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。

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。

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。

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。

Optimization of Bearing Locations for Maximizing First Mode Natural Frequency of Motorized Spindle-Bearing Systems Using a Genetic Algorithm

This paper has developed a genetic algorithm (GA) optimization approach to search for the optimal locations to install bearings on the motorized spindle shaft to maximize its first-mode natural frequency (FMNF). First, a finite element method (FEM) dynamic model of the spindle-bearing system is formulated, and by solving the eigenvalue problem derived from the equations of motion, the natural frequencies of the spindle system can be acquired. Next, the mathematical model is built, which includes the objective function to maximize FMNF and the constraints to limit the locations of the bearings with respect to the geometrical boundaries of the segments they located and the spacings between adjacent bearings. Then, the Sequential Decoding Process (SDP) GA is designed to accommodate the dependent characteristics of the constraints in the mathematical model. To verify the proposed SDP-GA optimization approach, a four-bearing installation optimazation problem of an illustrative spindle system is investigated. The results show that the SDP-GA provides well convergence for the optimization searching process. By applying design of experiments and analysis of variance, the optimal values of GA parameters are determined under a certain number restriction in executing the eigenvalue calculation subroutine. A linear regression equation is derived also to estimate necessary calculation efforts with respect to the specific quality of the optimization solution. From the results of this illustrative example, we can conclude that the proposed SDP-GA optimization approach is effective and efficient.

Study on LQR control algorithm using superelement model

The conventional linear quadratic regulator(LQR) control algorithm is one of the most popular active control algorithms.One important issue for LQR control algorithm is the reduction of structure's degrees of freedom(DOF). In this work, an LQR control algorithm with superelement model is intended to solve this issue leading to the fact that LQR control algorithm can be used in large finite element(FE) model for structure. In proposed model, the Craig-Bampton(C-B) method, which is one of the component mode syntheses(CMS), is used to establish superelement modeling to reduce structure's DOF and applied to LQR control algorithm to calculate Kalman gain matrix and obtain control forces. And then, the control forces are applied to original structure to simulate the responses of structure by vibration control. And some examples are given. The results show the computational efficiency of proposed model using synthesized models is higher than that of the classical method of LQR control when the DOF of structure is large. And the accuracy of proposed model is well. Meanwhile, the results show that the proposed control has more effects of vibration absorption on the ground structures than underground structures.

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。

基于K-modes的安全算法抵御SSDF攻击

为保障频谱感知安全,针对协作频谱感知中频谱感知数据篡改攻击,在集中式认知无线电网络中提出了一种基于K-modes的安全算法抵御此类攻击。利用K-modes算法对在融合中心收集到的次用户感知报告进行分类,在识别并剔除攻击者后,最后通过低复杂度的传统投票规则获得更高的网络感知性能。通过Python仿真,分析该算法在不同条件下对攻击者的识别性能,并验证该算法比传统防御算法的检测概率提高了约37%。

Segmented second algorithm of empirical mode decomposition

A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.

Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.

Design of Second Order Sliding Mode and Sliding Mode Algorithms:A Practical Insight to DC-DC Buck Converter

This paper presents a simple and systematic approach to design second order sliding mode controller for buck converters.The second order sliding mode control(SOSMC)based on twisting algorithm has been implemented to control buck switch mode converter.The idea behind this strategy is to suppress chattering and maintain robustness and finite time convergence properties of the output voltage error to the equilibrium point under the load variations and parametric uncertainties.In addition,the influence of the twisting algorithm on the performance of closed-loop system is investigated and compared with other algorithms of first order sliding mode control such as adaptive sliding mode control(ASMC),nonsingular terminal sliding mode control(NTSMC).In comparative evaluation,the transient response of the output voltage with the step change in the load and the start-up response of the output voltage with the step change in the input voltage of buck converter were compared.Experimental results were obtained from a hardware setup constructed in laboratory.Finally,for all of the surveyed control methods,the theoretical considerations,numerical simulations,and experimental measurements from a laboratory prototype are compared for different operating points.It is shown that the proposed twisting method presents an improvement in steady state error and settling time of output voltage during load changes.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

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。

FUZZY GLOBAL SLIDING MODE CONTROL BASED ON GENETIC ALGORITHM AND ITS APPLICATION FOR FLIGHT SIMULATOR SERVO SYSTEM

To alleviate the chattering problem, a new type of fuzzy global sliding mode controller （FGSMC） is presented. In this controller, the switching gain is estimated by fuzzy logic system based on the reachable conditions of sliding mode controller（SMC）, and genetic algorithm （GA） is used to optimize scaling factor of the switching gain, thus the switch chattering of SMC can be alleviated. Moreover, global sliding mode is realized by designing an exponential dynamic sliding surface. Simulation and real-time application for flight simulator servo system with Lugre friction are given to indicate that the proposed controller can guarantee high robust performance all the time and can alleviate chattering phenomenon effectively.

