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25 August 2022, Volume 35 Issue 4
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Measuring Environmental Performance of Provincial Thermal Power Plants in China: A Malmquist DEA Approach with FixedSum Undesirable Outputs
HOU Wenhui, ZHENG Yunwen, LIANG Liang, LI Yongjun
2022, 35(4): 12011224. DOI:
10.1007/s1142402200556
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With the development of China's economy, environmental pollution has become cumulatively serious. The primary source of environmental pollution is thermal power generation, which has attracted the attention of governments and academia in recent years. To effectively reduce environmental pollution, research should study how to constrain the undesirable output of thermal power plants, that is, to limit the total undesirable output of the plants to a certain fixed sum. However, few studies have suggested that these undesirable outputs should be fixedsum outputs. Moreover, no previous research publication about thermal power plants has analyzed their environmental performance changes. To address these gaps, a novel MalmquistDEA approach is proposed for evaluate the environmental performance of thermal power plants in this paper. This approach generalizes the equilibrium efficient frontier DEA model with fixedsum undesirable outputs and incorporates the model into the Malmquist productivity index (MPI). The authors apply this approach to the analysis of provincial thermal power plant environmental performance in China and analyze such plants' trends based on panel data from 2011 to 2014. The empirical research shows that the environmental performance of regional thermal power plants was positively affected by efficiency change and negatively affected by technical change. Finally, the authors provide policy suggestions based on our findings.
PositiveControllability, PositiveNearControllability, and Canonical Forms of Driftless DiscreteTime Bilinear Systems
TIE Lin
2022, 35(4): 12251243. DOI:
10.1007/s1142402203351
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Controllable canonical forms play important roles in the analysis and design of control systems. In this paper, a fundamental class of discretetime bilinear systems are considered. Such systems are of interest since, on one hand, they have the most complete controllability theory. On the other hand, they can be nearlycontrollable even if controllability fails. Firstly, controllability of the systems with positive control inputs is studied and necessary and sufficient algebraic criteria for positivecontrollability and positivenearcontrollability are derived. Then, controllable canonical forms and nearlycontrollable canonical forms of the systems are presented, respectively, where the corresponding transformation matrices are also explicitly constructed. Examples are given to demonstrate the effectiveness of the derived controllability criteria and controllable canonical forms.
Distributed CommunicationSliding MirrorDescent Algorithm for Nonsmooth Resource Allocation Problem
WANG Yinghui, TU Zhipeng, QIN Huashu
2022, 35(4): 12441261. DOI:
10.1007/s1142402201878
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This paper considers a distributed nonsmooth resource allocation problem of minimizing a global convex function formed by a sum of local nonsmooth convex functions with coupled constraints. A distributed communicationefficient mirrordescent algorithm, which can reduce communication rounds between agents over the network, is designed for the distributed resource allocation problem. By employing communicationsliding methods, agents can find a
ε
solution in
O
($\frac{1}{\varepsilon }$) communication rounds while maintaining
O
($\frac{1}{\varepsilon ^2}$) subgradient evaluations for nonsmooth convex functions. A numerical example is also given to illustrate the effectiveness of the proposed algorithm.
RHCBased Consensus of MultiAgent Systems with Simultaneous Packet Dropout and Input Delay
XU Juanjuan, ZHANG Zhaorong
2022, 35(4): 12621277. DOI:
10.1007/s1142402202603
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This paper is concerned with the multiagent systems with both packet dropout and input delay. A novel receding horizon control (RHC) based consensus protocol is proposed by solving a distributed RHC based optimization problem. The novelty of the optimization problem lines in the involvement of the neighbours' predictor information in the cost functions. Based on the derived RHC based consensus protocol, the necessary and sufficient condition for the meansquare consensus is obtained. In addition, the authors give a specific sufficient condition to guarantee the meansquare consensus.
MeanSquare Stabilization of Networked SampledData Systems with Packet Losses: Critical Sampling Intervals
WANG Chao, WANG Bingchang
2022, 35(4): 12781292. DOI:
10.1007/s1142402201887
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This paper studies the stabilizing problem of networked sampleddata linear systems with independent and identically distribution (i.i.d.) packet dropouts. First of all, for the sampleddata system without packet losses, the necessary and sufficient conditions for the critical sampling intervals are given to ensure the mean square stability where zero order holder and generalized holder are adopted, respectively. Secondly, for the sampleddata system with i.i.d. packet losses, necessary and sufficient conditions for the critical sampling intervals are given to ensure the mean square stability where the zero order holder control is adopted. The random sampling framework is used to examine the case that the value of the control signal is not zero when the packet loss occurs, but the value of the previous control signal when the data packet is not lost. For the sampleddata system with i.i.d. packet losses, the range of the sampling interval is given to ensure the mean square stability where the generalized holder control is adopted. Some numerical simulations are provided to validate the theoretical results.
