Projection pursuit cluster model and its application in water quality assessment

One of the difficulties frequently encountered in water quality assessment is that there are many factors and they cannot be assessed according to one factor, all the effect factors associated with water quality must be used. In order to overcome this issues the projection pursuit principle is introduced into water quality assessment, and projection pursuit cluster(PPC) model is developed in this study. The PPC model makes the transition from high dimension to one-dimension. In other words, based on the PPC model, multifactor problem can be converted to one factor problem. The application of PPC model can be divided into four parts: (1) to estimate projection index function Q(); (2) to find the right projection direction ; (3) to calculate projection characteristic value of the i th sample z-i, and (4) to draw comprehensive analysis on the basis of z-i. On the other hand, the empirical formula of cutoff radius R is developed, which is benefit for the model to be used in practice. Finally, a case study of water quality assessment is proposed in this paper. The results showed that the PPC model is reasonable, and it is more objective and less subjective in water quality assessment. It is a new method for multivariate problem comprehensive analysis.

A SPARSE PROJECTION CLUSTERING ALGORITHM

A clustering algorithm based on Sparse Projection (SP), called Sparse Projection Clus- tering (SPC), is proposed in this letter. The basic idea is applying SP to project the observed data onto a high-dimensional sparse space, which is a nonlinear mapping with an explicit form and the K-means clustering algorithm can be therefore used to explore the inherent data patterns in the new space. The proposed algorithm is applied to cluster a complete artificial dataset and an incomplete real dataset. In comparison with the kernel K-means clustering algorithm, the proposed algorithm is more efficient.

Projection Pursuit Dynamic Cluster Model and its Application to Water Resources Carrying Capacity Evaluation

The research shows that projection pursuit cluster (PPC) model is able to form a suitable index for overcom-ing the difficulties in comprehensive evaluation, which can be used to analyze complex multivariate prob-lems. The PPC model is widely used in multifactor cluster and evaluation analysis, but there are a few prob-lems needed to be solved in practice, such as cutoff radius parameter calibration. In this study, a new model-projection pursuit dynamic cluster (PPDC) model-based on projection pursuit principle is developed and used in water resources carrying capacity evaluation in China for the first time. In the PPDC model, there are two improvements compared with the PPC model, 1) a new projection index is constructed based on dynamic cluster principle, which avoids the problem of parameter calibration in the PPC model success-fully;2) the cluster results can be outputted directly according to the PPDC model, but the cluster results can be got based on the scatter points of projected characteristic values or the re-analysis for projected character-istic values in the PPC model. The results show that the PPDC model is a very effective and powerful tool in multifactor data exploratory analysis. It is a new method for water resources carrying capacity evaluation. The PPDC model and its application to water resources carrying capacity evaluation are introduced in detail in this paper.

Cluster projective synchronization of complex networks with nonidentical dynamical nodes

We investigate a new cluster projective synchronization （CPS） scheme in time-varying delay coupled complex dynamical networks with nonidentical nodes. Based on the community structure of the networks, the controllers are designed differently for the nodes in one community, which have direct connections to the nodes in the other communities and the nodes without direct connections to the nodes in the other communities. Some sufficient criteria are derived to ensure the nodes in the same group projectively synchronize and there is also projective synchronization between nodes in different groups. Particularly, the weight configuration matrix is not assumed to be symmetric or irreducible. The numerical simulations are performed to verify the effectiveness of the theoretical results.

Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

Strategic Measures for Implementing 300 Demonstration Projects to Lead the Construction of Modern Agricultural Industrial Clusters with Output of 100 Billion Yuan

To carefully implement the rural revitalization strategy,further promote the structural reform of the agricultural supply front,implement the "155 Development Strategy",speed up the cultivation of the modern agricultural industrial clusters with output of 100 billion yuan,and constantly consolidate the industrial foundation of the " Second City in Chengdu-Chongqing Line",the Municipal Party Committee and the Municipal Government of Nanchong City decided to implement the " 300 Demonstration Projects" of modern agriculture. It firmly grasps the " bull nose"( key part) of the project,energetically implements the construction of 100 modern circular agricultural demonstration parks,100 agricultural leading enterprises with output value of 100 billion yuan,and 100 agricultural new business model demonstration projects( hereinafter referred to as 300 Demonstration Projects),to realize the goal of the output value of the modern agriculture industrial clusters of Nanchong City exceeds 100 billion yuan in 2020.

