Statistical Characterization of a Small World Network Applied to Forest Fires

In this paper, a fault tolerant CNN (cellular neural network) using a small world network connection for image processing in embedded systems is described. In embedded systems, there are problems such as compactness, power consumption, reliability and speed, so new processor architecture is required. In this paper, we apply a small world network connection to cellular neural networks. The small world network features small links to the destination from the place of origin using local connection information of a random nature. The concept is derived from sociology. We then provide neural image processing algorithms suitable for CNN with the small world network model. Special attention has been given to typical neural algorithms for image processing, such as noise reduction and edge extraction. Further, we are proposing a fault tolerant architecture for the CNN, using majority voting.

The anti-interference characteristics of the neural network have a key impact on its information processing ability in complex environments. Most of the existing research focuses on small-scale networks and simplified models, and there is still a lack of systematic discussion on the influence mechanism of large-scale network expansion and topological complexity. In this study, a large-scale neural network model with different topologies is constructed to explore the influence mechanism of network size and connection complexity on the anti-disturbance characteristics. The optimal synchronization characteristics of complex NW small-world networks under noise interference are revealed, which provides a theoretical reference for the topology design and anti-interference ability improvement of artificial neural networks. Based on the Hodgkin-Huxley neuron dynamics model and Leonid chemical synapse theory, a complex Newman-Watts (NW) small-world network model containing 500 neurons is established for the first time, and the dynamic response characteristics of three topologies of simple ring network, simple NW small-world and complex NW small-world under music noise interference are compared and analyzed. The signal synchronization of the network is quantitatively evaluated by Pearson correlation, and the variation law of the anti-interference performance of different topologies is systematically revealed when the scale of the neural network is expanded from 100 to 500 neurons. The research shows that the expansion of network size and the increase of topological connection complexity can significantly enhance the anti-interference performance of neural network. Among them, the complex NW small-world network performs best in the noise interference environment, and the correlation coefficient increases significantly at the scale of 500 neurons. In this study, the network scale is extended to 500 neurons for the first time. By constructing a complex NW small-world topology, the influence of scale expansion and connection complexity improvement on the network anti-interference performance is systematically quantified, which provides reference simulation data for the simulation research of artificial neural networks.

UAV communication network is one of the most important study aspects of UAV. The introduction of small world network theory into wireless sensor networks (WSNs) can help building the network topology of higher performance, finding hidden rules and improving network performance, especially in the intelligent transportation and military. In this paper, due to the high dynamic topology and real-time of UAV, the small-world network is introduced, and the ant colony algorithm is used to realize real-time update and data transmission.

Synchronizability of multi-layer small-world dynamical networks

Based on the solution to the strictly piecewise linear state equation of a photovoltaic (PV) supplied micro-grid inverter, the nonlinear dynamic behavior of PV supplied micro-grid inverter is analyzed in this paper, and each PV supplied micro-grid inverter is considered as a network node, then a synchronous method for PV supplied micro-grid is studied using the small-world network model. Studies have found that, compared with the nearest-neighbor coupling network model, the PV supplied micro-grid based on the small-world network model has a faster synchronization time, and also has a faster recovery time in case of plus disturbance.

Fractal analysis of wildfire pattern dynamics using a Small World Network model

Optimal synchronization in small-world biological neural networks with time-varying weights

Parameter inference in small world network disease models with approximate Bayesian Computational methods

With the widespread application of intelligent systems in major industries, the traditional equipment manufacturing industry has gradually shifted to advanced equipment manufacturing. The innovation network of an industry directly affects the innovation capability of the industry, so the research of innovation network has always been the focus of attention. However, there are few researches on the innovation network of advanced equipment manufacturing industry, so this article has launched an in-depth study on it. First, it analyzes the basic geometric characteristics of the innovation network. Then, based on the small-world network model, an evolution model of the advanced equipment manufacturing innovation network was created, and simulation experiments were performed on the evolution model. The experimental results show that the clustering coefficient and the average distance showed different changes in different stages, but in the end they all gradually stabilized. The clustering coefficient was 0.484, the average distance was 1.854, and the degree of small cosmos was 0.497. This shows that the innovation network evolution model established in this study can reveal the evolution process of the innovation network of advanced equipment manufacturing through simulation experiments.

