Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering

To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition(VMD),fuzzy entropy(FE)and fuzzy clustering(FC).Firstly,based on the OTDR curve data collected in the field,VMD is used to extract the different modal components(IMF)of the original signal and calculate the fuzzy entropy(FE)values of different components to characterize the subtle differences between them.The fuzzy entropy of each curve is used as the feature vector,which in turn constructs the communication optical fibre feature vector matrix,and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre.The VMD-FE combination can extract subtle differences in features,and the fuzzy clustering algorithm does not require sample training.The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models.

Micro-Sized Pinhole Inspection with Segmented Time Reversal and High-Order Modes Cluster Lamb Waves Based on EMATs

Pinhole corrosion is difficult to discover through conventional ultrasonic guided waves inspection,particularly for micro-sized pinholes less than 1 mm in diameter.This study proposes a new micro-sized pinhole inspection method based on segmented time reversal(STR)and high-order modes cluster(HOMC)Lamb waves.First,the principle of defect echo enhancement using STR is introduced.Conventional and STR inspection experiments were conducted on aluminum plates with a thickness of 3 mm and defects with different diameters and depths.The parameters of the segment window are discussed in detail.The results indicate that the proposed method had an amplitude four times larger than of conventional ultrasonic guided waves inspection method for pinhole defect detection and could detect micro-sized pinhole defects as small as 0.5 mm in diameter and 0.5 mm in depth.Moreover,the segment window location and width(5-10 times width of the conventional excitation signal)did not affect the detection sensitivity.The combination of low-power and STR is more conducive to detection in different environments,indicating the robustness of the proposed method.Compared with conventional ultrasonic guided wave inspection methods,the proposed method can detect much smaller defect echoes usually obscured by noise that are difficult to detect with a lower excitation power and thus this study would be a good reference for pinhole defect detection.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

一种基于粗糙熵的改进K-modes聚类算法

K-modes聚类算法被广泛应用于人工智能、数据挖掘等领域。传统的K-modes聚类算法有不错的聚类效果,但是存在迭代次数多、计算量大、容易受到冗余属性的干扰等问题,且仅采用简单的0-1匹配的方法来定义2个样本属性值之间的距离,没有充分考虑每个属性对聚类结果的影响。针对上述问题,该文将粗糙熵引入K-modes算法。首先利用粗糙集属性约简算法消除冗余属性,确定各属性的重要程度;然后利用粗糙熵确定每个属性的权重,从而定义新的类内距离。将该文所提算法与传统的K-modes聚类算法分别在4组公开数据集上进行对比试验。试验结果表明,该文所提算法聚类准确率比传统的K-modes聚类算法更高。

基于K-modes聚类算法的山东省传统村落空间风貌类型及区划研究

传统村落的类型解析及空间区划是开展传统村落整体性保护和区域性发展的基础前提,本文在对山东省传统村落调查的基础上,基于空间基因理论视角,从地景、聚落、建筑、文化4个层次构建起13个指标的传统村落空间风貌分类指标体系,并采用K-modes聚类算法对山东省177个传统村落进行聚类分析,得到八大空间风貌类型,进一步结合区域文化、地理特点及行政区划,划分出山东省5个传统村落风貌区,从宏观视角分析了山东省传统村落空间风貌特征及其形成与发展的内在逻辑和地理分布规律,为更加整体全面地认识山东省传统村落特点、开展区域性传统村落集中连片保护利用等工作提供科学参考。

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

K-Modes聚类数据收集和发布过程中的混洗差分隐私保护方法

针对目前聚类数据收集与发布安全性不足的问题,为保护聚类数据中的用户隐私并提高数据质量,基于混洗差分隐私模型,提出一种去可信第三方的K-Modes聚类数据收集和发布的隐私保护方法。首先,使用K-Modes聚类数据收集算法对用户数据进行采样并加噪,再通过填补取值域随机排列发布算法打乱采样数据的初始顺序,使恶意攻击者不能根据用户与数据之间的关系识别出目标用户。然后,尽可能减小噪声的干扰,利用循环迭代的方式计算出新的质心完成聚类。最后,从理论层面上分析了以上3种方法的隐私性、可行性和复杂度,并利用3个真实数据集和近年来具有权威性的同类算法KM、DPLM、LDPKM等进行准确率、熵值的对比,验证所提方法的有效性。实验结果表明,所提方法的隐私保护和发布数据质量均优于当前同类算法。