基于扎根理论的城市轨道交通PPP项目风险因素识别与评价

城市轨道交通PPP项目有其独有的风险特征,如何管控生命周期内的复杂风险是项目推进过程中所面临的重要问题。针对城市轨道交通PPP项目风险因素识别不够全面、适用范围较窄等问题,应用扎根理论构建城市轨道交通PPP项目风险评价指标体系,并运用熵权法-灰色聚类分析对项目风险进行评价。通过实证研究,发现当前城市轨道交通PPP项目仍然面临一定程度的风险,政府行为在项目实施中发挥着重要作用。

面向开环扫描系统的超声图像畸变校正方法

超声显微成像技术广泛应用于工业无损检测领域。相较于闭环、半闭环扫描系统硬件复杂、成本高,开环扫描系统结构简单、成本低,但由于无反馈机制会导致步进电机的非线性运动引起图像像素错位畸变。因此,消除非线性运动带来的错位畸变是采用开环扫描系统实现高质量超声成像的关键。该文提出集最大值投影法、最大类间方差法和中心坐标校正法于一体的MIP-Otsu-C3M方法,对开环扫描系统获得的硬币回波数据采用最大值投影法获取初始灰度图像,采用最大类间方差法获取感兴趣区域的B扫描图像边缘像素位置,并采用中心坐标校正法成功消除像素错位,解决了超声C扫描图像畸变问题。对消除错位畸变的回波数据进行飞行时间法和傅里叶变换法图像重建,直接获得了非畸变的三维图像和透视图像。该新颖算法也验证了最大值投影法可拓展至图像畸变校正应用。

面向智能电表大数据分析的数据降维方法

针对智能电表大数据聚类分析过程中出现的计算复杂度过大的问题,提出基于极坐标投影的数据降维方法。采用极坐标投影的方法将由电能总消耗量和峰值电能消耗量所组成的数据集映射成由极角和极径组成的投影矩阵,设计一个由电能总消耗量和峰值电能消耗量所组成的改进聚类距离度量,使用误差平方和方法确定最优的聚类数量,将该方法引入于k均值算法进行算例分析。结果表明,该降维方法能够有效提升聚类算法的性能,并降低聚类算法的计算复杂度。

超大规模工程项目集群管理创新实践——西安交通大学科技创新港科创基地项目管理最佳实践案例

针对超大规模工程项目集群管理,立足于工程总承包实践创新,从项目管理机制、组织建设、施工图设计深化与优化,以及在质量、安全、技术、文明、绿色等全过程管理中,创新形成了超大规模工程项目集群管理的新理论、新模式、新方法,为建筑企业建立了具有针对性、实用性、创造性、前瞻性的集群项目综合管理理论,有效引领建筑业管理模式的创新发展,推动新发展阶段建筑业转型升级和高质量发展。

基于相似图投影学习的多视图聚类

随着数据来源方式的多样化发展,多视图聚类成为研究热点。大多数算法过于专注利用图结构寻求一致表示,却忽视了如何学习图结构本身;此外,一些方法通常基于固定视图进行算法优化。为了解决这些问题,提出了一种基于相似图投影学习的多视图聚类算法(multi-view clustering based on similarity graph projection learning, MCSGP),通过利用投影图有效地融合了全局结构信息和局部潜在信息到一个共识图中,而不仅是追求每个视图与共识图的一致性。通过在共识图矩阵的图拉普拉斯矩阵上施加秩约束,该算法能够自然地将数据点划分到所需数量的簇中。在两个人工数据集和七个真实数据集的实验中,MCSGP算法在人工数据集上的聚类效果表现出色,同时在涉及21个指标的真实数据集中,有17个指标达到了最优水平,从而充分证明了该算法的优越性能。

用Cluster态实现任意两粒子态的远程态制备方案(英文)

提出一个用Cluster态作为量子信道远态制备两量子态的方案,并计算了此方案成功的慨率和消耗的经典信息。一般地,Bob能成功地制备初态的慨率是1/4,消耗的经典信息是1/4经典比特。然而,在六种特殊情形下,消耗一些多余经典信息后,成功的概率能达到1/2甚至1。

基于二分网络加权投影的急性鼻咽炎中药配伍规律挖掘及组合用药预测

目的从二分网络加权投影角度挖掘中药方剂配伍规律,提出一种新的挖掘中药配伍规律的方法,并预测新型药物组合,为指导临床治疗急性鼻咽炎用药提供依据。方法以中医药综合数据库(TCMID)中的急性鼻咽炎方剂数据为数据源,通过提取方剂和药物构建二分网络,继而使用加权投影得到药物网络投影图,结合二分网络加权投影进行社会网络分析,基于皮尔逊相关性进行系统聚类的研究方法挖掘中药“君臣佐使”的配伍规律,并使用链路预测进行核心药物预测。结果二分网络加权投影与皮尔逊相关性进行系统聚类分析相结合的方式在中药方剂的配伍规律研究中作用显著。在链路预测中,选用11个链路预测指标,区分加权与无权算法后最终计算出的加权指标曲线下面积(AUC)大于无权指标,且在加权指标中,AUC最大指标为网络资源分配指标,预测出7组药物组合,包括白头翁与毛诃子、安息香与石椒草、白花茶与附子等。结论二分网络加权投影方法在揭示中药配伍规律与药物组合预测方面具有一定实用性和有效性。

