Likelihood and Quadratic Distance Methods for the Generalized Asymmetric Laplace Distribution for Financial Data

Maximum likelihood (ML) estimation for the generalized asymmetric Laplace (GAL) distribution also known as Variance gamma using simplex direct search algorithms is investigated. In this paper, we use numerical direct search techniques for maximizing the log-likelihood to obtain ML estimators instead of using the traditional EM algorithm. The density function of the GAL is only continuous but not differentiable with respect to the parameters and the appearance of the Bessel function in the density make it difficult to obtain the asymptotic covariance matrix for the entire GAL family. Using M-estimation theory, the properties of the ML estimators are investigated in this paper. The ML estimators are shown to be consistent for the GAL family and their asymptotic normality can only be guaranteed for the asymmetric Laplace (AL) family. The asymptotic covariance matrix is obtained for the AL family and it completes the results obtained previously in the literature. For the general GAL model, alternative methods of inferences based on quadratic distances (QD) are proposed. The QD methods appear to be overall more efficient than likelihood methods infinite samples using sample sizes n ≤5000 and the range of parameters often encountered for financial data. The proposed methods only require that the moment generating function of the parametric model exists and has a closed form expression and can be used for other models.

Modified Generalized Degree Distance of Some Graph Operations

The modified generality degree distance, is defined as: ,?which is a modification of the generality degree distance. In this paper, we give some computing formulas of the modified generality degree distance of some graph operations, such as, composition, join, etc.

Generalized Minimum Perpendicular Distance Square Method of Estimation

In case of heteroscedasticity, a Generalized Minimum Perpendicular Distance Square (GMPDS) method has been suggested instead of traditionally used Generalized Least Square (GLS) method to fit a regression line, with an aim to get a better fitted regression line, so that the estimated line will be closest one to the observed points. Mathematical form of the estimator for the parameters has been presented. A logical argument behind the relationship between the slopes of the lines and has been placed.

<i>d-Distance</i>Coloring of Generalized Petersen Graphs <i>P(n, k)</i>

A coloring of G is d-distance if any two vertices at distance at most d from each other get different colors. The minimum number of colors in d-distance colorings of G is its d-distance chromatic number, denoted by χd(G). In this paper, we give the exact value of χd(G) (d = 1, 2), for some types of generalized Petersen graphs P(n, k) where k = 1, 2, 3 and arbitrary n.

Some Results on Generalized Degree Distance

In [1], Hamzeh, Iranmanesh and Hossein-Zadeh and M. V. Diudea recently introduced the generalized degree distance of graphs. In this paper, we present explicit formulas for this new graph invariant of the Cartesian product, composition, join, disjunction and symmetric difference of graphs and introduce generalized and modified generalized degree distance polynomials of graphs, such that their first derivatives at x = 1 are respectively, equal to the generalized degree distance and the modified generalized degree distance. These polynomials are related to Wiener-type invariant polynomial of graphs.

Generalized nested logit-based stochasticuser equilibrium model with distance constraint of electric vehicles

Considering the range anxiety issue caused by the limited driving range and the scarcity of battery charging stations,the conventional multinomial logit(MNL)model with the overlapping path issue was used in route choice modeling to describe the route choice behavior of travelers effectively.Furthermore,the generalized nested logit-based stochastic user equilibrium(GNL-SUE)model with the constraints of multiple user classes and distance limits was proposed.A mathematical model was developed and solved by the method of successive averages.The mathematical model was proven to be analytically equivalent to the modified GNL-SUE model,and the uniqueness of the solution was also confirmed.The proposed mathematical model was tested and compared with the GNL-SUE model without a distance limit and the MNL-SUE model with a distance limit.Results show that the proposed mathematical model can effectively handle the range anxiety and overlapping path challenges.

Calibration of parallel kinematics machine using generalized distance error model

This paper focus on the accuracy enhancement of parallel kinematics machine through kinematics calibration. In the calibration processing, well-structured identification Jacobian matrix construction and end-effector position and orientation measurement are two main difficulties. In this paper, the identification Jacobian matrix is constructed easily by numerical calculation utilizing the unit virtual velocity method. The generalized distance errors model is presented for avoiding measuring the position and orientation directly which is difficult to be measured. At last, a measurement tool is given for acquiring the data points in the calibration processing. Experimental studies confirmed the effectiveness of method. It is also shown in the paper that the proposed approach can be applied to other typed parallel manipulators.

A Unified Bit-to-Symbol Mapping for Generalized Constellation Modulation

Gray mapping is a well-known way to improve the performance of regular constellation modulation,but it is challenging to be applied directly for irregular alternative.To address this issue,in this paper,a unified bit-to-symbol mapping method is designed for generalized constellation modulation(i.e.,regular and irregular shaping).The objective of the proposed approach is to minimize the average bit error probability by reducing the hamming distance(HD)of symbols with larger values of pairwise error probability.Simulation results show that the conventional constellation modulation(i.e.,phase shift keying and quadrature amplitude modulation(QAM)with the proposed mapping rule yield the same performance as that of classical gray mapping.Moreover,the recently developed golden angle modulation(GAM)with the proposed mapping method is capable of providing around1 d B gain over the conventional mapping counterpart and offers comparable performance to QAM with Gray mapping.

