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  • GAO Jinming, WANG Yijing, ZUO Zhiqiang, ZHANG Wentao
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-15
    This paper studies the periodic zero-dynamics attacks (ZDAs) in multi-agent systems without velocity measurements under directed graph. Specifically, two types of attack modes are addressed, i.e., infinite number and finite number of zero-dynamics attacks. For the former case, we show that the consensus of the considered system cannot be guaranteed. For the latter case, the dynamic evolution of the agents is investigated and it is found that only attacking the rooted agents can destroy the consensus. Then, a sufficient condition which quantifies whether or not the consensus value is destroyed is given, revealing the relationship among parameters of system model, filter and attack signal. Finally, simulations are carried out to verify the effectiveness of the theoretical findings.
  • SHE Xuehua, MA Hui, REN Hongru, LI Hongyi
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-15
    This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle (UAV) under the performance constraint and scaled relative velocity constraint, in which the states of the uncooperative target can only be estimated through a vision sensor. Considering the limited detection range, a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking system.Meanwhile, the scaled relative velocity constraint in the dynamic phase is taken into account, and a time-varying nonlinear transformation is used to solve the constraint problem, which not only overcomes the feasibility condition but also fails to violate the constraint boundaries. Finally, the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are bounded. It is proven that the UAV can follow the uncooperative target at the desired relative position within a prescribed time, thereby improving the applicability of the vision-based tracking approach. Simulation results have been presented to prove the validity of the proposed control strategy.
  • WANG Yeshunying, MENG Hui, LIAO Pu
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-08
    In this paper, we investigate the optimal per-claim reinsurance problem under the continuoustime framework to minimize the insurer’s ruin probability based on the Lundberg exponent. Considering reinsurance participants’ diversified risk preferences, we assume that the reinsurance premium is calculated by a combined premium principle, including the expected value premium principle and upper moment premium principle. Then, we derive the insurer’s optimal reinsurance strategy satisfying the principle of indemnity and the incentive compatibility condition in an infinite reinsurance space based on the point-wise optimization approach. Besides, our work emphasizes the optimality and admissibility of the combination of the excess of loss reinsurance and its dual form when a piecewise reinsurance premium principle is considered. As a special case, the optimal reinsurance strategy under the expected value premium principle reduces to the classic result. Furthermore, the numerical analyses are provided to illustrate the effects of the main parameters on the maximal Lundberg exponent and the optimal reinsurance strategy.
  • NI Xuanming, ZHENG Tiantian, GAO Feng, ZHAO Huimin
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-08
    Government-sponsored venture capital (GVC) has been used to support financially constrained start-ups as an important policy tool in China. Typically, to motivate the social capital to invest in start-ups, GVC provides subsidies for them to bridge the funding gap in the early-stage venture capital market. However, the effect and mechanism of GVC affecting social capital investment have not been clearly studied. In this paper, we not only develop a game model to analyze this issue in theory, but conduct an empirical study by analyzing the 14741 records matched by propensity score matching (PSM) of Chinese venture capital market data from 2011 to 2021. Our findings are as the follows. Firstly, the subsidies offered by GVC will simultaneously increase the returns and risks of investments in start-ups of social capital with more volatile incentive effect of GVC. The incentive effect of GVC is only effective when the returns resulting from the subsidies outweigh the risks they introduce. In the context of the Chinese venture capital market, the incentive effect of GVC is effective. Secondly, the incentive effect of GVC is more pronounced in high-tech industries, which can be attributed to the signaling effect facilitated by GVC. In this context, the subsidy mainly helps social capital to bear the costs associated with screening potential investments. Thirdly, the incentive effect of GVC is more significant in underdeveloped venture capital markets, which can be explained by the “virtuous cycle” effect, in which GVC plays a pioneering role in establishing a more robust early-stage market trading system. By examining these three points, this study contributes to a better understanding of how GVC can effectively guide social capital investment, especially in the Chinese landscape.
