Response surface method using grey relational analysis for decision making in weapon system selection

A proper weapon system is very important for a na- tional defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multiple- attribute decision making （MADM） problem. This paper proposes a new mathematical model based on the response surface method （RSM） and the grey relational analysis （GRA）. RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to an- alyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.

GREY RELATIONAL ANALYSIS: A NEW STATISTICAL METHOD OF MULTIFACTORIAL ANALYSIS IN MEDICINE

The concept, fundamental theory, analytical steps and formulae of grey relational analysis (GRA)-a new statistical method or multifactorial analysis in the field of medicine were introduced. GRA of grouping sequence that is applied to medical study was built by the authors. An example was given to demonstrate it. The superiority of GRA was recounted briefly.

Multi-attributed decision making for mining methods based on grey system and interval numbers

In the case of unknown weights, theories of multi-attributed decision making based on interval numbers and grey related analysis were used to optimize mining methods. As the representative of independence for the indicator, the smaller the correlation of indicators is, the greater the weight is. Hence, the weights of interval numbers of indicators were determined by using correlation coefficient. Relative closeness based on positive and negative ideal methods was calculated by introducing distance between interval numbers, which made decision making more rational and comprehensive. A new method of ranking interval numbers based on normal distribution was proposed for the optimization of mining methods, whose basic properties were discussed. Finally, the feasibility and effectiveness of this method were verified by theories and practice.

Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology

There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.

Method“Monte Carlo”in healthcare

In public health,simulation modeling stands as an invaluable asset,enabling the evaluation of new systems without their physical implementation,experimentation with existing systems without operational adjustments,and testing system limits without real-world repercussions.In simulation modeling,the Monte Carlo method emerges as a powerful yet underutilized tool.Although the Monte Carlo method has not yet gained widespread prominence in healthcare,its technological capabilities hold promise for substantial cost reduction and risk mitigation.In this review article,we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts.We underscore the significance of experiential insights derived from simulated experimentation,especially in resource-constrained scenarios where time,financial constraints,and limited resources necessitate innovative and efficient approaches.As public health faces increasing challenges,incorporating the Monte Carlo method presents an opportunity for enhanced system construction,analysis,and evaluation.

桥梁监测数据的车辆荷载效应分析与预测方法研究

桥梁监测积累了大量数据,通过对这些数据的深入分析和挖掘,可进一步掌握桥梁结构受力状态,对桥梁养护决策具有重要意义。从车辆荷载效应分析入手,提出了一种基于监测数据概率分析的桥梁安全评估方法。在经验模态分解(EMD)的基础上从原始监测数据中分离出车辆荷载产生的瞬态效应,并进行统计分析,获取每日最大挠度的概率分布,利用平稳随机过程外推其一段时期内的最大值概率分布,从而获得桥梁今后一段时间内车辆荷载效应的最大值,并与设计值进行对比分析,为在役桥梁的科学养护提供可靠的预测依据。

An Empirical Bayes Approach to Robust Variance Estimation: A Statistical Proposal for Quantitative Medical Image Testing

The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, stipulate four response categories: CR (complete response), PR (partial response), PD (progressive disease), SD (stable disease) based purely on percentage changes without consideration of any measurement uncertainty. In this paper, we propose a statistical procedure for tumor response assessment based on uncertainty measures of radiologist’s measurement data. We present several variance estimation methods using time series methods and empirical Bayes methods when a small number of serial observations are available on each member of a group of subjects. We use a publically available database which contains a set of over 100 CT scan images on 23 patients with annotated RECIST measurements by two radiologist readers. We show that despite of bias in each individual reader’s measurements, statistical decisions on tumor change can be made on each individual subject. The consistency of the two readers can be established based on the intra-reader change assessments. Our proposal compares favorably with the RECIST standard protocol, raising the hope that, statistically sound decision on change analysis can be made in future based on careful variability and measurement uncertainty analysis.

Image Classification using Statistical Learning Methods

In general, digital images can be classified into photographs, textual and mixed documents. This taxonomy is very useful in many applications, such as archiving task. However, there are no effective methods to perform this classification automatically. In this paper, we present a method for classifying and archiving document into the following semantic classes: photographs, textual and mixed documents. Our method is based on combining low-level image features, such as mean, Standard deviation, Skewness. Both the Decision Tree and Neuronal Network Classifiers are used for classification task.