改进的k-modes聚类算法在协同过滤就业推荐算法中的应用

为了给高校毕业生提供精准的个性化就业推荐服务,将基于动态权重相互依存距离的改进k-modes聚类算法应用于协同过滤推荐算法中。定义不同样本点属性之间的距离等于属性值内部距离和属性间外部距离的加权和,选择初始簇质心时,动态调整样本点与簇质心的距离以及簇密度的组合权重,动态设置簇密度计算公式的半径,根据样本点的概率值选出初始簇质心;迭代计算和优化得到满足精度的学生簇和职位簇;构建学生-职位矩阵,计算应届生和往届生的相似度、往届生和入职岗位的相似度,选择二者的相似度超过阈值的应届生簇和职位簇组合为匹配对进行匹配,并将匹配信息降序排列形成匹配列表,依据匹配列表进行双向推荐和信息推送,为高校的就业推荐和指导提供信息导向和技术支持。

Modes of Occurrence and Cleaning Potential of Trace Elements in Coals from the Northern Ordos Basin and Shanxi Province, China

Based on the analyses of 43 elements in 16 samples of the raw coal and feed coal collected from the northern Ordos basin and Shanxi Province, the modes of occurrence of these elements were studied using the method of cluster analysis and factor analysis, and the cleaning potential of the hazardous elements relatively enriched in the coals was discussed by analyzing six samples of the cleaned coal from the coal-washing plants and coal cleaning simulation experiments. The results shows that the elements Br and Ba show a strong affinity to the organic matter, Cs, Cd, Pb, Zn and Hg partly to the organic matter, and the other trace elements are mainly associated with the mineral matter. Cs, Mo, P, Pb, Zn and S have positive correlations with the two principal factors, reflecting the complexity of their modes of occurrence. Some elements that were thought to show a faint relationship (Be with S and Sb with carbonates) in other rocks are found to have a strong interrelation in the coals. Clay minerals (mainly kaolinite) dominate in the coals, and Ta, Th, Ti, Sc, REE, Hf, U, Se, W, V, Nb, Mo, Al, P, Cr, Pb and Zn are distributed mostly in kaolinite, while K, Rb, Cs, and Na have much to do with illite. Conventional cleaning can reduce the concentrations of most hazardous elements in various degrees. The hazardous elements S, As, Sb, Se, Mo, Pb, Cd and Hg relatively enriched in some coals from the area studied have a relatively high potential of environmental risks. However, by physical coal cleaning processes, more than 60% of As and Hg were removed, showing a high degree of removal, more than 30% of Sb, as well as S, Pb and Cd partly associated with the inorganic matter were removed. Se and Mo showing a relatively low degree of removal could be further removed by deep crushing of the coal during physical cleaning processes, and the concentrations of S, Pb, Cd and Hg with a partial association with the organic matter could be decreased in such ways as the coal blending. Cluster analysis together with factor analysis is a rapid and effective way to deduce the mode of occurrence of an element from bulk samples, and the removability data of most hazardous elements are basically consistent with their modes of occurrence suggested, which indicates that the statistical analysis could predict the cleaning potential of hazardous elements during the physical coal cleaning.

Parallel and Hierarchical Mode Association Clustering with an R Package <i>Modalclust</i>

Modalclust is an R package which performs Hierarchical Mode Association Clustering (HMAC) along with its parallel implementation over several processors. Modal clustering techniques are especially designed to efficiently extract clusters in high dimensions with arbitrary density shapes. Further, clustering is performed over several resolutions and the results are summarized as a hierarchical tree, thus providing a model based multi resolution cluster analysis. Finally we implement a novel parallel implementation of HMAC which performs the clustering job over several processors thereby dramatically increasing the speed of clustering procedure especially for large data sets. This package also provides a number of functions for visualizing clusters in high dimensions, which can also be used with other clustering softwares.

Softening of C-H Symmetric Stretching Vibrational Modes for CH2 and CH3 Radicals Adsorbed on Cun （n=1-6） Clusters

The properties of C-H vibration softening for CH2 and CHa radicals absorbed on Cun（n=1-6） clusters have been investigated, using the density functional theory with hybrid functional. The results indicate that the absorption of CH2 on Cu clusters is stronger than the case of CH3. The vibrational frequencies of C-H bonding agree with the experimental results obtained for CH2 and CH3 absorbed on Cu（111）. With the increase of cluster size, the softening （Einstein shift） of C-H vibrational modes become stronger.

