基于时空卷积动态知识图谱的新能源消纳评估方法

为构建新能源消纳知识图谱,首先,将电网积累的海量调度运行数据以动态四元组的形式,显式地表达调度运行数据的时空关联关系。通过滑动时间窗口快速搜索、提取局部时空图,构建子图数据集。然后,时空同步图卷积网络对局部时空图进行高维特征提取,充分挖掘历史数据的时空关联关系,利用新能源消纳知识图谱中存储的机理知识对模型进行引导,并通过多子图并行训练提升模型的学习效率。最后,基于中国西北某省级电网算例进行仿真和实验验证。结果表明,所提方法可以有效避免复杂的数学建模以及模型求解,相比于传统方法具有更高的评估精度与速度。

基于图卷积神经网络与K-means聚类的居民用户集群短期负荷预测

随着智能电表等高级量测装置在用户侧的广泛部署与使用,海量多源异构的居民用户数据得以采集与存储,为用户级负荷预测提供良好的数据基础。精准的居民用户集群负荷预测是促进智能配电网需求侧管理、辅助电网公司实现削峰填谷的重要基础。然而,现有的用户级负荷预测方法大多利用历史负荷序列的时间相关性构建数据驱动模型,却忽视相邻用户用电行为之间存在的潜在空间相关性。因此,提出一种基于K-means聚类和自适应时空同步图卷积神经网络的居民用户集群负荷预测方法。首先,采用K-means聚类将居民用户集群按照用电行为相似性划分成不同组;然后,基于居民用户集群的分组数量、各组居民用户的历史负荷数据以及各组居民用户负荷序列之间的相关性,构建面向居民用户集群负荷预测的时空图数据;最后,使用自适应时空同步图卷积神经网络实现居民用户集群短期负荷预测。文章通过真实的爱尔兰居民用户负荷公开数据集测试并验证所提方法的准确性和有效性,实验结果表明,相较于各个基准预测方法,所提方法能够充分挖掘并利用不同居民用户用电负荷之间的时空相关性,进而提高居民用户集群负荷预测精度。

基于线性时态逻辑的Petri网模型检测

Petri网是一种重要的数学工具,它能有效地对并发系统进行描述和建模。线性时态逻辑LTL则是描述和验证并发系统特性的一种重要的形式化工具,它能方便准确地描述并发系统的重要性质,如安全性和活性。文章深入描述了线性时态逻辑、Büchi自动机、Petri网和同步积之间的内在联系,并探讨了基于线性时态逻辑的Petri网模型检测策略。与其它方法比较,这种模型检测的策略结合了线性时态逻辑和Petri网模型的不同优点,增强了Petri网的模型分析和验证能力。最后,通过对一个并发系统形式化的模型检测分析,验证了相应的结论。

綦江“彩虹桥”坍塌事件给我们的警示

1999年新年刚过,一则新闻令还流连于快乐中的人们触目惊心:1月4日,重庆綦江县彩虹桥发生了坍塌事件,造成40人死亡,14人受伤,直接经济损失631万元。从各方面调查取证和专家组技术鉴定的情况来看,在该桥的整个建设过程中,有关领导急功近利,有关部门严重失职,有关人员玩忽职守,工程立项、发(承)包、设计、施工、监理、验收等环节均严重违反了基本建设程序,且设计、施工主体资格均不合法,工程管理十分混乱,导致施工质量极为低劣,致使该桥建成时就是一座危桥。血的教训是深刻的,更是发人深省的,如果在工程建设的同时,就能从工程建设档案制度上严格约束和监督管理。

Study on Synchro-Epitaxy of Poly-and Single Crystal Silicon

Synchro-epitaxy is introduced and a “two periods epitaxy” process is proposed.The influence of the flows of SiH 4 N 1,N 2,deposition time t 1,t 2,and epitaxial temperature T on epilayer quality (embodied by α) is reported.The shorter initial inducing time t 1 and larger flows of SiH 4 are,the wider single crystal strips are.But the quality of epilayer may be poor.The optimum conditions are:N 1=13.1～17.5sccm,N 2=7.0～7.88sccm,and t 1=30～50s.The influence of temperature is complex:when T is lower than 980℃,single crystal strips increase with T ;when T is higher than 980℃,single crystal strips decrease with T.It reaches maximum near 980℃.

Anomalies of bromine in the estuarine sediments as a signal of floods associated with typhoons

X-ray fluorescence scanning with synchrotron radiation was performed to study sediment core records of floods in Amur Bay,Sea of Japan.Interlayers of 3–8 mm with abnormally low bromine content were formed by the Razdolnaya River discharge to the central part of the bay during extreme floods,accompanied by severe storms at sea.Such conditions in the region are typical for periods of deep tropical cyclones(typhoons),to which the distinguished interlayers were compared on the timescale.This approach was made possible thanks to the high rate of sedimentation in the bay(3–5 mm/a) and low bioturbation of sediments under anoxic conditions.

