Research and Discussion on Project Cluster Teaching Mode based on the Enterprise Level Application

Currently, the country promotes with great effort the university should the application specific education, speeds up constructing to take getting employed as the guidance modern vocational education system. In order to strengthen the vocational skill ability of student and enhance the employment competitiveness, this article proposes enterprise application-based project colony educational model. In the teaching process, the school subject knowledge education and business skills needs of the enterprise integration, the use of enterprise program teaching, so that students can not only receive professional knowledge of the system education, but also the ability of professional application of formal training and training, after graduation the students can quickly adapt to the work of the business requirements, to achieve the purpose of application-oriented teaching.

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.

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.

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。

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.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

一种全概率联合远程制备四粒子cluster类态的方案

为了解决四粒子cluster类态的联合远程制备问题,提出了一种新的制备方法。采用两个GreenbergerHorne-Zeilinger(GHZ)态作为量子信道,每个制备者只需依次执行2×2维Hilbert空间二粒子投影测量。在接收端,接收者将对手中两个粒子进行某种酉操作,并分别对两个辅助粒子进行受控非门操作,从而最终获取需制备的cluster类态。经分析,该协议可实现全概率制备,即Psuc=1,并且效率比同类协议更高,因而更加经济、实用。

基于EPR对的任意四粒子Cluster类态全概率联合远程制备

采用4个EPR(Einstein-Podolsky-Rosen)对作为量子信道,提出一种新的全概率联合远程制备任意四粒子Cluster类态方案。在这个新的方案中,一个制备者执行四粒子投影测量,另一个制备者对手中的粒子执行二粒子投影测量。然后,接收者对手中二粒子进行某种幺正操作,并分别对两个辅助粒子进行受控非门操作,从而最终得到需要制备的Cluster类态。与其他制备任意四粒子Cluster类态的全概率制备方案相比,所提协议效率较高,而且在实验上更经济可行。

组合聚类和深度学习模型的风电场群风速预测

为提高规模化风电场群的风速预测精度,进而保障中国电网的安全稳定运行,提出了一种基于粒子群-投影寻踪聚类算法结合NS-L-Transformer的风电场群短期风速混合预测模型。首先,通过变分模态分解、去伪分量和小波变换的方法对采集的风速数据集进行处理,得到滤除噪声干扰后的风速数据集。其次,考虑风电场群间的风速空间关联特性,根据其风速波动特征,采用粒子群-投影寻踪聚类算法分析了风电场群间的空间相关性,根据算法所得到的评价指标对风电场群进行了场群关联性最优分类,并构造了分类后的高维风速数据集。最后,通过Transformer模型的自注意力机制结合LSTM模型的门控单元机制捕捉风速时间序列的局部特征,提出了NS-L-Transformer模型对所构造的具有局部特性的高维风速数据集进行了风速预测。选用中国东南某地区风电场群的风速数据进行了仿真分析,研究结果表明,采用分类后的高维数据集进行风速预测较单一风速数据集的预测精度有较大的提升;相较于Transformer模型,NS-L-Transformer的预测误差减少,从而验证了本研究所提混合预测模型的有效性。

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。

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。

基于能量的模糊最小二乘投影双支持向量聚类的不平衡学习

针对类不平衡场景中噪声数据导致聚类性能下降的现象,提出一种新的基于能量的模糊最小二乘投影双支持向量聚类的不平衡学习算法。该算法通过引入模糊隶属度函数,为各类数据点提供模糊权重来保持聚类过程中每一个数据点的显著贡献,同时为每个聚类投影轴引入能量因子,来降低噪声和异常值对算法的影响,并使用凹凸迭代过程求解目标函数的优化问题。分别在合成数据集和真实数据集上进行实验,实验结果证明了所提算法的有效性。

实验室集群项目超大复杂筏板施工综合技术研究

超大筏板结构能满足大构件的承载面积和承载能力的需求,还能起到分散荷载的效果,从而满足结构的整体稳定性,但该结构在设计及施工中均面临着复杂设计、筏板超厚和多变标高的技术难题。以深圳市南山区某实验室集群项目为例,通过对筏板变标高构造、承台底板砖胎模、超厚底板施工钢筋支撑等工艺的分析及应用,证明超高砖胎模配合分层回填施工能很好地为防水提供平整基层面、提高防水施工质量,超厚筏板采用优化后的型钢支撑既能方便施工、降低施工成本,还能保证施工过程中的安全性,可为类似工程提供技术参考。

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。