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.     

A hierarchical simple particle swarm optimization with mean dimensional information

To reduce the negative influence of the overemphasis of gbest the dimensional information of particle is introduced to be a new example. This additional information source is incorporated into simple PSO to establish a simpler position model. Another two simpler position updating models, cognition only model and social only model, based on the simple PSO algorithm are presented as well. Time hierarchy strategy is extended from probability hierarchy, both aiming to make full use of advantages of three models. Three models are used with time or probability hierarchy to update each particle’s position. Thus, two proposed algorithms THSPSO and PHSPSO are finally obtained. Experiments are conducted on fifteen benchmark functions. The results demonstrate the two proposed algorithms both have excellent performances for basic functions compared with other popular PSO variants. Probability hierarchy strategy is more effective than time hierarchy strategy in general.     

基于Fusion Compute虚拟化平台的HIRFL数据库迁移及性能优化

为解决兰州重离子加速器（HIRFL）数据中心资源利用率低下、硬件和维护成本较高等问题，HIRFL数据中心正在向虚拟化集群模式进行迁移。本文以华为Fusion Compute虚拟化平台为基础，构建了HIRFL数据库的迁移方案。通过物理主机、虚拟机计算资源、内存、存储和网络资源性能优化，提出了1套完整的HIRFL数据库虚拟机性能优化方案。采用业界主流的数据库性能测试工具Benchmark Factory对迁移前后的数据库进行了测试，对比实验表明，本文提出的方案具有有效性和可行性，降低了数据中心的运行和维护成本，提升了HIRFL数据库系统的安全性和稳定性。     

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.     

Advanced controller design based on gain and phase margin for microgrid containing PV/WTG/Fuel cell/Electrolyzer/BESS

Nowadays, with the increase in the amount of power generation related to renewable energy resources, the need for energy storage and management is raised. In this regard, the hydrogen energy plays a critical role in the development of renewable technologies. In view of the above, advanced controller design is presented in this paper to effectively perform load frequency control of islanded fuel cell microgrid based on the wind turbine, photovoltaic, fuel cell, electrolyzer, battery energy storage systems, and residential and commercial loads. The controller design is based on the determination of the controller parameters that the fuel cell microgrid system will provide the desired dynamic properties. In the proposed controller design, virtual gain and phase margin testers are added to provide the desired dynamic properties. The controller's stable parameter plane is determined with the help of the stability boundary locus method, taking into account time delay, gain, and phase margin. First, the accuracy of the stable parameter plane determined for the proposed controller design is demonstrated by means of time domain and eigenvalue analyzes. Finally, in order to show the performance of the advanced controller design and the success of the fuel cell as a backup generator, analysis studies have been carried out using actual data of solar and wind, and appropriate changes of load in studied microgrid.     

Concise dynamic model to accurately calculate the hydrogen yield during the reaction process

Estimation of hydrogen production rate and the total amount hydrogen during any reaction process require an accurate and concise dynamic model to describe the variation of hydrogen production rate with reaction time. There must be two comprehensive features of such model, the first one is that the hydrogen production rate calculated by this model is zero at the initial time is zero and the second one is that the integration of this model over time result in an explicit function. In this paper, we present a comprehensive model which meets above two requirements via amendments to the shifted logistic model. The model is validated against measured results through comparison of 25 experimental and theoretical hydrogen production rate. The experimentally validated model is used to calculate the total amount hydrogen during each reaction process. The impact of some experimental conditions on the total hydrogen production was mathematically explained for the first time, and this result reveal the excellent predictive ability of framework suggested in this paper. In addition, that is for an experimental condition history, predictions are achieved by developing a set of “secondary models” that describe some experimental conditions dependence of the “primary models” parameters. The method to obtain the parameters in “secondary models” described in this paper indicated that another major departure from the conventional models is the notion that the “secondary models” do not need to follow any preconceived formula and that they can be derived solely from the observed growth patterns. The presented framework can help the hydrogen production industry.     

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.