高潜水位矿区生态系统演变趋势研究:以淮南潘谢矿区为例

为了有效地应对高潜水位矿区煤炭资源开采后带来的生态环境问题,以淮南潘谢矿区为研究区域,应用遥感技术分析了耕地、水体分布以及景观格局,分析了生态系统结构的变化,得出了矿区生态系统结构总体变化规律以及不同生态系统相互转化规律。应用开采沉陷预测技术,基于矿区生态系统结构的变化规律,预测了生态系统演化趋势,得出的结论为:耕地面积逐年减少,矿区煤炭资源完全开采结束时,矿区的生态景观将由水域占绝对主导优势,矿区由陆地生态系统向水陆复合生态系统演变,土地利用模式将优化。     

开采沉陷预计与控制仿真试验教学系统

仿真教学是计算机辅助教学的高级阶段。从传统教学的不足与仿真教学的优点入手,针对传统矿山开采沉陷课程教学,以提高教学效果为目的,开发了基于VB的开采沉陷预计与控制仿真试验教学系统,将仿真教学引入到矿山开采沉陷学教学过程中。在论述系统开发原理及功能设计的基础上,详细介绍了系统3大模块——地表移动变形分析模块、动态演示模块及开采沉陷控制技术模块的功能与特点。基于本系统,通过人机交互界面与系统进行交流,可以让学生充分理解并掌握地表及岩层破坏机理,开采沉陷预计、开采沉陷防治等重点内容,从而有效提高教学质量和效果。教学实践表明,将计算机仿真教学纳入到矿山开采沉陷学教学过程中,可以极大地提高学生理解能力,培养学生的学习兴趣。     

基于密实核理论的单滚刀多阶段受力预测模型

基于密实核理论,针对特定岩石条件,考虑滚刀在破岩过程中复杂的受力情况,将滚刀楔形刃破岩过程分为四个阶段:弹性变形阶段、挤压破碎阶段、密实核破碎阶段与卸载阶段,且认为在密实核破碎阶段,密实核处于静水压状态,静水压力增大,会使受力物体的体积缩小,但不会改变其形状。并提出了单滚刀多阶段受力预测模型。进而开展了单滚刀线性切割岩石试验研究,并与理论模型进行了对比。结果表明:预测模型与实验数据垂直力、侧向力和滚动力的误差分别在(8~18)k N,(2~5)k N和(0~3)k N;在此基础上对预测模型进行了系数修正,使其能够更加真实地模拟滚刀破岩的受力情况,为滚刀以及刀盘的设计提供技术支撑。     

夏热冬冷地区居住建筑夏季人员开窗行为实测与建模研究

开关窗是建筑使用者控制室内热环 境和改善室内空气品质最直接且简单的手 段,同时对建筑节能也有重要影响,因此对 人员开关窗行为的定量化描述是非常必要 的。本研究通过对夏热冬冷地区—?—自贡市 7户住宅夏季人员开窗行为的实际监测,得到 以下结论：发现实测样本中存在三种明显差 异的典型开窗行为,分别是习惯性开窗型、 高强度开窗型和习惯性关窗型;对非环境因 素分析得出,一天中不同时段以及家庭成员 吸烟与否对开窗概率影响显著,而是否为工 作日以及住户所在楼层对开窗概率无明显影 夏热冬冷地区居住建筑夏季人员开窗行为实测与建模研究 * 刘奕巧　王新如　崔　颖　魏　绅　潘　嵩　LIU Yiqiao, WANG Xinru, CUI Ying, WEI Shen, PAN Song Measurement and Modelling of Occupants’ Window Opening Behavior in Residential Buildings in the Hot Summer and Cold Winter Regions in Summer 中图分类号 TU228 文献标识码 B 文 章 编 号 2095-6304（2021）05-15-09 作者简介 刘奕巧： 吉林建筑大学寒地建筑综合节能教育部重点实 验室,马来亚大学机械工程学院,博士研究生 王新如（ 通讯作者）：天津商业大学机械工程学院,讲 师,xinru5263@ticu.edu.cn 崔　颖：宝武清洁能源武汉有限公司 魏　绅： 伦敦大学学院巴特莱特可持续建筑学院,副教授 潘　嵩： 吉林建筑大学寒地建筑综合节能教育部重点 实验室,北京工业大学绿色建筑环境与节能 技术重点实验室,教授 *寒地建筑综合节能教育部重点实验室开放课题项目; 河北省国际科技合作基地（20594501D）;“十三五”国 家重点研发计划(2017YFC0702200) 响;对于环境因素而言,当室外温度超过32 ℃,开窗概率开始降低,空调开启比例升高;室外温 度超过37 ℃,空调开启比例达到100%,但开窗比例仍有11.1%;建立了基于二元逻辑回归的夏季 高强度开窗行为预测模型,模型的预测准确率达到了86%,而室外温度是该模型最为重要的预 测因子。本文的研究成果可以用于细化能耗模拟软件及室内空气品质评价软件对该类型住宅建 筑开窗行为特征的描述,对住宅建筑节能设计、运行、改造以及住宅室内空气品质和热舒适研 究均有一定程度的参考意义。     

Wall thickness error prediction and compensation in end milling of thin-plate parts

End milling has been widely adopted to machine the thin-plate parts that play increasingly important role in the aerospace industry, due to the advantages of high machining accuracy and fine machined surface quality. In this paper, a systematic method is proposed to predict and compensate the wall thickness errors in end milling of thin-plate parts. The errors are caused by the static deflections induced by the varying cutting force imposed on the weakly rigid part. To improve the efficiency of computing the part deformation, a novel FE model is firstly developed by combing the methods of substructure analysis, special mesh generation and structural static stiffness modification. Then, the time- and position-dependent deformations of the part are calculated based on the proposed FE model to predict the wall thickness errors left on the finished part. It reveals for the first time that the surface topography of the finished thin-plate part is formed by the repeated cutting with the bottom edge of the cutter (BEC) in end milling. Owing to the coupling between the axial cutting depth (ACD) and the force-induced deflection, the modified ACDs for compensation of the static wall thickness errors are finally determined by an iterative adjustment method. The proposed method is verified by three-axis end milling experiments. The experiment results show that the predicted wall thickness errors match well with the really measured ones, and the errors are reduced by 77.18% with the help of the proposed compensation method. Moreover, the proposed FE model reduces the computational time elapsed for error prediction by 67.44% as compared with the benchmark FE model.     