基于ESG理念的水生态PPP项目运营期绩效评价

PPP项目运营期绩效评价对保障项目健康合理运营、促进项目可持续发展具有重要意义。以ESG理念为基础建立水生态PPP项目运营期绩效评价指标体系,并综合使用熵权法和序关系分析法确定指标权重,结合灰色聚类评价模型确定绩效评价结果。最后,通过案例验证了该方法的可行性与适用性,希望为水生态PPP项目运营期的绩效评价提供有效支撑。

情报知识簇赋能科研的模式深化与实践

[目的/意义]伴随大数据“5V”特点引发的数据“爆炸”、事实真相隐匿“双刃剑”效应的不断增强,在科技项目系统工程建设从需求分析到产品交付的整个科研过程中,对科技情报的知识广泛启发、方案清晰直达等迫切需求日益突出,可灵活聚合,且契合专业的体系化情报正在成为赋能科技项目系统工程建设过程的重要基础和骨干中坚。[方法/过程]文章围绕科技项目系统工程实施过程“V”字模型,锚定启发式牵引、案例式牵引两个切入点,创新探索专业化专家标定情报中心位+特征化算法拓展情报扩展圈的两步法,实践了适用于科技项目系统工程建设科研过程的情报知识簇解决方案,并结合智能化技术趋势,规划了基于大模型的情报知识融合中心发展突破口。[结果/结论]通过工程实践,验证算法有效性,并依据两步法在自研情报平台中快速构建了多个情报知识簇,增强了面向科技项目建设科研需求、设计前端过程的情报启发效果,以及面向科技项目建设研制集成、验证交付后端过程的情报案例参鉴效果,对于进一步发挥科技情报在体系工程层面的宏观效应,以及系统工程实施层面的方案构思作用具有重要意义。

基于知识视角的研发项目相似性测度及对项目集聚类的影响研究

为了企业高效地管理多个新产品研发(NPD)项目,提出基于知识的多项目管理方法,该方法集成知识的可复用性及其相似性测度项目间的相似性,并结合依赖结构矩阵(DSM)方法,以更全面地分析知识对项目相似度及项目集聚类的影响。研究表明:迭代不仅对项目相似性产生影响,还能提升活动因学习而产生的知识增量;依据知识关系测度项目相似性,进而将相似度高的项目聚在一个项目集中,便于项目集内的项目进行知识转移与共享,使企业的知识资源得到有效、合理的利用。

基于自表示和投影映射的不完整多视图聚类

针对不完整多视图聚类存在的缺陷,提出一种融合自表示和投影映射的统一框架.首先,利用自表示和样本存在指示矩阵学习一致相似图,它反映了样本间的公共相似关系;其次,利用投影映射将样本矩阵投影到超球面上,得到公共低维表示;最后,将两者通过谱表示嵌入在一起,解决了因多视图数据缺失引起的不完整多视图聚类问题.该算法在真实数据集上的实验结果优于其他算法,证明了算法的有效性.

“东数西算”工程背景下韶关数据中心集群建设思路

当今全球已经进入数字化时代,信息数据量迅猛增长,算力资源的重要性也日益提升。为了支持处理这些海量数据,需要建设大量的数据中心建筑。文章基于“东数西算”工程的架构指引,通过总结韶关地区区位环境和产业资源现状,分析了建设韶关数据中心集群在能源利用和算力利用方面面临的挑战,并提出了针对性的建设思路。

“双碳”背景下建筑工程管理废料处理技术

随着国家“碳达峰、碳中和”战略目标的提出,作为碳排放大户的建筑领域开始积极进行技术变革、发展绿色经济。因此,为了解决建筑工程在施工前、施工中、竣工后以及装修、维护和拆除等阶段产生的废料处理问题,提出了一种基于POSC技术的建筑废料处理新途径。该技术以建筑废料处理装置为依托,通过对设备已有部件的科学设计与合理组合,将筛分、聚类原理灵活运用到建筑废料处理的各个阶段中,达到对废料分速、分层、聚类的目的,实现建筑废料处理的高效化、能耗最低化、有效寿命最长化和处理成本最低化,减少建筑领域相关技术活动的碳排放,达成科学工程管理,有效推动建筑行业的低碳发展,响应国家“碳达峰、碳中和”号召。