Stock Market Prediction Using Generative Adversarial Networks(GANs):Hybrid Intelligent Model

The key indication of a nation’s economic development and strength is the stock market.Inflation and economic expansion affect the volatility of the stock market.Given the multitude of factors,predicting stock prices is intrinsically challenging.Predicting the movement of stock price indexes is a difficult component of predicting financial time series.Accurately predicting the price movement of stocks can result in financial advantages for investors.Due to the complexity of stock market data,it is extremely challenging to create accurate forecasting models.Using machine learning and other algorithms to anticipate stock prices is an interesting area.The purpose of this article is to forecast stock market values to assist investors to make better informed and precise investing decisions.Statistics,Machine Learning(ML),Natural language processing(NLP),and sentiment analysis will be used to accomplish the study’s objectives.Using both qualitative and quantitative information,the study developed a hybrid model.The hybrid model has been handled with GANs.Based on the model’s predictions,a buy-or-sell trading strategy is offered.The conclusions of this study will assist investors in selecting the ideal choice while selling,holding,or buying shares.

自然资源统计分析与辅助决策系统设计与实现

针对自然资源专项统计数据存储分散、归集简单、来源不一,无法为自然资源部门精准决策提供有力依据的问题,本文探索利用空间地理信息、互联网、大数据、云计算等信息处理技术,设计了自然资源统计分析与辅助决策系统,实现了基于大数据的自然资源态势感知、综合研判和辅助决策支持。本文重点介绍了指标体系构建和基于“国土调查云”的内外网数据交换与更新两个关键技术,以及系统的指标体系设计与总体框架设计;详细阐述了系统指标展示与查询、专题分析报告、关键指标预警、业务应用等具体功能的实现。

带式输送机智能运维专家系统的设计与应用

为实现带式输送机运输系统及设备状态监测、故障预警、预防性维护、远程维护、维修决策、优化运行、检修指导等功能,以带式输送机运行、维护、检修和设备管理为中心,构建带式输送机装备智能运维专家系统,系统通过井下环网采集带式输送机的实时数据、故障记录,并结合带式输送机的机械特性、设备机理模型和知识库,经过数据分析和推理,为带式输送机的运维管理提供检修计划和辅助决策,避免重大设备故障,可有效提高带式输送机的运行效率和可靠性。

支持石油勘探开发决策的新方法——分形几何与地质统计学

在油气勘探开发中，科学决策的依据是由各种概念模型或数学模型给出的有关油气藏规模和空间分布的预测及储量的合理估算。根据油气勘探中、后期阶段决策的特点，把决策分为战略决策和管理决策，重点讨论了分形几何在油气藏规模分布和空间分布预测以支持战略决策方面的应用前景和地质统计学在储量计算、风险评价以支持管理决策方面的应用前景，并针对我国东部盆地处于勘探开发的中、后期，建议今后应加强有关方面的应用研究。

基坑支护方案灰色多目标决策优选模型的建立与应用

针对基坑支护方案影响因素的信息不完全性、模糊性以及影响因素与评价指标之间灰色关联性特点,综合应用灰色系统理论和多目标决策理论,以及无限方案多目标决策方法,确定有限方案多目标决策权系数的客观赋权法,建立基坑支护方案的灰色多目标决策优选模型。结合工程实例,对3种不同基坑支护方案进行优选。研究结果表明:拱型水泥土加刚架式钻孔桩空间组合支护方案为最优方案,实际工程采纳及实施效果良好;该决策模型科学、实用,可避免指标权重确定的主观随意性,可为类似工程提供参考。

灰色局势决策在水稻新品种评价中的应用

根据灰色系统理论中的灰色局势决策原理,利用目标赋权获得加权局势决策方法,分析1999～2000年云南省中北部粳稻区域试验中的13个农艺性状指标,对13个参试水稻品种的表现进行了综合分析。结果表明:灰色局势决策法是分析水稻品种的一种较为全面的量化分析方法,能对品种的优劣势进行更为客观的评价,并且弥补了方差分析方法的不足。

