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Prof. Dr. Zhiwu Li
Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau, China

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Research Keywords & Expertise

0 Game Theory
0 Data and process mining
0 Petri net theory and application
0 Supervisory control of discrete event systems
0 System reconfiguration

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Supervisory control of discrete event systems
System reconfiguration
Petri net theory and application

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Journal article
Published: 29 August 2021 in Expert Systems with Applications
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Since time series are characterized by a substantial volume of data, high levels of noise and the correlation between data in the time series attributes, it becomes challenging to mine crucial information from the series and apply it to anomaly detection. In this study, inspired by the concept of information granularity being applied to the process of system modelling, a granular Markov model is proposed for time series anomaly detection. Anomalies are generally caused by the changes in amplitude and shape; in this study we take both the original time series data and their amplitude change data into consideration. First, we utilize an interval information granularity representation based on the principle of justifiable granularity to represent the original time series data in an abstract manner to arrive at the corresponding representation results-- that is, interval information granules. Then, based on the results of the interval information granularity representation and the Fuzzy C-Means (FCM) clustering algorithm, a granular Markov model is developed to produce anomaly scores to quantify possible anomalies. Compared with state-of-the-art methods, experimental studies completed for a large number of datasets demonstrate that the proposed method can significantly improve the anomaly detection process with higher data anomaly resolution. The obtained results are consistent across all datasets.

ACS Style

Yanjun Zhou; Huorong Ren; Zhiwu Li; Witold Pedrycz. Anomaly detection based on a granular Markov model. Expert Systems with Applications 2021, 115744 .

AMA Style

Yanjun Zhou, Huorong Ren, Zhiwu Li, Witold Pedrycz. Anomaly detection based on a granular Markov model. Expert Systems with Applications. 2021; ():115744.

Chicago/Turabian Style

Yanjun Zhou; Huorong Ren; Zhiwu Li; Witold Pedrycz. 2021. "Anomaly detection based on a granular Markov model." Expert Systems with Applications , no. : 115744.

Journal article
Published: 24 August 2021 in IEEE Transactions on Automatic Control
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In this paper we develop an algorithm for designing an optimal control sequence in Petri nets which drives a plant net from a source marking to a set of target markings without passing any pre-given forbidden markings. Such control sequences are useful in flexible, reconfigurable automated systems where a plant necessarily respond promptly to a request of reconfiguration. We develop a Dijkstra searching algorithm that is carried out in the basis marking space of a plant net instead of the conventional reachability space. Hence, only a small subset of the reachability set is explored while the unpromising branches are reduced. Moreover, we propose a transition selecting rule to expose all forbidden trajectories and all first-met target markings during the searching process. The main advantage of the proposed method is wide applicability and low computational effort.

ACS Style

Ziyue Ma; Minqiang Zou; Jiafeng Zhang; Zhiwu Li. Design of Optimal Control Sequences in Petri Nets Using Basis Marking Analysis. IEEE Transactions on Automatic Control 2021, PP, 1 -1.

AMA Style

Ziyue Ma, Minqiang Zou, Jiafeng Zhang, Zhiwu Li. Design of Optimal Control Sequences in Petri Nets Using Basis Marking Analysis. IEEE Transactions on Automatic Control. 2021; PP (99):1-1.

Chicago/Turabian Style

Ziyue Ma; Minqiang Zou; Jiafeng Zhang; Zhiwu Li. 2021. "Design of Optimal Control Sequences in Petri Nets Using Basis Marking Analysis." IEEE Transactions on Automatic Control PP, no. 99: 1-1.

Journal article
Published: 17 August 2021 in Applied Soft Computing
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This study is devoted to the generalization of information granules by forming higher order, namely, order-2 information granules. Information granules are semantically meaningful entities, which play a central role in knowledge representation and system modeling in the framework of Granular Computing. The encountered information granules could exhibit significant heterogeneity because of the diversified formal formalisms. To facilitate an effective generalization of heterogeneous granular data when using clustering algorithms, an efficient scheme has been proposed to form a unified representation of various types of granular data by using Possibility–necessity measures. Once the clustering process has been completed in the possibility–necessity feature space, the higher order information granules come as results of decoding by involving the possibility–necessity metrics and fuzzy relational calculus. The extent to which the higher order information granules are supported by the granular data present at a lower level of hierarchy is quantified in terms of the membership degrees obtained in the clustering process. Experimental studies concerning a series of publicly available datasets coming from UCI and KEEL machine learning repositories are carried out in this study.

