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双语推荐：改进主成分分析法

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针对基于局部模式的人脸识别方法特征维数高、计算复杂度高、识别时间长的问题，提出一种结合主成分分析和局部导数模式的人脸识别方法，并针对如何解决光照、人脸表情等方面的问题提出了改进的编码方法。该方法首先将人脸图像分成很多小的区域，然后在每一个小区域中用改进的编码方法进行编码，并建立该区域的局部导数直方图，然后采用主成分分析法对所有直方图向量进行降维得到特征向量，最后利用最近邻分类器计算相似度。实验表明，这里提出的结合主成分分析和局部导数模式方法无论在识别率还是在运算速度上都优于传统的识别算法。
In order to solve the problems of high feature dimension,high computational complexity and long recognition time caused by the face recognition method based on local pattern,a face recognition method combining principal component analysis and local derivative model is presented in this paper. The improved coding method is introduced to solve the problems of illumination,facial expression and so on. The face image is divided into many small regions,and then each small area is coded by the improved coding method and a LDP histogram of the region is created. The dimension of histogram vectors are reduced by using principal component analysis. Finally,the nearest neighbor classifier is used to calculate the similarity. Experimental re-sults show that the presented method is superior to the traditional method in the recognition rate and the speed.

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Shapelet序列分析为时间序列分类提供了一种快速分类的方法，但Shapelet序列抽取速度很慢，限制了它的应用范围。为了加快 Shapelet 序列的提取，提出了一种基于主成分分析的改进方法。首先运用主成分分析法（PCA）对时间序列数据集进行降维，采用降维后的数据表示原数据，然后对降维后的数据提取出最能代表类特征的Shapelet序列。实验结果表明：本方法在保证分类准确率的前提下，提高了运算速度。
Shapelet provides a fast classification method in time series classification, but the extraction of time series Shapelet is so slow that it restricts the application of the Shapelet. In order to speed up the extraction of time series Shapelet, an improved method is proposed based on the principal component analysis. Firstly, it uses the principal component analysis (PCA) to reduce the dimension of time series data set and chooses the reduced data to represent the original data. Secondly, it can extract the most discriminatory Shapelet sequence from the reduced data. Lastly, the experimental results show that the improved method improves the speed of the extraction and ensures the accuracy of classification.

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在利用工业CT对产品进行检测过程中,扫描重建模型并与产品原始模型进行配准是其中一个关键步骤。经典的主成分分析方法在粗配准中存在主方向不确定的问题,可能导致配准失败,为此,提出了主方向的修正方法。首先利用经典主成分分析方法得到配准结果,再求出两模型形心与质心的位置关系矢量,通过分析各种反向情况中该矢量的投影特征,判断配准结果是否存在反向问题,并通过计算旋转轴线来修正模型方向。开发了粗配准实验软件,对改进后的方法进行了实验,实际应用验证了该方法可以有效解决使用主成分分析法配准时产生的模型反向问题。
Scan of the rebuilt model and original product model is one of the key steps for the registration when scanning the prod -uct by the industrial CT .The problem of the uncertain main direction method existed in the rough registration algorithm for Princi-pal Component Analysis (PCA),which could result in the registration failure.Proposed the main direction method to solve the problem.Firstly a registration result was worked out by PCA ,then calculated the relative vector from model centroid to geometric center according to analyze the projection characteristics of the vector in each reverse situation ,determined whether there was the opposite problem or not for the registration result ,and the correct direction was worked out by the rotation axis .The experiment software is developed for the rough registration algorithm and the improved method is used to make experiment .It shows that the method can effectively solve the opposite direction problem of the model by PCA .

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运用改进S型主成分分析法对我国31省市经济发展和生态环境两大系统进行综合评价,采用模糊隶属度方法测度了各地区的协调水平,在此基础上,对不同空间尺度的区域协调发展程度差异进行比较分析,揭示区域经济与生态环境协调发展的凸显问题并提出相应的对策建议。
This paper firstly uses the improved s - principal component analysis to evaluate the economic development sys-tem and the ecological environment system synthetically,and then measures the level of coordination of different regions, applying the fuzzy membership theory. On this basis the paper compares and analyzes the differences of coordinating degree of different regions,from which we find the issues in the coordinated development of regional economy and ecological envi-ronment and propose some corresponding suggestions and countermeasures.