Active Disturbance Rejection Control of SecondOrder Nonlinear Uncertain Systems with Guaranteed Transient and Steady State Tracking Error Bounds
CHEN Zhixiang
2022, 35(4): 12931309. DOI:
10.1007/s1142402210102
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This paper presents an active disturbance rejection control (ADRC) method for a class of secondorder nonlinear uncertain systems with guaranteed transient and steady state tracking error bounds. To deal with the tracking error constraint, an output error transformation and sliding surface technique are introduced to transform the constrained secondorder system into an equivalent unconstrained firstorder one. Then, an ADRC method is developed to achieve output tracking of the transformed uncertain system. The author proves that the closedloop system is semiglobally uniformly bounded and it is sufficient to guarantee the tracking error constraint for the original system. Simulation results of a system of two inverted pendulums connected by a spring and a damper demonstrate the effectiveness of the proposed control method.
Boundary Control of Coupled Wave Systems with SpatiallyVarying Coefficients
FENG Xiaodan, ZHANG Zhifei
2022, 35(4): 13101329. DOI:
10.1007/s1142402102141
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This paper considers the stabilization of the coupled wave systems with spatiallyvarying coefficients. The authors design a state feedback controller by backstepping method. In contrast to the previous work in the literature, the kernel equations become more complicated and the main difficulty lies in proving the existence and uniqueness of the solution to the kernel equations. Firstly, using the backstepping approach, the authors verify the kernel equations, which is a system of coupled hyperbolic equations with spatiallyvarying coefficients. Then, the existence and uniqueness of the kernel matrices is obtained. Finally, the authors use a Lyapunov function to get the exponential stabilization of the closedloop system. A numerical example is presented to illustrate the effectiveness of the proposed controller.
Distributed EventTriggered Tracking Control of Heterogeneous DiscreteTime MultiAgent Systems with Unknown Parameters
YANG Ruohan, ZHOU Deyun
2022, 35(4): 13301347. DOI:
10.1007/s1142402210031
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In this paper, a novel design framework is developed to solve the cooperative tracking problem of heterogeneous discretetime multiagent systems with unknown agent parameters and directed communication topologies. First, a distributed eventtriggered observer is developed to handle the heterogeneity of the multiagent systems. An eventtriggering mechanism is proposed to reduce the amount of data transmission between neighboring agents. Then, based on the proposed observer, a discretetime distributed model reference adaptive controller is presented to deal with the unknown parameters of the multiagent systems. It is shown that by using the proposed observer and the model reference adaptive controller, the proposed design framework could achieve output tracking of the unknown multiagent systems for any communication graph containing a directed spanning tree. Finally, an example is presented to illustrate the effectiveness of the proposed controller.
Equilibria and Stability Analysis of CohenGrossberg BAM Neural Networks on Time Scale
LIU Mingshuo, FANG Yong, DONG Huanhe
2022, 35(4): 13481373. DOI:
10.1007/s1142402202505
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This paper considers the CohenGrossberg BAM neural networks (CGBAMNNs) on time scale, which can unify and generalize the continuous and discrete systems. First, the criteria for the existence and uniqueness of the equilibrium of CGBAMNNs are derived on time scale. Then based on that, the authors give the criteria for the stability and estimation of equilibrium of the CGBAMNNs on time scale. The method proposed in this paper unifies and generalizes the continuous and discrete CGBAMNNs systems, and is applicable to some other neural network systems on time scale with practical meaning. The effectiveness of the proposed criteria for delayed CGBAMNNs is demonstrated by numerical simulation.
Asynchronous Dissipative Control and Robust Exponential Mean Square Stabilization for Uncertain Fuzzy Neutral Markov Jump Systems
WANG Jie, ZHUANG Guangming, XIA Jianwei, CHEN Guoliang, ZHAO Junsheng
2022, 35(4): 13741397. DOI:
10.1007/s1142402110054
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This paper researches the strict dissipative control problem for uncertain fuzzy neutral Markov jump systems by TakagiSugeno fuzzy rules. The asynchronous phenomenon is considered between the uncertain fuzzy neutral Markov jump systems modes and asynchronous fuzzy PD feedback controller modes, which is described by a hidden Markov model. Via using linear matrix inequalities, the desired asynchronous fuzzy PD feedback controller is obtained, which can ensure that the closedloop uncertain fuzzy neutral Markov jump systems satisfies robustly exponential mean square stabilization with strict dissipativity. A numerical example and a singlelink robot arm are utilized to demonstrate the effectiveness of the method.
