SVM application in hazard assessment: Self-heating for sulfurized rust

In order to assess the oxidation self-heating hazard of sulfurized rust, for particular ambient conditions in crude oil tanks, the support vector machine (SVM) technique is applied to predict the maximum temperature (T_max) of oxidation self-heating process. Five governing parameters are selected, i.e. the water content, mass of sulfurized rust, operating temperature, air flow rate and oxygen concentration in the respiratory/safety valve. The efficiency and validity of the SVM predictions are investigated in the case of two sets of data: more than 85 experiments performed in academic lab (China) and almost 17 additional results collected from existing literature. Two main steps are also discussed: the training process (on selected subsets of data) and prediction process (for the remaining subsets of data). It can be concluded that for both datasets the maximum temperature (T_max) values calculated by SVM technique were in good accordance with the experimental results, with relative errors smaller than 15% except for a few cases.The SVM technique seems therefore to be relevant and very helpful for complex implicit processes such as chemical reactions, as it is the case of the oxidation of sulfurized rust in oil tanks. Furthermore, such predictive methods can be continuously be improved through additional experiments feedback (larger databases) and can then be of crucial help for monitoring and early warning of hazardous reactions.     

城市规划区农用地价格评估的实证研究--以江苏泰兴市为例

城市规划区农用地价格的评估既不同于城市建成区各类用地价格评估，也不同于基本农田保护区的农用地的价格评估，是农用地价格评估中的热点和难点，其区别于其他土地评估的特点主要表现在：一是城市规划区农用地价格是一种农用地转用价格，其实质是农用地所有权价格；二是城市规划区农用地用途将发生变更；三是城市规划区农用地存在着预期收益及其收益年限问题。在此基础上，以江苏泰兴市为例，对城市规划区农用地价格的评估思路进行了探讨，思路之一是运用投入产出法将城市规划区农用地的未来预期净收益还原为无限年期的所有权价格；思路之二是运用假设开发法求取“剩余之数、即为地价；思路之三是运用征购价格法从农用地价格构成角度探讨了规划区农用地的价格，并对三种估价思路及其估价结果进行了比较分析。     

Evaluation of electronic components in life cycle assessment

Life cycle assessment (LCA), a quantitative method for evaluating the total environmental impact of a product, from the materials in its manufacture to its final disposal, is playing an increasingly important role in manufacturing. When the LCA method is applied to a product containing many kinds of electronic components, there is a need for life cycle inventory (LCI) data on the components. This paper provides an original calculation of the LCI data for each electronic components industry. These data show the amount of input energy and emissions into the atmosphere per yen of production yield. It is demonstrated that the magnitude of the LCI data for each industry is essentially equal to that of the other industries. Furthermore, we conclude that the LCI data for all electronic components are roughly equivalent, making it possible to calculate the LCI data of any electronic component by simply multiplying the LCI data for the industry by the price of the component. Furthermore, after comparing the materials production stages with the component manufacturing stage in the calculation, it became clear that for several component industries the materials production stage could not be omitted from the calculation. Received: April 10, 1998 / Accepted: February 8, 1999     

关于模糊综合指数与模糊向量加权记分

通过将模糊向量加权记分与模糊综合指数的计算步骤、计算内容和方法一一进行比较,证明模糊向量加权记分与模糊综合指数无本质差异。所采用的分制规定差异,只能使模糊向量加权记分所得结果更加不合理。采用模糊综合指数法,选用恰当的权重,可以克服“优劣倒置”现象。     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.     