  • CHEN Sheng, HU Haofei
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-08
    In this paper, we aim to study Kronecker canonical form theory for T-type digraphs, which can be used to construct trees by tensor product with some directed paths. Firstly, we show that some bicyclic digraphs and multicyclic digraphs are T-type digraphs. Secondly, we provide a characterization for T-type digraphs by their Kronecker canonical form. Moreover, we present an algorithm for computing the Kronecker canonical form, which can be used to determine whether or not a digraph is a T-type digraph. Lastly, for a class of T-type digraphs, we show that their incidence matrix pair can be transformed into Kronecker canonical form using unimodular matrices. We also present an algorithm related to finding such unimodular matrices.
  • WANG Linpeng, MOU Chenqi
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-08
    Characteristic pairs consist of lexicographical Gröbner bases and the minimal triangular sets, called W-characteristic sets, contained in them, and they are good representations of multivariate polynomial ideals in terms of Gröbner bases and triangular sets simultaneously. In this paper, we study how to decompose a polynomial set of arbitrary dimensions into characteristic pairs with simple W-characteristic sets, and two algorithms are proposed over fields of characteristic zero and over finite fields respectively. Both of the algorithms rely on the concept of strong regular characteristic divisors, and the one for fields of characteristic zero also uses Lazard Lemma to test whether an ideal is radical. Experimental results are presented to illustrate the effectiveness of the proposed algorithms.
  • SHI Yufeng, WANG Jinghan, ZHAO Nana
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-08
    In this paper, we study a class of general mean-field BDSDEs whose coefficients satisfy some stochastic conditions. Specifically, we prove the existence and uniqueness theorem of solution under stochastic Lipschitz condition and obtain the related comparison theorem. Besides, we further relax the conditions and deduce the existence theorem of solutions under stochastic linear growth and continuous conditions, and we also prove the associated comparison theorem. Finally, an asset pricing problem is discussed, which demonstrates the application of the general mean-field BDSDEs in finance.
  • DING Chengjun, YANG Weiguo, TANG Niansheng
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-04
    Continuous state nonhomogeneous Markov chains are widely used to model the performance of random variables continuously varied over time in many fields such as population biology. Existing works mainly focus on their strong law of large numbers. There is little work developed on their limit theorems. To this end, this paper investigates the limiting properties of continuous state nonhomogeneous Markov chains, and establishes limit theorems for multivariate functions of continuous state nonhomogeneous Markov chains, including the strong law of lager numbers, the central limit theorem and almost sure central limit theorem under some mild conditions, which are some basic theoretical properties for statistical inference and predictions of continuous-time-varying random variables.
  • SHANG Juan, MO Lipo, MI Rongxin, CAO Xianbing
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-02
    This paper investigates region tracking and perimeter surveillance of second-order multi-agent systems, where all agents move within a star-shaped set. First, by coordination transformations, the region tracking problem is converted from the star-shaped sets to a circular region. We employ communication and collaboration to complete region tracking and perimeter surveillance tasks, and then revert back to the star-shaped set by using inverse transformations. Second, we propose a distributed control strategy based on attractive and interaction potential functions, under which all agents can quickly track a given circular region and move around the perimeter. Finally, we validate the effectiveness and performance advantages of the proposed method through simulation experiments.
  • ZHANG Mengyue, ZHAO Shishun, XU Da, HU Tao, SUN Jianguo
    Journal of Systems Science & Complexity.
    Accepted: 2024-01-02
    The paper discusses the regression analysis of current status data, which is common in various fields such as tumorigenic research and demographic studies. Analyzing this type of data poses a significant challenge and has recently gained considerable interest. Furthermore we consider an even more difficult scenario where, apart from censoring, one also faces left-truncation and informative censoring, meaning that there is a potential correlation between the examination time and the failure time of interest. We propose a sieve maximum likelihood estimation (MLE) method and in the proposed method for inference, a copula-based procedure is applied to depict the informative censoring. Also we utilise the splines to estimate the unknown nonparametric functions in the model, and the asymptotic properties of the proposed estimator are established. The simulation results indicate that the developed approach is effective in practice, and it has been successfully applied a set of real data.
