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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.
Yanjun Zhou; Huorong Ren; Zhiwu Li; Witold Pedrycz. Anomaly detection based on a granular Markov model. Expert Systems with Applications 2021, 115744 .
AMA StyleYanjun Zhou, Huorong Ren, Zhiwu Li, Witold Pedrycz. Anomaly detection based on a granular Markov model. Expert Systems with Applications. 2021; ():115744.
Chicago/Turabian StyleYanjun Zhou; Huorong Ren; Zhiwu Li; Witold Pedrycz. 2021. "Anomaly detection based on a granular Markov model." Expert Systems with Applications , no. : 115744.
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.
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 StyleDan 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 StyleDan 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.
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.
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 StyleKaijie 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 StyleKaijie Xu; Witold Pedrycz; Zhiwu Li. 2021. "Granular computing: An augmented scheme of degranulation through a modified partition matrix." Fuzzy Sets and Systems , no. : 1.
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.
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 StyleChao 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 StyleChao 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.
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.
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 StyleHusam 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 StyleHusam 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.
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.
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 StyleYanjun 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 StyleYanjun 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.
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.
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 StyleYufeng 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 StyleYufeng 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.
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.
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 StyleKaijie 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 StyleKaijie 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.
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.
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 StyleYi 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 StyleYi 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.
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.
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 StyleZiliang 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 StyleZiliang 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.
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.
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 StyleXuya 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 StyleXuya 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.
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.
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 StyleLi 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 StyleLi 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.
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.
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 StyleXubin 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 StyleXubin 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.
This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimization problem to design a look-up table and an online tube-based output feedback RMPC with tightened constraints and scaled terminal constraint sets. In the offline optimization problem, a sequence of nested robust positively invariant (RPI) sets and robust control invariant (RCI) sets, respectively, for estimation errors and control errors is optimized and stored in the look-up table. In the online optimization problem, real-time control parameters are searched based on the bounds of time-varying estimation error sets. Considering the characteristics of the uncertain scheduling parameter in LPV systems, the online tube-based output feedback RMPC scheme adopts one-step nominal system prediction with scaled terminal constraint sets. The formulated simple and efficient online optimization problem with fewer decision variables and constraints has a lower online computational burden. Recursive feasibility of the optimization problem and robust stability of the controlled LPV system are guaranteed by ensuring that the nominal system converges to the terminal constraint set, and uncertain state trajectories are constrained within robust tubes with the center of the nominal system. A numerical example is given to verify the approach.
Xubin Ping; Junying Yao; Baocang Ding; Zhiwu Li. Tube-Based Output Feedback Robust MPC for LPV Systems With Scaled Terminal Constraint Sets. IEEE Transactions on Cybernetics 2021, PP, 1 -14.
AMA StyleXubin Ping, Junying Yao, Baocang Ding, Zhiwu Li. Tube-Based Output Feedback Robust MPC for LPV Systems With Scaled Terminal Constraint Sets. IEEE Transactions on Cybernetics. 2021; PP (99):1-14.
Chicago/Turabian StyleXubin Ping; Junying Yao; Baocang Ding; Zhiwu Li. 2021. "Tube-Based Output Feedback Robust MPC for LPV Systems With Scaled Terminal Constraint Sets." IEEE Transactions on Cybernetics PP, no. 99: 1-14.
Since the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in this paper, a combination-model-based approach is proposed by combining a similarity-measurement-based method and a model-based method for anomaly detection. First, the process of data representation is performed to generate a new data form to arrive at the purpose of reducing data volume. Furthermore, due to the anomalies being generally caused by changes in amplitude and shape, we take both the original time series data and their amplitude change data into consideration of the process of data representation to capture the shape and morphological features. Then, the results of data representation are employed to establish a model for anomaly detection. Compared with the state-of-the-art methods, experimental studies on a large number of datasets show that the proposed method can significantly improve the performance of anomaly detection with higher data anomaly resolution.
Yanjun Zhou; Huorong Ren; Zhiwu Li; NaiQi Wu; Abdulrahman M. Al-Ahmari. Anomaly detection via a combination model in time series data. Applied Intelligence 2021, 51, 4874 -4887.