Exploring price effects on the residential water conservation technology diffusion process: a case study of Tianjin city

Reforms of the water pricing management system and the establishment of a flexible water pricing system are significant for cities in northern China to tackle their critical water issues. The WATAP (Water conservation Technology Adoption Processes) model is developed in order to capture the water conservation technology adoption process under different price scenarios with disaggregate water demands down to the end use level. This model is explicitly characterized by the technological selection process under maximum marginal benefit assumption by different categories of households. In particular, when households need to purchase water devices in the provision market with the consideration of complex factors such as the life span, investment and operating costs of the device, as well as the regulated water price by the government. Applied to Tianjin city, four scenarios of water price evolutions for a long-term perspective (from year 2011 to 2030) are considered, including BAU (Business As Usual), SP1 (Scenario of Price increase with constant annual rate), SP2 (Scenario of Price increase every four years) and SP3 (Scenario of Price increase with affordable constraint), considering many factors such as historic trends, affordability and incentives for conservation. Results show that on aggregate 2.3%, 11.0% and 18.2% of fresh water can be saved in the residential sector in scenario SP1, SP2 and SP3, respectively, compared with the BAU scenario in the year 2030. The water price signals can change the market shares of different water appliances, as well as the water end use structure of households, and ultimately improve water use efficiency. TheWATAP model may potentially be a helpful tool to provide insights for policy makers on water conservation technology policy analysis and assessment.     

三维原子场全息作用矢量用于芳香类化合物的三维QSAR研究

采用新型的的三维原子场全息作用矢量(3D-HoVAIF)研究了200种芳香化合物的化学结构与其生物毒性的定量构效关系(QSAR).首先对芳香化合物进行了结构参数化表达,然后采用逐步回归(SMR)对变量进行筛选,建立了三维定量构效关系模型.其87个无氢键分子的模型和113个有氢键分子的模型的复相关系数和标准偏差分别为R2=0.801,SD=0.473和R2=0.929,SD=0.318.模型具有良好的稳定性和预测能力,证明了该三维原子场全息作用矢量在分子结构表征和生物毒性预测上的适用性.     

具双T-DNA区的大麦黄矮病毒复制酶基因RNAi植物表达载体的构建(英文)

RNAi系双链RNA诱导同源mRNA降解,导致特定基因表达沉默的一种现象.RNAi技术是通过基因工程防治植物病毒病的最佳途径.由大麦黄矮病毒(Barely yellow dwarfvirus,简称BYDV)引起的禾谷类黄矮病发病面积和危害程度在我国呈加重趋势.BYDV-GAV是当前流行的大麦黄矮病毒株系,本文针对BYDV-GAV复制酶基因序列,构建能在细胞内转录形成发卡RNA双链结构的表达盒,并将其置于中间载体pVec8-2b的T-DNA区,pVec8-2b的另一T-DNA区含有hpt选择报告基因表达盒.具双T-DNA区的病毒复制酶基因发卡RNA高效表达载体已导入根癌农杆菌,可用于诱导植物RNAi,创制无选择标记基因的抗大麦黄矮病转基因作物.     

基于支持向量机的采空区稳定性分级

针对采空区稳定性分级的影响因素众多且关系复杂的特点,提出采用支持向量机理论对采空区稳定性进行分级。根据分级评价指标选取原则,选取岩体结构、地质构造、岩石的质量指标、地下可见水、地下水体、周边开采的影响、相邻空区的情况、工程布置、跨度、面积、高度、矿柱的尺寸及布置、埋藏深度和采空区的规格14个影响因子,建立了采空区稳定性评价指标体系,引入支持向量机理论,选择有向无环图方式构造多类分类器,得到采空区稳定性分级的支持向量机模型。将该模型用于山东黄金矿业西山矿区的25个采空区进行分级,并与未确知测度方法的分级情况     

煤与瓦斯突出预测的支持向量机(SVM)模型

基于支持向量机(SVM)分类算法,考虑影响煤与瓦斯突出的主要因素,建立了煤与瓦斯突出预测的SVM模型。该模型选取开采深度、瓦斯压力、瓦斯放散初速度、煤的坚固性系数以及地质破坏程度5个指标作为模型输入量,同时将煤与瓦斯突出程度划分为无突出、小型突出、中型突出和大型突出4个等级,进而使其评判结果更为细化。以实测数据作为学习样本进行训练,建立相应判别函数对待判样本进行预测。通过算例分析,表明该模型的方法对煤与瓦斯突出预测的合理性与有效性,可以在实际工程中推广。