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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Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

An Investment Decision Support System for Process Industries

Most studies on investment evaluation mainly focus on enterprise economic benefits only, without process operability and sustainability considered. In this paper, we suggest that investment evaluation in process industries should be executed under three strategic objectives--enterprise benefits, social benefits and customer benefits. A systematic investment evaluation and decision-making method with a four-step procedure based on the analytic hierarchy process (AHP) is proposed to evaluate various qualitative and quantitative elements with various criteria. At the first step, the decision hierarchy is constructed under the three strategic objectives. Second, pair-wise comparison is utilized to evaluate the weights of elements and criteria. Third, qualitative elements are quantified by pair-wise comparison and quantitative elements are re-scaled by a uniform criterion. At the last, the best choice is made through synthesizing values upward in the hierarchy. An investment decision support system (DSS) is developed based on Microsoft Excel, and applied to a retrofit investment of united fluid catalytic cracking(FCC) and liquefied gas separation process in a refinery plant.

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.

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.

Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.

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。

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。

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。

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。

基于TFN-AHP和云模型的母舰对气垫船保障能力评价

母舰对气垫船的保障能力是气垫船任务执行能力的重要组成部分,对母舰的保障能力进行综合评价是提升气垫船与母舰保障适配能力的重要环节。母舰对气垫船的保障活动具有高度的随机性、复杂性和动态性,导致其保障能力的评价也存在不确定性和模糊性。为此,基于三角模糊数(triangular fuzzy number,TFN)-层次分析法(analytic hierarchy process,AHP)与云模型提出了一种新的母舰对气垫船保障能力评价方法,并对某型母舰对气垫船的保障能力进行了评价。该方法通过特定的结构算法完成定性概念与定量表示的相互结合,降低了保障能力评价中权重计算的主观性和随机性,为母舰对气垫船保障能力评价提供了借鉴。

基于临时支护新装置的工序优化与支护性能分析

针对掘进工作面存在的掘支平行作业难、用时比重失调问题,为实现平行作业、快速支护,设计了新的临时支护装置,具有尺寸可调、快速响应、及时支护的特点。采用理论分析加数值模拟的方法进行研究;介绍了装置的结构设计;以新巨龙煤矿综掘施工技术为例进行工艺优化,从理论上提出一种分区平行作业的施工技术;通过顶板两端简支岩梁力学模型,展开装置临时支护作用机理分析;并对装置的支护性能进行结构静力学分析。结果表明:在巷道顶板松动范围0~3.30 m时,装置支护所需承受的载荷为158.40 kN;仿真得到最大等效应力为57.37 MPa,在装置顶梁下端与耳座位置交接处,最大位移量出现在中间梁上端中部,为0.30 mm,装置的结构设计能满足支护要求。此研究可为实现快速掘进的平行作业,高效支护和安全生产,提供有益参考。

大型钻探设备支承底盘焊接工艺优化研究

大型支承底盘作为大型钻探设备的支承平台,通常由大量钢板通过焊接装配而成,焊接残余应力和残余变形会对底盘的强度和刚度产生一定的影响。本文运用有限元分析方法,选取高斯移动热源,针对大型支承底盘中具有代表性的T形焊接接头建立了有限元模型,分别采用4种不同的焊接顺序和焊接速度,分析了其对焊接应力和变形的影响,得到了优化的焊接顺序和焊接速度。在此基础上,对底盘的整体焊接方案做出优化,对比分析了优化前后的焊接应力和变形,结果表明:优化后残余应力减少了约7%,最大变形减少了约26%。研究成果可为焊接工艺的制定提供参考。