An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

During tunnel boring machine(TBM)excavation,lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation.However,site investigation generally lacks ground samples and the information is subjective,heterogeneous,and imbalanced due to mixed ground conditions.In this study,an unsupervised(K-means)and synthetic minority oversampling technique(SMOTE)-guided light-gradient boosting machine(LightGBM)classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data.During the tunnel excavation,an earth pressure balance(EPB)TBM recorded 18 different operational parameters along with the three main tunnel lithologies.The proposed model is applied using Python low-code PyCaret library.Next,four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application.In addition,the Shapley additive explanation(SHAP)was implemented to avoid the model black box problem.The proposed model was evaluated using different metrics such as accuracy,F1 score,precision,recall,and receiver operating characteristics(ROC)curve to obtain a reasonable outcome for the minority class.It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM.The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Metallic Softness Influence on Magic Numbers of Clusters

The metallic softness parameterαr 0 determines the structure of the cluster and governs the rule of magic numbers. Using molecular dynamic method, the stable structures and magic numbers are determined for the clusters consisting of 13 up to 147 atoms in medium range Morse potentials, which is suitable for most of metals. As the number of atoms constituting the cluster increases, the stable structures undergo transition from face-centered (FC) to edge-centered (EC) structures. The magic number take ones of FC series before transition and take ones of EC series after that. The transition point from FC to EC structures depends on the value of softness parameter.

SOFT IMAGE SEGMENTATION BASED ON CENTER-FREE FUZZY CLUSTERING

Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new soft image segmentation method based on center-free fuzzy clustering is proposed.The center-free fuzzy clustering is the modified version of the classical fuzzy C-means ( FCM ) clustering.Different from traditional fuzzy clustering , the center-free fuzzy clustering does not need to calculate the cluster center , so it can be applied to pairwise relational data.In the proposed method , the mean-shift method is chosen for initial segmentation firstly , then the center-free clustering is used to merge regions and the final segmented images are obtained at last.Experimental results show that the proposed method is better than other image segmentation methods based on traditional clustering.

Disordered Multi-view Registration Method Based on the Soft Trimmed Deep Network

Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.

基于软聚类的深度图增强方法

针对现有的深度获取方式存在数据缺失、分辨率低等问题,提出一种基于软聚类的深度图增强方法,称为软聚类求解器.该方法利用软聚类的强边缘保持特性提高深度图增强的精度.将软聚类仿射矩阵和加权最小二乘模型有机结合,构建了软聚类求解器中的置信加权最小二乘模型,提出了基于迭代的求解方法.为评估所提出的方法,在多项深度图增强任务上进行试验,包括深度图补洞、深度图超分辨率和深度图纠正,评价指标包含了峰值信噪比(PSNR)、结构相似度(SSIM)、均方根差(RMSE)和运行效率.结果表明:文中方法在深度图补洞任务中的平均PSNR达到了42.28,平均SSIM达到了98.83%;在深度图超分辨率、深度图纠正任务中的平均RMSE达到了8.96、 2.36.文中方法处理1张分辨率为2 048×1 024像素的图像仅需5.03 s.

基于SC-XGBoost的电站燃煤低位发热量软测量方法

随着国家大力推进能源供给侧结构性改革,新能源装机容量不断提升,电力市场竞争愈加激烈。另一方面,全球煤炭市场的复杂多变,导致以煤炭为能量来源的发电企业成本上涨。燃煤发热量是衡量煤质的重要评价标准之一,也是采购煤炭最重要的依据,对燃煤发热量进行准确预测能够有效地控制电厂运行采购成本。为了实现燃煤发热量的高效预测,采用Pearson系数对相关变量进行特征选取,采用基于密度的噪点空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法对某电厂自备煤厂近2年1733条化验数据进行去噪,对去噪后数据进行谱聚类(Spectral Clustering,SC)分析。将分类后的子样本集采用极致梯度提升(Extreme Gradient Boosting,XGBoost)算法分别建立预测模型,并与最小二乘法回归(Ordinary Least Squares,OLS)、支持向量机(Support Vector Machines,SVM)模型进行性能比较。结果表明,基于XGBoost的电站燃煤发热量预测模型相较于其他算法准确性有明显提升,泛化能力更强。对经过SC算法分类后的燃煤分别建立预测模型能够进一步提高模型的精细化水平,为燃煤电站发热量预测提供一种可靠高效的方法。

多样性引导的深度多视图聚类算法

多视图聚类旨在从不同视图的多样性信息中,学习到更加全面和准确的共识表示,以提高模型的聚类性能.目前大部分多视图聚类算法采用希尔伯特-施密特独立性准则(HSIC)或自适应加权方法从全局考虑各视图的多样性,忽略了各视图样本之间的局部多样性信息学习.针对上述问题,提出了多样性引导的深度多视图聚类算法.首先,提出了融合多头自注意力机制的软聚类模块,多头自注意力机制用来学习全局多样性,软聚类模糊C均值算法用来学习局部多样性;其次,在深度图自编码器网络结构中引入软聚类模块,以达到多样性信息引导潜在表示生成的目的;然后,将得到的各视图潜在表示进行加权融合得到共识表示,并采用谱聚类算法对共识表示进行聚类;最后,在3个常用数据集上进行了对比实验和消融实验.实验结果表明,提出的聚类算法具有良好的聚类效果,以及提出的多样性信息学习模块可以有效提高算法聚类性能.