基于RBF神经网络的露天矿爆破效果预测研究

露天矿爆破是一个受诸多因素共同影响的系统工程,是露天开采的重要环节之一,其爆破效果的优劣直接影响后续工序的完成。提高爆破技术水平和爆破质量,对矿山安全和生产具有重要的意义。本文通过随机森林选择影响爆破效果的主要参数,结合模糊评价确定爆破综合效果,建立了RBF神经网络爆破效果预测模型。将该模型应用于矿山爆破效果预测中,并将爆破现场实测的11组数据作为模型训练样本,另外5组现场数据作为预测样本进行测试,通过与BP神经网络比较,发现RBF神经网络的预测性能更为优越,可广泛应用于现场实践中。     

Least fresh first cache replacement policy for NDN-based IoT networks

In-network caching in Named Data Networking (NDN) based Internet of Things (IoT) plays a central role for efficient data dissemination. Data cached throughout the network may quickly become obsolete as they are transient and frequently updated by their producers. As such, NDN-based IoT networks impose stringent requirement in terms of data freshness. While various cache replacement policies were proposed, none has considered the cache freshness requirement. In this paper, we introduce a novel cache replacement policy called Least Fresh First (LFF) integrating the cache freshness requirement. LFF evicts invalid cached contents based on time series forecasting of sensors future events. Extensive simulations are performed to evaluate the performance of LFF and to compare it to the different well-known cache replacement policies in ICN-based IoT networks. The obtained results show that LFF significantly improves data freshness compared to other policies, while enhancing the server hit reduction ratio, the hop reduction ratio and the response latency.     

Use of a quantile regression based echo state network ensemble for construction of prediction Intervals of gas flow in a blast furnace

The usual huge fluctuations in the blast furnace gas (BFG) generation make the scheduling of the gas system become a difficult problem. Considering that there are high level noises and outliers mixed in original industrial data, a quantile regression-based echo state network ensemble (QR-ESNE) is modeled to construct the prediction intervals (PIs) of the BFG generation. In the process of network training, a linear regression model of the output matrix is reported by the proposed quantile regression to improve the generalization ability. Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles. To verify the performance of the proposed method, the practical data coming from a steel plant are employed, and the results indicate that the proposed method exhibits high accuracy and reliability for the industrial data. Furthermore, an application software system based on the proposed method is developed and applied to the practice of this plant.     

Evaluation and Predictive Model Development of Oxidative Stability of Biodiesel on Storage

Biodiesel comprises mono-alkyl esters of long-chain fatty acid methyl esters (FAME) derived from a renewable lipid feedstock. A major technical issue with the use of biodiesel is that it is more prone to oxidation during storage, when compared to petroleum fuel, due to the high content of polyunsaturated methyl esters that are easily oxidizable to compounds such as acids, aldehydes, and alcohols. Biodiesel (Jatropha and Pongamia) and antioxidants (Turmeric and butylated hydroxytoulene) were used for this study. We found that the acid value and viscosity for pongamia biodiesel increased significantly by 41.17% and 44.0% and that for jatropha biodiesel increased by 31.5% and 37.0%, respectively, after being stored for 3 months. The impact of antioxidants on the storage stability of biodiesel was examined according to the ASTM D4625 12-week procedure, and best results were found at a concentration level of 2500 ppm. The specific objective of this investigation is to develop models to determine the viscosity of biodiesel at any time “t” during long-term storage based on these experimental trials for upto 12 weeks. In addition, the models were used to predict the level of antioxidants that are to be added to biodiesel in order to minimize the effects of oxidative degradation during storage. The developed model recorded an adjusted R² of 0.86 and a modeling efficiency of 0.88.     

Hazard prediction discriminates between novice and experienced drivers

Typical hazard perception tests often confound multiple processes in their responses. The current study tested hazard prediction in isolation to assess whether this component can discriminate between novice and experienced drivers. A variant of the hazard perception test, based on the Situation Awareness Global Assessment Technique, found experienced drivers to outperform novices across three experiments suggesting that the act of predicting an imminent hazard is a crucial part of the hazard-perception process. Furthermore three additional hypotheses were tested in these experiments. First, performance was compared across clips of different length. There was marginal evidence that novice drivers’ performance suffered with the longest clips, but experienced drivers’ performance did not, suggesting that experienced drivers find hazard prediction less effortful. Secondly, predictive accuracy was found to be dependent on the temporal proximity of visual precursors to the hazard. Thirdly the relationship between the hazard and its precursor was found to be important, with less obvious precursors improving the discrimination between novice and experience drivers. These findings demonstrate that a measure of hazard prediction, which is less confounded by the influence of risk appraisal than simple response time measures, can still discriminate between novice and experienced drivers. Application of this methodology under different conditions can produce insights into the underlying processes that may be at work, whilst also providing an alternative test of driver skill in relation to the detection of hazards.