A Fast Hidden-line Removal Algorithm for 3-Dimensional Buildings

in this paper, a fast hidden-line removal method for 3D buildings , which is based on thesubtraction of a convex polygon from another one in linear time, is presented. Also, the facetscontaining holes are quickly divided into a lot of convex polygons, say, triangles and convexquadrilaterals. The algorithm for the polygon division runs in O( (k+1) (n-3)) , where n is thetotal number of the vertices of the merged loop of the facet, and k is the number of the concavevertices of the merged one.

Selective harmonic elimination method for wide range of modulation indexes in multilevel inverters using ICA

Selective harmonic elimination(SHE) in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm(ICA). The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.

Unfolding analysis of LaBr3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration

With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.

Optimization Algorithm for Reduction the Size of Dixon Resultant Matrix:A Case Study on Mechanical Application

In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.

Genetic Searching Algorithm for Optimal Runlength-Limited Codes with Error Control

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Algebraic Cryptanalysis of GOST Encryption Algorithm

This paper observes approaches to algebraic analysis of GOST 28147-89 encryption algorithm (also known as simply GOST), which is the basis of most secure information systems in Russia. The general idea of algebraic analysis is based on the representation of initial encryption algorithm as a system of multivariate quadratic equations, which define relations between a secret key and a cipher text. Extended linearization method is evaluated as a method for solving the nonlinear sys- tem of equations.

Genetic Algorithm Based 7-Level Step-Up Inverter with Reduced Harmonics and Switching Devices

This paper presents a unique voltage-raising topology for a single-phase seven-level inverter with triple output voltage gain using single input source and two switched capacitors.The output voltage has been boosted up to three times the value of input voltage by configuring the switched capacitors in series and parallel combinations which eliminates the use of additional step-up converters and transformers.The selective harmonic elimination(SHE)approach is used to remove the lower-order harmonics.The optimal switching angles for SHE is determined using the genetic algorithm.These switching angles are com-bined with a level-shifted pulse width modulation(PWM)technique for pulse generation,resulting in reduced total harmonic distortion(THD).A detailed com-parison has been made against other relevant seven-level inverter topologies in terms of the number of switches,drivers,diodes,capacitors,and boosting facil-ities to emphasize the benefits of the proposed model.The proposed topology is simulated using MATLAB/SIMULINK and an experimental prototype has been developed to validate the results.The Digital Signal Processing(DSP)TMS320F2812 board is used to generate the switching pulses for the proposed technique and the experimental results concur with the simulated model outputs.

An Improved Differential Evolution Whale Algorithm for Economic Load Distribution

An improved optimization algorithm combining the differential evolution algorithm and the whale algorithm is proposed for the problem of not being able to get rid of the local optimum in the economic load distribution algorithm. The algorithm adopts a nonlinear convergence strategy, a crossover strategy of differential evolution and the introduction of an elimination mechanism, which balances the global search and local exploitation ability of the algorithm and improves the accuracy of the solved optimal solution. The 13-unit and 40-unit systems are selected for economic load distribution calculation, and the experimental results show that the proposed improved algorithm is superior in distributing the economic load of the power system and can effectively reduce the economic cost.

Harmonic Minimization in Seven-Level Cascaded Multilevel Inverter Using Evolutionary Algorithm

Inverters are power electronic devices that change over DC to sinusoidal AC quantity. Be that as it may, in down to earth, these devices produce non-sinusoidal yield which contains harmonics, so as to blend a close sinusoidal component and to lessen the harmonic distortion multilevel inverters developed. Mathematical methods, which were developed, are derivative based and need initial considerations. To overcome this, evolutionary algorithms, which are derivative free and accurate, were developed for obtaining multi levels of output voltage. The proposed work uses two evolutionary algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. These algorithms are used to generate the switching angles by satisfying the non linear transcendental equations that govern the low order harmonic components. A seven level cascaded full bridge inverter is designed using MATLAB/Simulink and the results validate the results for switching angles. The Total Harmonic Distortion (THD) value obtained for GA and PSO is 11.81% and 10.84% respectively. The solution obtained from GA algorithm was implemented in hardware using dsPIC controller to validate the simulation results. The THD value obtained for cascaded seven-level multilevel inverter in the hardware prototype is 25.9%.

