State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters

This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping parameters.The addressed neural networks have a finite number of modes,and the modes may jump from one to another according to a Markov process.By construction of a suitable Lyapunov-Krasovskii functional,a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square.The criterion is formulated in terms of a set of linear matrix inequalities（LMIs）,which can be checked efficiently by use of some standard numerical packages.

Study on Decision Method of Neutral Point Grounding Mode for Medium-Voltage Distribution Network

The neutral grounding mode of medium-voltage distribution network decides the reliability, overvoltage, relay protection and electrical safety. Therefore, a comprehensive consideration of the reliability, safety and economy is particularly important for the decision of neutral grounding mode. This paper proposes a new decision method of neutral point grounding mode for mediumvoltage distribution network. The objective function is constructed for the decision according the life cycle cost. The reliability of the neutral point grounding mode is taken into account through treating the outage cost as an operating cost. The safety condition of the neutral point grounding mode is preserved as the constraint condition of decision models, so the decision method can generate the most economical and reliable scheme of neutral point grounding mode within a safe limit. The example is used to verify the feasibility and effectiveness of the decision method.

The improved neutral network and its application for valuing rock mass mechanical parameter

The artificial neutral network(ANN) has the ability that self-study and self-remember, its 3 layers BP network has been applied extensively, but sometimes because of serious multi-correlation between the variables, and a few observations while many variables, there usually will result into paralyzing in study, and the neutral network further development is restricted in the system to some extent. The partial least square regression(PLS) has its advantage of building the calculation model between the variables with strong multi-correlation, especially much effective on a few data and many variables. So a new and effective method-improved neutral network has been introduced-the neutral network based on the PLS. The results of example show the improved method has a few calculations and high accuracy, and provide a new way for valuing the rock mass mechanical parameters.

Parasite-host network analysis provides insights into the evolution of two mistletoe lineages(Loranthaceae and Santalaceae)

Mistletoes are ecologically important parasitic plants,with> 1600 species from five lineages worldwide.Mistletoe lineages exhibit distinct patterns of species diversification and host specificity,however,the mechanisms underlying these differences are poorly understood.In this study,we analysed a comprehensive parasite-host network,including 280 host species from 60 families and 22 mistletoe species from two lineages(Santalaceae and Loranthaceae) in Xishuangbanna,located in a biodiversity hotspot of tropical Asia.We identified the factors that predict the infection strength of mistletoes.We also detected host specificity and the phylogenetic signal of mistletoes and their hosts.We found that this interaction network could be largely explained by a model based on the relative abundance of species.Host infection was positively correlated with diameter at breast height and tree coverage,but negatively correlated with wood density.Overall,closely related mistletoe species tend to interact more often with similar hosts.However,the two lineages showed a significantly different network pattern.Rates of host generality were higher in Loranthaceae than in Santalaceae,although neither lineage showed phylogenetic signal for host generality.This study demonstrates that the neutral interaction hypothesis provides suitable predictions of the mistletoe-host interaction network,and mistletoe species show significant phylogenetic signals for their hosts.Our findings also indicate that high species diversification in Loranthaceae may be explained by high rates of host generality and the evolutionary history shared by Loranthaceae species with diverse host plants in the tropics.

Existence of Periodic Solutions for Neutral-Type Neural Networks with Delays on Time Scales

In this paper, we employ a fixed point theorem due to Krasnosel’skii to attain the existence of periodic solutions for neutral-type neural networks with delays on a periodic time scale. Some new sufficient conditions are established to show that there exists a unique periodic solution by the contraction mapping principle.

Almost sure exponential stability of neutral stochastic delayed cellular neural networks

In this paper, almost sure exponential stability of neutral delayed cellular neural networks which are in the noised environment is studied by decomposing the state space to sub-regions in view of the saturation linearity of output functions of neurons of the cellular neural networks. Some algebraic criteria are obtained and easily verified. Some examples are given to illustrate the correctness of the results obtained.

