A Scheme for Mining State Association Rules of Process Object Based on Big Data

This paper devises a scheme which can discover the state association rules of process object. The scheme aims to dig the hidden close relationships of different links in process object. We adopt a method based on difference and extremum to compute the timing. Clustering is used to classifying the adjusted data, and the next is associating the clusters. Based on the rules of clusters, we produce the rules of links. Association degrees between each two links can be determined. It is easy to get association chains according to the degree. The state association rules that can be obtained in accordance with association rules are the final results. Some industry guidance can be directly summarized from the state association rules, and we can apply the guidance to improve the efficiency of production and operational in allied industries.

Multi-objective optimization of process parametersduring low-pressure die casting of AZ91Dmagnesium alloy wheel castings

Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

Energy retrofit and conservation of a historic building using multi-objective optimization and an analytic hierarchy process

When deciding on the best historic building retrofit,energy savings and thermal comfort can be quantitatively evaluated using an energy model,whereas conservation compatibility is intrinsically qualitative and reflects the perspective of the local heritage authority. We present a methodology that permits finding and comparing optimal retrofits for historic buildings in a multi-perspective and quantitative way. We use an analytic hierarchyprocess to quantify conservation compatibility by distilling a conservation score from the opinions of 10 experts in the field. This score,along with energy needs for heating and cooling and thermal comfort,are the three targets of a multi-objective optimization aimed at identifying optimal retrofits for a medieval building in the north of Italy,destined to become a museum. Retrofit measures considered were different kinds of external and internal envelope insulation,improvement of airtightness,replacement of windows,and ventilative cooling. The result is a portfolio of optimal retrofits that cover the whole range of conservation compatibility. We showthat in the analyzed case heritage preservation is compatible with a four-fold reduction in energy needs at a high thermal comfort level. Even higher energy savings are only achievable at the cost of heritage degradation.

Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process

In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic;this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.

Tufting Carpet Machine Information Model Based on Object Linking and Embedding for Process Control Unified Architecture

In view of the lack of research on the information model of tufting carpet machine in China,an information modeling method based on Object Linking and Embedding for Process Control Unified Architecture(OPC UA)framework was proposed to solve the problem of“information island”caused by the differentiated data interface between heterogeneous equipment and system in tufting carpet machine workshop.This paper established an information model of tufting carpet machine based on analyzing the system architecture,workshop equipment composition and information flow of the workshop,combined with the OPC UA information modeling specification.Subsequently,the OPC UA protocol is used to instantiate and map the information model,and the OPC UA server is developed.Finally,the practicability of tufting carpet machine information model under the OPC UA framework and the feasibility of realizing the information interconnection of heterogeneous devices in the tufting carpet machine digital workshop are verified.On this basis,the cloud and remote access to the underlying device data are realized.The application of this information model and information integration scheme in actual production explores and practices the application of OPC UA technology in the digital workshop of tufting carpet machine.

Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization (MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process (IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization, though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior. © 2016, Tianjin University and Springer-Verlag Berlin Heidelberg.

使用Object-Z获取形式需求

针对软件需求描述中用UML描述的模型与形式需求说明相比不利于推理和验证的问题,使用统一过程建立用UML描述的需求模型并对其进行形式化,获得用Object-Z描述形式需求说明的方法和步骤,并结合实例进行论述.提出利用形式方法验证和确认非形式需求规格说明的过程.该研究为验证和确认非形式规格说明提供1种有效方法.