基于POA-VMD-WT的MEMS去噪方法

针对MEMS传感器所测得的加速度和角速度输出信号噪声较大问题,提出一种基于鹈鹕优化算法(pelican optimization algorithm,POA)的变分模态分解(variational mode decomposition,VMD)结合小波阈值(wavelet threshold,WT)的去噪方法。首先利用POA对VMD的参数组合进行优化选择,然后应用POA-VMD将含噪信号自适应、非递归地分解为一系列本征模态函数(intrinsic mode function,IMF)。再通过计算每个IMF的余弦相似度对IMFs进行分类,根据计算结果将IMFs分为噪声主导分量与信号主导分量,对分类后的噪声主导分量进行改进小波阈值去噪处理,最后对处理后的噪声分量与信号主导分量进行重构,获得降噪后的MEMS传感器信号。静态和动态实验结果表明,该方法去噪处理后信号的信噪比分别提高12和10 dB,均方误差分别降低75.5%和46.6%,去噪效果显著,能够提高MEMS传感器的精度。

Greedy Algorithm Applied to Relay Selection for Cooperative Communication Systems in Amplify-and-Forward Mode

Using a relaying system to provide spatial diversity and improve the system performance is a tendency in the wireless cooperative communications. Amplify-and-forward （AF） mode with a low complexity is easy to be implemented. Under the consideration of cooperative communication systems, the scenario includes one information source, M relay stations and N destinations. This work proposes a relay selection algorithm in the Raleigh fading channel. Based on the exhaustive search method, easily to realize, the optimal selection scheme can be found with a highly complicated calculation. In order to reduce the computational complexity, an approximate optimal solution with a greedy algorithm applied for the relay station selection is proposed. With different situations of the communication systems, the performance evaluation obtained by both the proposed algorithm and the exhaustive search algorithm are given for comparison. It shows the proposed algorithm could provide a solution approach to the optimal one.

Aeroengine Nonlinear Sliding Mode Control Based on Artificial Bee Colony Algorithm

For a class of aeroengine nonlinear systems,a novel nonlinear sliding mode controller(SMC)design method based on artificial bee colony(ABC)algorithm is proposed.In view of the strong nonlinearity and uncertainty of aeroengines,sliding mode control strategy is adopted to design controller for the aeroengine.On basis of exact linearization approach,the nonlinear sliding mode controller is obtained conveniently.By using ABC algorithm,the parameters in the designed controller can be tuned to achieve optimal performance,resulting in a closedloop system with satisfactory dynamic performance and high steady accuracy.Simulation on an aeroengine verifies the effectiveness of the presented method.

USING TLS ALGORITHM TO IMPROVE GPS POSITIONING PRECISION WITH ALTITUDE HOLD MODE

This paper presents a new method of improving Global Positioning System(GPS)positioning precision. Based on the altitude hold mode, the method does not need any other equipment. Under this constraint condition, the Total Least Squares(TLS) algorithm is used to prove that the method is effective. Theoretical analysis shows that the algorithm can significantly improve the GPS positioning precision.

基于混沌控制的采摘机器人防碰撞系统设计

针对采摘机器人在作业过程中遇到障碍物导致路径规划出现混沌现象的问题,基于混沌控制对采摘机器人防碰撞系统进行设计,并采用模糊自适应控制算法和滑模变结构控制算法结构的方式对系统进行控制。为了验证防碰撞系统的有效性,对采摘机器人进行了防碰撞和采摘仿真试验,结果表明:采摘机器人在作业过程中可以避开障碍物,按照规划路线到达预设位置,完成果蔬的采摘。

基于WOA-VMD和香农熵的锂电池早期故障诊断研究

针对当前电池管理系统无法诊断早期故障的问题,本文提出了一种基于WOA-VMD和香农熵的锂电池早期故障诊断方法。首先引入鲸鱼优化算法对变分模态分解算法进行参数寻优,提高变分模态分解算法的分解效果,使之分解得到包含更多故障特征信息的本征模态函数分量,再对单体电池电压信号进行分解重构,减少测量噪声和额外激励电压造成的影响。进而采用滑动窗口计算单体电压的香农熵极差和单体电压离差的总体香农熵,设置合适的阈值进行早期故障诊断。经过实际车辆数据验证,该方法可以提前10 min左右进行故障预警,且对于无故障车辆不会产生虚假预警,具有较强的鲁棒性和可靠性。