Periodic Solution for Neutral Type Neural Networks

The principle aim of this paper is to explore the existence of periodic solution of neural networks model with neutral delay. Sufficient and realistic conditions are obtained by means of an abstract continuous theorem of k-set contractive operator and some analysis technique.

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.

基于信号图像化和CNN-ResNet的配电网单相接地故障选线方法

配电网发生单相接地故障时,零序电流呈现较强的非线性与非平稳性,故障选线较为困难,针对此问题,提出一种基于信号图像化和卷积神经网络-残差网络的配电网单相接地故障选线方法。首先,利用排列熵优化变分模态分解算法的参数,将零序电流信号分解成一系列固有模态函数;其次,引入新的数据预处理方式,将固有模态函数转成二维图像,获得零序电流信号的时频特征图;最后,利用一维卷积神经网络提取零序电流信号的相关性和特征,利用残差网络提取时频特征图的特征,将两个网络融合,构建混合卷积神经网络结构,实现故障选线。仿真与实验结果表明,该方法能够在高阻接地、采样时间不同步、强噪声等情况下准确地选择出故障线路,可满足配电网对故障选线准确性和可靠性的需求。

“双碳”目标下黄河流域绿色技术创新效率评价及影响因素

基于“双碳”目标视角,采用超效率动态网络SBM模型测算黄河流域82市(盟)2011—2021年绿色技术创新的综合效率、科技研发效率及成果转化效率,并利用面板Tobit回归模型检验外部环境因素对三种效率的影响。结果表明:将CO_(2)排放量纳入指标体系后,中下游地区绿色技术创新综合效率得以提升,上游地区效率反而下降;黄河流域绿色技术创新综合效率呈波动上升趋势,科技研发效率起引擎作用;黄河流域绿色技术创新综合效率区域差异明显,成果转化效率差异是综合效率差异的主要来源;经济发展水平、产业结构高级化、人力资本禀赋、对外开放水平、政府支持力度和环境规制强度对三种效率的影响具有区域异质性。建议黄河流域提高绿色技术创新能力,完善创新成果转化平台,推动能源和产业结构升级。

国家中心城市交通碳排放效率的空间网络结构及动因研究

为科学把握城市交通碳排放效率的空间网络结构,实现交通运输业可持续发展,基于2011—2020年我国9个国家中心城市交通碳排放数据,构建考虑非期望产出的全局超效率SBM模型(GB-US-Super-SBM模型)并测算交通碳排放效率,利用修改的引力模型建立空间关联网络,在此基础上应用社会网络分析方法厘清交通碳排放效率空间网络结构及其动因。结果表明:1)研究期内,9个国家中心城市交通碳排放效率整体水平不高,城市间存在较大差距。2)国家中心城市交通碳排放效率的空间关联呈现网络结构形态,并逐渐形成了天津、西安、郑州等多个网络中心;空间网络关联性以2017年为节点呈现先增强后减弱的趋势;天津、西安、郑州等城市发挥着“桥梁”和“中介”作用,对空间网络的形成发挥了重要作用。3)经济发展水平差异、城镇化水平差异、节能技术水平差异和空间邻接关系等因素在交通碳排放效率的空间网络结构中发挥显著作用,其中空间邻接关系和经济发展水平差异的影响最显著。

