Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

Rheological behavior of hydrolyzed polyacrylamide solution flowing through a molecular weight adjusting device with porous medium

The separate-layer injection in different interlayers and the injection of the same-molecular-weight polymer solution in a layer are necessary in the polymer flooding process because of heterogeneous multilayer sandstone reservoirs in EOR projects.To alleviate the matching problems between the layer permeability and the injected polymer molecular weight,a molecular weight adjusting device with porous medium was designed on the basis of mechanical degradation principle.In terms of four variables(polymer concentration,pore diameter,length of shear component and flow rate),the rheological behavior of hydrolyzed polyacrylamide(HPAM)solution flowing through the device was investigated in detail.The change of these variables is able to control the shear rate of HPAM solutions through ceramic foam,and achieve the desired degree of shear degradation and the final rheological parameters—viscosity loss,viscoelasticity and pressure drop.Therefore,a linear relationship between viscosity loss and shearing rate was established so as to obtain the targeted viscosity easily.Field tests in the Daqing Oil Field showed that the polymer molecular weight could drop 20%to 50%.In a word,the results could guide the industrial application of the novel device and the further study of polymer degradation flowing through the porous medium.

STATISTICAL SPACE-TIME ADAPTIVE PROCESSING ALGORITHM

For the slowly changed environment-range-dependent non-homogeneity, a new statistical space-time adaptive processing algorithm is proposed, which uses the statistical methods, such as Bayes or likelihood criterion to estimate the approximative covariance matrix in the non-homogeneous condition. According to the statistical characteristics of the space-time snapshot data, via defining the aggregate snapshot data and corresponding events, the conditional probability of the space-time snapshot data which is the effective training data is given, then the weighting coefficients are obtained for the weighting method. The theory analysis indicates that the statistical methods of the Bayes and likelihood criterion for covariance matrix estimation are more reasonable than other methods that estimate the covariance matrix with the use of training data except the detected outliers. The last simulations attest that the proposed algorithms can estimate the covariance in the non-homogeneous condition exactly and have favorable characteristics.

面向类别不平衡语义分割的损失函数的研究

在图像语义分割的任务中,数据集的类别不平衡分布是一种普遍存在的现象;通过构造更加有效的损失函数来减轻其带来的不利影响是具有较大研究价值的。以损失函数为研究对象,利用训练样本像素的空间分布特性和相关性,改进了Focal损失函数的尺度度量方法,提出了一个基于迭代批次的权重自适应调整的Adapt-Focal损失函数。以类别不平衡的SAR图像数据集GDUT-Nansha为实验对象,采用三个经典的卷积神经网络DeepLabV3+、U-Net和RWL-ENet完成了定量实验。实验结果表明,所提出的Adapt-Focal损失函数相比于CE、FL、dfl、acw、lovász和softiou损失函数,有效提高了少样本地物类别裸地和道路的分割精度IoU和PA值;同时,整体分割精度指标mPA和mIoU均得到较大幅度的提高。验证了Adapt-Focal损失函数在类别不平衡图像语义分割中的有效性。

SLAM精度的向量加权平均自适应调节研究

针对粒子滤波算法需要大量粒子以提高精度以及重采样导致的粒子多样性缺失的问题,提出一种自调INFO(向量加权平均算法)优化的粒子重组粒子滤波算法。首先,通过向量不同的加权平均规则,使得最优粒子引导粒子集向期望区域移动,以此提高估计精度;其次,实时计算最优粒子附近的粒子密度,当密度大于阈值时,自适应调整迭代次数,实时监测粒子密度,根据此指标引入次优粒子的作用自适应调整粒子集分布;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。通过仿真实验检验改进算法在SLAM中的性能,结果表明该算法较标准算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

