采用动态相关度权重的特征选择算法

基于互信息的特征选择算法在考虑候选特征提供的新分类信息时,通常忽略了候选特征的加入会使得已选特征和类标签的相关性发生变化而带来额外的新增信息量,以及在计算冗余信息时采用累加求和的形式可能导致低估候选特征的冗余程度。针对以上问题,提出动态相关度权重的定义,以更全面地考虑候选特征带来的新信息量成分;提出改进冗余项的定义,采用取最大值和归一化策略,以解决传统算法存在的低估冗余问题;在此基础上提出一种采用动态相关度权重的特征选择算法(feature selection using dynamic relevance weight,FSDRW)。选取五种当前主流的基于互信息的过滤式特征选择算法进行对比实验,在来自加州大学尔湾分校UCI和亚利桑那州立大学ASU的机器学习测试数据集上的实验表明,所提出的算法在分类准确率及综合性能方面具有较好的表现。最后将所提出算法应用于广西某水库工程的微震、爆破信号识别中,算法选取出的特征用于微震信号识别可达到98.86%的分类准确率,验证了算法在实际应用中的有效性。

抑制差异与错误信息持续影响效应的关系:信息相关度的作用

为考察个体抑制控制能力与错误信息持续影响效应的关系以及信息相关度在其中的影响,135名年龄范围在18~25岁的被试通过大学校园广告招募自愿参加了实验。研究采用直接衡量个体抑制能力的实验任务对被试进行了分组,同时在错误信息持续影响效应的范式中进一步区分了信息的不同相关度。结果发现,个体抑制能力的差异导致了错误信息更正的差异,高抑制能力组能够较好地更正之前的错误信息,而低抑制能力组的更正效果随信息相关度的不同而变化。结果提示抑制过程的失败也取决于信息相关度的差异。本研究结果为错误信息持续影响效应的提取失败解释提供了更为直接的实验依据并细化了已有的理论解释,为教育教学中知识的更正过程提供了支持与借鉴。

基于S变换相关度的配电网单相接地自适应有源消弧法

提出了基于S变换相关度的配电网单向接地自适应有源消弧法,有效消除了配电网单相接地故障电弧,提升了电力系统稳定性。采集配电网线路零序电流时序数据,经S变换获取零序电流时频信息矩阵后,计算并获取各线路间时频信息矩阵相关度矩阵,输入卷积神经网络模型,输出单相接地故障选线结果;在故障线路采用基于模糊准PR与PI控制的双闭环自适应控制方法,向配电网故障线路中性点注入零序电流,有效控制注入电流相位以及幅值,迫使配电网故障线路所在相的电压值为0,完成自适应有源消弧。实验结果表明:该方法可有效识别配电网单相接地故障线路,实现配电网单相接地自适应有源消弧,且可尽快完成单相接地自适应有源消弧工作,保证配电网安全稳定运行。

Alignment Techniques in Total Knee Arthroplasty:Where do We Stand Today?

Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral postoperative joint alignment. However, contemporary approaches, such as kinematic alignments and hybrid techniques including adjusted mechanical, restricted kinematic, inverse kinematic, and functional alignments, are gaining attention for their ability to restore native joint kinematics and anatomical alignment, potentially leading to enhanced functional outcomes and greater patient satisfaction. The ongoing debate on optimal alignment strategies considers the following factors: long-term implant durability, functional improvement, and resolution of individual anatomical variations. Furthermore, advancements of computer-navigated and robotic-assisted surgery have augmented the precision in implant positioning and objective measurements of soft tissue balance. Despite ongoing debates on balancing implant longevity and functional outcomes, there is an increasing advocacy for personalized alignment strategies that are tailored to individual anatomical variations. This review evaluates the spectrum of various alignment techniques in TKA, including mechanical alignment, patient-specific kinematic approaches, and emerging hybrid methods. Each technique is scrutinized based on its fundamental principles, procedural techniques, inherent advantages, and potential limitations, while identifying significant clinical gaps that underscore the need for further investigation.