AMA StyleYanjun Zhou, Huorong Ren, Zhiwu Li, NaiQi Wu, Abdulrahman M. Al-Ahmari. Anomaly detection via a combination model in time series data. Applied Intelligence. 2021; 51 (7):4874-4887.
Chicago/Turabian StyleYanjun Zhou; Huorong Ren; Zhiwu Li; NaiQi Wu; Abdulrahman M. Al-Ahmari. 2021. "Anomaly detection via a combination model in time series data." Applied Intelligence 51, no. 7: 4874-4887.
The study is concerned with a problem of relational factorization which engages fuzzy relational calculus. It forms an interesting alternative to the method of non-negative matrix factorization that has been commonly discussed and found in numerous applications. The relational factorization takes n-dimensional data located in the unit hypercube and factors it into data of lower dimensionality and some fuzzy relation. Owing to the logic nature of processing delivered by relational calculus, the dimensionality reduction exhibits transparency as the reduction itself is described in terms of logic expressions. Two types of factorizations are investigated by using s-t and t-s composition operators where t and s are triangular norms and conforms, respectively. A two-level process of factorization is designed. A gradient-based learning scheme is developed. The quantification of the performance of the factorization process is realized by bringing a concept of information granularity: the obtained fuzzy relations are made granular constructs and the quality of the produced factorization is assessed in terms of the coverage and specificity of the obtained granular results. A collection of experiments is included to present the performance of factorization and its parametric analysis.
Hanyu E; Ye Cui; Witold Pedrycz; Zhiwu Li. Fuzzy Relational Matrix Factorization and Its Granular Characterization in Data Description. IEEE Transactions on Fuzzy Systems 2020, PP, 1 -1.
AMA StyleHanyu E, Ye Cui, Witold Pedrycz, Zhiwu Li. Fuzzy Relational Matrix Factorization and Its Granular Characterization in Data Description. IEEE Transactions on Fuzzy Systems. 2020; PP (99):1-1.
Chicago/Turabian StyleHanyu E; Ye Cui; Witold Pedrycz; Zhiwu Li. 2020. "Fuzzy Relational Matrix Factorization and Its Granular Characterization in Data Description." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.
The estimation of the loss and prediction of the casualties in earthquake-stricken areas are vital for making rapid and accurate decisions during rescue efforts. The number of casualties is determined by various factors, necessitating a comprehensive system for earthquake-casualty prediction. To obtain accurate prediction results, an effective prediction method based on stacking ensemble learning and improved swarm intelligence algorithm is proposed in this study, which comprises three parts: (1) applying multiple base learners for training, (2) using a stacking strategy to integrate the results generated by multiple base learners to obtain the final prediction results, and (3) developing an improved swarm intelligence algorithm to optimize the key parameters in the prediction model. To verify the effectiveness of the model, we collected data pertaining to earthquake destruction from 1966 to 2017 in China. Experiments were conducted to compare the proposed method with popular machine learning methods. It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy. Moreover, the importance of each feature was evaluated, which has important implications for future work such as casualty prevention and rescue during earthquakes.
Shaoze Cui; Yunqiang Yin; Dujuan Wang; Zhiwu Li; Yanzhang Wang. A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing 2020, 101, 107038 .
AMA StyleShaoze Cui, Yunqiang Yin, Dujuan Wang, Zhiwu Li, Yanzhang Wang. A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing. 2020; 101 ():107038.
Chicago/Turabian StyleShaoze Cui; Yunqiang Yin; Dujuan Wang; Zhiwu Li; Yanzhang Wang. 2020. "A stacking-based ensemble learning method for earthquake casualty prediction." Applied Soft Computing 101, no. : 107038.
This paper investigates the enforcement of Generalized Mutual Exclusion Constraints (GMECs) and deadlock-freeness on a Time Petri Net (TPN) system with uncontrollable transitions, motivated by the fact that the existing methods enforcing GMECs may degrade the performance of a closed-loop system and lead to deadlock states. A supervisor enforcing a set of GMECs and deadlock-freeness on an underlying untimed Petri net system is assumed to be available. By exploiting timing information and mathematical programming, a control function is designed to restrict the firing intervals of transitions such that a TPN system can avoid entering forbidden states. The key idea behind the proposed approach is the online computation of a graph, called Reduced Modified State Class Graph (RMSCG), that is an extension of the partial modified state class graph recently introduced by the authors. Based on the RMSCG, an online control synthesis procedure is developed, which can enforce the originally given GMECs and deadlock-freeness in a maximally permissive way.
