基于Projection Pursuit-RAGA-NK-GERT的质量绩效提升路径优化——以组织质量特异性免疫和产品生命周期为主导逻辑的解释框架

依据组织免疫和组织质量特异性免疫的相关理论,引入情境变量产品生命周期,构建基于组织质量监视、组织质量防御软要素、组织质量防御硬要素和组织质量记忆的质量绩效提升路径理论框架。使用基于RAGA的投影寻踪模型、适应度景观和NK模型对产品生命周期不同阶段的质量绩效提升路径进行实证分析,实证结果表明:产品生命周期不同阶段有不同的质量绩效提升路径,产品导入期遵循的质量绩效提升路径为组织质量监视→组织质量防御→组织质量记忆,产品成长期遵循的质量绩效提升路径为组织质量防御软要素→组织质量防御硬要素→组织质量监视→组织质量记忆,产品成熟期遵循的质量绩效提升路径为组织质量防御硬要素→组织质量防御软要素→组织质量监视→组织质量记忆,产品衰退期遵循的质量绩效提升路径为组织质量记忆→组织质量监视→组织质量防御。并建立产品生命周期不同阶段质量绩效提升路径优化的GERT网络,优化产品生命周期不同阶段的质量绩效提升路径。

Projection pursuit cluster model and its application in water quality assessment

One of the difficulties frequently encountered in water quality assessment is that there are many factors and they cannot be assessed according to one factor, all the effect factors associated with water quality must be used. In order to overcome this issues the projection pursuit principle is introduced into water quality assessment, and projection pursuit cluster(PPC) model is developed in this study. The PPC model makes the transition from high dimension to one-dimension. In other words, based on the PPC model, multifactor problem can be converted to one factor problem. The application of PPC model can be divided into four parts: (1) to estimate projection index function Q(); (2) to find the right projection direction ; (3) to calculate projection characteristic value of the i th sample z-i, and (4) to draw comprehensive analysis on the basis of z-i. On the other hand, the empirical formula of cutoff radius R is developed, which is benefit for the model to be used in practice. Finally, a case study of water quality assessment is proposed in this paper. The results showed that the PPC model is reasonable, and it is more objective and less subjective in water quality assessment. It is a new method for multivariate problem comprehensive analysis.

Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain, China

A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.

Projection Pursuit Dynamic Cluster Model and its Application to Water Resources Carrying Capacity Evaluation

The research shows that projection pursuit cluster (PPC) model is able to form a suitable index for overcom-ing the difficulties in comprehensive evaluation, which can be used to analyze complex multivariate prob-lems. The PPC model is widely used in multifactor cluster and evaluation analysis, but there are a few prob-lems needed to be solved in practice, such as cutoff radius parameter calibration. In this study, a new model-projection pursuit dynamic cluster (PPDC) model-based on projection pursuit principle is developed and used in water resources carrying capacity evaluation in China for the first time. In the PPDC model, there are two improvements compared with the PPC model, 1) a new projection index is constructed based on dynamic cluster principle, which avoids the problem of parameter calibration in the PPC model success-fully;2) the cluster results can be outputted directly according to the PPDC model, but the cluster results can be got based on the scatter points of projected characteristic values or the re-analysis for projected character-istic values in the PPC model. The results show that the PPDC model is a very effective and powerful tool in multifactor data exploratory analysis. It is a new method for water resources carrying capacity evaluation. The PPDC model and its application to water resources carrying capacity evaluation are introduced in detail in this paper.

Seismic comprehensive forecast based on modified project pursuit regression

In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression （PPR） is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation： to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast.

Regional partitioning of agricultural non-point source pollution in China using a projection pursuit cluster model

A projection pursuit cluster（PPC） model was used to analyze the regional partitioning of agricultural non-point source pollution in China.The environmental factors impacting the agricultural non-point source pollution were compiled into a projection index to set up the projection index function.A novel optimization algorithm called Free search（FS） was introduced to optimize the projection direction of the PPC model.By making the appropriate improvements as we explored the use of the algorithm,it became simpler,and developed better exploration abilities.Thus,the multi-factor problem was converted into a single-factor cluster,according to the projection,which successfully avoided subjective disturbance and produced objective results.The cluster results of the PPC model mirror the actual regional partitioning of the agricultural non-point source pollution in China,indicating that the PPC model is a powerful tool in multi-factor cluster analysis,and could be a new method for the regional partitioning of agricultural non-point source pollution.