基坑支护方案的灰色模糊可变决策模型

针对影响基坑支护方案决策因素所具有的不确定性、模糊性以及影响因素与评价指标间的相互关联性特点,基于灰色理论和模糊数学理论,建立了基坑支护方案的灰色模糊可变决策模型。应用基于贴近度最大的组合赋权方法确定评价指标权重,并用集值统计法对定性指标进行量化,以解决影响模型决策结果准确程度的关键问题。工程实例分析表明,采用该模型更为科学、有效,易于实现程序化。更重要的是,该方法能够提高优选决策的合理性和可信度,为决策者提供有力的参考,具有一定推广和应用价值。

基于相容粗糙集技术的连续值属性决策树归纳

决策树是常用的数据挖掘方法,扩展属性的选择是决策树归纳的核心问题。基于离散化方法的连续值决策树归纳在选择扩展属性时,需要度量每一个条件属性的每一个割点的分类不确定性,并通过这些割点的不确定性选择扩展属性,其计算时间复杂度高。针对这一问题,提出了一种基于相容粗糙集技术的连续值属性决策树归纳方法。该方法首先利用相容粗糙集技术选择扩展属性,然后找出该属性的最优割点,分割样例集并递归地构建决策树。从理论上分析了该算法的计算时间复杂度,并在多个数据集上进行了实验。实验结果及对实验结果的统计分析均表明,提出的方法在计算复杂度和分类精度方面均优于其他相关方法。

污水处理BOT项目投资中的经济风险分析

针对污水处理BOT项目现金流的构成,探索性地对各经济要素进行预测,通过模拟计算方法进行投资项目经济风险指标的模拟计算与统计分析,应用风险概率积分模型对项目经济风险进行度量,并根据投资商对风险的好恶程度,进行项目投资风险决策.

基于矩阵分析的线性分组码盲识别

针对现有的矩阵分析法对线性分组码进行盲识别时,容错性能较差的问题,提出了一种改进的方法。首先利用截获的码字数据建立分析矩阵并进行高斯消元,然后计算各列列重的归一化值,按照判决门限找出分析矩阵中的线性相关列,并以此建立统计量,最后通过统计量极大值的分布规律完成码长的识别。识别出码长后,通过移位处理及随机交换分析矩阵的行进行多次平均,实现高误码率下码字起点的识别。仿真结果表明,该方法与传统矩阵分析法相比,计算量基本相当,但容错性能有很大提升,能在较高误码率下有效实现线性分组码的盲识别。

井底污染施工选井模糊决策方法研究

为解决泥浆污染、压裂液污染、水锁伤害、结垢堵塞等气井井底污染问题,川西气田每年都会进行20余井次的井底净化施工,但在对近5年井底净化施工井进行跟踪发现,不正确的选井施工不仅无法取得预期效果,而且浪费了作业费用。针对这个问题,利用模糊决策方法 ,以近3年的井底净化施工效果为基础,采用统计分析的方法,确定不同指标对决策结果的影响权重,并按照其相对比重进行评分:污染类型、井口压力、产量和剩余储量等四项正相关指标的最高分值为5分,一项负相关指标(根据施工成本、施工难度及是否污染储层确定综合评价排名)的最高分值定为-20分,进而形成井底净化选井模糊决策打分表。利用该表对2015年施工的井底净化井进行指导选井及背靠背评价,结果表明,该模糊决策打分系统与气井净化效果符合率达到100%,说明该方法能够有效指导施工选井。

运用决策支持对象实现短期电力负荷预测

运用微软通用的决策支持对象(DSO),结合区域电网气象负荷数据库设计了决策树形式的数据挖掘模型并实现了日负荷预测系统。在描述了 DSO 分层结构特性之后,分析研究了日负荷预测的决策树数据挖掘模型构造过程并给出了程序化实现方法,进一步实现了通过决策树算法的负荷预测过程。实际使用的效果统计分析结果表明本系统达到并超过实用标准,具有智能自适应、自学习和全过程自动化,通用可靠以及准确率高等特性,是值得推广的方便实用型负荷预测工具。