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.

基于HS-Clustering的风电场机组分组功率预测

为了寻求风电场功率预测精度和计算效率二者的平衡,提出了一种基于霍普金斯统计量与聚类算法(HSClustering)的风电场机组分组功率预测方法,该方法将霍普金斯统计量与聚类算法的优势有效结合,采用霍普金斯统计量确定场内机组分组个数,通过聚类算法识别不同机组的相似性将风电场分成不同的机组群,然后对每组机群分别建立功率预测模型,从而叠加得到整场输出功率;另外以实测风速、实测功率及二者组合作为机组分组模型输入,分析其对预测精度的影响程度。实例分析表明基于HSClustering的分组预测方法可以显著提高预测精度,同时保证较高的计算效率;风速是影响分组效果的主要因素,对于某些分组模型,功率又可以作为风速的重要补充。

改进的YoloV3轻量化多目标检测算法

为有效解决轻量化目标检测网络降低参数量导致的检测精度损失问题,设计一种轻量化多目标检测网络MSPF-YoloV3,可以通过低分组数、低内存占用的通道混洗结构,达到与高分组数同样的信息交流的效果;将位置信息丰富的浅层特征与深层特征进行融合,提高对不同大小目标的检测精度。经过实验验证了该方法的有效性与实时检测的能力。该网络在PASCAL VOC07+12数据集上的mAP达到86.31%;在COCO2014数据集中提取出与PASCAL VOC07+12数据集相同的20个类别,mAP达到67.71%;权重文件大小为46.8 MB,相较于YoloV3降低了198.2 MB;检测速度达到44 FPS。

A Study on Numerical Calculation Method of Small Cluster Density in Percolation Model

Percolation theory deals with the numbers and properties of the clusters formed in the different occupation probability. In this Paper, we study the calculation method of small clusters. We calcu-lated the small cluster density of 1, 2 and 3 in the percolation model with the exact method and the numerical method. The results of the two methods are very close, which can be verified by each other. We find that the cluster density of all three kinds of small clusters reaches the highest value when the occupation probability is between 0.1 and 0.2. It is very difficult to get the analytical formula for the exact method when the cluster area is relatively large (such as the area is more than 50), so we can get the density value of the cluster by numerical method. We find that the time required calculating the cluster density is proportional to the percolation area, which is indepen-dent of the cluster size and the occupation probability.

无线传感网LEACH协议的改进方法

为解决传统LEACH(Low Energy Adaptive Clustering Hierarchy)协议网络节点能量消耗高、存活数量少和生存寿命短等问题,提出了一种LEACH-AD改进方案。该算法引入最优簇头比率P值、加入距离因子、剩余能量因子和密度因子等因素更新的阈值公式进行分簇以及簇间的传输。实验结果表明,改进后的LEACH-AD协议在首个死亡节点、10%死亡节点以及全部死亡节点分别比原LEACH协议延长138轮、195轮、628轮。在能量消耗方面比原LEACH协议多持续了631轮,改进后的路由协议减少了网络节点的能量消耗量,从而有效延长了无线网络与传感节点的工作时间,这对无线监测系统的研究与开发意义重大。

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

The Effective Clustering Partition Algorithm Based on the Genetic Evolution

To the problem that it is hard to determine the clustering number and the abnormal points by using the clustering validity function, an effective clustering partition model based on the genetic algorithm is built in this paper. The solution to the problem is formed by the combination of the clustering partition and the encoding samples, and the fitness function is defined by the distances among and within clusters. The clustering number and the samples in each cluster are determined and the abnormal points are distinguished by implementing the triple random crossover operator and the mutation. Based on the known sample data, the results of the novel method and the clustering validity function are compared. Numerical experiments are given and the results show that the novel method is more effective.

A numerical model for cloud cavitation based on bubble cluster

The cavitation cloud of different internal structures results in different collapse pressures owing to the interaction among bubbles. The internal structure of cloud cavitation is required to accurately predict collapse pressure. A cavitation model was developed through dimensional analysis and direct numerical simulation of collapse of bubble cluster. Bubble number density was included in proposed model to characterize the internal structure of bubble cloud. Implemented on flows over a projectile, the proposed model predicts a higher collapse pressure compared with Singhal model. Results indicate that the collapse pressure of detached cavitation cloud is affected by bubble number density.

基于CiteSpace的国内患者安全教育研究现状的文献计量学分析

目的:采用CiteSpace软件对我国患者安全教育相关文献进行文献计量学分析,总结该领域研究热点,探索未来发展趋势。方法:检索中国知网建库至2023年7月25日发表的相关文献,使用文献计量学分析方法及CiteSpace软件进行统计分析。结果:共检索出163篇文献,发文量呈现逐年上升的趋势。发文量居前3位的机构分别为重庆医科大学附属第一医院、天津医科大学和华中科技大学同济医学院附属协和医院。研究热点主要包括安全教育、调查、新护士、安全管理、安全文化及医学生6个聚类。影响因素、护士和新护士成为近3年的研究热词,并呈现持续上升趋势。结论:国内患者安全教育的发文量呈现增长趋势,发文机构以高等医学院校及附属医院为主,作者间的合作较弱,鉴于患者安全教育对临床医疗质量与安全的重要性,未来研究者应给予重视,加强机构间的合作与交流,就患者安全教育方案进行深入探讨。

无人机集群编队自主协同控制方法综述

自主协同控制是实现无人机集群高效协同编队的一种主流控制方法,属于当前无人机控制领域的研究热点和难点。论文首先从两类无人机集群编队自主协同控制主流类型切入。然后,重点对集中式控制类型中的领航者-跟随者法、虚拟结构法,和分布式控制类型中的人工势场法、行为法、一致性理论法、深度强化学习法等典型方法的基本思想、特点和最新研究进展进行了对比分析。最后,对无人机集群编队自主协同控制方法的发展趋势进行展望,以期为后续研究提供参考。

Clustering based segmentation of text in complex color images

We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.

