considered for multi-agent systems with input constraint under directed graphs, including leaderless and leader-following cases. Different from existing related works, the distinct feature of this paper is that both the amplitude and rate of the agents’ input are ensured in the given ranges. For the leaderless case, the saturation control strategy is designed and employed for multi-agent systems consensus with the aid of a novel saturation function. For the leader-following case, the saturation-function-based distributed observer as well as the observer-based saturation controller are proposed to achieve consensus. Finally, simulation results show the effectiveness of the designed methods.