一种从Object-Z到CSP规格说明的转化方法

面向对象形式规格说明语言Object-Z与进程代数CSP相结合是当今的一个热点,它既可以表示复杂的模块化数据与算法,又可以表示系统的行为,但求精与验证对它们结合后的规格说明需要分别进行处理。本文提出了一个方法,把Object-Z规格说明转化为CSP规格说明,可以方便地处理结合后的规格说明,因此求精与推理对结合后的规格说明可以按CSP规则与方法一致来进行处理。此外,转化后的Object-Z规格说明可以按照CSP方法进行模型检查。

基于视觉显著熵与Object Bank特征的图像记忆性模型

为了提高图像的记忆性预测能力,提出了一种基于视觉显著熵与改进的Object Bank特征的图像记忆性自动预测方法。该方法改进了传统的Object Bank特征,提取图像的视觉显著熵特征,利用支持向量回归机(SVR)训练得到图像的记忆性预测模型。实验结果表明,在预测准确性方面,所提方法比现有的方法的相关系数高出3个百分点。所提出的模型可以应用于图像的记忆性预测、图像检索排序、广告评价分析等方向。

An Object Threat Assessment Method Based on Indefinite Multiple Attribute Decision Making

On the basis of characteristics of threat assessment of aerial defense, a kind of MADM (multiple attribute decision making) method with unknown attribute weights and biased project on decision-maker is put forward. It can be used in dealing with the object threat assessment problem. A computing formula on project preference of decision-maker and one model of obtaining attribute optimization weight are set up. The method improves the veracity and reliability of computing object threat assessment, and the simulating result shows that this method is valid.

基于Object ARX的木门加工代码生成系统

针对木门加工领域中客户的个性化定制需求,每加工一扇木门,即使尺寸只变动一点,也必须重新制图和规划加工路径,造成生产加工耗时过多、生产效率降低。文中基于Object ARX程序设计,通过参数驱动方式绘制CAD加工图纸,对于加工尺寸改动较小的木门,只需调整变动参数,即可快速绘制木门加工图纸。根据绘制的加工图纸,从菜单上选取加工所需的刀具类型及刀具半径等参数,该界面软件快速绘图、设计加工轨迹和生成CNC加工G代码。该方法已经应用于企业生产且效果良好。

Software Process Modeling with UML in Development of Medical Insurance MIS

This paper describes how to use the Unified Modeling Language (UML) to modeling software processes in medical insurance MIS, and compares UML Modeling method with classic PO(Process Oriented) Modeling method. It indicates that the whole performance of application system model described by UML is much better than the one described by PO.

Anatomy structure features extraction in the process of large across compressive deformation of wood

In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. This realized the rapid extraction of the anatologic shape features in across compression and make possible the wood species could be characterized quantitatively.

基于多路神经网络的黄花菜关节点检测算法研究

传统的目标检测算法仅能得到目标框,无法确定黄花菜的生长姿态。针对这一问题,在现有目标检测算法的基础上优化神经网络(Neural Network)结构,由检测框预测改为关节点的预测。首先,按照锚点匹配的方式确定黄花菜的生长方向及长度,统计黄花菜目标生长角度和长度,按照统计结果设置多个锚点,实际生长角度和长度与锚点进行比较,获得目标的相对长度和角度,并将其作为模型预测值进行训练;其次,在模型中加入热力图预测分支,对4个关节点进行预测;最后,利用目标长度和角度信息连接关节点得到黄花菜目标的生长姿态。设计针对线段拟合特点的评估模型方法,在计算精度的过程中引入部分亲和度字段。并据此改进非极大值抑制算法(Non-Maximum Suppression)。试验结果表明:采用热力图校准后的模型对采摘目标识别精度达91.02%,定位精度达99.8%。