ACS Style

Dan Wang; Peng Nie; Xiubin Zhu; Witold Pedrycz; Zhiwu Li. Designing of higher order information granules through clustering heterogeneous granular data. Applied Soft Computing 2021, 112, 107820 .

AMA Style

Dan Wang, Peng Nie, Xiubin Zhu, Witold Pedrycz, Zhiwu Li. Designing of higher order information granules through clustering heterogeneous granular data. Applied Soft Computing. 2021; 112 ():107820.

Chicago/Turabian Style

Dan Wang; Peng Nie; Xiubin Zhu; Witold Pedrycz; Zhiwu Li. 2021. "Designing of higher order information granules through clustering heterogeneous granular data." Applied Soft Computing 112, no. : 107820.

Short communication
Published: 14 August 2021 in Automatica
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In this paper, we study the verification and enforcement problems of strong infinite-step opacity and k-step opacity for partially observed discrete-event systems modeled by finite state automata. Strong infinite-step opacity is a property such that the visit of a secret state cannot be inferred by an intruder at any instance along the entire observation trajectory, while strong k-step opacity is a property such that the visit of a secret state cannot be inferred within k steps after the visit. We propose two information structures called an ∞-step recognizer and a k-step recognizer to verify these two properties. The complexities of our algorithms to verify strong infinite- and k-step opacity are O(22⋅|X|⋅|Eo|) and O(2(k+2)⋅|X|⋅|Eo|), respectively, which are lower than that of existing methods in the literature (|X| and |Eo| are the numbers of states and observable events in a plant, respectively). We also derive an upper bound for the value of k in strong k-step opacity, and propose an effective algorithm to determine the maximal value of k for a given plant. Finally, we note that enforcement of strong infinite- and k-step opacity can be transformed into a language specification enforcement problem and hence be solved using supervisory control.

ACS Style

Ziyue Ma; Xiang Yin; Zhiwu Li. Verification and enforcement of strong infinite- and k-step opacity using state recognizers. Automatica 2021, 133, 109838 .

AMA Style

Ziyue Ma, Xiang Yin, Zhiwu Li. Verification and enforcement of strong infinite- and k-step opacity using state recognizers. Automatica. 2021; 133 ():109838.

Chicago/Turabian Style

Ziyue Ma; Xiang Yin; Zhiwu Li. 2021. "Verification and enforcement of strong infinite- and k-step opacity using state recognizers." Automatica 133, no. : 109838.

Journal article
Published: 12 August 2021 in Knowledge-Based Systems
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This study contributes to a development of granular models, which are realized through an aggregation of the outputs produced by numeric models formed at a lower level of hierarchy. The aim is to form a consensus through adjusting local sources of knowledge such that the granular outputs could reflect and quantify the diversity of local knowledge. The aggregation process is accomplished in an active mode by introducing a certain level of randomness to the numeric outputs of each individual model. The numeric outputs produced by numeric models are weighted by the analysis of the root mean squared error associated with the corresponding numeric constructs. Granular models are formed at a higher level of abstraction as the results of the aggregation mechanism. An augmented version of the principle of justifiable granularity is used as the underlying development vehicle for constructing granular outputs (intervals, fuzzy sets, etc.). The performance of the granular model is optimized through adjusting the random deviation added to the outputs of each local model and quantified by the coverage and specificity of the granular results. Experimental studies demonstrate that the proposed active aggregation mechanism can effectively improve the performance of the resulting granular models.

ACS Style

Dan Wang; Xiubin Zhu; Witold Pedrycz; Zhiwu Li. A randomization mechanism for realizing granular models in distributed system modeling. Knowledge-Based Systems 2021, 107376 .

