Hierarchical interface-based supervisory control (HISC) decomposes a discrete-event system (DES) into a high-level subsystem which communicates with n ges 1 low-level subsystems, through separate interfaces which rest...
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Hierarchical interface-based supervisory control (HISC) decomposes a discrete-event system (DES) into a high-level subsystem which communicates with n ges 1 low-level subsystems, through separate interfaces which restrict the interaction of the subsystems. It provides a set of local conditions that can be used to verify global conditions such as nonblocking and controllability. the current HISC verification and synthesis algorithms are based upon explicit state and transition listings which limit the size of a given level to about 10 7 states when 1GB of memory is used. In this paper, we extend the HISC approach by introducing a set of predicate based fixed point operators that allow us to do a per level synthesis to construct for each level a maximally permissive supervisor that satisfies the corresponding HISC conditions. We prove that these fixpoint operators compute the required level-wise supremal languages. We then present algorithmsthat implement the fixpoint operators. Based on these algorithms, a symbolic implementation is briefly discussed which can be implemented using binary decision diagrams. We also discuss a method to implement our synthesized supervisors in a more compact manner. A large manufacturing system example (worst case state space on the order of 10 30 ) extended from the ALP example is briefly discussed. the example showed that we can now handle a given level with a statespace as large as 10 15 states, using less than 160MB of memory. this represents a significant improvement in the size of systems that can be handled by the HISC approach. A software tool for synthesis and verification of HISC systems using our approach was also developed
this paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PS...
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this paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the optimization problem.
Multiway Decision Graph (MDG) is a canonical representation of a subset of many-sorted first-order logic. It generalizes the logic of equality with abstract types and uninterpreted function symbols. the area of Satisf...
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Multiway Decision Graph (MDG) is a canonical representation of a subset of many-sorted first-order logic. It generalizes the logic of equality with abstract types and uninterpreted function symbols. the area of Satisfiability (SAT) ha s been the subject of intensive research in recent years, with significant theoretical and practical contributions. From a practical perspective, a large number of very effective SAT solvers have recently been proposed, most of which based on improvements made to the original Davis-Putnam algorithm. Local search algorithms have allowed solving extremely large satisfiable instances of SAT. the combination between various verification methodologies will enhance the capabilities of each and overcome their limitations. In this paper, we introduce a model checking methodology for MDG based models using MDG tool and SAT solver. We use SAT solver searching for feasible paths of reachable states satisfying the property under certain encoding constraints. Finally, we provide a case study showing the correctness and the efficiency of our approach.
In different application fields, heterogeneous data sets are structured into either matrices or higher-order tensors. In some cases, these structures present the property of having common underlying factors, which is ...
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ISBN:
(数字)9781728155494
ISBN:
(纸本)9781728155500
In different application fields, heterogeneous data sets are structured into either matrices or higher-order tensors. In some cases, these structures present the property of having common underlying factors, which is used to improve the efficiency of factor-matrices estimation in the process of the so-called coupled matrix-tensor factorization (CMTF). Many methods target the CMTF problem relying on alternating algorithms or gradient approaches. However, computational complexity remains a challenge when the data sets are tensors of high-order, which is linked to the well-known “curse of dimensionality”. In this paper, we present a methodological approach, using the Joint dImensionality Reduction And Factors rEtrieval (JIRAFE) algorithm for joint factorization of high-order tensor and matrix. this approach reduces the high-order CMTF problem into a set of 3-order CMTF and canonical polyadic decomposition (CPD) problems. the proposed algorithm is evaluated on simulation and compared with a gradient-based method.
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