Joint alignment and steering to manage interference

In wireless communication networks,mobile users in overlapping areas may experience severe interference,therefore,designing effective Interference Management(IM)methods is crucial to improving network performance.However,when managing multiple disturbances from the same source,it may not be feasible to use existing IM methods such as Interference Alignment(IA)and Interference Steering(IS)exclusively.It is because with IA,the aligned interference becomes indistinguishable at its desired Receiver(Rx)under the cost constraint of Degrees-of-Freedom(DoF),while with IS,more transmit power will be consumed in the direct and repeated application of IS to each interference.To remedy these deficiencies,Interference Alignment Steering(IAS)is proposed by incorporating IA and IS and exploiting their advantages in IM.With IAS,the interfering Transmitter(Tx)first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx,and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference.Moreover,two improved versions of IAS,i.e.,IAS with Full Adjustment at the Interfering Tx(IAS-FAIT)and Interference Steering and Alignment(ISA),are presented.The former considers the influence of IA on the interfering user-pair's performance.The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components,thus ensuring the Spectral Efficiency(SE)of the interfering communication pairs.Under ISA,the power cost for IS at the interfered Tx is minimized,hence improving SE performance of the interfered communication-pairs.Since the proposed methods are realized at the interfering and interfered Txs cooperatively,the expenses of IM are shared by both communication-pairs.Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

计及源荷相关度类线性紧缩法的配电网有功无功协同优化

随着分布式电源的高比例渗透,主动配电网的稳定性受到很大影响,因此主动配电网有功-无功协同优化至关重要。对此,提出一种计及源荷相关度类线性紧缩法(Quasi-Linear Contraction,QLC)的配电网有功无功协同优化方法。首先,基于相关性分析方法分析新能源出力以及负荷的相关度,给出源荷相关度概率约束,完善系统优化模型。然后,针对优化模型非凸非线性容易导致局部最优解问题,采用泰勒展开式求解分支电流的上下界并代入节点电压和功率流方程,完成主动配电网模型的类线性紧缩,再采用罚函数方法校正,当得出的解在缩小后的约束范围内时该解为实际最优解,当得出的解在紧缩的约束边界上时,在满足原有约束范围的前提下向外寻优,得到实际最优解。最后,通过Yalmip等仿真计算软件验证所提方法的稳定性、准确性以及有效性。

归一化Alignment距离

为了更好地度量不同序列之间的差异,本文在给定度量空间上的符号距离满足特定条件的前提下,以Alignment距离为基础,提出了一种归一化Alignment距离.接下来利用Alignment相关度证明了该距离满足度量定义的三个条件,并且取值在[0,1]区间上.最后对其进行了进一步讨论,从理论上说明该定义的合理性.该距离可以在序列比对、聚类分析以及模式识别等领域中发挥重要作用.

考虑属性相关度的大数据随机游走抽样仿真

大数据通常是非随机的,且大数据量下可能存在抽样偏倚的问题,部分群体可能被过采样或欠采样,从而导致结果准确性较低。为此,提出基于属性相关度的大数据随机游走抽样算法。获取大数据邻域关系矩阵,根据排序思想得到大数据单属性邻域关系矩阵。计算不同大数据属性的邻域关系矩阵,计算数据属性的相关度,得到大数据属性约简结果。采用区间密度相似性调整区间,建立可变网格空间,将网格空间和密度偏差抽样算法有效结合,完成大数据随机游走抽样。仿真实验分析表明,所提算法可以大幅度提升样本质量,且能耗明显更低一些,最高仅为280Wh,获取更加精准的大数据随机游走抽样结果。

On The Cauchy Problem for A 1D Euler-Alignment System in Besov Spaces

In this paper, we investigate a 1D pressureless Euler-alignment system with a non-local alignment term, describing a kind of self-organizing problem for flocking. As a result, by the transport equation theory and Lagrange coordinate transformation, the local well-posedness of the solutions for the 1D pressureless Euler-alignment in Besov spaces with 1≤p∞ is established. Next, the ill-posedness of the solutions for this model in Besov spaces with 1≤p and is also deduced. Finally, the precise blow-up criteria of the solutions for this system is presented in Besov spaces with 1≤p .

