夏比冲击功结合剪切面积评价管线钢管韧性的意义

描述了夏比冲击宏观断裂过程,并对管线钢管材料在冲击载荷作用下发生断裂全过程力的变化以及冲击吸收能量与宏观断口的关系进行分析,指出了夏比冲击功结合剪切面积评价管线钢管材料韧性的必要性。     

基于AD9834的简易信号源设计

采用模拟电路设计的信号源存在频率精度不高、调试与维修不方便以及直接升级困难等缺点。为了克服这些缺点,提出利用直接数字频率合成技术（AD9834）产生要求的正弦测井激励信号,利用小功率低压器件的串联实现信号的大功率大电压输出,利用电源跟踪技术在一定程度上降低功率器件的损耗,提高电路的工作效率。实验结果表明,信号源输出频率1～15Hz,频率分辨率为0.004Hz。     

基于交易成本视角的企业第四利润源泉研究

在市场分工日益深化、企业竞争日趋激烈、核心价值链日益复杂的微利时代,第四利润源泉是继第一、二、三利润源后,提升竞争优势、创新赢利模式、赢得利润先机的又一战略利器。交易领域蕴藏的大量交易成本表明,降低交易成本能够成为增加利润的第四个源泉。     

基于“三间距离”理论概念的旅游客源市场开发研究——以江门市为例

江门旅游客源市场有着巨大挖掘潜力,特提出用三间距离理论作为指导江门旅游客源市场开发的指导思想,提出要充分认识三间距离的互动作用,有针对性开拓不同距离的游客市场,利用各种媒介对特色旅游景点宣传,打造特色拳头旅游产品,构建更加完善的旅游环境等措施。     

“等效电源定理”的教学设计

为了培养学生的认知能力、应用能力和创新能力,从教学目标、学情、教学方法、教学组织、教学效果等5个方面对《等效电源定理》这一课的教学设计进行说明。     

含硫油气井中的硫化氢气体检测和防护应急程序

文章详细分析了含硫油气井勘探开发中硫化氢气体产生的来源、硫化氢的主要检测方法,并重点论述了防护硫化氢的应急管理程序。     

南水北调中线水源地生态与经济系统动态分析——以汉中市为例

把生态与经济系统耦合类型划分为生态良好经济发达型、生态良好经济欠发达型、生态脆弱经济发达型、生态脆弱经济欠发达型四种。基于生态足迹理论,构建了生态资本效用指数、生态&经济协调指数,对南水北调工程水源地汉中市1990～2009年生态经济系统运行态势进行了动态分析。结果如下:生态资本供需变化较大:1993年以前为生态盈余,1994～2001年为基本持平,2002～2009年为轻度赤字;生态压力逐渐增大,由1990年的0.74提高到2009年的1.15;2009年的生态资本转换效用是1990年的1.82倍,同期生态与经济协同发展指数提高了2.01倍。研究表明,汉中市生态与经济系统目前属于生态良好经济欠发达型。据此,提出了生态资源经济化的路径与建议。     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.