Multi-parameters uncertainty analysis of logistic support process based on GERT

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.

Supporting Processes of Syndiospecific Metallocene Catalyst for Polymerization of Propylene

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Constrained Run-to-Run Optimization for Batch Process Based on Support Vector Regression Model

An iterative (run-to-run) optimization method was presented for batch processes under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process.Because support vector machine is powerful for the problems characterized by small samples,nonlinearity, high dimension and local minima, support vector regression models were developed for the end-point optimization of batch processes. Since there is no analytical way to find the optimal trajectory, an iterative method was used to exploit the repetitive nature of batch processes to determine the optimal operating policy. The optimization algorithm is proved convergent. The numerical simulation shows that the method can improve the process performance through iterations.

Determination of reservoir induced earthquake using support vector machine and gaussian process regression

The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /

The Process of Support by Nursing Professionals for Families Having a Member with Borderline Personality Disorder

The purpose of this study was to explore the process of family support provided by nurses to families with a borderline personality disorder (BPD) patient. Semi-structured interviews were conducted with 16 nurses who had provided care to BPD patients. Data obtained from the interviews were qualitatively analyzed using a modified grounded theory approach. As an overall core category of family support processes practiced by nurses for families with BPD patients, family support practiced without awareness that the nurses were supporting families was extracted. Through this process, nurses held perceptions that were premises for family support, which were formed through their individual nursing experiences and perspectives. Nurses also had diverse perceptions concerning the image of families. Through the integration of perceptions that were premises for family support and perceptions of an image of the family, nurses underwent a process of “determination and ambivalence about the need for family support.” Then, nurses provided “family support practice” when they acknowledged the need for family support. During the “family support practice,” nurses had difficulties in providing family support. When family support was not successfully provided, nurses provided “family support practice with seeking more effective ways through trial and error.” For cases in which nurses did not acknowledge the need for intervention, they intentionally chose “not to provide family support.” Furthermore, during the “family support practice,” nurses had contradictory perspectives of family support. Such family support processes ultimately led to an awareness of the same family support required for the future. Family support was provided with “family support practice” and “family support practice with seeking more effective ways through trial and error.” In some cases, however, the process ended in “not to provide family support intentionally.” Experiences and perspectives in providing family support are important factors in carrying out future family support. Developing the positive implications of these factors and reducing psychological strain on nurses may ensure smooth implementation of family support. Thus, nurses need to recognize that they are supporting the family, which is identified as a core category.

Development of a Web-Based Decision Support System for Cell Formation Problems Considering Alternative Process Routings and Machine Sequences

In this study, we use the respective advantages of the tabu search (TS) and the Web-based technologies to develop a Web-based decision support system (DSS) for cell formation (CF) problems considering alternative process routings and machine sequences simultaneously. With the assistance of our developed Web-based system, the CF practitioners in the production departments can interact with the systems without knowing the details of algorithms and can get the best machine cells and part families with minimize the total intercellular movement wherever and whenever they may need it. To further verify the feasibility and effectiveness of the system developed, an example taken from the literature is ado- pted for illustrational purpose. Moreover, a set of test problems with various sizes drawn from the literature is used to test the performance of the proposed system. Corresponding results are compared to several well-known algorithms previously published. The results indicate that the proposed system improves the best results found in the literature for 67% of the test problems. These show that the proposed system should thus be useful to both practitioners and researchers.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Enterprise Process Modeling Methodology and Environment Supporting Project Management

Development of the global economy makes modem enterprises confront challenges of efficient managements for large projects and complex processes. Projects are typically managed in rather special manners. On the contrary, there exist many methodologies for product process management to achieve consistency and continuance. However, processes often lack flexibility offered by projects. This paper dis~ the relationship of conceptual characteristics between process and project, gives low-level details to tackle the difference between them, and proposes an enterprise process modeling method for project management. An integrated environment is designed to support the method from which both project management and process management can receive benefits and conform to the limitations.

Fault-Diagnosis Method Based on Support Vector Machine and Artificial Immune for Batch Process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase.Support vector machine is first used for phase identification,and for each phase,improved artificial immune network is developed to analyze and recognize fault patterns.A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network.The proposed method has been applied to glutamic acid fermentation,comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.