基于团簇微观结构分析的离子电活性聚合物驱动特性

首先,对离子交换膜吸附水分子微观过程进行分析,并结合团簇结构揭示离子电活性聚合物的传质动力学特性;其次,基于溶胀理论研究团簇受力情况,依据几何变形特点和变形传质机理建立物理模型;最后,对得到的驱动模型进行验证和分析讨论。研究结果表明:本文模型所得结果和实验结果较吻合。含水量对离子交换膜团簇通道的形成有重要影响,阳离子的迁移以及水分子运动是离子交换膜驱动的主控因素。随着阳离子浓度和水分子浓度增加,静水压力、渗透压力以及静电压力均逐渐增大,渗透压力对离子电活性聚合物弯曲变形起主导作用。

分布式稀疏软大间隔聚类

虽然软大间隔聚类(Soft large margin clustering,SLMC)相比其他诸如K-Means等算法具有更优的聚类性能与某种程度的可解释性,然而当面对大规模分布存储数据时,均遭遇了同样的可扩展瓶颈,其涉及的核矩阵计算需要高昂的时间代价。消减此代价的有效策略之一是采用随机Fourier特征变换逼近核函数,而逼近精度所依赖的特征维度常常过高,隐含着可能过拟合的风险。本文将稀疏性嵌入核SLMC,结合交替方向乘子法(Alternating direction method of multipliers,ADMM),给出了一个分布式稀疏软大间隔聚类算法(Distributed sparse SLMC,DS-SLMC)来克服可扩展问题,同时通过稀疏化获得更好的可解释性。

文化产业虚拟集群对我国省域网络文化软实力的影响

文化产业虚拟集群是文化企业成员基于数字技术,在信息共享与价值交换理念下形成的网络平台组织。数智时代,文化产业虚拟集群在数据要素效率倍增的中介作用下,能够借助展示平台、承载形式、表现方法、传播途径等实现网络文化软实力的提升。为此,以爱奇艺虚拟集群为例,选择空间杜宾双重差分模型佐证了上述观点。同时研究还发现,文化产业虚拟集群对周边省域网络文化软实力的提升存在“虹吸效应”,且相较于中西部地区,文化产业虚拟集群对东部地区的网络文化软实力影响更加显著。综上,我国应努力协调区域间的数字文化资源配置、改良文化软实力传播渠道、强化文化产品的精神属性与文化认同,从而赓续文化传承,实现我国文化软实力与影响力的进一步增强。

基于BCALoD的FPSO软刚臂系泊系统疲劳分析

针对软刚臂系泊系统铰节点在服役过程中出现的疲劳损伤问题,提出一种基于原型监测和局部密度双向聚类算法(Bidirectional Clustering Algorithm based on Local Density,BCALoD)的疲劳寿命计算方法。采用BCALoD算法对获得的船体六自由度进行工况分类,运用多体动力学将运动数据转算为受力时程,将其作为铰节点疲劳寿命分析的载荷谱。采用Abaqus软件建立各铰节点有限元模型以计算热点应力,结合Miner线性疲劳累积损伤理论和雨流计数方法计算疲劳寿命。进一步分析评估基于实测数据的铰节点疲劳设计指标,指出该FPSO软刚臂上铰节点的疲劳寿命不足以支持其完成服役,且各铰节点难以统一维护和更换。本研究可为在役软刚臂系泊系统的疲劳寿命计算提供一种新的载荷处理方法,为未来海洋平台的设计提供参考。

稀疏矩阵和改进归一化切割的快速多视图聚类

多视图聚类是一种新颖的聚类算法,它可以有效地探索出数据之间的内在聚类结构。大多数多视图聚类算法在构造相似图时容易受到噪声的影响,而且在聚类过程中还会面临信息损失问题,从而降低聚类结果的准确性。此外,现有多视图聚类算法通常使用交替迭代优化方法获得最优解,多次迭代会导致内存溢出或耗时过长。为了解决上述问题,提出了一种基于稀疏矩阵和改进归一化切割的快速多视图聚类算法(SINFMC)。该算法根据原始数据构造每个视图的相似图,并对相似图进行融合得到共识图矩阵。对共识图矩阵进行l1范数约束获得稀疏矩阵,实现数据降噪和加速计算。使用改进的归一化谱聚类算法对稀疏的共识图进行聚类得到聚类指标矩阵,这样不仅能够直接获得聚类结果,而且消除了聚类过程中的信息损失和偏差。该聚类算法无需交替迭代优化且通过稀疏矩阵表示精简计算过程,大幅降低了算法的时间和空间复杂度。人工和真实数据集上的比较实验结果表明该算法在质量和效率方面优于对比算法。