Fault diagnosis method of rolling bearing based onthreshold denoising synchrosqueezing transform and CNN

The rolling bearing vibration signal is non-stationary and is easily disturbed by background noise,so it is difficult to accurately diagnose bearing faults.A fault diagnosis method of rolling bearing based on the time-frequency threshold denoising synchrosqueezing transform(TDSST)and convolutional neural network(CNN)is proposed.Since the traditional methods of wavelet threshold denoising and wavelet adjacent coefficient denoising are greatly affected by the estimation accuracy of noise variance,a time-frequency denoising method based on the STFT spectral correlation coefficient threshold optimization is adopted,which is combined with a synchrosqueezing transform.The ability of the TDSST to reduce noise and improve time-frequency resolution was verified by simulated impact fault signals of rolling bearings.Finally,the CNN is utilized to diagnose the time-frequency diagrams obtained by the TDSST.The diagnostic results of the rolling bearing experimental data show that the proposed method can effectively improve the accuracy of diagnosis.When the SNR of the bearing signal is larger than 0 dB,the accuracy is over 95%,even when the SNR reduces to-4 dB,the accuracy is still around 80%.Moreover,the standard deviation of multiple test results is small,which means that the method has good robustness.

Passive control of Permanent Magnet Synchronous Motor chaotic system based on state observer

Passive system theory was applied to propose a new passive control method with nonlinear observer of the Permanent Magnet Synchronous Motor chaotic system. Through constructing a Lyapunov function, the subsystem of the Permanent Magnet Synchronous Motor chaotic system could be proved to be globally stable at the equilibrium point. Then a controller with smooth state feedback is designed so that the Permanent Magnet Synchronous Motor chaotic system can be equivalent to a passive system. To get the state variables of the controller, the nonlinear observer is also studied. It is found that the outputs of the nonlinear observer can approximate the state variables of the Permanent Magnet Synchronous Motor chaotic system if the system’s nonlinear function is a globally Lipschitz function. Simulation results showed that the equivalent passive system of Permanent Magnet Synchronous Motor chaotic system could be globally asymptotically stabilized by smooth state feedback in the observed parameter convergence condition area.

A study on synchronization error accumulation over hop count for synchronization scheme of two-way packet exchange

Two-way packet exchange synchronization scheme has been widely used in wireless sensor networks. However, due to the fact that its synchronization error accumulates rapidly over hop count, its applications are greatly restricted. In this paper, the factors that cause the accumulation of synchronization error over hop count are investigated. Theoretical analysis shows that two factors including the clock drift and the asymmetry of two-way packet exchange, have distinct influences on synchronization error between two adjacent nodes. Further, the clock frequency order along synchronization path is found to be vital to the accumulation of synchronization error. The above three factors jointly determine the accumulation of synchronization error over hop count in wireless network. Theoretic results are also verified by three fine-grained experiments on wireless sensor network testbed. The conclusions can be used to decrease synchronization error for large-scale wireless network by careful network deployment.

Acid volatile sulfide and simultaneously extracted metals in superficial sediments from Baihua Lake, China

The bioavailability of five divalent cationic heavy metals （Pb, Cd, Cu, Zn and Ni） in 10 superficial sediment samples from Baihua Lake was assessed based on the molar ratio of simultaneously extracted metals （SEMs） to acid volatile sulfide （AVS）. Atomic absorption spectrometry （AAS） and X-ray powder diffraction （XRD） were used to determine the heavy metal concentrations and examine the mineralogy of the crystalline phases, respectively. The AVS loadings in sediments from Baihua Lake ranged from 64.30 to 350.08 ~rnol/g （dry weight）. The corresponding SEM levels for the sampling sites varied from 1.770 to 14.660 vrnol/g. The molar ratio of SEMs to AVS ranged from 0.014 to 0.084 with a mean value of 0.034. The XRD analysis also confirmed the presence of some metal sulfides in sediments from Baihua Lake. The SEMs/AVS ratios for all sampling sites were significantly lower than 1.0, indicating that AVS in the sediments was sufficient to bind the five heavy metals; thus, these heavy metals are currently not significantly bioavailable to benthic organisms. Comparing the SEMs results to published guideline values for metal toxicity to benthic organisms in sediments, however, suggests that Zn and Ni pose a risk at some sampling locations in Baihua Lake.