基于电压补偿的双端直流配电网电压就地协调控制

双端直流配电网是一种双端电源供电的网络结构,可为直流负荷提供更加稳定和可靠的电源电压。然而,负荷的功率波动和不平衡使线路电压跌落增大,可能导致直流负荷的电压不平衡度和电压偏差指标越限,影响直流负荷的正常运行。文中基于电压源型换流器(VSC)外环电压控制,考虑中线电压补偿,提出基于电压补偿等效模型的直流配电网电压偏差及不平衡度联合抑制策略,实现直流配电网电压就地协调控制。首先,建立双端直流配电网潮流模型,将VSC电压下垂控制引入潮流模型,分析不同控制策略下的电压偏差和不平衡度的特性。其次,在此基础上,以电压最低点为分点获得双端电源供电回路压降的简化等效模型,并利用最小二乘法实现等效阻抗参数辨识,构建双端直流配电网的电压补偿等效模型。以参数辨识结果作为电压外环控制输入,提出考虑中线电压补偿的双端直流配电网电压就地协调控制策略。最后,在MATLAB/Simulink中搭建仿真模型,验证了双端直流配电网潮流模型的正确性和控制策略的有效性。

一类中立型时滞神经网络的稳定性分析

对一类时变时滞的中立型神经网络进行研究,首先利用同胚映射定理证明了系统平衡点的存在唯一性,其次通过构造适当的Lyapunov泛函并利用矩阵不等式的技巧得到了系统正平衡点全局渐近稳定的充分条件,最后通过2个数值算例来进一步验证所给条件的可行性和理论的正确性.

青少年网络霸凌刑法规制的不足及完善路径

青少年网络霸凌是当下的社会热点问题之一,它不仅违反了未成年人网络保护法律规范,同时也对清朗的网络生态环境产生滋扰。当前,对此类行为的刑法规制手段仍存在一些不足,例如,对涉关霸凌行为罪名的量刑区间设置不尽合理、尚未明确旁观者的入刑范围,以及对被霸凌者权利维护渠道不畅等。故而,建议从以下几个方面完善此类行为的刑法规制,一是合理调整罪名的量刑区间,二是划定旁观者可能的入罪范围,三是健全被霸凌者的权利救济体系。

配电网间歇性重燃电弧模型的建立与断续弧光接地故障特征分析研究

电弧间歇性重燃是配电网单相接地故障最显著的特征。现有的电弧模型甚少考虑电弧间歇性重燃特性,导致无法精确描述断续弧光接地特征,进而影响继电保护动作。为此,论文提出一种间歇性重燃电弧模型的建立方法,并在此基础上对断续弧光接地故障特征进行了分析。弧道阻抗的随机变化是电弧间歇性重燃的重要标志,故论文重点围绕弧道阻抗变化的随机性和重燃时间间隔的随机性开展间歇性重燃电弧模型的研究。黑盒电弧模型中,Cassie-Mayr联合模型能完整的描述电弧电流从大电流到小电流的变化过程,但存在从大电流变化为小电流的判据模糊,转换过程突变的问题。为此,论文通过引入连续过渡函数解决上述问题。同时,为描述弧道电阻的变化特性,利用Fermi函数对联合模型中Mayr模型和Cassie模型进行权重分配。以改进的Cassie-Mayr单次燃弧模型为基础,根据工频熄弧理论,通过设置燃弧时间长短表征间歇性重燃的随机性,从而建立了间歇性重燃电弧模型。利用该模型,对典型10kV配电网单辐射型网架结构的接地故障进行模拟仿真,采用快速傅里叶变换(fast Fourier transform,FFT)和小波包分析提取了不同条件下故障电压、电流、高次谐波、零序分量以及频率分布等故障特征。研究结果表明:改进后Cassie-Mayr联合模型不但解决了电弧电流从大电流到小电流的转换突变问题,且不同模型权重占比的分配更能准确地表征实际燃弧弧道阻抗变化的随机性;通过设置电弧燃弧时间长短,准确地描述间歇性重燃的随机性;电弧断续时刻为非整数周期下的过电压、过电流幅值高于整数周期;电缆线路增大了故障线路电流,过电流可达3.81~7.20pu,不利于熄弧;大电流系统故障相零序电流主频在0~400Hz,小电流系统故障相零序电流主频在1200~1600Hz。

