Dual-PISA: An index for aggregation operations on time series data

Aggregation operations play an essential role in time series database management. As the number of data increases, it is difficult for current solutions, such as summary table and MapReduce-based methods to respond to such queries with low latency. Other approaches, such as segment tree-based methods, have a poor insertion performance when the data size exceeds the available memory. This paper proposes a Persistent Index for Segmented Aggregations (PISA), which has fast insertion performance and low latency for aggregation queries. PISA uses a forest to overcome the performance disadvantage of insertion in traditional segment trees. By defining two kinds of tags, namely code number and serial number, we propose an algorithm to accelerate queries by avoiding unnecessary reading data on disk. Additionally, we extend it to Dual-PISA to tolerate a range of unordered data, which is very important in the real world. Dual-PISA is stored on disk and is hugely memory-efficient — only takes a few hundred bytes of memory for billions of data points. Dual-PISA can be easily implemented on both traditional databases and NoSQL systems. It handles aggregation queries within milliseconds on a commodity server, for a time range that contains tens of billions of data points.     

A finite line source simulation model for geothermal systems with series- and parallel-connected boreholes and independent fluid loops

A model for the simulation of geothermal systems with parallel- and series-connected boreholes is presented. Mass and heat balance problems are formulated for each component in the system and are assembled into system-level problems. A third problem is formulated to account for heat transfer in the bore field, using the finite line source solution. This third problem is coupled to the system-level heat balance problem by an analytical solution of the heat transfer inside boreholes with multiple U-tubes. The simulation model allows for any number of independent fluid loops within the bore field or within individual boreholes and allows for combinations of specified inlet fluid temperatures and heat extraction rates in independent fluid loops. The model accounts for the axial variation of the fluid and borehole wall temperatures and heat extraction rates. The capabilities of the model are demonstrated through three example simulations.     

基于改进雷达图的配电系统电能质量评估方法

本文针对配电系统电能质量综合评估存在的评估指标权重分配过于主观或客观的问题,提出一种基于改进雷达图法的配电系统电能质量评估方法。该方法利用最小偏差组合权重法计算各评估指标的最优组合权重。并在传统雷达图的基础上对各指标轴间夹角值的确定和特征向量的选取两方面做了进一步的改进。首先采用扇形区域取代原始的三角形区域,并利用最小偏差组合权重法计算各指标轴间夹角值。其次选择新的特征向量构造评估函数对配电系统电能质量进行综合评估。最后通过对某钢铁厂棒材变电站10kV配电系统电能质量进行综合评估,验证了该方法的可行性和合理性。较传统雷达图而言,改进雷达图法解决了传统雷达图法中各指标间信息共用以及评估结果不唯一的问题,使评估结果更加准确合理。     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

A mechanism informed neural network for predicting machining deformation of annular parts

Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods.     

Remaining useful life prediction of PEMFC based on cycle reservoir with jump model

Accurate prognosis of limited durability is one of the key factors for the commercialization of proton exchange membrane fuel cell (PEMFC) on a large scale. Thanks to ignoring the structure of the PEMFC and simplifying the prognostic process, the data-driven prognostic approaches was the commonly used for predicting remaining useful life (RUL) at present. In this paper, the proposed cycle reservoir with jump (CRJ) model improves the ESN model, changes the connection mode of neurons in the reservoir and speeds up the linear fitting process. The experiment will verify the performance of CRJ model to predict stacks voltage under static current and quasi-dynamic current conditions. In addition, the reliability of the CRJ model is verified with different amount of data as the training and test sets. The experimental results demonstrate that the CRJ model can achieve better effect in the remaining useful life prognosis of fuel cells.     

ChronoCorrelator: Enriching Events with Time Series

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.     

熔融制样-X射线荧光光谱法测定硅铁中硅磷锰铝钙铬

熔融制样-X射线荧光光谱法(XRF)测定硅铁合金样品,需重点解决样品前处理中合金样品侵蚀铂-黄坩埚的难题。硅铁样品以四硼酸锂-碳酸锂预氧化剂在石墨垫底瓷坩埚中高温预氧化熔融后,再将熔融物转移至铂-黄坩埚中,用四硼酸锂熔融制成玻璃熔片,实现了熔融制样-X射线荧光光谱法对硅铁合金中硅、磷、锰、铝、钙、铬的测定。实验讨论了预氧化熔融的熔剂体系及氧化方法、试样与熔剂的稀释比,结果表明,试样与熔剂以1∶35的稀释比,以10滴300g/L碘化钾溶液为脱模剂,在1100℃熔融30min,熔融制得的玻璃片均匀、透明、无气泡,符合测定要求。用具有浓度梯度的系列硅铁有证标准样品制作校准曲线,各待测元素校准曲线的线性相关系数均大于0.9995。方法应用于硅铁合金实际样品中硅、磷、锰、铝、钙、铬的测定, 结果的相对标准偏差(RSD,n=11)在0.1%~5.8%之间;正确度试验表明,硅铁标准样品的测定结果与认定值相符,硅铁实际样品的测定结果与国家标准方法测定值一致,能满足常规分析要求。     

Eliciting and measuring hesitance in decision-making

Hesitance is an innate human psychological phenomenon pervasive in our daily life. Although it is very complex and still not fully understood, there have been many studies related to this interesting topic. In this study, we proposed two fundamental problems about hesitance: the first is the generating problem, which is related to how to elicit hesitance information from human thinking; the second is the measuring problem, which is related to how to devise effective and reasonable methods to measure hesitance degrees from given hesitance information. With these two fundamental questions, we first discussed and analyzed several examples in real life involving hesitance, showing that it can indeed be recorded and represented. Then we roughly classified the hesitance information into two major classes: static hesitance and dynamic hesitance. Some simple and interesting methods were proposed to elicit hesitance, and more reasonable methods were proposed to measure the hesitance degree from the two classes of hesitance information. All methods that have been discussed in this study try to avoid complexity in every aspect while reserving strictness and reasonability; therefore, the present study provides suitable information for practitioners with different backgrounds.