三端口柔性多状态开关多模式平滑切换控制策略

光伏整县推进和电动汽车快速发展造成配电网末端电压偏高等电能质量问题,基于区域柔性互联的柔性多状态开关(flexible multi-state switch,FMSS)技术成为解决方案之一。针对柔性多状态开关PQ模式与VF模式间切换的波动及冲击性问题,提出在状态跟随控制器之后添加惯性环节,并改进相位预同步环节,可实现FMSS并离网工作模式的平滑切换。针对FMSS直流母线控制端的馈线故障导致系统运行崩溃问题,提出一种引入权值调节的控制策略,可实现系统稳定运行情况下,有功控制和直流母线电压控制的平滑切换。在MATLAB/Simulink环境下搭建了三端口FMSS仿真模型,对提出的控制策略进行了仿真验证,仿真结果实现了任意馈线故障下三端口FMSS无缝转供电功能。最后搭建物理实验平台,进一步验证了所提平滑切换策略的有效性。

负载横移扰动下人体步态频率变化特性

为了研究人体行走时适应负载横向动态干扰的机制,从而设计可靠的穿戴式机器人辅助策略,提出采用基于双自由度受迫振动模型构建人-负载耦合系统。基于机械振动原理分析人-负载耦合系统特性,给出不同行走频率对人和负载位移的影响,提出人体通过调节行走频率适应负载横移扰动影响的假设;搭建负载横向动态干扰人体行走实验平台,获得实验对象无负重行走以及负载横向扰动下的肌肉协同特征及腿部激励频率,并与仿真结果进行对比分析。实验结果表明:负载横移扰动下人体行走是一个适应性调整并最后进入动态稳定的过程。其中,在负重情况下的人体上肢躯干摆动幅度比无负重情况下摆动增幅大60%,人体通过调整行走频率以及相关肌群肌肉协同策略来减少负载横向动态干扰带来的上肢躯干摆动影响。在负重情况下,不同行走速度的步态频率均趋向于人-负载耦合系统的固有频率,设定常速频率与固有频率相同,慢速下行走到达稳态时腿部激励频率上升4.30%,快速下腿部激励频率下降2.71%,负重下的行走控制策略与无负重下的行走控制策略相比发生了改变,以适应负载横移扰动带来的影响。

60岁及以上人群体重调整腰围指数与低eGFR的相关性研究

目的探讨60岁及以上人群体重调整腰围指数(Weight-adjusted-waist index,WWI)与低估算肾小球滤过率(Estimated glomerular filtration rate,eGFR)之间的关系,为早期预防老年人肾功能损害提供科学参考。方法基于“新疆多民族自然人群队列建设及健康随访研究”的基线调查数据,通过腰围(cm)除以体重(kg)的平方根计算WWI。采用Logistic回归模型分析WWI与低eGFR之间的相关性,并采用限制性立方样条(RCS)回归函数来探索WWI水平与低eGFR之间的非线性剂量反应关系。结果共纳入10987例研究对象,低eGFR的总检出率为8.9%。调整相关因素后,Logistic回归结果显示,WWI与低eGFR有相关性(模型2:OR=1.085,95%CI:1.018~1.157,P=0.012;模型3:OR=1.098,95%CI:1.027~1.173,P=0.006)。使用限制性立方样条发现WWI和低eGFR之间存在非线性联系(P<0.05)。WWI和低eGFR发生风险拐点的参考值为11.316。当WWI>11.316时,WWI每增加一个单位,低eGFR的发生风险增加0.281倍(OR=1.281,95%CI:1.123~1.462)。结论在60岁及以上人群中WWI与低eGFR存在相关性,并且当人群WWI>11.316时,随着WWI的增加低eGFR的发生风险更大,该人群应当关注WWI的水平,这对其慢性肾功能损害的早发现早诊断早治疗有积极作用。

