基于人工神经网络的城市火灾事故的预测方法

随着社会经济的飞速发展,城市化进程的加快和人口的迅速增长,城市火灾频繁发生,造成的损失呈上升趋势.针对城市火灾事故发生的特点,根据人工神经网络基本原理和特性,建立了城市火灾事故神经网络预测模型;为了更精确预测城市火灾事故的发生,将城市火灾事故分为了高峰期(春节)和非高峰期两个时段分别进行预测;应用神经网络预测模型和分时段相结合方法对某城市火灾事故进行了实际预测.结果表明,神经网络模型是城市火灾事故预测的有效工具,该模型与时段法的结合能准确预测火灾事故发生的趋势.     

An alternative work measurement method and its application to a manufacturing industry

Difficulties in determining the standard time justify the need to develop alternative methods to direct measurement procedures. The indirect methods which are comparison and prediction, standard data and formulation, predefined movement-time systems have several deficiencies in time measurement procedures. In this study, an alternative indirect work measurement method based on artificial neural networks (ANNs) is presented which is simple and inexpensive. For the application of the proposed method, the products that have similar production processes are selected among the whole product family produced in a manufacturing company. The standard times of the sampled products that are previously measured are used and the standard times of the remaining several products and semi-products are predicted by the proposed method. The model results show that the proposed method can be applied accurately in companies which produce similar products.     

基于神经网络的煤层自然发火的非线性预测

煤炭自燃是一典型的非线性现象.笔者论述了煤炭自燃的危害,从非线性理论的角度分析了煤炭自燃的本质特征;应用神经网络中BP网络这一高度非线性关系映射建立了自然发火预测模型,克服了传统预测方法的不足并在山东枣庄矿业集团公司柴里煤矿进行了预测分析,预测结果与验证结果基本吻合,取得了满意的效果, 为解决煤炭自燃的预测提供了一条良好的思路和方法,具有较大的理论意义和应用价值.     

基于人工神经网络的矿井通风系统评价研究

以矿井通风系统的\"安全可靠、经济合理\"和其定义所包含的各项内容为依据,从矿井通风动力、通风网络、通风设施、通风质量、通风监测、防灾抗灾能力、通风电耗、通风能力 8个方面,确立了 16项矿井通风系统评价指标,建立了一个新的矿井通风系统评价指标体系.采用人工神经网络中的BP网络算法,在 Visual C++6.0平台上研制开发了矿井通风系统评价BP网络模型的计算机程序.并经过实际生产矿井检验,预测结果与实际完全吻合,说明了笔者所确定的矿井通风系统评价指标体系可以反映矿井通风系统的状况,所采用的BP网络算法正确,可以用来指导实际工作.该计算程序简单,易于操作,有一定的推广应用价值.     

岩层移动角选取的神经网络方法研究

岩层移动角是进行各类保护煤柱设计时的关键性参数,涉及地表建(构)筑物的安全.在综合分析影响岩层移动角因素的基础上,采用人工神经网络方法建立岩层移动角选取的模型.该模型采用改进的BP算法,运用我国典型的地表移动观测站资料作为学习训练样本和测试样本,对模型的计算结果与实测值进行了对比分析.分析结果表明:用人工神经网络方法求算岩层移动角考虑的因素更为全面,结果更接近于实际.笔者为岩层移动角的理论计算探索出了一种新的方法.     

三峡库区场地建筑安全评价研究

三峡水库自2003年蓄水后,水对斜坡(滑坡)的软化作用和库岸再造大大改变了库区的工程地质条件,库区的地质灾害严重制约了库区移民迁镇工程,场地建筑安全评价显得尤为重要.笔者根据库区工程地质的特点,提出了场地建筑适宜性评价的指标体系,建立了场地建筑安全评价人工神经网络模型.通过对红石包滑坡进行各种工况下的稳定性评价,利用稳态坡形、坡角工程地质类比法对红石包进行库岸再造的预测,对三峡库区巴东县新城区红石包油库建筑进行安全评价.为库区移民迁镇工作提供科学依据.     

Degradation of Rhodamine B by an electrochemical ozone generating system consist of a Ti anode coated with nanocomposite of Sn–Sb–Ni oxide

An ozonation process was performed using a recycled electrochemical ozone generator system. A titanium based electrode, coated with nanocomposite of Sn–Sb–Ni was applied as anode in a laboratory-made electrochemical reactor. A constant flow rate of 192 mg/h of generated ozone was entered to an ozonation reactor to contact with a typical target pollutant, i.e., Rhodamine B (Rh.B) molecules in aqueous solution. Four operational parameters such as: initial dye concentration, pH, temperature and the contact time were evaluated for the ozonation process. Experimental findings revealed that for a solution of 8 mg/L of the dye, the degradation efficiency could reach to 99.5% after 30 min at pH 3.7 and temperature of 45 °C as the optimum conditions. Kinetic studies showed that a second order equation can describe the ozonation adequately well under different temperatures. Also, considering to the importance of process simulation, a three-layered feed forward back propagation artificial neural network model was developed. Sensitivity analysis indicated order of the operational parameter's relative importance on the model output as: time ≫ pH > Rh . B initial concentration > temperature.     