Performances of Clustering Methods Considering Data Transformation and Sample Size: An Evaluation with Fisheries Survey Data

Clustering is a group of unsupervised statistical techniques commonly used in many disciplines. Considering their applications to fish abundance data, many technical details need to be considered to ensure reasonable interpretation. However, the reliability and stability of the clustering methods have rarely been studied in the contexts of fisheries. This study presents an intensive evaluation of three common clustering methods, including hierarchical clustering(HC), K-means(KM), and expectation-maximization(EM) methods, based on fish community surveys in the coastal waters of Shandong, China. We evaluated the performances of these three methods considering different numbers of clusters, data size, and data transformation approaches, focusing on the consistency validation using the index of average proportion of non-overlap(APN). The results indicate that the three methods tend to be inconsistent in the optimal number of clusters. EM showed relatively better performances to avoid unbalanced classification, whereas HC and KM provided more stable clustering results. Data transformation including scaling, square-root, and log-transformation had substantial influences on the clustering results, especially for KM. Moreover, transformation also influenced clustering stability, wherein scaling tended to provide a stable solution at the same number of clusters. The APN values indicated improved stability with increasing data size, and the effect leveled off over 70 samples in general and most quickly in EM. We conclude that the best clustering method can be chosen depending on the aim of the study and the number of clusters. In general, KM is relatively robust in our tests. We also provide recommendations for future application of clustering analyses. This study is helpful to ensure the credibility of the application and interpretation of clustering methods.

Particle swarm optimization computer simulation of Ni clusters

The stable structures and energies of Ni clusters were investigated using particle swarm optimization(PSO)combined with simulated annealing(SA).Sutton-Chen many-body potential was used in describing the interatomic interactions.The simulation results indicate that the structures of Ni clusters are icosahedral-like and binding energy per atom tends to approach that of bulk materials when the atoms number increases.The stability of Ni clusters depends not only on size but also on symmetrical characterization.The structure stability of Nin clusters increases with the increase of total atom number n.It is also found that there exists direct correlation between stability and geometrical structures of the clusters,and relatively higher symmetry clusters are more stable.From the results of the second difference in the binding energy,the clusters at n=3 is more stable than others,and the magic numbers effect is also found.

Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

Application of a New Fuzzy Clustering Algorithm in Intrusion Detection

This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the architecture of FCM al- gorithm,enhanced the analysis for effective clustering.During the clustering processing,it may adjust clustering numbers dy- namically.Finally,it used the method of section set decreasing the time of classification.By experiments,the algorithm can im- prove dependability of clustering and correctness of classification.

PC Cluster系统无法运行JSS作业的原因分析及解决方法

本文在简要介绍PC Cluster系统组成的基础上,着重分析了系统不能运行JSS作业的原因——inode被占满了而引起的,并介绍了通过删除mailman软件包排除故障的方法。

A heuristic clustering algorithm based on high density-connected partitions

Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.

Proposal of PAPR Reduction Method for OFDM Signal by Re-Ordering of Clusters in Frequency Domain

One of the limitations of using OFDM technique is its higher PAPR in the time domain signal. The higher PAPR OFDM signal would cause the fatal degradation of BER performance and undesirable spectrum regrowth in the nonlinear channel. One of the promising PAPR reduction methods for OFDM signal is the Partial Transmit Sequence (PTS) method which can achieve better PAPR performance with reasonable computation complexity. However the PTS method is required to inform the phase coefficients of PTS as the side information to the receiver for the correct demodulation of data information through the data or separate channels. To simplify the transceiver of OFDM system with the PTS method, the phase coefficients of PTS are usually embedded in the data information. However since the phase coefficients of PTS are obtained after the PTS processing only for the data information at each OFDM symbol, it is hard to embed the phase coefficients of PTS in the data information separately without degradation of PAPR performance. To solve this problem, this paper proposes a new PAPR reduction method based on the packet-switched transmission systems in which all the clusters within the certain number of OFDM symbols have the sequential cluster ID numbers embedded in the header of each cluster. The salient features of the proposed method are to reduce the PAPR performance by re-ordering of clusters (ROC) in the frequency domain at the transmitter and to reconstruct the original ordering of clusters by using the cluster ID number demodulated from each cluster at the receiver. This paper also proposes a reduction technique of computation complexity for the proposed ROC method by using the feature of IFFT processing. This paper presents various computer simulation results to verify the effectiveness of the proposed ROC method with the reduction technique of computation complexity.

Multivariate Cluster and Principle Component Analyses of Selected Yield Traits in Uzbek Bread Wheat Cultivars

Investigation of genetic diversity of geographically distant wheat genotypes is a useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues > 1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was the greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18,373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).