Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

辉县部分景区地质灾害危险性评估及防治措施研究

为了给辉县景区减灾防灾提供地质依据,根据实际工程概况,研究了辉县部分景区地质灾害危险性预测评估及防治措施,预测评估了工程建设中、建设后可能引发或加剧地质灾害危险性,建设工程自身可能遭受已存在地质灾害危险性;评估了地质灾害危险性综合分区以及根据地质灾害危险性、防治难度和防治效益,对评估区适宜性作出评价,提出了防治地质灾害的措施和建议。     

甘肃省陇东地区极端降水的时空特征

为阐明甘肃省陇东地区极端降水的变化规律,利用该地区13个气象台站1961~2015年的逐日降水资料,应用超门限峰值法、反距离加权法、线性倾向估计法、滑动平均法、Mann-Kendall突变分析法、复值Morlet小波分析法研究极端降水的时空变化特征。结果表明,陇东地区各站第95百分位极端降水阈值在27.8mm附近变化,即可以大雨事件作为极端降水事件进行监测;极端降水指数主要表现为从南向北递减的趋势,极端降水事件对环县和崆峒的影响较小;极端降水事件虽然在总量、强度上表现为减小趋势,但其发生频次及对总降水的贡献率逐渐增强;极端降水总量和极端降水强度周期变化均在21年左右能量最强,而极端降水频次和极端降水贡献率周期变化分别在28、5年左右能量最强。     

Machine learning methods for short‐term bid forecasting in the renewable energy market: A case study in Italy

In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play.     

化工园区Na-Tech风险定量评价方法

自然灾害引发的技术灾害（Na-Tech）对工业设施及生产设备带来了风险，加上化工园区聚集着大量危险化学品，这种耦合风险的扩大效应极易造成园区及周围区域的巨大经济损失和人员伤亡，因此化工园区Na-Tech事故逐渐受到广泛关注。本文通过对化工园区多灾种耦合风险影响因素进行辨识，分析耦合风险系统结构及作用机理，建立耦合风险系统结构模型与耦合风险演化模型，提出地震灾害引发的Na-Tech事故中设备泄漏概率模型；以某化工园区为例，应用耦合风险模型及设备泄漏概率模型，利用QRA软件对多灾种耦合下化工园区风险进行定量评价，对比传统工业灾害和地震灾害下个人风险和社会风险，得出地震灾害对化工园区风险影响，为化工园区多灾种耦合风险研究提供理论与方法支撑。     

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real‐Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel‐like environments. As will be discussed, this type of environment presents several special characteristics that often make well‐established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.     

江西省铅山河流域山洪灾害风险区划

为绘制江西省铅山河流域山洪灾害风险区划图,从危险性和易损性两方面构建了风险评价指标体系,基于层次分析法和GIS技术建立了山洪灾害风险评价模型,绘制了研究区山洪灾害危险性、易损性和风险分布图,并结合实际山洪灾害调查评价成果对其验证分析。结果表明,铅山河流域山洪灾害风险度相对较高地区主要分布于武夷山镇、篁碧畲族乡、天柱山乡、石塘镇、英将乡等流域中上游段,风险度较低地区则集中于流域下游永平镇附近,评价结果与该流域实际情况较为吻合,说明所构建的风险评价指标体系和评价模型有效可靠。研究结果可为其他地区山洪灾害风险区划图绘制提供借鉴。     

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling

This study considers an efficient method for the estimation of quantiles associated to very small levels of probability (up to O(10⁻⁹)), where the scalar performance function J is complex (eg, output of an expensive-to-run finite element model), under a probability measure that can be recast as a multivariate standard Gaussian law using an isoprobabilistic transformation. A surrogate-based approach (Gaussian Processes) combined with adaptive experimental designs allows to iteratively increase the accuracy of the surrogate while keeping the overall number of J evaluations low. Direct use of Monte-Carlo simulation even on the surrogate model being too expensive, the key idea consists in using an importance sampling method based on an isotropic-centered Gaussian with large standard deviation permitting a cheap estimation of small quantiles based on the surrogate model. Similar to AK-MCS as presented in the work of Schöbi et al., (2016), the surrogate is adaptively refined using a parallel infill criterion of an algorithm suitable for very small failure probability estimation. Additionally, a multi-quantile selection approach is developed, allowing to further exploit high-performance computing architectures. We illustrate the performances of the proposed method on several two to eight-dimensional cases. Accurate results are obtained with less than 100 evaluations of J on the considered benchmark cases.     

Simulation of performance of intermediate fluid vaporizer under wide operation conditions

The intermediate fluid vaporizer (IFV) is a typical vaporizer of liquefied natural gas (LNG), which in general consists of three shell-and-tube heat exchangers (an evaporator, a condenser, and a thermolator). LNG is heated by seawater and the intermediate fluid in these heat exchangers. A one-dimensional heat transfer model for IFV is established in this paper in order to investigate the influences of structure and operation parameters on the heat transfer performance. In the rated condition, it is suggested to reduce tube diameters appropriately to get a large total heat transfer coefficient and increase the tube number to ensure the sufficient heat transfer area. According to simulation results, although the IFV capacity is much larger than the simplified-IFV (SIFV) capacity, the mode of SIFV could be recommended in some low-load cases as well. In some cases at high loads exceeding the capacity of a single IFV, it is better to add an AAV or an SCV operating to the IFV than just to increase the mass flow rate of seawater in the IFV in LNG receiving terminals.