水箱安装支架级进模设计

通过对安装支架的冲压工艺及单工序模成形进行分析,确定采用11道工序的级进模进行成形。为保证条料的同步移动和送料顺畅,下模采用浮动顶料机构,还介绍了条料的粗定位和精定位。模具经试冲,结果表明:设计的排样方案和模具结构合理可行,能满足安装支架的大批量生产需求。

计及子过程贡献度的电压暂降经济损失评估方法

现有电压暂降经济损失评估方法一般通过计算过程中断概率评估用户经济损失,然而实际工业用户各子过程对电压暂降经济损失影响程度不同,计算过程中断概率未能表征各子过程对电压暂降经济损失的影响程度,由此得到的损失评估值无法真实反映用户受损程度。为此,提出一种计及子过程贡献度的电压暂降经济损失评估方法:构建基于支持向量机概率输出的敏感设备失效概率评估模型;建立考虑子过程参数变化的参数偏移度函数,修正子过程中断概率;同时,提出子过程贡献度指标量化各子过程对电压暂降经济损失的影响程度,以评估值与损失样本偏差最小为目标求解最优子过程贡献度,建立用户损失评估模型。对某企业的实例分析验证了所提方法的有效性。

液压支架立柱防冲吸能构件优化仿真及压溃实验研究

液压支架作为煤炭开采主要支护设备,经常受到冲击地压影响,防冲吸能构件起到保护液压支架作用,为获得更大的初始支反力峰值与吸能量、更小的支反力分散度,对吸能构件参数细化并通过ABAQUS有限元软件进行吸能构件的建模与压溃冲击仿真,获取吸能构件的吸能性能及屈曲变形形态,验证最优尺寸的吸能性能;对比吸能构件平均支反力的预测数据与有限元仿真数据,误差在15%以内,最优尺寸构件的平均支反力模型预测误差为−3.40%,验证吸能构件预测模型所预测数据较为准确;搭建吸能构件压溃实验台,选择5种加载速度,以准静态压溃方式对定制加工的吸能构件进行轴向加载压溃实验,实验结果表明:不同加载速度轴向压溃实验,支反力波动基本一致,最大初始支反力峰值为2253.52 kN,最大支反力标准差为206.23 kN,最小初始支反力峰值为2096.26 kN,最小支反力标准差为189.83 kN,初始支反力峰值的平均值为2149.32 kN,支反力标准差平均值为196.77 kN;不同压溃速度下的轴向压溃实验数据与吸能构件有限元仿真数据对比,初始支反力峰值、支反力标准差的相对误差分别为5.6%、11.07%;对最优尺寸吸能构件通过预测模型法、有限元仿真法、压溃实验法三种方法进行吸能性能分析,预测模型法的平均支反力为1879.7 kN,有限元仿真法的平均支反力为1945.9 kN,压溃实验法平均支反力为1919.8 kN,预测模型误差3.41%,压溃实验误差−1.3%,通过3种方法的数据验证结果,证明了吸能构件分析方法的可靠性和可行性。

大数据时代高职院校课程质量诊断与改进研究

随着大数据时代的不断发展,职业人才的教育培养环境得到了显著改善。数据决策、数据分析及数据整合逐渐成为高职院校课程体系建设的重要组成部分。通过运用大数据技术,教育培养环境更加智能化、个性化,为职业人才的培养提供了有力支持。高职院校课程质量的诊断,也要基于大数据技术的运用,增强课程质量诊断的科学性与针对性,通过对课程教学方法及教育模式的改进实现对教学有效性的提升,并利用统一质量诊断标准、加强数据信息筛选,以及深化教育信息反馈等多种策略,增强高职院校课程教学成果的转化能力。

基于Abaqus的CFRP薄壁弹性杆卷绕应力仿真与研究

CFRP(Carbon-Fiber-Reinforced-Plastic)薄壁弹性杆可展机构当前在空间可展支撑机构领域应用广泛,关于其力学性能的研究和设计优化是当前研究的热点。本研究基于Abaqus有限元仿真软件,对厚度为0.8~0.9mm的CFRP薄壁弹性杆的收展过程进行了力学仿真模拟,得到了CFRP薄壁弹性杆在直径100mm的卷筒上达到指定弯曲状态时的应力分布结果,验证了CFRP薄壁弹性杆的强度并归纳总结了CFRP薄壁弹性杆弯曲时的应力分布规律。

An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

湖南农产品加工业高质量发展战略研究

湖南农产品加工业实现了持续平稳较快发展,已发展成为湖南省三大万亿产业之一,但加工业依然存在大而不强、产业链不长且不完整等问题,高质量发展面临诸多挑战。为此,应按照全产业链发展思维,以结构调整优化与转型升级为主线,以延链补链强链为目标,构建农产品加工业全产业链“63”一体化发展体系,即大力实施“厚植产业发展基础”“做强产业发展主体”“做大总量做优质量”“做强产业发展支撑”“健全质量保障体系”“提升精准管理服务能力”等六大战略重点,提出“强化组织领导与部门统筹”“强化考核评估与实施监督”“构建协同发展机制”等三大战略保障措施,打造层次分明且功能齐全的现代农产品加工业体系,助力建设全国知名的一流农产品加工业发展高地,加快促进湖南省农产品加工业的高质量发展,为全面推进农业农村现代化、实现乡村振兴提供重要支撑。