规模以下工业企业抽样调查的权数调整研究

为解决规模以下工业企业估计中的问题,对现有的权数调整范围进行了拓展,以期提高对规模以下工业企业的估计精度。一方面,解决目录企业的非自然消亡问题。分别讨论样本单元非自然消亡和样本层非自然消亡两种情况,将非自然消亡视为一种单元无回答,引入样本匹配方法,选择最为“相近”的正常上报企业与非自然消亡企业匹配,把非自然消亡的样本企业权数调整到正常上报的样本企业中。另一方面,解决非目录企业的估计偏差问题。分别讨论了基于超总体模型估计和倾向得分逆加权估计的权数调整思路,超总体模型估计选取了线性和非线性两种。倾向得分逆加权估计中,重点研究了倾向得分的求解,基于GBM(Generalized Boosted Model)算法,在其迭代求解过程中引入了权重,提出了w-GBM算法,同时提出将参数估计方法中的Logistic回归估计和非参数估计方法中的w-GBM算法或GBM算法进行加权的组合估计方法。实证结果表明,以上思路具有可行性。

一种自适应调整权重向量的多目标进化算法

基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)作为一种重要的多目标优化方法,已经成功地应用于解决各种多目标优化问题。然而,MOEA/D算法在解决具有高维目标和复杂帕累托前沿(Pareto frontier,PF)的问题时,容易陷入局部最优并难以获得可行解。本文提出一种改进的MOEA/D算法,包括3个优化策略:首先,使用拉丁超立方抽样方法代替随机方法初始化种群,得到分布均匀的初始种群,同时对权重向量关联解的策略进行优化;其次,提出一种稀疏度函数,用于计算种群中个体的稀疏度并维护外部种群;最后,提出了自适应调整权向量的方法,用于引导种群收敛到帕累托前沿,并且有效平衡种群的多样性和收敛性。将提出算法和4种对比算法在DTLZ和WFG系列问题以及多目标旅行商问题(multi-objective travel salesman problem,MOTSP)上进行对比实验,实验结果表明本文提出自适应调整权重向量的多目标进化(MOEA/D with cosine similarity adaptive weight adjustment,MOEA/D-CSAW)算法在处理具有复杂帕累托前沿和高维多目标的问题时,算法的综合性能要优于对比算法。

抗差Helmert分量估计在地铁CPⅢ控制网平差中的应用

为使CPⅢ控制网数据定权合理、提高平差成果精度,并具有抗粗差特性,论文提出采用结合Huber的Helmert抗差分量估计定权方法对CPⅢ控制网中的水平角、竖直角和斜距三类观测值进行合理定权平差,以福州市轨道交通4号线工程西门站至东街口站区间右线CPⅢ控制网测量项目为例,验证了该方法的定权合理性和可靠性,并可获得点位偏差小、精度可靠且有抗粗差能力的平差成果。

动态调整语义的词性加权多模态情感分析模型

为更好地利用单词词性包含的语义信息和伴随单词出现时的非自然语言上下文信息,提出动态调整语义的词性加权多模态情感分析(part of speech weighted multi-modal sentiment analysis model with dynamic semantics adjustment,PW-DS)模型.该模型以自然语言为主体,分别使用基于Transformer的双向编码器表示(bidirectional encoder representation from Transformers,BERT)模型、广义自回归预训练(generalized autoregressive pretraining for language understanding,XLNet)模型和一种鲁棒优化的BERT预训练(robustly optimized BERT pretraining approach,RoBERTa)模型为文本模态做词嵌入编码;创建动态调整语义模块将自然语言和非自然语言信息有效结合;设计词性加权模块,提取单词词性并赋权以优化情感判别.与张量融合网络和低秩多模态融合等当前先进模型的对比实验结果表明,PW-DS模型在公共数据集CMU-MOSI和CMU-MOSEI上的平均绝对误差分别达到了0.607和0.510,二分类准确率分别为89.02%和86.93%,优于对比模型.通过消融实验分析了不同模块对模型效果的影响,验证了模型的有效性.