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针对煤与瓦斯突出预测效率和准确率不高这一问题,提出将主成分分析(PCA)法与改进的极端学习机(PSO-ELM)相结合的方法对煤与瓦斯突出进行预测。根据某煤矿地质动力区划方法,在划分活动断裂,岩体应力计算等工作基础上获取影响突出的相关数据;通过主成分分析法对原始数据进行降维处理,消除变量间的线性相关性;利用粒子群算法(PSO)对极端学习机(ELM)的输入权值和隐层阈值进行优化,建立PSO-ELM预测模型,将提取的主成分作为该模型的输入,煤与瓦斯突出强度作为模型输出。实验结果表明,该方法的预测精度高、结构简化,具有较强的泛化性能力强。
In order to solve the problems of low efficiency and accuracy of the coal and gas outburst prediction,in the paper, primary component analysis ( PCA ) combined with improved extreme learning machine ( PSO-ELM ) method for prediction of the coal and gas outburst is proposed. According to a coal mine geology dynamic division method,prominent influenced relevant data is acquired by the basic work of divisions of active faults and rock mass stress calculation. Through the primary component analyze method to reduce the dimension of the original data, eliminate the linear correlation volume. Using particle swarm optimization( PSO) to optimize the input weights and hidden layer threshold of extreme learning machine ( ELM ) , establish PSO-ELM prediction model, treat the extractive principal components as the input of the prediction model,the outburst intensity of coal and gas as the model output. The results show that the method has high accuracy of the prediction, simplification of

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评价指标体系建立的是否合理决定着人们能否对评价对象有个正确的认识。本文提出一种基于改进灰色关联度的评价指标体系构建方法，该方法首先计算出各因素与系统的关联度，采用德尔菲法对专家打分进行处理，打分结果作为各因素重要性的得分；然后将因素的关联度与重要性得分相结合，排序后筛选出初始指标；最后通过对初选指标之间的相关性分析，求解出有效的指标体系。比较实验是在真实的数据集上进行的，实验结果证明改进的灰色关联度分析法明显优于主成分分析法。因此可以认为改进后的方法能够有效的实现指标筛选。
@@@@Whether evaluation objects can be understood comprehensively or not depends on a reasonable evaluation index system. In this paper, a novel evaluation index system construction method based on improved grey correlative degree analysis has been proposed. Firstly, the correlation between various factors and the system has been calculated, dealing with expert scores based on the Delphi method and assigning the results to the importance score of each factor. Secondly, the correlative degree has been combined with the importance score and the initial indicators have been screened after sorting. Finally, an effective index system has been drawn out through analyzing the correlations of primary indexes. Comparison experiment has been conducted on real data sets. The experimental results demonstrate that the improved grey correlation analysis method outperforms the principal component analysis method. It suggests that the proposed method can be applied for achieving effective ind

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车辆在特定道路工况条件下的性能能够为汽车系统的设计和改进提供参考。目前，深圳市还没有建立起典型的城市道路工况。文章基于深圳市 M347路公交车的行驶数据，利用主成分分析法和聚类分析法来构建公交车线路行驶工况。由聚类分析将原始短行程数据分为两类，可以体现公交线路中拥挤和畅通两种不同行驶状况。文章所构建的综合工况能较好地反映该线路的道路工况，对于研究分析具有深圳特点的行驶工况和优化车辆性能具有一定参考意义。
The performance of automotive under speciifc drive cycles can provide a reference for the development and improvement of vehicle systems. At present, there is no representative typical driving cycle in Shenzhen. In this paper, the development of M347 bus route driving cycle in Shenzhen was reported, combining principal component analysis and clustering analysis methods, and the rationality and the validity of the driving cycle were tested and veriifed. The original micro-trips are divided into two series by the clustering analysis method, which may explain trafifc jam and smooth trafifc driving conditions of M347 bus route. The M347 route multiple driving cycle constructed at last can relfect the real-world road conditions.