AMA Style

Dan Wang, Xiubin Zhu, Witold Pedrycz, Zhiwu Li. A randomization mechanism for realizing granular models in distributed system modeling. Knowledge-Based Systems. 2021; ():107376.

Chicago/Turabian Style

Dan Wang; Xiubin Zhu; Witold Pedrycz; Zhiwu Li. 2021. "A randomization mechanism for realizing granular models in distributed system modeling." Knowledge-Based Systems , no. : 107376.

Journal article
Published: 01 July 2021 in Automatica
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In this article, we deal with the active diagnosis problem in labeled Petri nets by developing a supervisor for a plant such that the closed-loop system is diagnosable. Since control actions may introduce deadlocks even if an original plant is deadlock-free, we first generalize the classical notion of diagnosability in labeled Petri nets to the nets that may contain potential deadlocks. To avoid enumerating all reachable markings of a plant, we develop a structure called quiescent basis reachability graph, and accordingly propose a structure named Q-diagnoser to verify the diagnosability of a net. We prove that a plant is diagnosable if and only if there does not exist any indeterminate cycle in its Q-diagnoser. Finally, for an undiagnosable plant, we introduce a diagnosability enforcing supervisor to enforce the diagnosability by trimming a Q-diagnoser. Moreover, our approach guarantees that the closed-loop system cannot reach a dead marking unless a fault transition has fired.

ACS Style

Yihui Hu; Ziyue Ma; Zhiwu Li; Alessandro Giua. Diagnosability enforcement in labeled Petri nets using supervisory control. Automatica 2021, 131, 109776 .

AMA Style

Yihui Hu, Ziyue Ma, Zhiwu Li, Alessandro Giua. Diagnosability enforcement in labeled Petri nets using supervisory control. Automatica. 2021; 131 ():109776.

Chicago/Turabian Style

Yihui Hu; Ziyue Ma; Zhiwu Li; Alessandro Giua. 2021. "Diagnosability enforcement in labeled Petri nets using supervisory control." Automatica 131, no. : 109776.

Journal article
Published: 11 June 2021 in Fuzzy Sets and Systems
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As an important technology in artificial intelligence, Granular Computing has emerged as a new multi-disciplinary paradigm and received much attention in recent years. Information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of Granular Computing. By generating centroids (prototypes) and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. As a reverse process of granulation, degranulation involves data reconstruction completed on a basis of the granular representatives (decoding information granules into numeric data). Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation process becomes. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of reconstruction (degranulation), in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we elaborate on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the underlying conceptual framework. The results obtained on both synthetic and publicly available datasets are reported to show the enhancement of the data reconstruction performance thanks to the proposed method. It is pointed out that by using the proposed approach in some cases the reconstruction errors can be reduced close to zero by using the proposed approach.

ACS Style

Kaijie Xu; Witold Pedrycz; Zhiwu Li. Granular computing: An augmented scheme of degranulation through a modified partition matrix. Fuzzy Sets and Systems 2021, 1 .

AMA Style

Kaijie Xu, Witold Pedrycz, Zhiwu Li. Granular computing: An augmented scheme of degranulation through a modified partition matrix. Fuzzy Sets and Systems. 2021; ():1.

Chicago/Turabian Style

Kaijie Xu; Witold Pedrycz; Zhiwu Li. 2021. "Granular computing: An augmented scheme of degranulation through a modified partition matrix." Fuzzy Sets and Systems , no. : 1.

Journal article
Published: 09 June 2021 in IEEE Control Systems Letters
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In this letter, we study the problem of non-blockingness verification by tapping into the basis reachability graph (BRG). Non-blockingness is a property that ensures that all pre-specified tasks can be completed, which is a mandatory requirement during the system design stage. We develop a condition of transition partition of a given net such that the corresponding conflict-increase BRG contains sufficient information on verifying non-blockingness of its corresponding Petri net. Thanks to the compactness of the BRG, our approach possesses practical efficiency since the exhaustive enumeration of the state space can be avoided. In particular, our method does not require that the net is deadlock-free.

ACS Style

Chao Gu; Ziyue Ma; Zhiwu Li; Alessandro Giua. Non-Blockingness Verification of Bounded Petri Nets Using Basis Reachability Graphs. IEEE Control Systems Letters 2021, 6, 1220 -1225.

