Accelerating consensus of self-driven swarm via adaptive speed

In resent years, Vicsek model has attracted more and more attention and been well developed. However, the in-depth analysis on the convergence time are scarce thus far. In this paper, we study some certain factors that mainly govern the convergence time of Vicsek model. By extensively numerical simulations, we find the convergence time scales in a power law with $r^2\ln N$ in the noise-free case, where $r$ and $N$ are horizon radius and the number of particles. Furthermore, to accelerate the convergence, we propose a new model in which the speed of each particle is variable. The convergence time can be remarkably shortened compared with the standard Vicsek model.Comment: 11 pages, 6 figure

Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation

Abstract Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable to small perturbations on the inputs. This has led to safety concerns on applying DNNs to safety-critical domains. Several verification approaches based on constraint solving have been developed to automatically prove or disprove safety properties for DNNs. However, these approaches suffer from the scalability problem, i.e., only small DNNs can be handled. To deal with this, abstraction based approaches have been proposed, but are unfortunately facing the precision problem, i.e., the obtained bounds are often loose. In this paper, we focus on a variety of local robustness properties and a ( δ , ε ) -global robustness property of DNNs, and investigate novel strategies to combine the constraint solving and abstraction-based approaches to work with these properties: We propose a method to verify local robustness, which improves a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. Specifically, the values of neurons are represented symbolically and propagated from the input layer to the output layer, on top of the underlying abstract domains. It achieves significantly higher precision and thus can prove more properties. We propose a Lipschitz constant based verification framework. By utilising Lipschitz constants solved by semidefinite programming, we can prove global robustness of DNNs. We show how the Lipschitz constant can be tightened if it is restricted to small regions. A tightened Lipschitz constantcan be helpful in proving local robustness properties. Furthermore, a global Lipschitz constant can be used to accelerate batch local robustness verification, and thus support the verification of global robustness. We show how the proposed abstract interpretation and Lipschitz constant based approaches can benefit from each other to obtain more precise results. Moreover, they can be also exploited and combined to improve constraints based approach. We implement our methods in the tool PRODeep, and conduct detailed experimental results on several benchmarks </jats:p

Envirostore: A cooperative storage system for disconnected operation in sensor networks

Abstract — This paper presents a new cooperative storage system for sensor networks geared for disconnected operation (where sensor nodes do not have a connected path to a basestation). The goal of the system is to maximize its data storage capacity by appropriately distributing storage utilization and opportunistically offloading data to external devices when possible. The system is motivated by the observation that a larg

Research on grounding grid corrosion classification method based on convolutional neural network

Aiming at the problem that the traditional detection methods can not accurately classify the corrosion degree of grounding grids. The corrosion image is taken as the research object, the convolution neural network is used as the algorithm firstly to classify the corrosion degree. Firstly, the corrosion simulation experiment was carried out, and the sample library was established by using the corrosion image collected in different stages. Then, according to the LeNet-5 model, the traditional CNN and improved CNN models were designed for corrosion classification of grounding grid. Simulation experiments were carried out in the preprocessed samples. Finally, the experimental results of Soft-max and SVM classifier are compared and analyzed. The results show: the classification results of the two models were better than those of the original samples, and the classification performance of SVM is better than that of Soft-max. The improved model can improve classification accuracy. This study fills the blank of detecting the corrosion degree of grounding grid by image method, and it is significant to quickly grasp the corrosion degree to avoid faults or accidents

Studies of Moisture Absorption and Release Behaviour of Akund Fiber

Akund fiber is a new type of natural cellulose fiber. Because of its excellent properties, akund fiber has become one of the new ecological materials which have huge development potential. Recently natural fibers have shown great promise in a variety of applications that were previously dominated by synthetic fibers due to their important aspects of biocompatibility, possible biodegradation, nontoxicity, and abundance. Moisture absorption and release behaviour of natural fiber plastic composites is one major concern in their outdoor applications. So the knowledge of the moisture content and the moisture absorption and release rate is very much essential for the application of akund fiber as an excellent reinforcement in polymers. An effort has been made to study the moisture absorption and release behaviour of akund fiber and the mechanical performance of it at relative air humidity from 0% to 100%. The gain and loss in moisture content in akund fiber due to water absorption and release were measured as a function of exposure time under the environment, in which temperature is 20°C and humidity is 65%. The regression equations of the absorption and release process were established

Research on grounding grid corrosion classification method based on convolutional neural network

Aiming at the problem that the traditional detection methods can not accurately classify the corrosion degree of grounding grids. The corrosion image is taken as the research object, the convolution neural network is used as the algorithm firstly to classify the corrosion degree. Firstly, the corrosion simulation experiment was carried out, and the sample library was established by using the corrosion image collected in different stages. Then, according to the LeNet-5 model, the traditional CNN and improved CNN models were designed for corrosion classification of grounding grid. Simulation experiments were carried out in the preprocessed samples. Finally, the experimental results of Soft-max and SVM classifier are compared and analyzed. The results show: the classification results of the two models were better than those of the original samples, and the classification performance of SVM is better than that of Soft-max. The improved model can improve classification accuracy. This study fills the blank of detecting the corrosion degree of grounding grid by image method, and it is significant to quickly grasp the corrosion degree to avoid faults or accidents