顾及起算点误差的区域参考框架约束方法研究

利用Helmert转换模型,首先对起算点先验坐标可能存在的粗差进行探测并剔除,其次依据剩余起算点坐标的相似变换构建最小约束条件,最后实现整网坐标框架的确定。选取中国境内及周边IGS站和陆态网GNSS基准站单周观测数据,采用IGS站ITRF2014坐标分别构建参数加权约束、最小约束、顾及IGS站先验坐标误差的Helmert模型约束,然后利用不同的基准约束平差方法获取整网坐标成果。实验结果表明,利用Helmert模型能有效削弱起算点坐标粗差对网形扭曲的影响,在一定程度上可提高区域参考框架的精度和可靠性。

5G波束动态调度应对用户分布潮汐效应

现网中存在较多潮汐特性的场景,用户在特定的时间范围内规律性迁移和聚集,但现有5G基站以固定的波束配置覆盖,无法随业务和人员的聚集而变化,在该场景下最优波束覆盖范围与用户位置存在不匹配的情况,也无法发挥5G有源天线单元(Active Antenna Unit,AAU)能够波束赋形的优势。此类场景的网络优化存在人工无法计算最优波束权值、突发情况网络覆盖应对慢以及人工上塔调整天线成本高等痛点。针对这些痛点,提出波束动态调度方案,通过大数据计算和蚁群算法的应用,实现自动波束寻优,智能识别潮汐场景,自适应调整AAU的广播权值,使同步信号块(Synchronization Signal/PBCH Block,SSB)广播权值始终指向用户集中的区域,从而达到最优的覆盖效果和天线增益。同时,方案在基站突发故障、人员突发聚集等场景也能发挥重要作用,实现5G网络应用发展和用户感知提升的双重目标。

大数据平台应用场景成熟度评价模型与方法研究

大数据平台应用场景展示了大数据平台在各个领域的广泛适用性和对提高效率、优化决策的重要作用,对大数据平台应用场景成熟度进行评价研究,有助于更好地研判应用场景所处的状态或发展阶段,提升大数据平台应用场景建设水平。本文研究构建了大数据平台应用场景成熟度评价模型,包括7个一级指标和29个二级指标,设计了权重可调整的评价计算方法,并以某省工业大数据平台应用场景成熟度评价为例,对模型进行了应用验证。实践证明,该评价模型可快速识别大数据平台应用场景能力的短板,为平台应用场景能力提升提供科学指导。

基于模糊一致性调整算法的推荐系统评估研究

考虑到一致性调整和排序权重向量确定方法是犹豫模糊语言群决策中的2个重要课题,构造基于一致性调整算法的犹豫模糊语言群决策模型。具体来说,该模型首先运用对数最小二乘法计算犹豫模糊语言偏好关系(hesitant fuzzy linguistic preference relation,HFLPR)的排序权重向量;其次,将一致性指数最小的语言偏好关系对应的排序权重向量作为HFLPR的序权重向量;随后,基于一致性调整算法建立了一种收敛的犹豫模糊语言群决策模型。该模型既没有改变原始犹豫模糊语言术语集,又可以尽可能地避免信息的损失;最后,将建立的模型应用于推荐系统的优选过程。实验表明,构建的群决策算法更合理有效。

无人机构架航线设计在地籍测量中的应用研究

针对传统地籍测量方法效率低、成本高、测量周期长等问题,本文探讨了一种基于无人机摄影测量技术的地籍测量方法。首先,在航线规划时,采取了一种构架航线设计方案,提升影像之间的系统关联度,使影像匹配更加充分;其次,在空中三角测量过程,为高效发挥差分无人机定位优势,考虑采用最佳摄站坐标权值下的辅助光束法平差策略;最后,将生产的数字地表模型(DSM)应用于地籍图矢量采集当中,并开展相应的精度分析。经实验验证,本文的无人机构架航线设计方法能协助完成任务区完整的地籍测量任务,在确定最佳摄站坐标权值后,无需野外控制点即可满足1∶500比例尺地籍图生产精度要求,大大提高了测量效率,具有较强的工程应用价值。