AMA Style

Chao Gu, Ziyue Ma, Zhiwu Li, Alessandro Giua. Non-Blockingness Verification of Bounded Petri Nets Using Basis Reachability Graphs. IEEE Control Systems Letters. 2021; 6 ():1220-1225.

Chicago/Turabian Style

Chao Gu; Ziyue Ma; Zhiwu Li; Alessandro Giua. 2021. "Non-Blockingness Verification of Bounded Petri Nets Using Basis Reachability Graphs." IEEE Control Systems Letters 6, no. : 1220-1225.

Short communication
Published: 07 June 2021 in Automatica
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This paper studies the marking diagnosability verification problem in labeled Petri nets. Marking diagnosability is a property implying the fact that a plant Petri net has ever reached a pre-defined set of faulty markings can be detected in a finite number of future steps. We first show that the conventional basis-reachability-graph-based methods cannot be used due to the existence of partially faulty basis markings. To overcome such a problem, we propose a transition partition rule to obtain two particular graphs called the positive basis reachability graph and the negative basis reachability graph. Then we develop an information structure, called a dual verifier, that is a parallel composition of the two basis reachability graphs and can be used to determine the marking diagnosability of a plant net. The proposed method has polynomial complexity in the number of basis markings.

ACS Style

Ziyue Ma; Xiang Yin; Zhiwu Li. Marking diagnosability verification in labeled Petri nets. Automatica 2021, 131, 109713 .

AMA Style

Ziyue Ma, Xiang Yin, Zhiwu Li. Marking diagnosability verification in labeled Petri nets. Automatica. 2021; 131 ():109713.

Chicago/Turabian Style

Ziyue Ma; Xiang Yin; Zhiwu Li. 2021. "Marking diagnosability verification in labeled Petri nets." Automatica 131, no. : 109713.

Journal article
Published: 31 May 2021 in IEEE Access
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A reconfigurable manufacturing system (RMS) means that it can be reconfigured and become more complex during its operation. In RMSs, deadlocks may occur because of sharing of reliable or unreliable resources. Various deadlock control techniques are proposed for RMSs with reliable and unreliable resources. However, when the system is large-sized, the complexity of these techniques will increase. To overcome this problem, this paper develops a four-step deadlock control policy for the detection and treatment of faults in an RMS. In the first step, a colored resource-oriented timed Petri net (CROTPN) is designed for rapid and effective reconfiguration of the RMS without considering resource failures. In the second step, "sufficient and necessary conditions" for the liveness of a CROTPN are introduced to guarantee that the model is live. The third step considers the problems of failures of all resources in the CROTPN model and guarantees that the model is reliable by designing a common recovery subnet and adding it to the obtained CROTPN model at the second step. The fourth step designs a new hybrid method that combines the CROTPN with neural networks for fault detection and treatment. A simulation is performed using the GPenSIM tool to evaluate the proposed policy under the RMS configuration changes and the results are compared with the existing approaches in the literature. It is shown that the proposed approach can handle any complex RMS configurations, solve the deadlock problem in an RMS, and detect and treat failures. Furthermore, is simpler in its structure.

ACS Style

Husam Kaid; Abdulrahman Al-Ahmari; Zhiwu Li; Wadea Ameen. Deadlock Control and Fault Detection and Treatment in Reconfigurable Manufacturing Systems Using Colored Resource-Oriented Petri Nets Based on Neural Network. IEEE Access 2021, 9, 84932 -84947.

AMA Style

Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li, Wadea Ameen. Deadlock Control and Fault Detection and Treatment in Reconfigurable Manufacturing Systems Using Colored Resource-Oriented Petri Nets Based on Neural Network. IEEE Access. 2021; 9 (99):84932-84947.

Chicago/Turabian Style

Husam Kaid; Abdulrahman Al-Ahmari; Zhiwu Li; Wadea Ameen. 2021. "Deadlock Control and Fault Detection and Treatment in Reconfigurable Manufacturing Systems Using Colored Resource-Oriented Petri Nets Based on Neural Network." IEEE Access 9, no. 99: 84932-84947.