Liang Li; Francesco Basile; Zhiwu Li. Closed-Loop Deadlock-Free Supervision for GMECs in Time Petri Net Systems. IEEE Transactions on Automatic Control 2020, PP, 1 -1.
AMA StyleLiang Li, Francesco Basile, Zhiwu Li. Closed-Loop Deadlock-Free Supervision for GMECs in Time Petri Net Systems. IEEE Transactions on Automatic Control. 2020; PP (99):1-1.
Chicago/Turabian StyleLiang Li; Francesco Basile; Zhiwu Li. 2020. "Closed-Loop Deadlock-Free Supervision for GMECs in Time Petri Net Systems." IEEE Transactions on Automatic Control PP, no. 99: 1-1.
While granular computing has experienced rapid growth in the past decades and some milestones have been reached, a comprehensive study of the representation capabilities delivered by numeric prototypes and granular prototypes produced by different techniques still calls for comprehensive research and a comparative analysis. Well-constructed information granules are reflective of the nature of the numeric evidence and serve as backbones of granular classifiers and granular models. The objective of this study is to review a number of clustering paradigms aimed at the construction of information granules, discuss the development of granular prototypes, and conduct a comprehensive evaluation of quality of numeric prototypes and their corresponding augmentations coming in the form of granular prototypes. We have been witnessing many studies devoted to the construction of information granules, but a comparative analysis of the quality of information granules constructed on a basis of prototypes produced by different clustering algorithms is still lacking. In this regard, the review of the clustering algorithms supporting the formation of information granules and the comprehensive comparative study of their usefulness in classification and modeling tasks offered in this study make sense. This will promote the usage of information granules in various future works, especially classification problem and system modeling.
Xiubin Zhu; Witold Pedrycz; Zhiwu Li. Construction and Evaluation of Information Granules: From the Perspective of Clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, PP, 1 -14.
AMA StyleXiubin Zhu, Witold Pedrycz, Zhiwu Li. Construction and Evaluation of Information Granules: From the Perspective of Clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; PP (99):1-14.
Chicago/Turabian StyleXiubin Zhu; Witold Pedrycz; Zhiwu Li. 2020. "Construction and Evaluation of Information Granules: From the Perspective of Clustering." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-14.
A typical example of discrete event systems (DESs) is a reconfigurable manufacturing system (RMS) whose can be changed and become more complicated during its operation. Thus, an effective tool needs to be developed to create both high- and low-level DES implementation. The ladder diagram (LD) is a common programming method for controlling DESs. Petri nets (PNs) represent the most important graphical and mathematical tool that can be used to provide an integrated solution for the design, modeling, analysis, control, and implementation of DESs. Various types of PN-based LDs are developed for DESs, which have high structural complexity of the LDs. Therefore, an approach that can facilitate the elimination of the LDs’ structural complexity needs to be developed. The main objective of this paper is to develop a two-step approach for LDs implementation for RMSs with dynamic changes. First, a colored resource-oriented Petri net (CROPN)-based algorithm is proposed for a rapid and valid configurations of an RMS. Second, a ladder diagram colored resource-oriented Petri net (LDCROPN) is developed to convert the CROPN into an LD. The developed approach is evaluated using examples in the literature. Moreover, the results demonstrate the effectiveness of the developed approach for LD implementation under RMS specification changes.
Husam Kaid; Abdulrahman Al-Ahmari; Zhiwu Li. Colored Resource-Oriented Petri Net Based Ladder Diagrams for PLC Implementation in Reconfigurable Manufacturing Systems. IEEE Access 2020, 8, 217573 -217591.
AMA StyleHusam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li. Colored Resource-Oriented Petri Net Based Ladder Diagrams for PLC Implementation in Reconfigurable Manufacturing Systems. IEEE Access. 2020; 8 (99):217573-217591.
Chicago/Turabian StyleHusam Kaid; Abdulrahman Al-Ahmari; Zhiwu Li. 2020. "Colored Resource-Oriented Petri Net Based Ladder Diagrams for PLC Implementation in Reconfigurable Manufacturing Systems." IEEE Access 8, no. 99: 217573-217591.