基于聚类分析的石油储层中含油性识别方法

储层含油性预测是油藏描述的重要内容之一。用聚类分析方法对测井资料进行油气、水层识别的思路。油储层的预测即是在井位处获取测井数据,进行储层含油性识别,从而得到储层在一个井位的精确认识,可以使得识别更加客观,进而得到对储层的完整认识,而选择一个合适的算法进行属性提取,可以提高方法的准确性和高效性。所以本文将重点研究,基于K-Means聚类分析的石油储层中含油性识别。

超柔浓染仿HOY 83 dtex/144 f涤纶全拉伸丝的生产工艺

为生产超柔浓染仿高取向丝(HOY)工艺的83 dtex/144 f涤纶全拉伸丝(FDY),讨论了底部加热器温度、无风区高度、集束高度、预网络压力以及GR_(1)辊壳材质对涤纶FDY的生产及物理性能的影响。结果表明:底部加热器温度为305℃,无风区高度为69.5 mm,集束高度为510 mm,预网络压力为0.025 MPa,GR_(1)辊壳材质选择镀陶瓷时,生产的83 dtex/144 f涤纶FDY具有较好的柔软度和吸色性。

基于指纹图谱与化学计量学的藿香正气软胶囊质量控制方法研究

目的:建立藿香正气软胶囊的高效液相色谱法(HPLC)指纹图谱及多指标成分测定方法,结合化学计量学方法评价藿香正气软胶囊的质量。方法:采用HPLC,SHIMADZU VP-ODSC18色谱柱(250 mm×4.6 mm,5μm),以乙腈-0.1%磷酸水溶液为流动相,梯度洗脱,流速为1.0 mL·min^(–1),梯度波长为254、283 nm,柱温为30℃,进样量为10μL,建立指纹图谱及多指标成分测定方法,结合化学计量学方法对不同批次藿香正气软胶囊质量进行分析和评价。结果:建立了藿香正气软胶囊的HPLC指纹图谱,共标定了22个共有峰,15批藿香正气软胶囊指纹图谱的相似度均在0.8以上;对甘草苷、柚皮芸香苷、橙皮苷、水合氧化前胡素、白当归脑、欧前胡素、和厚朴酚、珊瑚菜素、异欧前胡素、厚朴酚10个成分进行了含量测定;化学计量学分析表明,不同企业生产的藿香正气软胶囊质量存在一定差异,区分各样品的质量差异标志物为水合氧化前胡素、和厚朴酚、异欧前胡素、珊瑚菜素、厚朴酚和甘草苷。结论:建立的HPLC指纹图谱及多指标成分测定方法准确、简便,结合化学计量学方法可用于评价藿香正气软胶囊的质量。

基于简化Janbu法的夹炭质泥岩软弱层的溶蚀峰丛斜坡边坡稳定性分析

通过对因开挖导致溶蚀峰丛斜坡陡坡段出现破坏裂缝影响区域的补勘,获取破坏区主要岩土力学参数,并借助简化Janbu法对夹炭质泥岩软弱层的溶蚀峰丛斜坡进行滑移稳定性分析,采用反算法反算已滑坡裂缝区的抗剪强度指标,用于指导边坡修复和加固,同时给予安全合理的支护建议,以期为类似边坡失稳和支护问题提供科学的参考依据。

Modelling method with missing values based on clustering and support vector regression

Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real processes, the available data set is usually obtained with missing values. To overcome the shortcomings of global modeling and missing data values, a new modeling method is proposed. Firstly, an incomplete data set with missing values is partitioned into several clusters by a K-means with soft constraints （KSC） algorithm, which incorporates soft constraints to enable clustering with missing values. Then a local model based on each group is developed by using SVR algorithm, which adopts a missing value insensitive （MVI） kernel to investigate the missing value estimation problem. For each local model, its valid area is gotten as well. Simulation results prove the effectiveness of the current local model and the estimation algorithm.

CSFW-SC: Cuckoo Search Fuzzy-Weighting Algorithm for Subspace Clustering Applying to High-Dimensional Clustering

Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.

Mobile cluster rekeying in tracking sensor network

The wireless sensor network has a broad application in target tracking and locating, and is especially fit for military detection or guard. By arranging the sensor nodes around the target, this article establishes a tracking eluster which can follow the target logically, process data on the target and report to the sink node, thus achieving the tracking function. To improve the security, this article proposes a mobile cluster rekeying protocol （MCRP） to manage the tracking elusterg season key. It is based on a random key predistribution algorithm （RKP） , which is composed of a multi-path reinforcement scheme, a q-composition scheme and a oneway cryptographie hash function.