Journal article
Published: 28 May 2021 in Knowledge-Based Systems
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Due to the high data volume and non-stationarity of time series data, it is very difficult to directly use the original data for anomaly detection. In this study, a novel framework of anomaly detection is proposed, whose intent is to capture more detailed data of time series’ shape and morphology characteristics by data representation to carry out anomaly detection. First, high-order differences and intervals are employed to realize data representation, and then such rectangles and cubes are constructed with the results of data representation for similarity measurement and anomaly detection. Compared with existing state-of-the-art methods, based on the experimental studies completed on large amount of datasets, the methods proposed in this framework are effective in detecting anomalies caused by changes in shape and amplitude. Meanwhile, it can detect anomalies with higher accuracy and better performance of data anomaly resolution.

ACS Style

Yanjun Zhou; Huorong Ren; Zhiwu Li; Witold Pedrycz. An anomaly detection framework for time series data: An interval-based approach. Knowledge-Based Systems 2021, 228, 107153 .

AMA Style

Yanjun Zhou, Huorong Ren, Zhiwu Li, Witold Pedrycz. An anomaly detection framework for time series data: An interval-based approach. Knowledge-Based Systems. 2021; 228 ():107153.

Chicago/Turabian Style

Yanjun Zhou; Huorong Ren; Zhiwu Li; Witold Pedrycz. 2021. "An anomaly detection framework for time series data: An interval-based approach." Knowledge-Based Systems 228, no. : 107153.

Journal article
Published: 10 May 2021 in Nonlinear Analysis: Hybrid Systems
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This paper studies a performance safety enforcing problem in stochastic event graphs, a subclass of stochastic Petri net models. We assume that an intruder can attack part of the transitions to increase/decrease their firing rate such that the performance of the system violates a given safety interval. The difficulty in solving this problem is that the capability of the intruder, i.e., the number of transitions that can be simultaneously attacked, is limited. The control aim is to find a protecting policy such that the performance of the protected plant is guaranteed to be in a given safety interval. We show that this problem can be formulated as a two-player game between the intruder and the operator of the plant. By using mixed integer linear programming technique, we develop a heuristic method to compute a protecting policy that is locally optimal.

ACS Style

Zhou He; Ziyue Ma. Performance safety enforcement in stochastic event graphs against boost and slow attacks. Nonlinear Analysis: Hybrid Systems 2021, 41, 101057 .

AMA Style

Zhou He, Ziyue Ma. Performance safety enforcement in stochastic event graphs against boost and slow attacks. Nonlinear Analysis: Hybrid Systems. 2021; 41 ():101057.

Chicago/Turabian Style

Zhou He; Ziyue Ma. 2021. "Performance safety enforcement in stochastic event graphs against boost and slow attacks." Nonlinear Analysis: Hybrid Systems 41, no. : 101057.

Journal article
Published: 16 April 2021 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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With integer linear programming problems (ILPPs) being formulated and solved, the existing approaches design optimal Petri-net supervisors via nonpure net structures, including self-loops and data inhibitor arcs. Nonpure net structures are powerful for control of Petri-net-modeled discrete-event systems. However, in the existing work, the formulated ILPPs contain a large number of constraints, which is computationally inefficient. In this article, we propose approaches that formulate ILPPs with fewer constraints such that the computational efficiency is significantly improved. To do so, in formulating ILPPs for optimal Petri-net controllers by using self-loops and data inhibitor arcs, we remove the reachability conditions for legal markings. By doing so, an obtained solution may result in some legal markings unreachable. To solve this problem, a novel technique is developed to design an optimal controller by modifying the initial marking and structure of the obtained supervisor. It is shown that, by the reduced ILPPs, one can find the same feasible solutions as that obtained by the existing work. Finally, the proposed approaches are demonstrated by examples.

ACS Style

Yufeng Chen; Yuting Li; Zhiwu Li; NaiQi Wu. On Optimal Supervisor Design for Discrete-Event Systems Modeled With Petri Nets via Constraint Simplification. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -15.