中低渗油藏适用性工艺技术的研究与应用

河口采油厂管辖的油田是中低渗油藏。在复杂低渗油藏条件下,目前进入特高含水开发阶段。该油田中低渗油藏的特性开发中面临着注采不对应、开发效果下降、工艺技术适应性变差等问题。因此,为了改善油藏开发效果和最终采收率,要求采用特殊适用的工艺技术。为了提高措施效果和油藏采收率,开展了工艺的适用性研究,以确保工艺技术与不同类型的油藏特点相适应。2021年的现场实际应用表明,这些工艺技术体系具有较强的适应性,取得较好的增注、增油、降水效果。

产业共性技术跨组织合作研发激励模型研究

本文研究了一个由高校院所进行基础研究和制造企业负责后续商业开发的共性技术跨组织合作研发激励模型及其选择问题。本文将政府支持与合同激励放在同一框架,运用博弈论方法为制造企业设计产出导向单一激励模型、产出导向+过程监控激励模型及“政府支持+”综合激励模型以激发高校院所基础研究的积极性,同时解析了激励模型选择、影响因素及机理。研究表明:“政府支持+”综合激励模型是三种激励模型中的占优模型、产出导向+过程监控激励模型优于产出导向单一激励模型;双方收益分享系数具有促进和抑制高校院所认真履责行为的双重作用,且对其机会主义行为具有抑制作用,对制造企业后续商业开发行为及预期收益有“倒U型”影响;高校院所基础研究的影响系数在双方收益分享系数对制造企业后续商业开发努力的影响中发挥调节作用;政府补贴对高校院所和制造企业的合作研发行为具有明显的正向激励作用;惩罚系数可有效抑制高校院所的机会主义行为。

基于图像处理的水培生菜冠层图像叶面积估测研究

为实现精准、高效、无损地获取植物工厂环境下水培生菜相关长势参数叶面积(Leaf area,LA),基于数字图像处理和机器学习回归方法建立单株水培生菜冠层图像LA估测模型。首先,通过智能手机获取2个生菜品种不同生长期的冠层可见光图像,利用Photoshop图像处理软件将原始图像统一剪裁为900像素×900像素大小,采用中值滤波(MedianBlur)法对剪裁后的图像进行去噪运算,将RGB图像转化为HSV颜色空间,再采用mask掩膜法分割彩色图像;然后,利用图像法获取单株生菜LA实测值,构建以LA实测值为因变量,以生菜冠层投影面积(Projected leaf area,PLA)为自变量的线性回归(Linear regression,LR)模型和以全局图像特征(颜色、形状、纹理等)为自变量的支持向量回归(Support vector regression,SVR)、多元线性回归(Multiple linear regression,MLR)和随机森林(Random forest,RF)等LA估测模型进行对比分析;最后,采用决定系数(Coefficient of determination,R^(2))和均方根误差(Root mean square error,RMSE)评估模型的准确性。结果表明:RF模型估测效果最好,对于生菜品种‘绿萝’单株LA估测结果的R^(2)为0.9714、RMSE为8.89 cm2,对于品种‘碧霄’估测结果的R^(2)为0.9201、RMSE为23.34 cm2。本研究验证了RF回归模型能够较准确地估测生菜单株叶面积,可为植物工厂水培生菜LA无损估测提供新的解决方案和研究基础。

轮辐式索承网格屋盖钢结构单道胎架施工技术

泰安体育场作为山东省泰安市的新地标,其钢结构屋盖采用轮辐式索承网格结构,通过68榀径向索和1道中央环索形成张拉结构体系,最大悬挑跨度为45 m。通过数值分析对施工过程进行了精细化稳定性分析,针对不规则构件吊装的工程技术难点,进行了吊装方案的设计与优化;考虑到成本控制与施工周期,极具创新性地采用了径向单道胎架作为屋盖施工过程中的临时支撑,结合工程实际情况设计了胎架转换结构,并对单道胎架整体在多种可能的荷载组合作用下进行验算与校核。通过对体育场屋盖施工全过程的模拟以及对安装过程中的重难点进行优化分析,为现场施工的安全顺利开展提供了技术保障,可为其他类似采用单道胎架施工方法的工程提供施工方案设计的新思路。