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41.
BP神经网络技术在交通工具火灾预警中的应用 总被引:2,自引:2,他引:2
通过对交通工具火灾成因机理以及现有典型交通工具火灾实例的分析研究,建立了预警评价指标体系。根据非线性理论和模式识别原理以及交通工具火灾的特点,采用基于BP神经网络的智能灾害诊断方法,对交通工具火灾发生的可能性和危险性进行评估和预测。研究表明:BP神经网络方法是解决非线性系统问题的一种有效方法,与传统的预警方法相比,该方法具有速度快、效率高、可信度好、自学能力强等特点。采用BP网络进行交通工具火灾预警时,只需输入影响交通工具火灾发生的相关指标因素,网络便可在较短的时间内得出可靠的预警结果。 相似文献
42.
以消防安全工程学与系统安全工程理论为基础,结合我国城市发展特征及消防安全管理状况,建立了城市区域火灾风险评价指标体系;针对神经网络易陷入局部极小而引起评价指标权值分布不合理的缺陷,提出了基于神经网络和遗传算法的城市火灾风险评价模型,该模型以火灾发生的可能性以及灾后的严重程度为输入单元,火灾风险等级为输出单元,采用误差反算法训练BP网络,最终得出火灾风险等级范围,有效地解决了城市火灾的动态性和非线性特征;研究实例证明了该模型的有效性,可为城市的消防安全管理提供确实可行的参考依据。 相似文献
43.
高晓勤 《中国安全生产科学技术》2006,2(6):108-110
定量化风险管理技术有其无可比拟的优势,但无法对风险因素中的人为因素进行管理。本文通过分析量化管理的缺陷和定性化管理的优势,提出应根据具体情况,采用定量化与定性化管理相结合的管理方式,并提出定性化管理的具体方法。 相似文献
44.
基于BP网络的建筑安装施工现场安全综合评价的研究 总被引:2,自引:0,他引:2
针对目前我国建筑安装施工现场安全评价技术的不成熟和欠科学性的现状 ,笔者分析和综合了目前安全评价技术 ,结合建筑业特点 ,提出了基于BP神经网络的建筑安装施工现场安全评价方法 ,并对该评价模型的原理、方法及算法进行了研究。首先 ,结合建筑安装施工现场安全生产的特点建立评价指标体系 ,随后 ,运用层次分析法确定指标及准则层的权重 ,并运用模糊综合评价法生成评价样本集 ,最后 ,利用样本集训练BP网络 ,待误差满足要求后 ,即可运用训练成功的BP神经网络进行安全评价。 相似文献
45.
46.
新疆中低产田发展人工草地的初步研究 总被引:3,自引:0,他引:3
从新疆天然草地的利用,农业面临的问题出发,说明在绿洲内发展人工草地的必要性。针对全疆中低产田特点,将其分成7种基本类型。按类型总结出相应适生的饲草种类。通过人工种植这些饲草,达到合理利用改良耕地的目的,进而带动整个农业产业结构的调整,建立起适宜新疆农业发展的草地农业模式。 相似文献
47.
Ashu Jain Lindell E. Ormsbee 《Journal of the American Water Resources Association》2004,40(6):1617-1630
ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set. 相似文献
48.
We examined the principal effects of different information network topologies for local adaptive management of natural resources.
We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete
understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation
and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states:
one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold
between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities
significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized
behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility
returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns
with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability
to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter
the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the
systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers
lose their capacity to reorganize back to the desired state.
An erratum to this article can be found at . 相似文献
49.
Abedalrazq F. Khalil Mac McKee Mariush Kemblowski Tirusew Asefa 《Journal of the American Water Resources Association》2005,41(1):195-208
ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources. 相似文献
50.
The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life. 相似文献