基于案例推理的化工园区应急资源需求预测

为确定化工园区突发事故的应急资源需求,提出了一种基于案例推理的化工园区应急资源需求预测方法.该方法主要由三部分组成:(1)基于案例推理的模型构建;(2)化工园区事故的案例描述;(3)基于人工神经网络的案例适应.最后,以石化园区火灾爆炸事故的应急资源需求预测验证该方法的有效性.研究表明,该方法可以实时地对化工园区应急资源...     

基于BP网络的建筑安装施工现场安全综合评价的研究

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.     

Test case based risk predictions using artificial neural network

INTRODUCTION: The traditional fuzzy-rule-based risk assessment technique has been applied in many industries due to the capability of combining different parameters to obtain an overall risk. However, a drawback occurs as the technique is applied in circumstances where there are multiple parameters to be evaluated that are described by multiple linguistic terms. METHOD: In this study, a risk prediction model incorporating fuzzy set theory and Artificial Neural Network (ANN) capable of resolving the problem encountered is proposed. An algorithm capable of converting the risk-related parameters and the overall risk level from the fuzzy property to the crisp-valued attribute is also developed. Its application is demonstrated by a test case evaluating the navigational safety within port areas. RESULTS: It is concluded that a risk predicting ANN model is capable of generating reliable results as long as the training data takes into account any potential circumstance that may be met. IMPACT ON INDUSTRY: This paper provides safety assessment practitioners with a novel and flexible framework of modelling risks using a fuzzy-rule-base technique. It is especially applicable in circumstances where there are multiple parameters to be considered. The proposed framework also enables the port industry to manage navigational safety in a rational manner.     

一种处理爆炸多能法的新方法

以多能法中距离－超压关系图为例,介绍了一种运用前馈多层感知器网络对现行多能法中查图步骤进行改进的方法。此法从很大程度上解决了多能法在爆炸灾害评估中手工查图误差大且不方便的缺点。模拟结果表明,该处理方法能够很好的逼近多能法中的超压函数P＝f(R0,C),最后提出了人工神经网络方法在爆炸破坏效应评估中进一步应用的设想。     

化工园区应急物资需求决策模型研究

为实现化工园区高效应急救援,针对化工园区应急物资需求及特点,从基本物资需求量、应急物资消耗量、应急物资损耗量3个方面,构建化工园区应急物资需求优先级指标体系;基于TOPSIS方法对各应急物资需求进行重要性分级排序,设计求解出应急物资需求量的BP神经网络预测模型。以化工园区历史事故案例进行分析,结果表明,BP神经网络应急物资需求预测结果和实际应急物资需求量相差很小,结合TOPSIS方法后的应急物资需求分级决策方案,能有效辅助应急物资的组织和调度,最大限度地发挥应急效用。     

An adaptive neural network algorithm for assessment and improvement of job satisfaction with respect to HSE and ergonomics program: The case of a gas refinery

Researchers have been continuously trying to improve human performance with respect to Health, Safety and Environment (HSE) and ergonomics (hence HSEE). This study proposes an adaptive neural network (ANN) algorithm for measuring and improving job satisfaction among operators with respect to HSEE in a gas refinery. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and job satisfaction is used as output for the ANN algorithm. Moreover, ANN is used to rank operators performance with respect to HSEE and job satisfaction. Finally, Normal probability technique is used to identify outlier operators. Moreover, operators with inadequate job satisfaction with respect to HSEE are identified. This would help managers to see if operators are satisfied with their jobs in the context of HSEE. This is the first study that introduces an integrated ANN algorithm for assessment and improvement of human job satisfaction with respect to HSEE program in complex systems.     

遗传神经网络在煤层自燃预测中的应用

煤层开采自燃危险性程度与许多影响因素之间存在着复杂的非线性关系,利用人工神经网络技术和人工生命学中的遗传算法,建立了预测模型,不但很好地解决了该问题,同时也大大提高了运算速度。     

多环芳烃污染土壤化学氧化修复技术应用研究

本文在分析了PAHs法污染土壤修复常用技术基础上,以化学氧化修复技术为例,结合案例,探讨了该技术在多环芳烃污染土壤修复中的应用,对打赢蓝天碧水净土保卫战具有积极的现实意义。     

基于小波神经网络的煤层底板突水非线性预测方法研究

针对煤层底板突水系统为一非线性动力学系统的特性,并在考察目前煤层底板突水预测方法的基础上,给出利用小波神经网络对煤层底板突水进行预测的可行性和优越性;阐述了小波神经网络的基本原理;提出和分析了基于小波神经网络的煤层底板突水预测模型及算法;并通过实例证明,应用小波神经网络解决煤层底板突水预测的可行性和优越性.研究及实践表明:小波神经网络的预测精度更高、更准确.