含T接逆变型分布式电源配电网的自适应距离保护

大量逆变型分布式电源(Inverter Interfaced Distributed Generation,IIDG)接入配电网,传统继电保护技术的可靠性无法满足。为了提高配电网供电可靠性,适应可再生能源大量渗透的新型电力系统,针对含T形接入IIDG的配电网,文章提出了一种自适应性的距离保护方案。分析了故障发生在不同位置对保护的影响,根据IIDG在系统发生故障时的输出特性,通过BP神经网络利用保护本地电气信息计算IIDG的输出电流,实时整定动作值。该方法由于无需与远方通信,动作速度快,并且减少了通信系统的投资。最后在MATLAB中建立了含T形接入IIDG的10 kV配电网模型,并与传统距离保护进行了对比,验证了该保护方法的优越性。

基于深度生成模型的医院网络异常信息入侵检测算法

为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserstein距离算法与MMD(Maximun Mean Discrepancy)距离算法,在深度生成模型中,对医院网络数据展开降维处理。向异常检测模型中输入降维后网络正常运行数据样本,并提取样本特征。利用深度学习策略中的Adam算法,生成异常信息判别函数,通过待测网络运行数据与正常网络运行数据的特征对比,实现医院网络异常信息入侵检测。实验结果表明,算法能实现对医院网络异常信息入侵的高效检测,精准检测多类型网络入侵行为,为医疗机构网络运行提供安全保障。

变工况下动态卷积域对抗图神经网络故障诊断

针对基于无监督域自适应故障诊断方法忽略了域间数据结构信息、传统域对齐平均最大差异法全局泛化能力差等问题,本文提出一种基于无监督域自适应理论的动态卷积域对抗图神经网络故障诊断模型,分别通过对数据的类别标签、域标签和数据结构信息进行建模。通过分类器和域鉴别器分别建模类别标签和域标签,通过图神经网络将数据结构信息嵌入到实例图节点中,利用高斯Wasserstein距离来度量不同领域的实例图之间的差异。本文对比了不同工况下共14种迁移任务在各模型下故障识别的准确率。实验结果表明:基于动态卷积的域对抗图神经网络模型在变工况下的故障诊断效果均优于其他对比模型,且模型性能稳定。

面向光伏集群扩展规划的两阶段分布鲁棒优化

随着双碳目标的深入推进,可再生能源的渗透率逐年攀升,其消纳问题备受关注。分布式可再生能源集群是一种消纳可再生能源的新模式,在规划时需要着重考虑源荷不确定性的影响。以分布式光伏集群新增光伏并网规划为背景,计及源荷的不确定性,提出了一种基于两阶段分布鲁棒优化的光伏集群扩展规划方法。考虑规划阶段和运行阶段侧重的不同,建立了以年等值成本最小为目标、考虑机组出力约束和电网承载能力的两阶段分布鲁棒优化模型。为了提高计算效率,结合K-means聚类和极限场景法对区域分布式光伏和随机负荷的历史数据进行削减和修正,并基于修正后的场景集构造了基于Wasserstein距离的概率分布模糊集。接着采用列和约束生成算法将建立的两阶段分布鲁棒优化模型分解为主问题和子问题,通过主问题和子问题的迭代进行求解,进一步提高了求解效率。其中,为实现子问题的求解,引入了拉格朗日对偶法将子问题转化为确定性优化问题。最后,以某分布式光伏集群为例开展算例分析,结果表明:所提的基于两阶段分布鲁棒优化的光伏集群扩展规划方法可以有效协调规划运行方案的经济性和鲁棒性,根据历史场景集的大小和可靠程度可以灵活调整模型控制参数以满足多种工程应用场景下对可靠性和经济性的不同需求。

改进的MVO-GRNN神经网络岩爆预测模型研究

准确预测岩爆烈度等级能有效指导岩爆灾害的防控。根据影响岩爆发生及烈度等级的3个因素构建岩爆评价指标体系,提出一种基于改进多元宇宙算法(Improved Multi-Verse Optimizer,IMVO)优化广义回归神经网络(General Regression Neural Network,GRNN)的岩爆预测模型。在普通多元宇宙算法(MVO)的基础上,运用自适应平衡机制调节MVO算法中的虫洞存在概率(V_(WEP))和旅行距离率(V_(TDR))两个重要参数来改进该算法;再运用改进的多元宇宙算法优化广义回归神经网络的光滑度,通过训练数据优选出最佳光滑因子σ,得到IMVO-GRNN神经网络岩爆烈度预测模型;最后结合工程实例验证模型的性能。研究表明,该模型相比传统模型寻优能力更强,精度更高,为岩爆预测提供了一种新的思路。

