Use of Neural Net Models to Forecast Atmospheric Pollution

The forecast of the CO and NO₂ concentration levels has been obtained by the 3-layer Perceptron Neural Network with Error Backpropagation learning rule.This study shows the 3-layer Perceptron performances in relationship with the choice of the activation functions parameters (depending on the statistical values of the input/output variables).A first simulation set using data at 1 hour before has been utilized to forecast CO levels. A second simulation set with data at 12–24 hours before has been used to forecast NO₂ levels.The Neural Net's performance appears to be very good both for the parameters activation function optimisation and the variables choice.     

Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning

A model to mimic the search behaviour of fishermen is built with two neural networks to cope with two separate decision-making processes in fishing activities. One neural network deals with decisions to stay or move to new fishing grounds and the other is constructed for the purpose of finding prey within the fishing areas. Some similarities with the behaviour of real fishermen are found: concentrated local search once a prey has been located to increase the probability of remaining near a prey patch and the straightforward movement to other fishing grounds. The artificial fisherman prefers areas near the port when conditions in different fishing grounds are similar or when there is high uncertainty in its world. In the latter case a reluctance to navigate to other areas is observed. The artificial fisherman selects areas with higher concentration of prey, even if they are far from the port of departure, unless a high uncertainty is related to the fishing ground. Connected areas are preferred and followed in orderly fashion if a higher catch is expected. The observed behaviour of the artificial fisherman in uncertain scenarios can be described as a risk-averse attitude. The approach seems appropriate for an individual-based modelling of fishery systems, focusing on the learning and adaptive characteristics of fishermen and on interactions that take place at a fine scale.     

基于神经网络的泥石流预测

作者用神经网络方法对泥石流预测进行了探讨，讨论了训练参数的选择及训练方法，并结合泥石流预报的具体情况，对ＢＰ模型进行了适当的改进。研究表明，模型及算法是可行的。     

Hedgerows and hedgerow networks in landscape ecology

Hedgerows originated and coexist with agriculture. Their internal structure and species diversity vary widely with origin (planted, spontaneous, or remnant), farming practices in adjacent fields, and the refined art of hedgerow management. Most hedgerow species are forest-edge species, and apparently none is limited to hedgerows. Wide hedgerows composed of trees and shrubs appear to function as corridors for movement of many plants and animals across a landscape. The reduction of crop loss, by dampening pest population fluctuations with hedgerow predators, remains a hypothesis for study.Field microclimate downwind of a hedgerow is modified about 16 times the hedgerow height (h) for evaporation, and approximately 28 h for wind speed. A turbulent wind pattern with harsher microclimate is present at 6–8 h if a second hedgerow is nearby downwind. Zones of higher crop productivity at 3- to 6-h downwind, and 2- to 6-h upwind of a second hedgerow may be expected. Overall, we expect little short-term difference in farm-field production with or without hedgerows.Evidence suggests that hedgerow networks, and especially their mesh size (of fields), exert a major control on many major landscape fluxes. Such fluxes include animal populations, wind speed, evapotranspiration and soil desiccation, soil erosion and nutrient runoff, species movement along network lines, and movement of field species across the network. In a relatively short period, the hedgerow ecosystem, with no unique species, has attained a metastable equilibrium, which is regulated by enormous human inputs.More than 20 economic roles of hedgerows are pinpointed. The roles, providing resources and protection of resources, are poorly known quantitatively. We conclude that hedgerows perform diverse functions for society and the farmer that are both economically and ecologically significant.     

基于人工神经网络的巢湖富营养化分时分区评价

应用人工神经网络方法,以温度、高锰酸盐指数、总磷、总氮作为评价参数,对巢湖湖区12个点位的营养状态进行了分时段评价,一共评价了2000年至2003年四个年度的各月份的营养状态。因为湖泊的营养状态水平是变化的,不同区域、不同时段营养状态水平是不同的,因此采用分区分时评价更能全面客观地反映巢湖的营养状态水平及其变化特征。     

基于安全心理学的主动式防火墙设计

当前防火墙的防御策略为被动防御 ,它只能被动等待攻击者的攻击 ,直接降低了其安全性能。从安全心理学的角度出发 ,分析了被动式防火墙的弱点并设计了一种新型防火墙。该防火墙采用主动式防御策略 ,对于在防火墙中识别出的非法访问 ,通过重定向 ,引导至识别区来保护主系统 ,降低攻击者的攻击欲望 ,从而提高了防火墙的安全性能。防火墙中关键技术为攻击识别技术和重定向技术。     

Learning Machines Applied to Potential Forest Distribution

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.     

用ART2神经网络方法进行结构的损伤识别

用ART2神经网络方法对建筑结构的损伤进行了识别 ,实验证明该方法的识别精度高 ,具有自动从环境中学习的能力 ,对未知的损伤 ,ART2网络将能自动地给出新的识别输出 ,即对未知的损伤类型也能够进行识别     

网络路由信息安全应用研究——基于流量预测的路由选择新算法

根据灰色系统的预测理论 ,笔者提出了一种关于网络数据流量变化发展的预测方法 ,并为其建立了基于网络流量变化趋势的预测模型。依据该模型对网络中各结点未来流量的预测结果 ,可用实时动态来确定最佳的路由选择策略 ,从而在根本上解决网络拥塞现象的发生 ,大大地提高了网络数据通信时的安全可靠性。     

网络传播中的环境保护宣传

随着电脑、互联网和多媒体在人们生活中的不断应用,网络传播成为了一种重要的信息传播方式,学会利用网络传播的优势,开展环境保护宣传,必将为未来的环保工作打开一个新的局面。