  • XIN Guoce, ZHANG Yingrui, ZHANG Zihao
    Journal of Systems Science & Complexity.
    Accepted: 2023-12-20
    We find by applying MacMahon’s partition analysis that all magic labellings of the cube are of eight types, each generated by six basic elements. A combinatorial proof of this fact is given. The number of magic labellings of the cube is thus reobtained as a polynomial in the magic sum of degree 5. Then we enumerate magic distinct labellings, the number of which turns out to be a quasipolynomial of period 720720. We also find the symmetry group can be used to significantly simplify the computation.
  • LIAN Chunbo, HAN Ning, GE Bin, LI Lin
    Journal of Systems Science and Complexity.
    Accepted: 2023-12-08
    In this paper, the flocking behavior of a Cucker-Smale model with a leader and noise is studied in a finite time. We present a Cucker-Smale system with two nonlinear controls for a complex network with stochastic synchronization in probability. Based on the finite-time stability theory of stochastic differential equations, the sufficient conditions for the flocking of stochastic systems in a finite time are obtained by using the Lyapunov function method. Finally, the numerical simulation of the particle system is carried out for the leader and noise, and the correctness of the results is verified.
  • CHEN Luefeng, LIU Xiao, WU Min, LU Chengda, PEDRYCZ Witold, HIROTA Kaoru
    Journal of Systems Science and Complexity.
    Accepted: 2023-12-08
    In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer (LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then a LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is ±1r/min, and the overshoot is reduced to 5.8 %. This greatly improves the stability and security, which is exactly what the drilling process requires.
  • ZHANG Zhibing, ZHOU Dapeng, WANG Yeguang, GAO Wanxin, ZHANG Yanjun
    Journal of Systems Science and Complexity.
    Accepted: 2023-12-08
    The stability margin is a vital indicator for assessing the safety level of aircraft control systems. It should maintain sufficient stability margin to ensure safety during flight, especially in the process of large maneuver operations. The stability margin is generally quantified by the Bode diagram, which strictly depends on the system parameters and the open-loop transfer function. However, due to the uncertain flight environments, transmission delays of sensors and mode switchings, etc., there exist large parameter and structure uncertainties in the aircraft control systems, which makes it difficult to precisely configure the stability margin to the desired value by the usual control methods. To address this problem, an indirect adaptive control strategy is proposed in this paper, where an adaptive PI control law with the capability of self-configuration of stability margin is developed. The developed control law not only achieves stable time-varying command tracking in the time domain, but also is able to automatically configure the phase margin and gain margin in the frequency domain. Finally, the simulation of the one-degree-of-freedom roll rate control model of the air vehicle verifies the validity of the proposed control method.
  • GE Zhaoqiang
    Journal of Systems Science & Complexity.
    Accepted: 2023-12-08
    In this paper, the approximate controllability of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions is investigated in the sense of integral solution in Hilbert spaces. Some sufficient and necessary conditions are obtained. Firstly, the existence and uniqueness of integral solutions of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions are considered by GE-evolution operator theory and Sadovskii’s fixed point theorem, the existence and uniqueness theorem of solutions is given. Secondly, the approximate controllability of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions is studied in the sense of integral solution. The criterion for approximate controllability is provided. The obtained results have important theoretical and practical value for the study of controllability of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions.
  • CHEN Shaoshi, DU Hao, GAO Yiman, LI Ziming
    Journal of Systems Science & Complexity.
    Accepted: 2023-12-06
    We extend the shell and kernel reductions for hyperexponential functions over the field of rational functions to a monomial extension. Both of the reductions are incorporated into one algorithm. As an application, we present an additive decomposition in rationally hyperexponential towers. The decomposition yields an alternative algorithm for computing elementary integrals over such towers. The alternative can find some elementary integrals that are unevaluated by the integrators in the latest versions of maple and mathematica.