AMA Style

Yufeng Chen, Yuting Li, Zhiwu Li, NaiQi Wu. On Optimal Supervisor Design for Discrete-Event Systems Modeled With Petri Nets via Constraint Simplification. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Yufeng Chen; Yuting Li; Zhiwu Li; NaiQi Wu. 2021. "On Optimal Supervisor Design for Discrete-Event Systems Modeled With Petri Nets via Constraint Simplification." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-15.

Journal article
Published: 13 April 2021 in Applied Energy
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Deep recurrent neural networks, such as gated recurrent units and long short-term memories, have been widely applied in wind speed forecasting. However, the simulations of the dynamics of the neurons in these models are different from the dynamics of natural neurons, and the useful temporal information is not fully extracted. This results in an unsatisfactory forecasting accuracy for practical wind energy management. In this study, under the hypothesis that a wind speed series can be forecasted using only previous observations (without any other information from the outer environment), a hybrid dual temporal information wind speed forecasting system comprising a third-generation spiking neural network is proposed, aiming to better extract temporal information. A fluctuating feature decomposition strategy is adopted to separate the different modes and adaptively transform the original series into several subseries. Subsequently, the third-generation spiking neural network is integrated with a convolution operation to correct and optimize the forecasting performance of a single recurrent deep learning model. Finally, an effective optimization algorithm is applied to obtain a linear combination of the forecasting outputs of each subseries. Four wind datasets collected from the Liaotung Peninsula in China are used to verify the effectiveness of the designed forecasting system. The experiments indicate that the proposed forecasting system achieves MAPEhengshan=1.43%, MAPExianren=1.40%, MAPEdonggang=1.49%, and MAPEdandong=2.56%, thereby showing excellent forecasting performance.

ACS Style

Danxiang Wei; Jianzhou Wang; Xinsong Niu; Zhiwu Li. Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks. Applied Energy 2021, 292, 116842 .

AMA Style

Danxiang Wei, Jianzhou Wang, Xinsong Niu, Zhiwu Li. Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks. Applied Energy. 2021; 292 ():116842.

Chicago/Turabian Style

Danxiang Wei; Jianzhou Wang; Xinsong Niu; Zhiwu Li. 2021. "Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks." Applied Energy 292, no. : 116842.

Journal article
Published: 22 March 2021 in Knowledge-Based Systems
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Information granules have been considered as the fundamental constructs of Granular Computing. As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information granules. The FCM-based granulation–degranulation mechanism plays a pivotal role in Granular Computing. In this paper, to enhance the quality of the degranulation (reconstruction) process, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors (fuzzification factor vector) and setting up an adjustment mechanism to modify the prototypes and the partition matrix. The design is regarded as an optimization problem, which is guided by a reconstruction criterion. In the proposed scheme, the initial partition matrix and prototypes are generated by the FCM. Then a fuzzification factor vector is introduced to form an appropriate fuzzification factor for each cluster to build up an adjustment scheme of modifying the prototypes and the partition matrix. With the supervised learning mode of the granulation–degranulation​ process, we construct a composite objective function of the fuzzification factor vector, the prototypes and the partition matrix. Subsequently, the particle swarm optimization is employed to optimize the fuzzification factor vector to refine the prototypes and develop the optimal partition matrix. Finally, the reconstruction performance of the FCM algorithm is enhanced. Overall, we show that the enhanced version of the degranulation process is beneficial to reduce the deterioration of the reconstruction results and improve the performance of the mechanism of granulation–degranulation, which is also meaningful for transforming data between numeric form and granular format. We offer a thorough analysis of the developed scheme. In particular, we show that the classical FCM algorithm forms a special case of the proposed scheme. Experiments completed for both synthetic and publicly available datasets demonstrate that the proposed approach outperforms the generic data reconstruction approach.

ACS Style

Kaijie Xu; Witold Pedrycz; Zhiwu Li. Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector. Knowledge-Based Systems 2021, 222, 106951 .

AMA Style

Kaijie Xu, Witold Pedrycz, Zhiwu Li. Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector. Knowledge-Based Systems. 2021; 222 ():106951.