Migrating a Desktop Simulator of a Chemical Process to the Cloud

This paper shows how a desktop simulation can be migrated into its cloud equivalence using Windows Azure. It is undeniable that simulators are expensive and cost-intensive regarding maintenance and upgrading, and thus, it is not always feasible to buy such a simulator. Therefore, it will be of great significance if we have an approach, which provides simulators with services through the Internet with the aim of making them accessible from anywhere and at any time. That is, researchers and developers can focus on their actual researches and experiments and the intended output results. The cloud simulation infrastructure of this contribution is capable of hosting different simulations with the ability to be cloned as cloud services. The simulator example used here mimics the process of a distillation column to be seen as a widely used plant in several industrial applications. The cloud simulation core embedded in the cloud environment is fully independent from the developed user-interface of the simulator meaning that the cloud simulator can be connected to any user-interface. This allows simulation users such as process control and alarm management designers to connect to the cloud simulator in order to design, develop and experiment their systems on a “pay-as-you-go” basis as it is the case of most cloud computing services, aimed at providing computing services as utilities like water and electricity. For coding convenience, Windows Azure was selected for both developing the cloud simulation and hosting it in the cloud because of the fact that the source code of the desktop simulator is already available in C# based on dot Net technology. From a software technical point of view, UML graphical notations were applied in order to express the software requirement specifications of the distributed cloud simulation, representing a widespread technology in the object-oriented design and analysis.

A Multiple Random Feature Extraction Algorithm for Image Object Tracking

This paper proposes an object-tracking algorithm with multiple randomly-generated features. We mainly improve the tracking performance which is sometimes good and sometimes bad in compressive tracking. In compressive tracking, the image features are generated by random projection. The resulting image features are affected by the random numbers so that the results of each execution are different. If the obvious features of the target are not captured, the tracker is likely to fail. Therefore the tracking results are inconsistent for each execution. The proposed algorithm uses a number of different image features to track, and chooses the best tracking result by measuring the similarity with the target model. It reduces the chances to determine the target location by the poor image features. In this paper, we use the Bhattacharyya coefficient to choose the best tracking result. The experimental results show that the proposed tracking algorithm can greatly reduce the tracking errors. The best performance improvements in terms of center location error, bounding box overlap ratio and success rate are from 63.62 pixels to 15.45 pixels, from 31.75% to 64.48% and from 38.51% to 82.58%, respectively.

原油直接催化裂解UPC工艺过程模拟与反应参数多目标优化

原油直接催化裂解技术可以实现从原油到三烯(乙烯、丙烯、丁烯)、三苯(苯、甲苯、二甲苯)的一步跨越,对我国炼化行业的转型升级具有重要意义。以全馏分石蜡基中原原油(159~780℃)为原料,采用混合馏分结构与分子组成集总建模策略,对原油直接催化裂解制烯烃(UPC)反应过程进行建模与工艺参数校准,并基于工业试验数据验证了其准确性。以多产烯烃、控制焦炭生成为目的,利用NSGA-Ⅲ多目标优化算法对模型进行了优化,得到优化后的操作参数。采用优化后的操作参数,即在第一段预热闪蒸温度为194.32℃、第二段预热闪蒸温度为228.16℃、第一段提升管反应器出口温度为615.73℃、第二段提升管反应器出口温度为622.59℃的条件下,UPC工艺得到的乙烯和丙烯收率之和为53.367%,焦炭收率为8.311%。此外,由于现行税费体系燃料型工艺路线税收贡献明显高于化工型工艺路线,UPC工艺参数优化后,烯烃收率大幅度提升,燃料油收率大幅度下降,原油生产总值降低44.30元t。

BOSD: Business Object Based Flexible Software Development for Enterprises

The enterprise software need adapt to new requirements from the continuous change management. The recent development methods have increased the flexibility of software. However, previous studies have ignored the stability of business object and the particular business relationships to support the software development. In this paper, a coarse-grained business object based software development, BOSD, is presented to resolve this problem. By analyzing the characteristics of variable requirement, business objects are abstracted as the separately-developed unit from business process, and are assembled to system through their relationships. The methodology of BOSD is combined with MDA (Model Driven Architecture) and implemented on the semiautomatic platform.

Development of the Data Processing and Analysis System Framework for ICF Experiments

An idea is presented about the development of a data processing and analysis system for ICF experiments, which is based on an object oriented framework. The design and preliminary implementation of the data processing and analysis framework based on the ROOT system have been completed. Software for unfolding soft X-ray spectra has been developed to test the functions of this framework.