Projection Pursuit Flood Disaster Classification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization

The indicators of flood damage assessment in the flood classification are often incompatible, and it is very difficult to use those indicators value directly for classification assessment. Projection pursuit technology can project higher dimensional incompatible data into lower dimensional sub-space, and find the projection values for optimal projection index function to get the higher dimensional data structure features, which has been improved to be reasonable and effective for flood disaster classification assessment. However, it is a bit difficult to optimize the parameters of projection index functions, as a result, that limits the applications of this method. As an emerging heuristic global optimization algorithm based on swarm intelligence, particle swarm optimization algorithm has the ability of solving complex optimization problem, but it still be easily convergent early, and can not search the global optimal solution. In this paper, a flood disaster classification assessment method based on multi-swarm cooperative particle swarm optimization is proposed, which adopts a tri-parameter Logistic curve to construct the flood disaster projection pursuit model, and uses mul-ti-swarm system particle swarm optimization method to optimize the parameters of the projection index functions. The typical test function experiment shows that this optimization method can solve the early convergence commonly found in standard particle swarm optimization algorithm, which global optimized ability is improved greatly. Applied in flood disaster assessment in HeNan Province, the results using this method comparing with others indicates that it can assess effectively the flood disaster, and has better assessment accuracy and disaster resolution.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

A Genetic Fuzzy Analytical Hierarchy Process Based Projection Pursuit Method for Selecting Schemes of Water Transportation Projects

The optimal selection of schemes of water transportation projects is a process of choosing a relatively optimal scheme from a number of schemes of water transportation programming and management projects, which is of importance in both theory and practice in water resource systems engineering. In order to achieve consistency and eliminate the dimensions of fuzzy qualitative and fuzzy quantitative evaluation indexes, to determine the weights of the indexes objectively, and to increase the differences among the comprehensive evaluation index values of water transportation project schemes, a projection pursuit method, named FPRM-PP for short, was developed in this work for selecting the optimal water transportation project scheme based on the fuzzy preference relation matrix. The research results show that FPRM-PP is intuitive and practical, the correction range of the fuzzy rained is both stable and accurate; preference relation matrix A it produces is relatively small, and the result obtherefore FPRM-PP can be widely used in the optimal selection of different multi-factor decision-making schemes.

The application of projection pursuit classification in the process of strategy selection and evaluation based on the real coded accelerating genetic algorithm

During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.

A Case Retrieval Projection Pursuit Model for Configuration Design of a Product Family

The paper studies on case-based reasoning of uncertain product attributes in configuration design of a product family. Interval numbers characterize uncertain product attributes. By interpolating a number of certain values randomly to replace interval numbers and making projection pursuit analysis on source cases and target cases of expanded numbers, we can get a projection value in the optimal projection direction. Based on projection value, we can construct a case retrieval model of projection pursuit that can handle coexisting certain and uncertain product attributes. The application examples of chainsaw configuration design show that case retrieval is highly sensitive to reliable results.

Benefit Evaluation Model of Small Watershed Control Based on Projection Pursuit

A projection pursuit model is presented in this paper for comprehensive evaluation of benefits of small watershed control.By using the model,small watershed control samples with many benefit evaluation indexes can be synthesized projective values with one dimension.The samples can be naturally evaluated according to the projective values.The parameters of the model is optimized by using real coding beased accelerating genetic aglrothm,which overcomes the shortcomings of large computation amount and difficulty of computer programming in traditional projection prusuit methods,and provides a new way for wide applications of projection pursuit technique to different evaluation problems in agricultural systems engineering.

Projection pursuit model of vehicle emission on air pollution at intersections based on the improved bat algorithm

The projection pursuit model is used to study the assessment of air pollution caused by vehicle emissions at intersections. Based on the analysis of the characteristics and regularities of vehicle emissions at intersections, a vehicle emission model based on projection pursuit is established, and the bat algorithm is used to solve the optimization function. The research results show that the projection pursuit model can not only measure the air pollution of vehicle emissions at intersections, but also effectively evaluate the level of vehicle exhaust emissions at intersections. Taking the air pollution caused by vehicle emissions at intersections as the research object and considering the influence factors of vehicle emissions on air pollution comprehensively, the evaluation index system of vehicle emissions at intersections on air pollution is constructed. Based on large data analysis, a prediction model of air pollution caused by vehicle emissions at intersections is constructed, and an improved bat algorithm is used to realize the assessment process. The application results show that the prediction model of vehicle emissions at intersections can define the degree of air pollution caused by vehicle emissions, and it has good guiding significance and practical value for solving the problem of air pollution caused by vehicle emissions.

CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

Approach to Weighted Geometric Evaluation Based on Projection Pursuit

Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.

A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

Accelerated Matrix Recovery via Random Projection Based on Inexact Augmented Lagrange Multiplier Method

In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.