Chicago/Turabian Style

Kaijie Xu; Witold Pedrycz; Zhiwu Li. 2021. "Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector." Knowledge-Based Systems 222, no. : 106951.

Journal article
Published: 18 March 2021 in Information Sciences
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Resilience is a critical criterion to evaluate a networked system including discrete-event systems (DESs). This research touches upon the supervisory control problem of a DES modeled with labeled Petri nets under malicious attacks. Attacks on a system can be categorized into actuator attacks and sensor attacks. The former may cause a failure of an actuator for executing the commands issued from a supervisor that enforces a specification. The latter may corrupt an observation (i.e., a sequence of observable transition labels) from a sensor by different types of attacks such as insertion, removal, and replacement of transition labels. For actuator attacks, if we can detect them and disable some particular controllable transition labels before reaching a state that does not satisfy the specification, then we can find a modified supervisor to enforce the specification. For sensor attacks, we assume that, once a time, only one attack can be carried out, i.e., the attacker does not change the attack during an observation corruption. Given a specification, we consider in a plant model any two feasible transition sequences that share the same corrupted observation under attacks. It is shown that there exists a supervisor to enforce the specification if the one-step controllable extensions of the two transition sequences either satisfy or violate the specification simultaneously. To this end, a novel structure, namely a product observation reachability graph constructed from a plant and its specification, is proposed to decide the existence of such a supervisor by checking whether each state in the graph satisfies a particular condition. The application of the reported methods is demonstrated through examples.

ACS Style

Yi Wang; Yuting Li; Zhenhua Yu; NaiQi Wu; Zhiwu Li. Supervisory control of discrete-event systems under external attacks. Information Sciences 2021, 562, 398 -413.

AMA Style

Yi Wang, Yuting Li, Zhenhua Yu, NaiQi Wu, Zhiwu Li. Supervisory control of discrete-event systems under external attacks. Information Sciences. 2021; 562 ():398-413.

Chicago/Turabian Style

Yi Wang; Yuting Li; Zhenhua Yu; NaiQi Wu; Zhiwu Li. 2021. "Supervisory control of discrete-event systems under external attacks." Information Sciences 562, no. : 398-413.

Journal article
Published: 12 March 2021 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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In an automated manufacturing system (AMS), resources are, in general, subject to unpredictable failures, which invalidate many existing deadlock control strategies. In this article, we propose an adaptive deadlock control policy for an AMS with multiple types of unreliable resources. The considered AMS is modeled with a system of simple sequential processes with resources. First, based on an elementary siphon control method, monitors are added for elementary siphons and some particular dependent siphons to ensure the liveness of a system if there are no resource failures. By considering the fact that an unreliable resource may fail in a system, recovery subnets are added to describe the resource failures and recoveries. Since a monitor added for a siphon may not be able to guarantee that the corresponding siphon is always marked if the failure of a resource in the siphon occurs, the concept of switch controllers is presented so as to make the siphon always remarked if it is emptied by resource failures. It is verified that the adaptive controller proposed in this article can guarantee the liveness of the controlled system no matter whether unreliable resources break down or not. More importantly, if there is no resource failure, the system can maintain predefined production without degrading planned system performance. Finally, examples are presented to illustrate the validity of the proposed method.

ACS Style

Ziliang Zhang; GaiYun Liu; Kamel Barkaoui; Zhiwu Li. Adaptive Deadlock Control for a Class of Petri Nets With Unreliable Resources. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -13.

AMA Style

Ziliang Zhang, GaiYun Liu, Kamel Barkaoui, Zhiwu Li. Adaptive Deadlock Control for a Class of Petri Nets With Unreliable Resources. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-13.

Chicago/Turabian Style

Ziliang Zhang; GaiYun Liu; Kamel Barkaoui; Zhiwu Li. 2021. "Adaptive Deadlock Control for a Class of Petri Nets With Unreliable Resources." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-13.