An Automatic Classification Pipeline for the Complex Synaptic Structure Based on Deep Learning
SHEN Lijun, MA Chao, LUO Jie, HONG Bei
2022, 35(4): 13981414. DOI:
10.1007/s1142402203075
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As a hallmark of brain complexity, synapses in the nervous system have always received extensive attentions. The diversity of the synaptic structure reflects various functions and mechanisms, some research indicates that, as one of the complex synaptic structures, multiple synapses can strengthen the synaptic connection, what's more, it is closely associated with the procedure of memory and learning. Accompanied by the fast advancement of electron microscopy (EM) technology, it is possible to detect the composition of multiple synapse with high resolution. On this basis, there have been various meaningful studies concerning the relationship between the multiple synapse and cognitive abilities. Despite the extensive studies have been made by different researchers on multiple synapse, no attention has been paid to the classification accuracy of the type of multiple synapse. The current research puts forward an effective method for the automatic classification of multiple synapse, which should be performed in three steps, namely the segmentation of synaptic clefts, the segmentation of vesicle bands, as well as the segmentation of multiple synapses. According to experimental results based on four data sets, the mean classification rate of the method is approximately 97%. In addition, the experimental result on the public dataset shows that the accuracy can reach 96.5%. The classification results provide a basis for quantitative statistics of followup studies. Moreover, this automatic classification method can reduce the time in artificial statistics, and thus researchers can focus more attention on the analysis of statistical results.
On Basis and Pure Nash Equilibrium of Finite Pure Harmonic Games
LIU Aixin, LI Haitao, LI Ping, YANG Xinrong
2022, 35(4): 14151428. DOI:
10.1007/s1142402200320
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This paper investigates the basis and pure Nash equilibrium of finite pure harmonic games (FPHGs) based on the vector space structure. First, a new criterion is proposed for the construction of pure harmonic subspace, based on which, a more concise basis is constructed for the pure harmonic subspace. Second, based on the new basis of FPHGs and auxiliary harmonic vector, a more easily verifiable criterion is presented for the existence of pure Nash equilibrium in basis FPHGs. Third, by constructing a pure Nash equilibrium cubic matrix, the verification of pure Nash equilibrium in threeplayer FPHGs is given.
Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach
PENG Siyang, GUO Shaojun, LONG Yonghong
2022, 35(4): 14291457. DOI:
10.1007/s1142402101683
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The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space (RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.
Optimal Reinsurance and Investment Strategies Under MeanVariance Criteria: Partial and Full Information
ZHU Shihao, SHI Jingtao
2022, 35(4): 14581479. DOI:
10.1007/s1142402202363
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This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of meanvariance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the shortselling of stocks is prohibited. The problem is formulated as a stochastic linearquadratic control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution to the HJB equation in the full information case. Some numerical illustrations are also provided.
Stock Efficiency Evaluation Based on Multiple Risk Measures: A DEALike Envelopment Approach
LI Jun, GAO Hengxuan, LI Yongjun, JIN Xi, LIANG Liang
2022, 35(4): 14801499. DOI:
10.1007/s114240220034y
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This paper proposes a new approach for stock efficiency evaluation based on multiple risk measures. A derived programming model with quadratic constraints is developed based on the envelopment form of data envelopment analysis (DEA). The derived model serves as an inputoriented DEA model by minimizing inputs such as multiple risk measures. In addition, the Russell input measure is introduced and the corresponding efficiency results are evaluated. The findings show that stock efficiency evaluation under the new framework is also effective. The efficiency values indicate that the portfolio frontier under the new framework is more externally enveloped than the DEA efficient surface under the standard DEA framework.
Limited Memory BFGS Method for Least Squares Semidefinite Programming with Banded Structure
XUE Wenjuan, SHEN Chungen, YU Zhensheng
2022, 35(4): 15001519. DOI:
10.1007/s1142402200151
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This work is intended to solve the least squares semidefinite program with a banded structure. A limited memory BFGS method is presented to solve this structured program of high dimension. In the algorithm, the inverse power iteration and orthogonal iteration are employed to calculate partial eigenvectors instead of full decomposition of n×n matrices. One key feature of the algorithm is that it is proved to be globally convergent under inexact gradient information. Preliminary numerical results indicate that the proposed algorithm is comparable with the inexact smoothing Newton method on some large instances of the structured problem.
Regression Analysis of IntervalCensored Data with Informative Observation Times Under the Accelerated Failure Time Model
ZHAO Shishun, DONG Lijian, SUN Jianguo
2022, 35(4): 15201534. DOI:
10.1007/s114240210209y
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This paper discusses regression analysis of intervalcensored failure time data arising from the accelerated failure time model in the presence of informative censoring. For the problem, a sieve maximum likelihood estimation approach is proposed and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process. Also Ispline functions are used to approximate the unknown functions in the model, and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations. In addition, an illustrative example is provided.