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全球低碳化进程方兴未艾,低碳电力的发展模式与技术方案层出不穷,亟须对其生产运营效率进行准确评价。文中提出一种以低碳电力效率指数为核心的低碳电力生产效率评估新思路。在超效率数据包络分析(DEA)的基础上,充分考虑模型非径向松弛变量带来的非期望产出误差以及低碳评价指标间的信息交叠,利用主成分分析法对超效率DEA模型进行改进,并引入低碳投入偏好因子与低碳投入主成分,重构DEA优化模型目标函数以提高模型精度。最后通过具体算例完成不同省(市)间低碳电力生产效率水平测算、综合评价、模型对比及参数灵敏度分析,从而验证了评估模型的有效性和准确性。
With the global low-carbon revolution growing rapidly,the development patterns and technical plans of low-carbon electricity emerge in an endless stream.It is imperative to make precise evaluation of the production efficiency of low-carbon electricity.A new line of thought for low-carbon electricity benefits evaluation with the low-carbon index as the core is proposed.Based on the data envelopment analysis(DEA),the super efficiency DEA model is improved by principal component analysis,with the unexpected error caused by non-radial relaxation variables and the information overlap existing in low-carbon evaluation indices considered.The low-carbon input preference factor and low-carbon input principal component are also introduced while the objective function of optimization model is modified to improve model accuracy.Finally an example is given to prove the validity and correctness of the model with low-carbon electricity production efficiency calculation, comprehensive evaluation,model

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对电能质量各项指标进行综合评估，可挝化现有各层面电能质碴状况，便于实现电力市场的按质定价？利用改进的层次分析法币¨主成分分析法确定各指标的组合权重，同时将改进的逼近理想解排序（TOPSlS）法与秩和比（RSR）法应州于电能质量综合评估，将观测点电能质最状况与础想解、闰怀允许值和自设的等级值结合到一起进行排序和分卡当，从而快速实现电能质域综合评估。实例计算结果表明，采用上述方法具有一定的有效性和可行性。
The quantized result from synthetic evaluation of power quality is one of the foundations to assess power quality and is favorable to implement the market mechanism of pricing electric ener-gy according to power quality. This paper uses the improved AHP and the principal component analysis (PCA) to determine the index of the combination weights;meanwhile, the improved TOPSIS and rank sum ratio(RSR) method is applied to the comprehensive assessment of the power quality, so the observation point of the power quality and the ideal value, standard value and rank values set up can be sorted and classified together, and power quality comprehensive evaluation can be achieved quickly. Calculation results of case study show that the proposed synthetical evaluation method is effective and feasible.

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为解决指挥信息系统在复杂电磁环境下对抗训练效果的评估问题,分析了复杂电磁环境影响信息系统效能的相关因素,建立了指挥信息系统对抗训练效果评估指标体系.并针对传统模糊综合评估模型在确定指标权重时主观性较大、利用信息不充分的问题,提出一种改进的模糊综合评判算法,采用主成分分析法代替专家法确定指标权重,采用加权平均算子代替取大取小算子,克服了以上问题.实例分析表明该模型能够实现训练效果的量化分析,为提高训练质量提供参考依据.
In order to evaluate the exercise efficiency of command information system in complex electromagnetic environments ,establish the command information system combat training effect evaluation index system base on the analysis of the factors what impact the system Effectiveness .Aiming at the problems of traditional fuzzy synthetic evaluation model in practical application ,which are much subjective as calculating weight of index and do not make full use of all information ,the following methods were improved :using the principal component analysis to replace the Experts to to calculate the weight of index ,using weighted average operator to replace classical big or small operator .Finally ,the results show that the method can realize the quantitative analysis of efficiency evaluation , which prove a reference foundation to improve of the quality of training .

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