Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

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.

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.

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.

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。

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。

基于GA-BP神经网络的发动机失火诊断

为了提高神经网络在发动机失火故障诊断中的准确率,提出了GA-BP神经网络算法。分析了发动机故障时的尾气变化情况,提出了发动机故障诊断规则;分析了BP神经网络原理,指出其训练速度慢、容易陷入局部极值问题;使用遗传算法对神经网络结构和参数进行优化,得到最优网络结构,将优化后的模型参数作为初始值再次进行BP算法优化;将此算法与自适应动量BP神经网络进行对比,GA-BP神经网络不仅缩短了训练时间,而且故障诊断准确率也大大提高。

基于主成分分析的GA-BP模型在城市需水预测中的应用

针对城市需水预测模型中需水量影响因子多、影响因子之间普遍存在多重共线问题,以及BP神经网络收敛速度慢、易陷入局部最优等缺点,提出一种由主成分分析、遗传算法及BP神经网络三者相结合的改进预测模型。以泰州市为实例,建立以主成分分析筛选需水量主要影响因子,遗传算法优化BP网络连接权值和阈值的需水预测模型,预测结果与BP神经网络预测模型预测结果做对比。结果表明:改进预测模型对泰州市2003-2014年需水量预测的平均相对误差为0.564%,最大相对误差为1.681%,精度优于BP神经网络预测模型;改进预测模型预测值与实际泰州市需水量吻合良好且训练速度更快、预测精度更高,可作为需水预测的一种有效方法。

基于GA-BP神经网络的施工区域水质预测及预警模型研究

现有监测手段虽能实时反映水质状况,但无法对水质状况进行预测从而指导施工。基于此,以影响施工区域水质的因素为预测参数并基于监测数据,构建了GA-BP神经网络水质预测模型,并针对不同水质状况提出了应对措施。结合不同施工阶段高速公路施工区域水库水质状况,验证该预测方法和应对措施的有效性。结果表明:GA-BP神经网络模型不仅能准确有效地预测现场水质状况,而且还能基于水质预测结果和水质预警模型及早调整施工方案,减少工程建设对施工区域水环境的影响。

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。

基于GA-BP神经网络的交叉口短时交通流预测

交叉口车辆运行效率不高主要由两个原因造成:一是目前很多交叉口交通信号的配时与实际交通流不匹配;二是当前短时交通流预测的时长集中在5~15min,过长的时间间隔无法给信号控制提供准确的数据支撑。为了改善这种状况,提出了一种基于优化的BP(Back Propagation)神经网络的交叉口短时交通流预测方法。首先针对流量预测提出了准实时的概念,其次引入BP神经网络,再利用遗传算法对BP神经网络进行优化,最后在优化的BP神经网络的基础上建立交叉口短时交通流预测模型,且将传统的预测时长从5min缩短到5s。利用南京市某一道路交叉口采集的数据对提出的预测方法进行验证,结果表明:与传统的BP神经网络以5min为预测时长相比,该预测方法以5s为预测时长能够将预测精度提高77%。