High-Accuracy Signal Subspace Separation Algorithm Based on Gaussian Kernel Soft Partition

Xu, Kaijie; Pedrycz, Witold; Li, Zhiwu; Nie, Weike

doi:10.1109/tie.2018.2823666

Cited by 29 publications

(14 citation statements)

References 36 publications

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“…We will explore state-tree strucutre to deal with deadlock avoidance problem in automated manufacturing systems [58], [59]. It is also interesting to use the subspace separation algorithm in [60] to find the deadlock zone. APPENDIX See Table 7.…”

Section: Examplementioning

confidence: 99%

On Algebraic Identification of Critical States for Deadlock Control in Automated Manufacturing Systems Modeled With Petri Nets

et al. 2019

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Petri nets are an important and popular tool to model and analyze deadlocks in automated manufacturing systems. The state space of a Petri net model can be divided into two disjoint parts: a live-zone and a dead-zone. A first-met bad marking (FBM) is a marking in the dead-zone, representing the very first entry from the live-zone to the dead-zone, and the calculation of FBMs to a large extent contributes to the complexity of designing optimal liveness-enforcing supervisors. Most existing studies have to fully enumerate the reachable markings of a Petri net model to obtain the FBMs, which exacerbates the computational overheads. This paper first explores a variation mechanism of calculating FBMs with respect to the resource capacity in a class of S 3 PR (Systems of Simple Sequential Processes with Resources) from the structural analysis perspective, which contains a ξ -resource. More generally, for the class of S 3 PR with an η-resource as defined in this paper, the FBMs can be calculated in an algebraic way by a customized structural analysis technique without enumerating all the reachable markings. Finally, the variation mechanism of calculating FBMs is revealed for these considered classes of Petri net models. Examples are given to demonstrate the proposed method.INDEX TERMS Petri net, first-met bad marking, deadlock control, automated manufacturing system.

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Section: Examplementioning

confidence: 99%

On Algebraic Identification of Critical States for Deadlock Control in Automated Manufacturing Systems Modeled With Petri Nets

et al. 2019

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“…They divide the covariance matrix of the received signals into two subspaces: signal-subspace and noise-subspace, and estimate the DOA of the received signals utilizing the orthogonal relation between the signal-subspace and noise-subspace [7][8][9]. Although the subspace-based techniques such as MUSIC and ESPRIT can estimate DOA with high-resolution, they need to know the exact number of sources to distinguish between the signal-and noise-subspace [10]. In practice, however, the number of sources is not known a priori; the source enumeration must be executed before the DOA techniques are performed.…”

Section: Introductionmentioning

confidence: 99%

Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

et al. 2021

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Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

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“…In addition, Petri nets are beneficial to design and analysis of discrete event systems or other formal systems [32, 33, 45 ̶ 57] since they have a simple mathematical representation using a linear matrix of algebra [7], [2]. Also, the modeling paradigm can be used in social networks [22,23], multi-agent systems [21,24] and data analysis systems [38]. Fault-tolerance, fault-recovery, and fault-repair in a system are important issues that should be taken into account in many industrial processes [19, 28, 34, 50 ̶ 52, 56].…”

Section: Introductionmentioning

confidence: 99%

Fault-Recovery and Repair Modeling of Discrete Event Systems Using Petri Nets

Alzalab

et al. 2020

IEEE Access

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Despite advances in automated manufacturing systems (AMSs), faults occur from time to time, which cannot be avoided in a complex real system. A fault is one of the primary causes of failures making some AMS operations unable to complete, and the diagnosis is one of the most important steps in fault-recovery and repair. This work develops a methodology for investigating the behavior of faults on the resources in discrete event systems that are failure-prone. We tackle the fault-tolerant problem and propose a method to make the system able to continue performing its duties, while the failed resources are under a repair and recovery process. In this work, a failure-safe model is proposed and, at the same time, a method for fault recovery and repair of a faulty element is presented without interrupting task processing due to the occurrence of fault to some elements. We use redundant elements to replace the target elements, and these redundant elements are used to do the same work as the target elements do, when faults occur to the target elements. A target element is an unreliable element that is prone to failure. After a faulty target element is repaired and recovered, its failure model is automatically replaced by its repaired model to indicate that the corresponding element has returned to work. The proposed method is tested using an application example. The results show, compared with those obtained by the studies in the literature, that the proposed method has a great performance and outperforms the existing studies.

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High-Accuracy Signal Subspace Separation Algorithm Based on Gaussian Kernel Soft Partition

Cited by 29 publications

References 36 publications

On Algebraic Identification of Critical States for Deadlock Control in Automated Manufacturing Systems Modeled With Petri Nets

On Algebraic Identification of Critical States for Deadlock Control in Automated Manufacturing Systems Modeled With Petri Nets

Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

Fault-Recovery and Repair Modeling of Discrete Event Systems Using Petri Nets

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