Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting d...Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting debris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and useful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time series of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collected in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.展开更多
The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flo...The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.展开更多
The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of inte...The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside [Current Zoology 61 （1）： 98-106, 2015].展开更多
In this paper, an SIRS epidemic model with high-risk immunization was investigated, where a susceptible neighbor of an infected node is immunized with rate h. Through analyzing the discrete-time model, we found that t...In this paper, an SIRS epidemic model with high-risk immunization was investigated, where a susceptible neighbor of an infected node is immunized with rate h. Through analyzing the discrete-time model, we found that the epidemic threshold above which an epidemic can prevail and persist in a population is inversely proportional to 1 - h value. We also studied the continuous-time epidemic model and obtained a different result： the epidemic threshold does not depend on the immunization parameter h. Our results suggest that the difference between the discrete-time epidemic model and the continuous-time epidemic model exists in the high-risk immunization.展开更多
文摘Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting debris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and useful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time series of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collected in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.
文摘The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.
文摘The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside [Current Zoology 61 （1）： 98-106, 2015].
基金This research is supported by the National Natural Science Foundation of China （No. 61203153）.
文摘In this paper, an SIRS epidemic model with high-risk immunization was investigated, where a susceptible neighbor of an infected node is immunized with rate h. Through analyzing the discrete-time model, we found that the epidemic threshold above which an epidemic can prevail and persist in a population is inversely proportional to 1 - h value. We also studied the continuous-time epidemic model and obtained a different result： the epidemic threshold does not depend on the immunization parameter h. Our results suggest that the difference between the discrete-time epidemic model and the continuous-time epidemic model exists in the high-risk immunization.