基于投影寻踪模型的特高含水油藏剩余油可采潜力评价方法

影响特高含水油藏剩余油可采潜力的因素极其复杂,且各因素的影响程度差异明显,常规方法多以剩余油饱和度或剩余油储量丰度等单一指标评价剩余油潜力,难以有效指导特高含水油藏剩余油挖潜。在充分考虑特高含水油藏剩余油可采潜力影响因素的基础上,综合表征储层非均质性、剩余油可采储量规模、水淹状况以及油水分流能力的差异,构建了特高含水油藏剩余油可采潜力量化评价指标体系,并考虑不同指标对剩余油可采潜力控制程度的差异,将加速遗传算法与投影寻踪模型相结合来确定各评价指标的客观权重,从而构建了剩余油可采潜力指数,形成特高含水油藏剩余油可采潜力量化评价新方法。以渤海Q油田南区主力产层NmIL砂体为例,开展特高含水油藏剩余油可采潜力量化评价,结果表明,新方法可综合表征不同区域位置的储层物性、可采储量丰度和油水分流能力对剩余油可采潜力的影响,实现了主力产层NmIL砂体剩余油可采潜力分布的差异化定量评价,优势可采潜力区域刻画明显,将其作为NmIL砂体下一步井网加密调整潜力区域,以精准指导加密水平井的部署,为特高含水油藏剩余油挖潜提供了一种全新的分析方法与思路。

基于蜣螂算法优化的投影寻踪生态环境评价方法构建

矿山开采会破坏和占用大量土地资源,对生态环境造成持久的负面影响,因此评价矿区生态环境质量变化迫在眉睫。为了准确监测矿区生态环境质量,以陕煤集团张家峁矿区2000—2023年24期逐年的Landsat影像为基础数据,通过计算绿度(Normalized Difference Vegetation Index,NDVI)、湿度(Humidity Index,WET)、热度(Land Surface Temperature,LST)、干度(Normalized Differential Build-up and bare Soil Index,NDBSI)4项生态指标,并采用基于果蝇优化算法的投影寻踪(Fruit Fly Optimization Algorithm-Projection Pursuit Clustering,FOA–PPC)、基于粒子群优化算法的投影寻踪(Particle Swarm Optimization-Projection Pursuit Clustering,PSO–PPC)、基于灰狼优化算法的投影寻踪(Grey Wolf Optimizer-Projection Pursuit Clustering,GWO–PPC)和基于蜣螂优化算法的投影寻踪(Dung Beetle Optimizer-Projection Pursuit Clustering,DBO–PPC)4种群智能优化算法构建矿区生态环境质量评价方法,并利用平均相关度进行精度验证。结果表明:①DBO–PPC模型的平均相关度和类内聚集度均高于PSO–PPC模型、FOA–PPC模型和GWO–PPC模型,且与EI指数更接近,表明DBO–PPC能更好的评估研究区生态环境;②基于DBO–PPC模型的张家峁矿区2000—2023年生态环境质量均值为0.4,生态环境质量以差和较差等级为主,面积占比约55.94%,空间上呈西部差东部好,常家沟水库的生态环境在研究期内表现为优等级,矿区东北部和中部区域的生态环境较好,植被覆盖较多;③矿区发生沉陷面积占比为81.28%,沉陷最大值达−0.15 m;采区发生沉陷现象面积占比明显高于矿区,占采区面积的89.56%,生态环境质量以−0.0004的速率下降,表明采矿活动使得研究区的地表发生了沉陷,进而影响到生态环境。综上所述DBO–PPC模型在监测评价矿区生态环境质量方面具有较强的合理性,从而为矿区生态环境可持续发展提供技术手段。

基于投影寻踪模型的水-能源-粮食-人口系统安全评价

河北省作为京津冀城市圈中不可或缺的一部分,其水、能源、粮食、人口等资源安全稳定对于整个区域经济和社会的发展尤为重要。为了深入了解河北省水-能源-粮食-人口系统的安全状况,将人口因素纳入水-能源-粮食系统进行分析,构建水-能源-粮食-人口系统的安全评价指标体系,运用基于麻雀算法改进的投影寻踪模型对河北省2010—2019年水-能源-粮食-人口系统安全进行时空演变综合评价。结果表明:在研究期,河北省水-能源-粮食-人口系统安全等级逐渐升高,各市的水-能源-粮食-人口系统安全差异性降低;河北省各市水-能源-粮食-人口系统安全等级值在1.32~3.95之间,说明该系统安全处于不安全、亚安全、安全3个等级。建议河北省根据自身资源优势,合理分配资源,实现水、能源、粮食和人口的协调发展。