  • CHEN Lingfan, YAO Shanshan
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-29
    The minimal basis of a univariate polynomial matrix $M(s)\in K[s]^{m\times n}$ is a basis of the syzygies of the polynomial matrix $M(s)$ with lowest possible degree, where $K[s]$ is the univariate polynomial ring over the field of $K$. It provides an efficient tool to compute the moving planes and moving quadratics of a rational parametric surface, which are employed to implicitize the parametric surface as a powerful implicitization method. In this paper, we develop two improved algorithms for computing the minimal bases of polynomial matrices. The algorithms are based on efficient methods to reduce the degrees of a set of univariate polynomial vectors. It is shown that the computational complexities of the two algorithms are $\mathcal{O}(m^{2}n^{3}d^2+d^2n^5-(2mn^4d^2-\frac{1}{6}m^3nd))$, and $\mathcal{O}(m^2nd^2+(n-m)n^3d^2+\frac{m^2n^2d^2}{n-m})$ respectively, where $m,n$ are the sizes of the polynomial matrix $M(s)$ and $d$ is the degree of each entry of the matrix. The new algorithms are faster than the state-of-the-art methods by experimental examples. Some properties about the degree of the minimal basis are also provided.
  • QI Niuniu, DEHBI Lydia, LIU Banglong, YANG Zhengfeng, ZENG Zhenbing
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-24
    This paper primarily focuses on solving the Heilbronn problem of convex polygons, which involves minimizing the area of a convex polygon $P_1P_2\cdots P_n$ while satisfying the condition that the areas of all triangles formed by consecutive vertices are equal to $\frac{1}{2}$. The problem is reformulated as a polynomial optimization problem with a bilinear objective function and bilinear constraints. A new method is presented to verify the upper and lower bounds for the optimization problem. The upper bound is obtained by the affine regular decagon. Then Bilinear Matrix Inequalities (BMI) theory and the branch-and-bound technique are used to verify the lower bound of the problem. The paper concludes by proving that the lower bound for the area minimization problem of a convex polygon with 10 vertices is 13.076548. The relative error compared to the global optimum is $0.104\%$.
  • Binrong WU, Lin WANG, Yu-Rong ZENG
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-24
    This study proposes a novel interpretable tourism demand forecasting framework that considers the impact of the COVID-19 pandemic by using multi-source heterogeneous data, namely, historical tourism volume, newly confirmed cases in tourist origins and destinations, and search engine data. This study introduces newly confirmed cases in tourist origins and tourist destinations to forecast tourism demand and proposes a new two-stage decomposition method called ensemble empirical mode decomposition-variational mode decomposition to deal with the tourist arrival sequence. To solve the problem of insufficient interpretability of existing tourism demand forecasting, this study also proposes a novel interpretable tourism demand forecasting model called JADE-TFT, which utilizes an adaptive differential evolution algorithm with external archiving (JADE) to intelligently and efficiently optimize the hyperparameters of temporal fusion transformers (TFT). The validity of the proposed prediction framework is verified by actual cases based on Hainan and Macau tourism data sets. The interpretable experimental results show that newly confirmed cases in tourist origins and tourist destinations can better reflect tourists’ concerns about travel in the post-pandemic era, and the two-stage decomposition method can effectively identify the inflection point of tourism prediction, thereby increasing the prediction accuracy of tourism demand.
  • XIE Siyu, ZHANG Shujun, WANG Ziming, GAN Die
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-17
    In this paper, we consider a sparse parameter estimation problem in continuous-time linear stochastic regression models using sampling data. Based on the compressed sensing (CS) method, we propose a compressed least squares (LS) algorithm to deal with the challenges of parameter sparsity. At each sampling time instant, the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter. Then, the original high-dimensional sparse unknown parameter is recovered by a reconstruction method. By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods, we establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals. At last, a simulation example is provided to verify our theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.