Test on Stochastic Block Model: Local Smoothing and Extreme Value Theory
WU Fan, KONG Xinbing, XU Chao
2022, 35(4): 15351556. DOI:
10.1007/s1142402101549
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In this paper, to obtain a consistent estimator of the number of communities, the authors present a new sequential testing procedure, based on the locally smoothed adjacency matrix and the extreme value theory. Under the null hypothesis, the test statistic converges to the type I extreme value distribution, and otherwise, it explodes fast and the divergence rate could even reach n in the strong signal case where n is the size of the network, guaranteeing high detection power. This method is simple to use and serves as an alternative approach to the novel one in Lei (2016) using random matrix theory. To detect the change of the community structure, the authors also propose a twosample test for the stochastic block model with two observed adjacency matrices. Simulation studies justify the theory. The authors apply the proposed method to the political blog data set and find reasonable group structures.
Genetic Pleiotropy Test by Quasi
p
Value with Application to Typhoon Data in China
WU Qiang, ZHONG Shaojun, TONG Xingwei
2022, 35(4): 15571572. DOI:
10.1007/s1142402202875
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To test genetic pleiotropy, the main difficulty lies in the failure to find a test statistic and calculate its
p
value for determining whether to reject the null hypothesis or not. To deal with this issue, the authors propose a quasi
p
value, which plays the similar role as the usual
p
value in genetic pleiotropy test. In the formula of the quasi
p
value, the main task is to determine the weights. In this paper, the authors present two weighted methods based on the Bayesian rule and extend the proposed methods to study a single binary trait using a datadriven EM algorithm. Extensive simulation studies are conducted for the assessment of the two proposed methods and illustrate that the proposed methods improve the performance of power by comparing with the twostage method. In addition, the authors apply the proposed methods to the data of tropical storms that occurred on the mainland of China since 1949, investigating the relationship between the landing site and predictive features of tropical storms, and showing that the landing site has a large influence on at least two features of typhoon.
Recurrences for Callan's Generalization of Narayana Polynomials
CHEN Xi, YANG Arthur Li Bo, ZHAO James Jing Yu
2022, 35(4): 15731585. DOI:
10.1007/s114240210216z
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By using Chen, Hou and Mu's extended Zeilberger algorithm, the authors obtain two recurrence relations for Callan's generalization of Narayana polynomials. Based on these recurrence relations, the authors further prove the realrootedness and asymptotic normality of Callan's Narayana polynomials.
High Speed Machining for Linear Paths Blended with G3 Continuous PythagoreanHodograph Curves
ZHAO Kai, LI Shurong
2022, 35(4): 15861607. DOI:
10.1007/s114240220258x
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Previously, many studies have illustrated corner blend problem with different parameter curves. Only a few of them take a Pythagoreanhodograph (PH) curve as the transition arc, let alone corresponding realtime interpolation methods. In this paper, an integrated cornertransition mixinginterpolationbased scheme (ICMS) is proposed, considering transition error and machine tool kinematics. Firstly, the ICMS smooths the sharp corners in a linear path through blending the linear path with
G
3 continuous PH transition curves. To obtain optimal PH transition curves globally, the problem of corner smoothing is formulated as an optimization problem with constraints. In order to improve optimization efficiency, the transition error constraint is deduced analytically, so is the curvature extreme of each transition curve. After being blended with PH transition curves, a linear path has become a blend curve. Secondly, the ICMS adopts a novel mixed interpolator to process this kind of blend curves by considering machine tool kinematics. The mixed interpolator can not only implement jerklimited feedrate scheduling with critical points detection, but also realize selfswitching of two interpolation modes. Finally, two patterns are machined with a carving platform based on ICMS. Experimental results show the effectiveness of ICMS.
The Center Problem and TimeReversibility with Respect to a Quadratic Involution for a Class of Polynomial Differential Systems with Order 2 or 3
YANG Jing, YANG Ming, LU Zhengyi
2022, 35(4): 16081636. DOI:
10.1007/s1142402100405
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Most studies of the timereversibility are limited to a linear or an affine involution. In this paper, the authors consider the case of a quadratic involution. For a polynomial differential system with a linear part in the standard form (
y
,
x
) in $\mathbb{R}^2$, by using the method of regular chains in a computer algebraic system, the computational procedure for finding the necessary and sufficient conditions of the system to be timereversible with respect to a quadratic involution is given. When the system is quadratic, the necessary and sufficient conditions can be completely obtained by this procedure. For some cubic systems, the necessary and sufficient conditions for these systems to be timereversible with respect to a quadratic involution are also obtained. These conditions can guarantee the corresponding systems to have a center. Meanwhile, a property of a centerfocus system is discovered that if the system is timereversible with respect to a quadratic involution, then its phase diagram is symmetric about a parabola.
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