基于边缘计算的低压配电网健康状态评价方法

分布式新能源在配网中的大规模接入是实现碳中和目标的必要过程,但由此引发的若干配网稳定性隐患问题需要重视。从庞大的配网数据中快速、有效地筛选出低压配电台区的薄弱环节,从而进行针对性的优化改造具有重要意义。文中提出了一种基于边缘计算的低压配电网健康状态评价方法,在众多指标中选取5+1项重要指标,包括5项基础指标以及1项数据合格率指标,随后对指标进行标准化处理;为提高评价方法的合理性,分别采用基于专家经验的序关系分析法和基于数据差异性的变异系数法,得到各个指标的主观权重和客观权重;通过拉格朗日最优乘子法对主观权重和客观权重进行优化,得到各个指标的综合权重与评价函数,并根据函数值得到低压配电网的健康状况进行最终评价结果。所提方法采用边缘计算的方式为通信和主站系统缓解计算压力,通过对淄博市的8个低压台区进行评价分析,结果表明所提方法更具有效性和客观性,有望在实际系统中实施。

基于网络流时空序列的加密流量分类

流量分类问题对于网络资源管理和安全非常重要。然而用户流量经常被加密处理,为流量分类问题带来极大的挑战。为此,提出一种新型的时间序列特征提取方法,用于解决加密应用程序流量分类问题。该方法通过分析数据包的空序列,提取加密网络流量的关键行为特征,并结合自注意力机制的长短时记忆网络来训练并对流量进行分类。为了评估方法的有效性,在公开网络数据集ISCXVPN2016上进行了详细的实验。结果表明,此方法能够显著提高识别加密应用程序流量的准确性和计算效率。

永磁同步电机BP神经网络 智能PID滑模观测矢量控制算法

针对永磁同步电机(PMSM)转速超调量大、转子位置检测精度低等问题,提出一种BP神经网络智能PID滑模观测器控制策略,将BP神经网络与传统PID控制相结合,利用BP神经网络实现对PID增益的在线调节,实现对永磁同步电机启动、突加负载干扰时稳定控制。采用无位置传感器控制,在永磁同步电机数学模型α-β坐标系中建立了滑模观测器结构,并且在Matlab/Simulink仿真系统中建立了仿真模型进行了仿真分析;从PID参数、电机转速等方面对BP神经网络智能PID控制的有效性进行了评估和仿真验证。通过仿真分析,采用滑模观测器检测转子实际位置与预期位置之间的误差小于7%,在0.3 s之后转子实际位置与预期位置完全重合。采用BP神经网络智能PID控制的永磁同步电机在启动时转速超调量减少了10.6%,在突加负载干扰时减少了1.4%。相比起传统PI控制,提出的BP神经网络智能PID控制能够有效提高PMSM的自适应性及抗干扰能力,并且显著减少了电机在启动及突加负载时超调量。

基于事件触发控制的中立型神经网络簇同步

研究了事件触发下带有Lévy噪声的中立型耦合神经网络的簇同步问题。首先,在混合时滞和不连续噪声的条件下,提出了一种事件触发机制与牵制控制方法结合的控制策略,应用于神经网络的簇同步中,以提高簇同步效率,缓解网络负担。然后,通过构建新的Lyapunov泛函,以及使用线性矩阵不等式(linear matrix inequalities,LMI)分析技术和广义Dynkin公式,得到误差系统的簇同步准则,进一步保证了耦合神经网络的簇同步。最后,通过一个数值仿真,验证了所得结论的可行性。

220kV变压器中压侧中性点经小电抗接地的运行分析

北京电网分区运行,其中枢纽站的接地方式关系整个电网运行,K分区中的A变电站采用220 kV变压器中压侧中性点经小电抗接地的运行方式。分析了该分区的主变中性点接地情况、A变电站的等值网络,同时计算了A变电站的短路容量、不同主变并列运行时的单相短路电流,计算表明,中性点经小电抗接地后可降低其单相短路电流,满足设备热稳定性要求。同时提出中性点小电抗投运后对现场运行、倒闸操作的要求。