  • FAN Xianrui, LI Yongming, TONG Shaocheng
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-17
    This paper studies the finite-time fuzzy adaptive output feedback resilient control problem for nonlinear cyber-physical systems (CPSs) with sensor attacks and actuator faults. Fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear functions, and a fuzzy state observer is constructed to estimate the unmeasured states. By combining the Nussbaum function with the backstepping control design technique, a fuzzy adaptive resilient control scheme is designed to successfully address the effects of sensor attacks and actuator faults. It is proved that the controlled system is semi-global practical finite-time stability (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite time interval. Finally, the simulation and comparison results further demonstrate the effectiveness of the designed control method.
  • XIE Haibin, ZHANG Jingjie, CHEN Yun, LU Zudi
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-15
    Under the assumption that asset prices follow a mixed gamma process, this paper first shows that return series can be presented as a difference of two gamma processes and then proposes a realized probability index for return direction forecasting. The underlying distribution of this new index is analyzed and found to be beta-distributed. Both theoretical and empirical results show that this new index is more efficient than the traditional binary index.
  • WANG Wenjun, YANG Zhihuang
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-01
    This paper investigates the moment selection and parameter estimation problem of high-dimensional unconditional moment conditions. First, we propose a Fantope projection and selection (FPS) approach to distinguish the informative and uninformative moments in high-dimensional unconditional moment conditions. Second, for the selected unconditional moment conditions, we present a generalized empirical likelihood (GEL) approach to estimate unknown parameters. The proposed method is computationally feasible, and can efficiently avoid the well-known ill-posed problem of GEL approach in the analysis of high-dimensional unconditional moment conditions. Under some regularity conditions, we show the consistency of the selected moment conditions, the consistency and asymptotic normality of the proposed GEL estimator. Two simulation studies are conducted to investigate the finite sample performance of the proposed methodologies. The proposed method is illustrated by a real example.
  • SHENG Ning, LIU Yang, CHI Ronghu, AI Zidong
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-01
    This paper considers the adaptive finite-time control and observer design method for a class of non-strict feedback systems with unmeasurable states, unknown nonlinear dynamics and actuator faults. In this paper, an observer is proposed to estimate the unmeasurable states in finite-time based on adaptive technique and neural networks, while the actuator faults are not included. Command filter is used to solve the computational explosion and singularity problems caused by the traditional backstepping and non-strict feedback structure, respectively. Since the fault efficiency indicators in real systems are not available, two-layer neural networks are adopted, where the first network is to estimate the unknown nonlinearities of systems and the second one is to estimate fault efficiency indicators and unknown nonlinear terms. The proposed scheme guarantees that states are bounded through stability theorem. Finally, two experiments including a numerical example and a spring-mass-damper system are given to verify the effectiveness of the proposed method.
  • YU Miao, FENG Jun-e, XIA Jianwei, FU Shihua, SHEN Hao
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-01
    In this paper, the approximate synchronization of leader-follower multiagent systems (MASs) over finite fields is studied in regard to local and global synchronization. First, the approximately synchronous state set (ASSS) is obtained. Second, combined with ASSS and transient periods, some criteria for the local and global approximate synchronization of systems are given. Moreover, the algorithms for calculating the maximum approximately synchronous basin (MASB) and the maximum control invariant set (MCIS) are presented. Third, the global approximate synchronization of the system is achieved by designing the state feedback control, and a design algorithm of the controller using the truth matrix method is proposed. Moreover, the results of approximate synchronization are degenerated to complete synchronization. Last, two examples are shown to demonstrate the results of this paper.
  • DAI Zhifeng, WU Tong
    Journal of Systems Science and Complexity.