基于角度轨迹和自适应权重的改进MOEA/D算法

基于分解的多目标优化算法是解决多目标优化问题的有效方法,其主要思想是通过一组均匀分布的权重向量对目标进行加权,形成不同的子问题,在其邻域内通过协同进化得到帕累托最优解集。然而,这些均匀分布的权重向量不能很好地适应所有前沿形状的多目标问题,导致解集的多样性下降。为使算法能够高效地判断前沿类型,并有效地调整权重向量,本文提出了基于角度轨迹和自适应权重的改进MOEA/D算法,该算法通过基于角度的聚类方法,降低聚类时间复杂度,并利用聚类-合并策略,提高对前沿划分的准确性。在根据聚类结果判断出前沿类型后,算法根据其前沿类型,采用根据角度划分区域的分配策略,或是根据前沿分段长度和数量分配。这样可以有效地分配计算资源,提高多目标问题求解的效率,通过实验证明,算法的分配策略行之有效,能提高尖峰、长尾和不连续前沿的解集多样性,在收敛性和多样性上均优于对比算法。

Bariatric surgery-mediated weight loss and its metabolic consequences for type-2 diabetes

The worldwide epidemic of obesity and its medical complications are being dealt with a combination of life style changes(e.g.,healthier diet and exercise),medications and a variety of surgical interventions.The Roux-en Y gastric bypass(RYGB) and laparoscopic adjustable gastric banding(LAGB) are two of the most common weight loss surgeries for morbid obesityassociated metabolic syndrome and insulin resistance.A vast majority of patients that undergo RYGB and LAGB are known to experience marked weight loss and attenuation of diabetes.A number of recent studies have indicated that the rates of remission in glycemic control and insulin sensitivity are significantly greater in patients that have undergone RYGB.A plausible hypothesis to explain this observation is that the gastric bypass surgery as opposed to the gastric banding procedure impinges on glucose homeostasis by a weight loss-independent mechanism.In a recent paper,Bradley et al have experimentally explored this hypothesis.The authors compared several clinical and laboratory parameters of insulin sensitivity and β-cell function in cohorts of RYGB and LAGB patients before and after they lost approximately 20% of their body mass.Afterweight loss,both groups of patients underwent similar changes in their intra-abdominal and total adipose tissue volume,hepatic triglyceride and circulating leptin levels.The RYGB patients who lost 20% body mass,manifested higher postprandial output of glucose,insulin and glucagon-like peptide-1;these laboratory parameters remained unchanged in LABG patients.Irrespective of the observed differences in transient responses of RYGB and LAGB patients to mixed meal,the overall glycemic control as judged by glucose tolerance,multi-organ insulin sensitivity and β-cell function were nearly identical in the two groups.Both RYGB and LAGB patient cohorts also experienced similar changes in the expression of a number of pro-and anti-inflammatory markers.Based on these analyses,Bradley et al concluded that similar restoration of insulin sensitivity and b-cell function in non-diabetic obese patients that have undergone RYGB and LAGB were directly due to marked weight loss.These data have important implications for the risk/benefit analysis of weight loss therapy by bariatric procedures.

Real-Time Update of Sequence Placement Logic for High Arch Dams Based on Evidence Weight Discount

Sequence placement logic plays a significant role in construction simulation of high arch dams and directly affects the simulation process and results.To establish a sequence logic for dam block placement,the construction scheme,real-time construction process,and random factors of the site all need to be considered in detail.There are few studies available currently that take all these factors into consideration.To address this problem,a real-time update of sequence placement logic for high arch dams based on evidence weight discount is proposed in this study.First,the subjective weight of the dam block sequence priority criteria is built using a consistent matrix method based on the construction scheme.Second,using evidence theory,dynamic objective weight of the priority criteria and basic probability assignment is built.Finally,using a weight self-adaptive adjustment method and comprehensive evidence discounting,the placing probabilities of different dam blocks are obtained.A case study indicates that this method can realize realtime update of sequence placement logic.