This study examined the application of network theory to skilled passing behaviour in a team sport by considering small-world and scale-free network models. Using data obtained from a 2006 soccer game in Japan between Japan and Ghana, we counted the number of passes by each player within 5-minute intervals. The structural properties of the passing behaviour, which included a characteristic path length and clustering coefficient, and the degree of distribution were analysed. This showed that the structural property of the passing behaviour represented neither a complete graph nor a random graph; rather, it reflected a small-world or scale-free network. In addition, the probabilities of outgoing and incoming passes reflected links that followed a power-law distribution. Passing behaviour in a soccer match appeared to be similar to behaviour in social networks with smaller vertices in terms of the scale-free property and a self-organising mechanism.

Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.

Small-world networks are characterized by large clustering coefficients and small characteristic path lengths. These properties are induced by replacing a small fraction of short-range local scale links of a geometric/Euclidean graph with long-range global scale links. Advances in wireless networks allow for cost-efficient addition of secondary long-range wireless interfaces in devices. We derive analytical mean-field solutions for 1) clustering coefficient and 2) characteristic path length of peer-to-peer D2D wireless networks with topologies mimicking small-world networks. In graph-theoretic terms these topologies correspond to geometric graphs of randomly deployed nodes in two-dimensions with range limited shortcuts. The models show that in spite of the fact that links used to create shortcuts are range limited, the network still retains the essential phase difference between characteristic path length and clustering coefficient that is the hallmark of small-world networks when a small fraction of all nodes, as little as 1%-5%, have range limited shortcut links. We also demonstrate the utility of these models as design tools for determining deployment parameters of small-world wireless sensor networks.

Considering the problems of potentially generating a disconnected network in the WS small-world network model [Watts and Strogatz, Nature393, 440 (1998)] and of adding edges in the NW small-world network model [Newman and Watts, Phys. Lett. A263, 341 (1999)], we propose a novel small-world network model. First, generate a regular ring lattice of N vertices. Second, randomly rewire each edge of the lattice with probability p. During the random rewiring procedure, keep the edges between the two nearest neighbor vertices, namely, always keep a connected ring. This model need not add edges and can maintain connectivity of the network at all times in the random rewiring procedure. Simulation results show that the novel model has the typical small-world properties which are small characteristic path length and high clustering coefficient. For large N, the model is approximately equal to the WS model. For large N and small p, the model is approximately equal to the WS model or the NW model.

Two characters, scale-free and hierarchical structure, are shown in lots of real systems all over the world. In this article, firstly, we build up a class of network models with hierarchical structure, denoted by N h (t), and prove it to be scale-free. By computing average degrees, we find these network models are all sparse. Secondly, based on their special topological structure, we research analytically average distance of each model belonging to N h (t) and present an iteration computational method of average distance corresponding to every one in N h (t). Finally, we discuss the affect by changing some tunable parameters to average distance in the set N h (t), and compare our hierarchical network models with previous ones, and show some meaning problems for gaining more universal methods of computing average distance in hierarchical networks.

The concept of small world phenomenon has been observed and applied in many types of networks. This paper presents a study of two small-world network models in clustering formation and routing in wireless network, the Watts and Stragtz’s (WS) and the Newman and Watts (NW) models and evaluates their properties in term of average node degree and path length. We tested the small world phenomenon in wireless networks by simulating a wireless mobile ad hoc network with distributed routing protocols. We present the research challenges of bridging the gap between theoretical models and practical wireless network implementation. This study provides insights on how wireless networks behave under small world network models with distributed routing protocols.