Initial Alignment Technique for SINS of Vehicles in the Moving State

An initial alignment technique for the strapdown inertial navigation system (SINS) of vehicles in the moving state is researched. By selecting an odometer as the system’s external sensor, the mathematical model for the alignment in the moving state is established and the observability of the system is analyzed. The results show that the SINS can successfully achieve the precision alignment in 10 min when the vehicle is moving toward the prearranged place after its staying for several seconds to perform the coarse alignment. The precision of alignment can also be improved in the moving state compared with that in the static state.

由一般拓扑度量空间所产生的Alignment空间

Alignment空间是一个在广义误差下定义的度量空间。在以往的信息处理问题中,一般只讨论离散状态下的序列比对Alignment问题,并由此产生一种新的非线性度量空间-Alignment空间。本文将离散状态下的Alignment空间推广到一般情况,得到了由一般拓扑度量空间所产生的Alignment空间仍然是度量空间,并证明了Alignment距离与Levenshtein距离的等价性。

语义查询扩展中词语-概念相关度的计算

在基于语义的查询扩展中,为了找到描述查询需求语义的相关概念,词语.概念相关度的计算是语义查询扩展中的关键一步.针对词语.概念相关度的计算,提出一种K2CM(keyword to concept method)方法.K2CM方法从词语.文档.概念所属程度和词语.概念共现程度两个方面来计算词语.概念相关度问语.文档.概念所属程度来源于标注的文档集中词语对概念的所属关系,即词语出现在若干文档中而文档被标注了若干概念.词语.概念共现程度是在词语概念对的共现性基础上增加了词语概念对的文本距离和文档分布特征的考虑.3种不同类型数据集上的语义检索实验结果表明,与传统方法相比,基于K2CM的语义查询扩展可以提高查询效果.

维基百科的中文语义相关词获取及相关度分析计算

为了研究在线百科全书维基百科在语义相关词抽取、语义相关度计算等方面的应用,从中文维基百科下载并整理了5万余篇解释页面文档,抽取了反映语义关系的站内超级链接网络,通过挖掘文档间相互引用的链接关系抽取了近40万对语义相关词,并研究了语义相关词之间的主题聚类特性,选择两两相关的多个词得到相关词集合并计算了平均语义相关度.相关度计算综合利用了链接在文档中的位置、频率等信息,通过对比经典算法在同一测试集上的计算结果对算法进行了分析评估,证明了维基百科语义相关词抽取方法的有效性.

基于中文维基百科链接结构与分类体系的语义相关度计算

自然语言词汇的语义相关度的计算需要获取大量的背景知识,而维基百科是当前规模最大的百科全书,其不仅是一个规模巨大的语料库,而且还是一个包含了大量人类背景知识和语义关系的知识库,研究表明,其是进行语义计算的理想资源.本文提出了一种将维基百科的链接结构和分类体系相结合计算中文词汇语义相关度的算法,算法只利用了维基百科的链接结构和分类体系,无需进行复杂的文本处理,计算所需的开销较小.在多个人工评测的数据集上的实验结果显示,获得了比单独使用链接结构或分类体系的算法更好的效果,在最好的情况下,Spearman相关系数提高了30.96%.

基于知网的语义相关度计算

为解决句法分析中的结构性歧义,引入了语义相关度计算.基于语义相似度计算及知网的语义信息,提出了语义相关度计算方法;利用知网的义原纵向与横向关系及实例信息计算不同词性的相关度.在计算义原距离时,考虑了义原之间的解释关系,对义原的距离进行修正.根据相似度的对称性,计算实例的影响因素提高了相关度的准确率.实验结果表明,使用该计算方法得出的语义相关度结果更加合理.

基于地理空间本体的语义检索相关度研究

以地理信息领域为应用背景,面向地理空间语义检索,基于地球信息科学中的空间拓扑理论,以空间本体为语义检索的概念空间,提出了一种语义相关度的算法。其特点是考虑了传统字面匹配相关度与语义关系相关度两部分的融合,同时引入了本体关系权值的机制控制在不同语义检索应用中本体的关联程度,并体现了其与语义距离的反比关系。通过所作的相关实验,验证了该语义相关度算法在地理空间语义检索应用中可以达到良好的效果,并且也为其他领域应用提供了较好的参考和借鉴价值。