More Efficient On-the-Fly Verification Methods of Colored Petri Nets

Colored Petri Nets (CP-nets or CPNs) are powerful modeling language for concurrent systems. As for CPNs' model checking, the mainstream method is unfolding that transforms a CPN into an equivalent P/T net. However the equivalent P/T net tends to be too enormous to be handled. As for checking CPN models without unfolding, we present three practical on-the-fly verification methods which are all focused on how to make state space generation more efficient. The first one is a basic one, based on a standard state space generation algorithm, but its efficiency is low. The second one is an overall improvement of the first. The third one sacrifices some applicability for higher efficiency. We implemented the three algorithms and validated great efficiency of latter two algorithms through experimental results

EnPAC: Petri Net Model Checking for Linear Temporal Logic

State generation and exploration (counterexample search) are two cores of explicit-state Petri net model checking for linear temporal logic (LTL). Traditional state generation updates a structure to reduce the computation of all transitions and frequently encodes/decodes to read each encoded state. We present the optimized calculation of enabled transitions on demand by dynamic fireset to avoid such a structure. And we propose direct read/write (DRW) operation on encoded markings without decoding and re-encoding to make state generation faster and reduce memory consumption. To search counterexamples more quickly under an on-the-fly framework, we add heuristic information to the Buchi automaton to guide the exploration in the direction of accepted states. The above strategies can optimize existing methods for LTL model checking. We implement these optimization strategies in a Petri net model-checking tool called EnPAC (Enhanced Petri-net Analyser and Checker) for linear temporal logic. Then, we evaluate it on the benchmarks of MCC (Model Checking Contest), which shows a drastic improvement over the existing methods.Comment: 11 pages, 5 figure

LOCAL WELL-POSEDNESS AND BLOW-UP FOR A FAMILY OF U(1)-INVARIANT PEAKON EQUATIONS

The Cauchy problem for a unified family of integrable U(1)-invariant peakon equations from the NLS hierarchy is studied. As main results, local well-posedness is proved in Besov spaces, and blow-up is established through use of an L 1 conservation law

Learning from Noisy Crowd Labels with Logics

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a ``pseudo-E-step'' that distills from the logic rules a new type of learning target, which is then used in the ``pseudo-M-step'' for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels.Comment: 12 pages, 7 figures, accepted by ICDE-202

Biomedical Applications of Graphene

Graphene exhibits unique 2-D structure and exceptional phyiscal and chemical properties that lead to many potential applications. Among various applications, biomedical applications of graphene have attracted ever-increasing interests over the last three years. In this review, we present an overview of current advances in applications of graphene in biomedicine with focus on drug delivery, cancer therapy and biological imaging, together with a brief discussion on the challenges and perspectives for future research in this field

Program Dependence Net and On-demand Slicing for Property Verification of Concurrent System and Software

When checking concurrent software using a finite-state model, we face a formidable state explosion problem. One solution to this problem is dependence-based program slicing, whose use can effectively reduce verification time. It is orthogonal to other model-checking reduction techniques. However, when slicing concurrent programs for model checking, there are conversions between multiple irreplaceable models, and dependencies need to be found for variables irrelevant to the verified property, which results in redundant computation. To resolve this issue, we propose a Program Dependence Net (PDNet) based on Petri net theory. It is a unified model that combines a control-flow structure with dependencies to avoid conversions. For reduction, we present a PDNet slicing method to capture the relevant variables' dependencies when needed. PDNet in verifying linear temporal logic and its on-demand slicing can be used to significantly reduce computation cost. We implement a model-checking tool based on PDNet and its on-demand slicing, and validate the advantages of our proposed methods.Comment: 17 pages, 3 figure

Target prediction in blast furnace based on BP network optimized by variable neighborhood PSO

This paper combine the improved PSO algorithm (Analysis of Particle Swarm Optimization Algorithm) with the BP neural network for prediction of Silicon content in hot metal. Firstly, the varying visual mechanism is drawing into the standard PSO through changing the neighbor structure dynamically with each particles, in order to enhance the local and global searching ability in particle swarm. Afterwards, the improved algorithm is used to optimize the weights and threshold of BP neural network to avoid falling into local extremum. Finally, the prediction model of Si content in hot metal is built based on BP network optimized by Variable neighborhood PSO. The average relative error of the prediction model is 6.7% based on the data from blast furnace

Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control