Load Cluster Characteristic Analysis and Modeling of Electric Vehicles

Electric vehicle, as a clean energy industry, is an important branch. Electric vehicles not only are the energy of the electric user, but also can be used as mobile and distributed energy storage unit to the grid. As a precondition of safety operation for power grid, studies of EVs’ charging load characteristics is also the theoretical basis of intelligent scheduling EVs charging orderly. This paper assesses the future of the electric vehicles development prospects, and secondly establishes a charging model of a single EV. Then, considering stochastic distribution of the initial state-of-charge (SOC0) and the arriving time of the vehicles, a cluster model of the charging station is proposed. Meanwhile, the paper from the types and charging mode of electric vehicles analyzes the behavior of EV. Finally, an example simulation is validated.

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.

Fuzzy BC-k-modes:一种分类矩阵对象数据的聚类算法

传统的聚类算法主要对具有单值属性的数据进行聚类研究,针对矩阵对象数据的研究较少,提出一种新的fuzzy between-cluster k-modes(简称Fuzzy BC-k-modes)聚类算法。在Fuzzy BC-k-modes算法中,采用增加簇间信息(不同类中的对象到其他类中心的距离)去修正目标函数,在对修正的目标函数寻求局部最优解时,提出隶属度矩阵的更新公式。最后,在四个真实数据集上验证了Fuzzy BC-k-modes算法的有效性,并且分析了模糊因子与隶属度间的关系。

基于K-modes聚类算法的辽宁传统村落划分及保护策略

本文基于传统村落的自然环境、人文历史、民居建筑等分类特征,使用K-modes聚类算法,对辽宁省30个国家级传统村落进行聚类划分,将其整合为景区依托型、民族特色型、生态文化型、休闲观光型、文化遗产型共五个类别,最后根据归纳的聚类典型特征,提出有针对性的辽宁省传统村落保护发展策略。

基于K-modes聚类算法的安徽历史文化名村分类及保护发展策略

以安徽省44个省级以上历史文化名村为研究对象,分析其空间分布特征与保护管理现状,并探讨影响其类型划分的具体因素。同时,借鉴学者关于历史文化名村和传统村落分类的研究,以地理条件、产业经济、社会生活、历史文化为主要维度,构建形成安徽历史文化名村类型划分的指标体系。基于此,运用K-modes聚类算法,将安徽历史文化名村划分为生态宜居型、文旅资源型、特色民俗型、综合发展型四种类型,并总结各类历史文化名村的典型特征,进而提出相应的保护与发展策略。

一种基于属性值权重的k-modes聚类分析算法

针对k-modes方法未考虑各属性值在属性空间的分布特征而导致分类变量间差异性度量不准确的问题,提出了一种基于属性值权重的k-modes聚类分析算法。该算法利用属性值之间的差异和属性值的权重,重新定义了相异度度量公式;采用属性值频率和各属性值的权重,给出一种聚类中心更新迭代公式,有效地体现了属性值在属性空间中的分布特征和属性之间的重要性差异;采用UCI数据集,验证了算法的有效性。

非独立同分布下的K-Modes算法

传统的K-Modes算法中,初始聚类中心是随机选取的,聚类结果过分依赖初始聚类中心的选择,影响聚类效果。在很多K-Modes算法的研究中假设数据是独立同分布的,在现实的数据中,数据对象和属性之间是根据某些耦合关系彼此关联的,是非独立同分布的。针对这两方面问题,通过基于层次聚类进行预聚类的方法改进选取初始中心的方法,引入非独立同分布思想计算相异度量,进行实验验证。实验结果表明,通过改进初始中心的选取方法和相异度量的计算方法很好改进了K-Modes算法,提高了算法的聚类精度。

Failure Modes of Machining Center in Test Run

To analysis the early failures of machining centers,the failure mode effect and criticality analysis( FMECA) method was used. Based on the failure data collected from production lines in test run,all the failure modes of machining centers were summarized and criticality of all subsystems is figured out. And the process of FMECA was improved. The most critical subsystem was manipulator subsystem. The most critical failure mode was impacted manipulator. Reasons and effect of some important failure modes were analyzed. And some suggestions to solve failures were given.

Third Order Current Mode Universal Filter Using Only Op.amp. and OTAs

A novel current mode active-only universal filter using four dual current output Operational Transconductance Amplifiers (OTAs) and three Operational Amplifiers (OAs) is presented. The circuit can realize low pass and high pass filter characteristics by choosing the suitable current output branches. The filter performance factors natural frequency (ω0), bandwidth , quality factor Q and transconductance gain gm are electronically tunable. The proposed circuit has very low sensitivities with respect to circuit active elements. From sensitivity analysis, it has been clearly shown that the proposed circuit has very low sensitivities with respect to the circuit active elements. The gain roll-off of high pass and low pass configuration is 18 dB/oc- tave. The proposed circuit facilitates integrability, programmability and ease of implementation.