    Accepted: 2023-11-01
    This study examines the influence of oil shocks on systemic risk spillover among the commodity markets. Specifically, this paper uses the DCC-GARCH approach combined with the TVP-VAR model to calculate risk connectedness and the GARCH-MIDAS model to explore how oil shocks from different sources affect the risk spillover effects among the commodity markets. The results are the following: first, there are significant risk spillovers among the commodity markets with important time-varying characteristics and with sharp changes in times of crisis. The industrial metals, agriculture, precious metals, and light energy commodity markets are risk recipients, and the energy and livestock commodity markets are risk exporters. Second, oil price shocks, particularly oil aggregate demand shocks, prominently affect the total risk connectedness among the commodity markets. In particular, the impact on the net risk spillover effect of different commodity market differs.
  • HU Haokun, MO Lipo, CAO Xianbing
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-23
    The optimization problem of heterogeneous networks under a time-varying topology. Each agent only accesses to one local objective function, which is nonsmooth. An improved algorithm with noisy measurement of local objective functions' sub-gradients and additive noises among information exchanging between each pair of agents is designed to minimize the sum of objective functions of all agents. To weaken the effect of these noises, two step sizes are introduced in the control protocol. By graph theory, stochastic analysis and martingale convergence theory, it is proved that if the sub-gradients are uniformly bounded, the sequence of digraphs is balanced and the union graph of all digraphs is joint strongly connected, then the designed control protocol can force all agents find the global optimal point almost surely. At last, we give some numerical examples to verify the effectiveness of the stochastic sub-gradient algorithms.
  • QIAO Nan, LI Tao
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-23
    We propose a data-driven direct adaptive control law based on the adaptive dynamic programming (ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive indices. We use online data of the system to iteratively solve the generalized algebraic Riccati equation (GARE) and to learn the optimal control law directly. For the case with measurable system noises, we show that the adaptive control law approximates the optimal control law as time goes on. For the case with unmeasurable system noises, we use the least-square solution calculated only from the measurable data instead of the real solution of the regression equation to iteratively solve the GARE. We also study the influences of the intensity of the system noises, the intensity of the exploration noises, the initial iterative matrix, and the sampling period on the convergence of the ADP algorithm. Finally, we present two numerical simulation examples to demonstrate the effectiveness of our algorithms.
  • YANG Xu, ZENG Shao-Feng, LIU Zhi-Yong
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-23
    Feature correspondence is a crucial aspect of various computer vision and robot vision tasks. Unlike traditional optimization-based matching techniques, researchers have recently adopted a learning-based approach for matching, but these methods face challenges in dealing with outlier features. This paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier issue. Additionally, we introduce a modified cross-entropy matching loss to handle the outlier problem. As a result, our proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier features. Benchmark experiments confirm the effectiveness of our approach.
  • QIN Yahang, ZHANG Chengye, CHEN Ci, XIE Shengli, LEWIS Frank L.
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-23
    This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou navigation. While navigating in urban canyons, the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou signal. Here, we formulate the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system, and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback control. Our learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system, without the full knowledge of the vehicle dynamics. It is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate. A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.
  • ZHU Sheng, WANG Jinting
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-17
    Facing various pieces of information disclosed by the system upon arrival, customers often exhibit different strategic responses. In this paper customers' strategic behavior is studied in a Markovian queue with Bernoulli-type working vacations. Upon completion of a service, the server starts a working vacation if the system is empty. If the system is found to be non-empty, the server takes a working vacation with a certain probability. During a working vacation, the server provides service at a lower service rate. Upon arrival, each customer decides whether to join the system or not based on the information disclosed and a reward-cost structure. We study the equilibrium balking strategies of customers at two information levels. For the fully observable case, we derive the two-dimensional threshold strategies, under which customers behave accordingly in the regular state and the working vacation state. For the partially observable case, we obtain a threshold strategy that completely depends on the queue length of the system. The influence of input parameters on the equilibrium strategies is discussed by numerical examples. Sensitivity analysis shows that reducing the vacation probability or rising the vacation rate will encourage more customers to join the system, thereby improving the system throughput. In addition, the disclosure of real-time server state information will also improve the system throughput.
