时序脉冲燃烧控制技术在回火炉上的应用

衡钢1#回火炉燃烧控制系统采用时序脉冲燃烧控制技术来控制炉膛温度,据此,重点介绍了该系统的组成、工作原理及动态性能.由于它具有控制温度波动小、节约燃料等优点,因此具有广泛的推广应用价值.     

用时间序列来判断电力系统中的混沌现象

由于混沌现象对初值、参数等的敏感依赖性,现有的基于电力系统的模型（方程）来研究混沌的方法,会由于建模时方程的形式、参数等误差带来可能的失误。为了弥补这一缺点,文中提出了直接利用对实际系统RS采样得到的时间序列的Lyapunov指数λi分析的方法来判断混沌的存在性。利用矩阵的特征值移动技术可以找到RS的伴随系统RS*,使RS* 的λ*i=λi+k(k为合适的正实数),于是可以利用现有的计算时间序列Lyapunov指数的方法来实现RS的混沌判断。     

BP网络在三江平原井灌区地下水埋深预测中的应用

近些年来,由于水田面积迅速增加,导致三江平原井灌水稻区地下水位持续下降,吊泵、局部超采现象时有发生。为解决上述问题,以853农场为例,采用BP神经网络方法建立了853农场地下水埋深动态预测模型,对地下水埋深进行模拟和预测,精度检验结果表明,模型拟合和预测精度均较高。该模型揭示了研究区域地下水动态变化规律,为三江平原井灌区地下水资源的可持续利用提供了科学依据。     

Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-Head Self-Attention

The source and load uncertainties arising from increased applications of renewable energy sources such as wind and photovoltaic energy in the power system have had adverse effects on optimal planning and dispatching. Models for generating typical renewable energy and load scenarios are constructed to reduce such effects and improve the applicability of a planning and optimal dispatching model of power systems with a high proportion of renewable energy. The traditional clustering-based model for representing such scenarios cannot handle high-dimensional time-series data and consequently the feature-related information obtained cannot fully reflect the characteristics of the data. Thus, a deep convolutional embedded clustering model based on multi-head self-attention is proposed. First, a variational mode decomposition model is optimized to reduce the influence of noise-related signals on the feature extraction. The deep features are then extracted from the data using an improved convolutional autoencoder, and the appropriate number of clusters is determined using the elbow method. Following this, the network parameters are optimized based on the sum of losses during reconstruction and clustering. Subsequently, typical scenarios are then generated based on the optimized network model. Finally, the proposed method is evaluated based on data visualization and evaluation metrics. It is shown that the quality of features and the accuracy of clustering can be effectively improved by the proposed scenario generation method.     

基于时序数据的软件可靠性模型组合新方法

基于时序数据的软件可靠性模型受到越来越多的关注,然而单一模型在精确度和通用性上都存在不足,鉴于此,提出一种新的软件可靠性模型组合的方法,该方法将反向传播（ BP ）神将网络模型和支持向量机回归（SVMR）模型进行组合,通过遗传算法（GA）和滑动窗口机制构造可靠性模型输入,使用粒子群（PSO）算法选择单一模型的最优参数,并使用BP神经网络确定两个模型的权重值建立组合模型,来预测下一阶段的软件失效数据。最后进行了仿真实验并做了对比分析,结果表明该方法较单一模型具有更高的精确度和较好的通用性。     

确定越流含水层参数的混沌序列优化算法

将混沌序列优化算法应用在第一类越流系统含水层非稳定流井流问题上,进行抽水试验数据的分析、含水层参数的求解,并就算法的搜索能力和结果与给定含水层各参数取值范围的关系进行探讨。结果表明:(1)求解越流条件下含水层参数的计算问题能用混沌序列优化算法得到很好的解决;(2)除越流因数上限取值会降低越流因数搜索结果的准确性外,储水系数、导水系数、越流因数上限取值对算法的搜索能力和搜索结果没有太明显的影响。相较于其他方法,混沌序列优化算法易于编程、运算简单、运算结果不被人为因素干扰等特点更为突出。     

基于图像帧间信息和FS-KFDA的极光序列图像检测算法

为了解决数据量巨大的极光序列图像难于用人工划分的方法来进行变化监测的问题,提出了一种基于极光序列图像特征和帧间信息的感兴趣极光变化区域(ROI)自动检测算法,用计算机进行辅助分类。首先提取样本图像特征,再利用离散小波变化算法对帧间信息进行特征分析,引入特征标度核Fisher分析算法(FS-KD-FA),结合K-均值聚类选择训练样本,构建分类器实现了变化检测。对北极黄河站采集到的实测极光图像数据进行极光区域划分检测,实验结果表明了算法的有效性。     

提高时序数据识别精度的方法及应用

通过建立多分类二叉树的SVM识别模型,充分利用SVM的优点,实现具有时序特征对象的识别。由于SVM没能利用时序特性有用的分类信息,导致部分类别判断的失误,因此通过引入数据窗口,利用时序对象的类别分布来校正SVM识别的奇异对象,进一步提高时序对象识别的精度。利用测井数据,以地层识别为应用背景,通过"识别"和"校正"两个阶段,大大提高地层的识别精度,取得很好的应用效果。     

Crime hotspot prediction based on dynamic spatial analysis

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.     

时间序列分析在地下水位预报中的应用

依据北京市地下水位观测井月平均水位资料,运用逐步自回归模型、指数平滑模型、季节性模型3种时间序列模型分别建立地下水位动态模拟和预测模型,并对模型的模拟和预测精度进行对比分析。通过应用实例分析反映,时间序列模型可较全面地反映地下水位动态变化规律,且计算简单,所需资料较少且易于获得,可以作为一种简易快速的地下水位模拟预测模型,能为地下水资源合理开发利用和科学管理提供参考依据。