Fuzzy reasoning Petri nets

This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. The issues of how to represent and reason about rules containing negative literals are addressed in the proposed PN model. The execution rules based on the model are defined formally using the operators in max-algebra. Then, a fuzzy reasoning algorithm is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional PNs and allows one to exploit the maximum parallel reasoning potential embedded in the model. The legitimacy and feasibility of the proposed approach are proved and validated through a turbine fault diagnosis expert system.     

Shortest Routing of Bidirectional Automated Guided Vehicles Avoiding Deadlock and Blocking

It is a great challenge to effectively manage automated manufacturing systems in which automated guided vehicles (AGVs) are adopted to handle materials, because of such features as prior unknown AGV requests and changing AGV initial positions. This paper aims to find the shortest time routes, while both deadlock and blocking in AGV systems are avoided. It first finds the shortest routes based on a guide path layout, and then performs rerouting, whenever necessary, to avoid deadlock and blocking according to a deadlock avoidance policy, previously developed by the authors. The proposed approach can offer better solutions than existing methods. For some cases, it can find a solution, while the existing ones cannot     

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding (CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure, thereby discovering desired patterns demonstrating involved users’ normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives, thereby promoting further research into this emerging and important field.      

Colored timed Petri nets for modeling and analysis of cluser tools

In this paper, a colored timed resource‐oriented Petri net (CTROPN) is developed to model the cluster tools in semiconductor fabrication. It is structurally simple and concise, since each module in a cluster tool corresponds to a unique place in CTROPN. It is also powerful in modeling the qualitative and temporal behavior of the cluster tools with a single‐blade or dual‐blade robot. The initial transient behavior, steady‐state behavior and the final transient behavior can all be investigated through a single model. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

On Siphon Computation for Deadlock Control in a Class of Petri Nets

As a structural object, siphons are well recognized in the analysis and control of deadlocks in resource allocation systems modeled with Petri nets. Many deadlock prevention policies characterize the deadlock behavior of the systems in terms of siphons and utilize this characterization to avoid deadlocks. This paper develops a novel methodology to find interesting siphons for deadlock control purposes in a class of Petri nets, i.e., a system of simple sequential processes with resources . Resource circuits in an are first detected, from which, in general, a small portion of emptiable minimal siphons can be derived. The remaining emptiable ones can be found by their composition. A polynomial-time algorithm for finding the set of elementary siphons is proposed, which avoids complete siphon enumeration. It is shown that a dependent siphon can always be controlled by properly supervising its elementary siphons. A computationally efficient deadlock control policy is accordingly developed. Experimental study shows the efficiency of the proposed siphon computation approach.     

Performance modeling and analysis of workflow

Workflow model analysis is performed at logic, temporal, and performance levels. This paper mainly deals with the performance level issues. Workflow net (WF-net) is extended with time information to the timing workflow net (TWF-net). To provide a formal framework for modeling and analyzing workflow, this paper proposes a multidimension workflow net (MWF-net) that include multiple TWF-nets and the organization and resource information. The algorithm to decompose a free-choice and acyclic Petri nets (PN) into a set of T-components is extended to a TWF-net containing iteration structures. Then, resource availability and workload analysis is performed. A method for computing the lower bound of average turnaround time of transaction instances processed in a MWF-net is proposed. Finally a case study is used to show that the proposed method can be effectively utilized in practice.     

Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

In this paper, a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism. First, a Petri net for the deadlock control of such systems is defined. Its admissible markings and first-met inadmissible markings (FIMs) are introduced. Next, place invariants are designed via an integer linear program (ILP) to survive all admissible markings and prohibit all FIMs, keeping the underlying system from reaching deadlocks, livelocks, bad markings, and the markings that may evolve into them by firing uncontrollable transitions. ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition. In addition, the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs. The condition under which the supervisor is maximally permissive in behavior is given. Finally, experimental results with the proposed method and existing ones are given to show its effectiveness.      

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

A recommender system (RS) relying on latent factor analysis usually adopts stochastic gradient descent (SGD) as its learning algorithm. However, owing to its serial mechanism, an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems. Aiming at addressing this issue, this study proposes a momentum-incorporated parallel stochastic gradient descent (MPSGD) algorithm, whose main idea is two-fold: a) implementing parallelization via a novel data-splitting strategy, and b) accelerating convergence rate by integrating momentum effects into its training process. With it, an MPSGD-based latent factor (MLF) model is achieved, which is capable of performing efficient and high-quality recommendations. Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm, an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.      

An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.