  • ZHOU Pei, ZHU Chungang
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-17
    Isogeometric collocation method (IGC) shows high computational efficiency compared with isogeometric Galerkin method (IGG) when solving partial differential equations (PDEs). However, few studies about IGC have focused on multi-sided physical domains. In this paper, we propose a new IGC method based on toric parameterization (IGCT) for the multi-sided planar physical domains. Due to the high order continuity of toric basis functions, the IGCT method shows more accurate numerical approximation. Moreover, we generalize the adaptive w-refinement method into IGCT (IGCT-w), in which the weights of basis functions in physical domains are optimized independently for geometry representation. The numerical accuracy of IGCT-w is significantly improved by an order of magnitude in comparison with IGCT method. To save the computational cost of IGCT-w, we devise a selection of weights scheme according to relative residuals. Finally, several numerical examples demonstrate the effectiveness and robustness of our proposed method.
  • SUN Jiuyun, DONG Huanhe, FANG Yong
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-17
    In this paper, physics-informed liquid networks (PILNs) are proposed based on liquid time-constant networks (LTC) for solving nonlinear partial differential equations (PDEs). In this approach, the network state is controlled via ordinary differential equations (ODEs). The significant advantage is that neurons controlled by ODEs are more expressive compared to simple activation functions. In addition, the PILNs use difference schemes instead of automatic differentiation to construct the residuals of PDEs, which avoid information loss in the neighborhood of sampling points. As this method draws on both the traveling wave method and physics-informed neural networks (PINNs), it has a better physical interpretation. Finally, the KdV equation and the nonlinear Schrödinger equation are solved to test the generalization ability of the PILNs. To the best of the authors' knowledge, this is the first deep learning method that uses ODEs to simulate the numerical solutions of PDEs.
  • SUN Jiuyun, DONG Huanhe, FANG Yong
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-17
    In this article, hybrid physics-informed neural networks (PINNs) are proposed for solving partial differential equations (PDEs). In this approach, we introduce a difference scheme based on local mesh to construct the physical residuals as part of the loss function. The obvious advantage is that the hybrid PINNs are not completely dependent on automatic differentiation techniques and are more sensitive to gradient changes in the solution. In addition, since the PINNs are continuous mappings, local meshes at arbitrary points can be built. Therefore, all local meshes are independent and the hybrid PINNs are not limited by dimension. Finally, the performance of the hybrid PINNs is verified by numerical experiments, and the effects of the order of differential schemes and the size of local mesh on accuracy are discussed. The results show that the generalization ability of the hybrid PINNs is significantly better than that of the PINNs.
  • XIN Hong, YI Dong
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-08
    With the development of intelligent technology, the problem of LiDAR-based localization has played an increasingly important role in the localization of robots in unmanned systems. Since the dense point clouds from the environment requires a large amount of memory, specific and stable structural features, such as pole-like objects in the environment, can serve as the ideal landmarks for localization in unmanned systems, which can effectively reduce the memory usage and mitigate the effects of dynamic environment changes. In this paper, we propose a pole-like objects extraction approach and then, it is applied into the localization system of mobile robots. Under the same experimental conditions, our method can extract more pole-like objects and achieve better long-term localization accuracy than some existing methods in different urban environmental datasets.
  • Pengli MAO, Yan LIN, Lin LI, Baochang ZHANG
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-08
    Remaining useful life (RUL) is a significant challenge in prognostics and health management. Existing methods suffer from a severe performance drop, as testing data from engine sensors exhibits high nonlinearity and complicated fault modes. In this study, we introduce a reinforcement neural architecture search technique based on upper confidence bound (UCB) to optimize an efficient model. UCB explores the combinatorial parameter space of a multi-head convolutional layers concatenate with recurrent layers to search for a suitable architecture. To address the highly nonlinear dataset in complicated working conditions, rainflow counting algorithm is applied to extract features. Experiments are conducted on C-MAPSS dataset. Compared with state-of-the-art, our approach yields better results in both RMSE and scoring function for all the sub-datasets. In multiple working conditions, we achieve lower RMSE with significant superiority. The experimental results confirm that our proposed method is an efficient approach for obtaining highly precise RUL predictions.