Using Two Levels DWT with Limited Sequential Search Algorithm for Image Compression

In this paper introduce new idea for image compression based on the two levels DWT. The low-frequency sub-band is minimized by using DCT with the Minimize-Matrix-Size-Algorithm, which is converting the AC-coefficients into array contains stream of real values, and then store the DC-coefficients are stored in a column called DC-Column. DC-Column is transformed by one-dimensional DCT to be converted into T-Matrix, then T-Matrix compressed by RLE and arithmetic coding. While the high frequency sub-bands are compressed by the technique;Eliminate Zeros and Store Data (EZSD). This technique eliminates each 8 × 8 sub-matrix contains zeros from the high frequencies sub-bands, in another hands store nonzero data in an array. The results of our compression algorithm compared with JPEG2000 by using four different gray level images.

基于不同算法筛选糖尿病足溃疡截肢预测模型的比较

目的 探讨不同算法筛选的糖尿病足溃疡(DFU)截肢预测模型。方法 收集2015年1月至2020年12月新疆医科大学第一附属医院收治的364例DFU患者的临床资料,按照截肢情况将其分为截肢组(n=213)和非截肢组(n=151),分别通过单因素分析、Boruta算法和随机森林-递归特征消除(RF-RFE)算法进行截肢危险因素分析,并构建临床预测模型,比较模型的c指数、F1分数和Brier分数,评估模型的预测效能和临床意义。结果 两组患者年龄、高血压病程、冠心病病程、Wagner评分、部位-缺血-神经病变-细菌感染-面积-深度(SINBAD)评分、国际糖尿病足工作组(IWGDF)分级比较,差异均有统计学意义(P﹤0.05)。实验室指标中截肢组患者低密度脂蛋白(LDL)、高密度脂蛋白(HDL)、甘油三酯(TG)、血钙、血磷、白蛋白与球蛋白比值(A/G)、平均血小板分布宽度(PDW)、血红蛋白(Hb)均低于非截肢组患者,截肢组患者球蛋白(GB)、中性粒细胞比例(N)、纤维蛋白原(FIB)、国际标准化比值(INR)、平均红细胞分布宽度(RDW)/白蛋白比率、中性粒细胞/淋巴细胞比值(NLR)、血小板与淋巴细胞比值(PLR)均高于非截肢组患者,差异均有统计学意义(P﹤0.05)。多因素分析结果显示,Wagner分级﹥2级、SINBAD评分﹥3分、FIB、Hb、PDW、INR、年龄均是DFU患者截肢的独立危险因素(P﹤0.05)。传统Logistic回归模型c指数、F1分数和Brier分数分别为0.771、0.809、0.163。采用Boruta算法得出对截肢影响最大的影响因素为年龄、Wagner分级﹥2级、SINBAD评分﹥3分、IWGDF分级﹥3级、A/G、INR、FIB、N、Hb、RDW比白蛋白比率、NLR和PLR,模型c指数、F1分数、Brier分数分别为0.686、0.744、0.163.RF-RFE算法得出DFU截肢危险因素为NLR、PLR、N、肌酐和PDW,模型c指数、F1分数和Brier分数分别为0.748、0.769、0.220。结论 不同算法从不同逻辑对DFU患者截肢的危险因素进行评估,可与传统统计学方法结合,为DFU的治疗决策提供依据互补。

混合级联H桥逆变器的新型SHEPWM功率均衡控制策略

对于直流侧电压比为1∶1∶2的混合级联H桥逆变器,传统的指定谐波消除脉宽调制法(SHEPWM)存在各单元间输出功率不均衡问题,因此,提出一种双层功率均衡调制策略。首先,通过对消谐方程组中的基波幅值方程表达式进行等效拆分,使2个低压H桥单元的输出电压基波幅值之和与高压H桥单元的输出电压基波幅值相等,实现了高低压H桥单元间的功率均衡,即外层功率均衡。其次,利用逻辑运算的方法对低压单元的初始驱动信号进行逻辑重组,实现2个低压单元间的功率均衡,即内层功率均衡;然后使用改进的实数型遗传算法(IRCGA-2)求出消谐方程组的解,消除指定的谐波,得到高质量的输出电压。最后,通过仿真和实验证明了调制策略的正确性与有效性。