基于多元精细复合多尺度波动散布熵和累积欧氏距离矩阵测度的风电机组变桨轴承退化状态评估

针对风电机组变桨轴承服役过程环境噪声干扰严重、退化状态评估精度低的问题,提出一种基于多元精细复合多尺度波动散布熵和累积欧氏距离矩阵测度的退化状态评估模型。该模型将监测数据状态特征获取过程由单通道拓展为多通道进行,通过提出的多元精细复合多尺度波动散布熵算法来获取多通道监测数据的多尺度状态特征,并将累积和检验算法与欧氏距离矩阵测度方法相结合,用于定量衡量基准样本与待分析样本间的差异,从而实现变桨轴承退化状态评估。风电机组变桨轴承全寿命周期加速疲劳实验验证结果表明:该模型能够及时捕捉到变桨轴承的初始退化时刻并且准确跟踪整个退化过程。

基于LSTM-WGAN的时间序列数据异常检测

在时序数据中发现隐藏的异常行为或事件,可以保障生产安全,具有重要意义。目前的异常检测模型存在训练不稳定、容易产生梯度消失的问题,影响异常检测效果,针对该问题,提出一种LSTM-WGAN模型,WGAN负责捕获变量之间的潜在关联,进一步提升了LSTM的检测能力。同时,以Wasserstein距离代替交叉熵损失训练判别器和生成器,结合重构损失以及判别损失实现异常检测。在NAB公开数据集上的实验结果表明LSTM-WGAN相较于基准模型在准确率、召回率以及F1得分上都有较大幅度的提升。

基于提升距离保护适应性的改进VSG控制策略研究

随着新能源的大规模并网,新能源虚拟同步发电机(VSG)控制技术得到广泛应用。针对现有VSG控制下传统距离保护动作失效的问题,提出1种基于提升距离保护适应性的改进VSG控制策略。首先,对传统距离保护失效的原理进行分析;然后,以减小故障点两侧电流相位差为控制目标,通过定量设计VSG内环控制中的虚拟阻抗值,实现对换流器输出故障电流特性的主动控制;最后,在Matlab/Simulink中搭建新能源并网交流线路发生短路故障时的仿真模型。仿真分析表明,所提改进VSG控制策略能有效支撑传统距离保护的动作可靠性。

基于门限的5G空间调制消息传递检测算法

针对经典消息传递检测算法中存在复杂度高与性能低的问题,文中基于门限和消息传递机制,提出一种适用于5G通信的空间调制信息检测算法。该算法通过引入组合数学表示方法,在广义空间调制系统完成大规模天线组合的表示,进而构建基本的分层消息传递检测模型。在此基础上,利用设定门限的方法快速地判定接收信号的欧氏距离,以减少消息传递检测算法执行无效的迭代,从而实现具有较低复杂度的消息传递检测算法。理论分析及实验仿真结果表明,与分层消息传递检测算法相比,基于门限的消息传递检测算法具有更高的检测性能与较低的计算复杂度。

含分布式电源配电网的自适应距离保护

针对逆变型分布式电源接入的配电网系统,对分布式电源采用低电压穿越及P/Q控制策略,分析其故障输出特性及对传统距离保护方案产生的影响;针对逆变型分布式电源接入造成传统距离保护易发生拒动或误动的问题,结合不同故障类型下的复合序网分析,提出一种基于逆变型分布式电源正序分量的自适应距离保护方案,通过检测逆变型分布式电源其输出的故障电流构造助增分支系数对原有分支系数进行自适应修正,讨论在相间短路故障和接地短路故障情况下该方案的适用性。结果表明,该自适应距离保护方案在逆变型分布式电源出力变化时具备优异可靠性和灵敏性。

往复式涡环生成装置性能的多目标优化

为了将涡环更好地应用到送风等流体输送领域,需要提高涡环生成频率以获得更高的流体输送效率。本文设计一种能够提高涡环生成频率的往复式涡环生成装置,该装置在一个活塞运动周期中可以生成两个涡环,有效地利用了活塞往复运动过程中的推力。针对涡环远距离输送的特点,设计并进行实验;基于响应曲面法与NSGA-II算法对往复式涡环生成装置性能进行多目标优化,使其能够将涡环输送更远距离。结果表明:涡环1与涡环2的输送距离难以同时达到最大值,涡环平均输送距离最大为295 cm,相比优化前增加了2.25%;单个涡环输送距离最大为377 cm,相比优化前增加了6.2%。

基于典型源荷耦合模式的中长期时序场景生成

高比例可再生能源电力系统中源荷功率在气象的影响下耦合程度增加。基于生成式模型的场景生成方法是分析源荷功率不确定性问题的成熟工具,然而实际应用中常受源荷数据匹配样本少的问题限制,直接生成的中长期源荷耦合时序场景质量较差。提出一种基于典型源荷耦合模式的中长期时序场景生成方法。首先,定义了源荷各自的气象模式概念。然后,通过气象距离将历史数据中典型气象模式相似的源荷聚合成定义的典型源荷耦合模式。接着,采用生成对抗网络生成典型风光场景,并将生成场景与典型源荷耦合模式进行匹配。最后,根据匹配的对应关系得到典型源荷耦合场景。以日本地区为例的仿真结果表明,所提方法能够提高场景在气象方面的可解释性,同时减小了源荷数据匹配样本少对中长期源荷耦合场景生成的影响。