  • LIU Haiyi, ZHANG Yabin, WANG Lei
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-08
    Recently, the physics-informed neural network shows remarkable ability in the context of solving the low-dimensional nonlinear partial differential equations. However, for some cases of high-dimensional systems, such technique may be time-consuming and inaccurate. In this paper, we put forward a pre-training physics-informed neural network with mixed sampling (pPINN) to address these issues. Just based on the initial and boundary conditions, we design the pre-training stage to filter out the set of the misfitting points, which is regarded as part of the training points in the next stage. We further take the parameters of the neural network in Stage~1 as the initialization in Stage~2. The advantage of our approach is that it takes less time to transfer the valuable information from the first stage to the second one to improve the calculation accuracy, especially for the high-dimensional systems. To verify the performance of the pPINN algorithm, we first focus on the growing-and-decaying mode of line rogue wave in the Davey-Stewartson I equation. Another case is the accelerated motion of lump in the inhomogeneous Kadomtsev-Petviashvili equation, which admits a more complex evolution than the uniform equation. The exact solution provides a perfect sample for data experiments, and can also be used as a reference frame to identify the performance of the algorithm. The experiments confirm that the pPINN algorithm can improve the prediction accuracy and training efficiency well, and reduce the training time to a large extent for simulating nonlinear waves of high-dimensional equations.
  • WENG Wuyan, CHU Chengbin, WU Peng
    Journal of Systems Science and Complexity.
    Accepted: 2023-10-07
    This paper investigates a new resource-allocation problem involving multi-resource operations, where completing an operation requires simultaneous use of multiple (renewable) resources, probably of different types. The goal of the study is to provide a solution method that minimizes the makespan. We formulate the problem into a novel mixed-integer linear program (MILP) model. To efficiently solve practical-sized instances, an exact Benders decomposition algorithm is developed. This algorithm divides the original problem into a master problem of allocating resources and a subproblem of calculating the makespan, and both are linked via Benders cuts. The convergence is sped up by improving the mathematical model and embedding the variable neighborhood search algorithm. Compared with CPLEX, a commonly used MILP solver, the computational results demonstrate that the proposed algorithm provides tighter upper and lower bounds in most instances. In particular, compared with CPLEX, the proposed method can on average improve the upper and lower bounds by 4.76% and 4.39%, respectively, in solving practical-sized instances.
  • XING Kai, LI Shang, YANG Xiaoguang
    Journal of Systems Science and Complexity.
    Accepted: 2023-09-28
    Using an unbalanced panel data covering 75 countries from 1991 to 2019, we explore how the political risk impacts on food reserve ratio. The empirical findings show that an increasing political risk negatively affects food reserve ratio, and the same effects hold for both internal risk and external risk. Moreover, we find that the increasing external or internal risks both negatively affect food production and food exports, but external risk does not significantly impact food imports and it positively impacts food consumption, while internal risk negatively impacts food imports and food consumption. The results suggest that most governments have difficulty raising subsequent food reserve ratios in face of an increasing political risk, no matter if it is an internal risk or an external risk although the mechanisms behind the impacts are different.
  • WU Qiang, TONG Xingwei, DUAN Xiaogang
    Journal of Systems Science and Complexity.
    Accepted: 2023-09-28
    Exclusive hypothesis testing is a new and special class of hypothesis testing. Since this kind of testing can be applied in survival analysis in order to understand the association between genomics information and clinical information about the survival time. And it is well known that Cox's proportional hazards model is the most commonly used model for regression analysis of failure time. So, in this paper, we consider doing the exclusive hypothesis testing for Cox's proportional hazards model with right-censored data. Thus, we propose the comprehensive test statistics to make decision. And we show that the corresponding decision rule can control the asymptotic Type I errors and have good powers in theory. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Rotterdam Breast Cancer Data study that motivated this study.