改进蛇算法的七电平逆变器SHEPWM研究

多电平逆变器控制策略中的特定谐波消除脉宽调制(SHEPWM)能够以较低的开关频率得到较好的电压输出波形,重点在于开关角的求取。在多电平逆变器SHEPWM调制的开关角求解算法上提出改进蛇优化算法,对传统蛇优化算法采用Tend混沌映射初始种群,使初始种群分布更加均匀;引入Levy飞行策略增强算法的全局搜素能力,能够更加精准快速得到全局开关角的最优解。仿真结果表明,改进蛇优化算法所求的开关角相较于传统算法更好地消除低次谐波,并降低了输出线电压的畸变率,充分验证了该方法的可行性。

基于决策树算法的电子通信网络数据流冗余消除方法

现有的消除方法在消除数据流冗余数据时,空间缩减比例过低,导致消除效果不理想。针对这一问题,文章引入决策树算法,设计了新的电子通信网络数据流冗余消除方法。在对电子通信网络数据进行集成处理后,文章利用决策树算法,对电子通信网络数据流进行分类。然后,结合相同类别数据的相似度计算结果,文章对数据流中的冗余数据进行了迭代消除。实验表明:应用新方法后,电子通信网络数据流空间缩减比例显著提升,说明新方法的消除效果更理想。

基于RF-RFE算法的地铁车站洪涝灾害预测研究

地铁车站形式以地下段为主,进入雨期时受到洪涝灾害的威胁,易发生雨水倒灌现象,严重影响居民出行和地铁安全运营。为进一步提高地铁车站洪涝灾害预测的效果,提出基于RF-RFE和DNN神经网络的地铁车站洪涝灾害预测方法。首先,通过收集并分析已发生地铁车站洪涝灾害的案例,采用文献综述结合专家访谈的方法,构建地铁车站洪涝灾害初始变量集;然后,利用随机森林—递归特征消除(RF-RFE)算法,计算初始变量重要性并完成变量分类正确率排序,从初始变量集中筛选出重要变量;最后,建立DNN神经网络预测模型,并以筛选出的重要变量作为输入样本,训练DNN神经网络,完成对地铁车站洪涝灾害的预测。研究结果表明:(1)变量选择可提高预测模型精度,与初始变量集的DNN神经网络预测模型相比,数据筛选后的DNN神经网络预测模型准确率提高了4.36%;(2)RF-RFE和DNN神经网络算法结合具有良好的效果,预测模型准确率为88.1%,F1分数为0.9。

基于改进型随机森林算法的页岩岩性识别——以准噶尔盆地芦草沟组为例

在储集层岩性识别的应用中,特别是对页岩等非均质性较强的非常规储集层的岩性识别,机器学习算法的高效性、准确性和有效信息整合能力已经得到了充分验证。考虑到岩性识别的特征参数优选问题,优选自然伽马、T2几何平均值、结构指数、骨架密度指数、密度和深侧向电阻率,采用结合递归特征消除的随机森林算法,对准噶尔盆地中二叠统芦草沟组页岩储集层的主要岩性进行识别;利用传统的随机森林算法和支持向量机法,对同一套资料进行岩性预测,并与岩石薄片鉴定结果对比。结合递归特征消除的随机森林算法只需选择一半的测井参数,便能够达到更好的效果,而且通过优选特征参数,缩短了算法的运行时间。因此,结合递归特征消除的随机森林算法能够实现测井特征参数的优选,提高页岩岩性识别的准确率,缩短运行时间,为复杂岩性识别和多参数选择提供了新的思路。

基于ORB-SLAM2的改进特征匹配与稠密地图算法

针对ORB-SLAM2算法在特征匹配时容易出现误匹配以及无法构建稠密地图的问题,引入GMS算法来改进ORB-SLAM2算法中的误匹配问题以及添加稠密地图线程。首先,建立图像金字塔,对每层图像金字塔进行网格划分,提取特征点,对每个网格中的特征点引入四叉树策略进行筛选,得到均匀的特征点;其次,在特征匹配阶段引入GMS算法剔除误匹配;最后,根据位姿估计和关键帧构建稠密点云地图。通过TUM数据集上的实验验证,结果表明改进算法的匹配数比原ORB-SLAM2算法增加了18.36%,匹配用时减少了8.53%,将改进算法应用于移动机器人自动导航和避障中,能够提高系统的可靠性和运行效率。