Journal article
Published: 10 March 2021 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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This article focuses on the issue of checking critical observability for labeled Petri nets. Critical observability is a property related to the safety concern of cyber-physical systems. With the aim of checking this property of a net system, it is required to detect whether a set of markings consistent with any observed word of the net system is a subset of a set of critical states representing undesirable operations or a set of noncritical states. In this work, we prove a necessary and sufficient condition to check critical observability when the critical state set is described by an arbitrary subset of reachable markings. Then, the result is extended to the case when a critical state set is modeled by all the reachable markings that satisfy disjunctions of generalized mutual exclusion constraints. The proposed method is derived from the solutions of integer linear programming problems and is applicable to net systems with liveness and boundness. Several case studies show the performance of the presented methodology for discrete-event systems.

ACS Style

Xuya Cong; Maria Pia Fanti; Agostino Marcello Mangini; Zhiwu Li. Critical Observability of Discrete-Event Systems in a Petri Net Framework. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -11.

AMA Style

Xuya Cong, Maria Pia Fanti, Agostino Marcello Mangini, Zhiwu Li. Critical Observability of Discrete-Event Systems in a Petri Net Framework. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-11.

Chicago/Turabian Style

Xuya Cong; Maria Pia Fanti; Agostino Marcello Mangini; Zhiwu Li. 2021. "Critical Observability of Discrete-Event Systems in a Petri Net Framework." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-11.

Journal article
Published: 08 March 2021 in IEEE Transactions on Automatic Control
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This paper develops design principles for observers of timed discrete event systems that behave according to specific time semantics. Observers devoted to discrete event systems usually ignore the timing aspects of the underlying systems although these aspects can be widely used in many problems, in particular for refinement of estimation and event inference tasks. The techniques proposed in this paper utilize the time stamps of the observations to refine the state estimation process for a class of timed automata where events occur according to constant values of time. Consequently, the resulting timed observers are helpful to refine privacy and security tasks. As an example of application, current-state opacity is discussed with timed observers.

ACS Style

Li Jun; Dimitri Lefebvre; Christoforos N. Hadjicostis; Zhiwu Li. Observers for a class of timed automata based on elapsed time graphs. IEEE Transactions on Automatic Control 2021, PP, 1 -1.

AMA Style

Li Jun, Dimitri Lefebvre, Christoforos N. Hadjicostis, Zhiwu Li. Observers for a class of timed automata based on elapsed time graphs. IEEE Transactions on Automatic Control. 2021; PP (99):1-1.

Chicago/Turabian Style

Li Jun; Dimitri Lefebvre; Christoforos N. Hadjicostis; Zhiwu Li. 2021. "Observers for a class of timed automata based on elapsed time graphs." IEEE Transactions on Automatic Control PP, no. 99: 1-1.

Journal article
Published: 25 February 2021 in IEEE Transactions on Fuzzy Systems
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For Takagi-Sugeno fuzzy systems subject to inexact membership functions, bounded disturbances and noises, an output feedback robust model predictive control approach with time-varying robust tubes is investigated. The membership functions errors are bounded within convex sets via the properties of zonotopes and interval matrices. An off-line table stores a series of structures that include nested robust positively invariant sets with the corresponding nominal feedback controller gains, ancillary controller gains, and observer gains. According to bounds of real-time estimation error sets, the time-varying structures in the off-lined table is searched. Then, the output feedback robust model predictive control problem with time-varying tightened constraints on inputs and states is optimized to stabilize the nominal system. The output feedback robust model predictive control approach can not only update bounds of the estimation errors and uncertain terms resulting from inexact membership functions, but also reduce the computational burden. The proposed robust model predictive control algorithm with recursively feasibility and robust stability guarantees the robust stability of the controlled systems.

ACS Style

Xubin Ping; Junying Yao; Bao-Cang Ding; Peng Wang; Zhiwu Li. Time-Varying Tube-based Output Feedback Robust MPC for T-S Fuzzy Systems. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Xubin Ping, Junying Yao, Bao-Cang Ding, Peng Wang, Zhiwu Li. Time-Varying Tube-based Output Feedback Robust MPC for T-S Fuzzy Systems. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Xubin Ping; Junying Yao; Bao-Cang Ding; Peng Wang; Zhiwu Li. 2021. "Time-Varying Tube-based Output Feedback Robust MPC for T-S Fuzzy Systems." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.