Application of synthetic principal component analysis model to mine area farmland heavy metal pollution assessment

Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA).The results show that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution.Sort and clustering were done to the synthetic principal components scores of soil sample,which is given by synthetic principal components analysis.Data structure of soil heavy metal contaminations relationships and pollution level of different soil samples are discovered.The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pol- lutants.Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Modelling the Sorption of 63Ni to Granitic Materials： Application of the Component Additive Model

The component additive modelling approach is based on summing the results from models already calibrated with pure mineral phases. The summation can occur as the sum of results for thermodynamic surface speciation models or as the sum of pseudo-thermodynamic models for adsorption on individual mineral phases. Static batch sorption experiments of 63Ni are with different granitic rocks and component minerals. XRD analyses have been used to calculate the percentage mineralogical composition of the granitic rocks. Sorption data has been modelled using non electrostatic correction models to obtain Rdfor the granitic rocks and mineral. Ra values for the granitic rocks predicted from the component additive model have been compared to experimental values. Results showed that predicted Rd values for granite adamellite, biotite granite and rapakivi granite were identical to the experimentally determined values, whereas, for graphic granite and grey Granite, the predicted and experimentally determined Ra values were much different. The results also showed a greater contribution to the bulk Raby feldspar while quartz showed the least contribution to the Rd.

Structural Analysis and Design of Steel Connections Using Component-Based Finite Element Model

This paper introduces CBFEM （component-based finite element model） which is a new method to analyze and design connections of steel structures. Design focused CM （component model） is compared to FEM （finite elements models）. Procedure for composition of a model based on usual production process is used in CBFEM. Its results are compared to those obtained by component method for portal frame eaves moment connection with good agreement. Design of moment resistant column base is demonstrated by a case loaded by two directional bending moments and normal force. Interaction of several connections in one complex joint is explained in the last example. This paper aims to provide structural engineers with a new tool to effectively analyze and design various joints of steel structures.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

多源激光点云建模在桥梁养护中的探索

运营已久的复杂山区高速公路桥梁在路面负荷严重、次生地质灾害的影响下,对道路日常的养护运维工作带来了很大挑战。利用桥梁三维建模技术进行在线可视化,同时在重点桥梁路段安装监测设备,在线进行桥梁健康监测成为目前新的技术手段。针对运营较长时间的道路桥梁设计竣工图纸缺失,无法通过图纸复现真实的桥梁模型,采用地面站式激光与车载激光相结合的方法,对长时间运营桥梁进行扫描,对两种方法得到的点云数据进行配准及滤波处理,然后进行单体化建模,真实还原运营桥梁的结构形态。同时,利用公路行业BIM模型标准,对单体化的桥梁进行构件拆分编码,形成规范的桥梁单元模块。在此基础上,利用GIS技术开发高速公路基础设施平台,关联桥梁养护相关作业模块,实现既有高速公路桥梁线上巡检养护。

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

A parameterization scheme of vertical mixing due to inertial internal wave breaking in the ocean general circulation model

Based on the theoretical spectral model of inertial internal wave breaking （fine structure） proposed previ- ously, in which the effects of the horizontal Coriolis frequency component f-tilde on a potential isopycnal are taken into account, a parameterization scheme of vertical mixing in the stably stratified interior be- low the surface mixed layer in the ocean general circulation model （OGCM） is put forward preliminarily in this paper. Besides turbulence, the impact of sub-mesoscale oceanic processes （including inertial internal wave breaking product） on oceanic interior mixing is emphasized. We suggest that adding the inertial inter- hal wave breaking mixing scheme （F-scheme for short） put forward in this paper to the turbulence mixing scheme of Canuto et al. （T-scheme for short） in the OGCM, except the region from 15°S to 15°N. The numeri- cal results ofF-scheme by usingWOA09 data and an OGCM （LICOM, LASG/IAP climate system ocean model） over the global ocean are given. A notable improvement in the simulation of salinity and temperature over the global ocean is attained by using T-scheme adding F-scheme, especially in the mid- and high-latitude regions in the simulation of the intermediate water and deep water. We conjecture that the inertial internal wave breaking mixing and inertial forcing of wind might be one of important mechanisms maintaining the ventilation process. The modeling strength of the Atlantic meridional overturning circulation （AMOC） by using T-scheme adding F-scheme may be more reasonable than that by using T-scheme alone, though the physical processes need to be further studied, and the overflow parameterization needs to be incorporated. A shortcoming in F-scheme is that in this paper the error of simulated salinity and temperature by using T-scheme adding F-scheme is larger than that by using T-scheme alone in the subsurface layer.

Influences of affiliated components and train length on the train wind

The induced airfl w from passing trains,which is recognized as train wind,usually has adverse impacts on people in the surroundings,i.e.,the aerodynamic forces generated by a high-speed train＇s wind may act on the human body and endanger the safety of pedestrians or roadside workers.In this paper,an improved delayed detached eddy simulation（IDDES） method is used to study train wind.The effects of the affiliate components and train length on train wind are analyzed.The results indicate that the aff liated components and train length have no effect on train wind in the area in front of the leading nose.In the downstream and wake regions,the longitudinal train wind becomes stronger as the length of the train increases,while the transverse train wind is not affected.The presence of affiliate components strengthens the train wind in the near fiel of the train because of strong fl w solid interactions but has limited effects on train wind in the far field.

Component modeling and updating method of integrated energy systems based on knowledge distillation

Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.

A COM-based Framework for Management,Analysis and Visualization of Large Scope Digital Elevation Models

This paper presents a component object model (COM) based framework for managing, analyzing and visualizing massive multi-scale digital elevation models (DEMs). The framework consists of a data management component (DMC), which is based on RDBMS/ORDBMS, a data analysis component (DAC) and a data render component (DRC). DMC can manage massive multi-scale data expressed at various reference frames within a pyramid database and can support fast access to data at variable resolution. DAC integrates many useful applied analytic functions whose results can be overlaid with the 3D scene rendered by DRC. DRC provides view-dependent data paging with the support of the underlying DMC and organizes the potential visible data at different levels into rendering.

Rapid prediction of floating and sinking components of raw coal

A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.

可重构流程模型驱动和组件化的MES流程进化

为使制造执行系统流程能够始终与用户业务流程保持一致,提出一种基于可重构流程模型和组件技术的流程进化实施方法。对业务流程可变性进行分析,建立了可重构流程模型,并对其可重构性进行了说明。定义了组件模型,使流程模型能以组件的形式封装和部署。给出了流程模型分析算法与流程组件调整算法,通过流程模型在重构前后发生的变化驱动流程组件进行重构,完成对系统流程的进化。结合某航天企业的开发实施验证了该方法的可行性。

一种基于角色的特征模型构件化方法

为了解决领域特征模型混杂交织及其与需求模型过度耦合的问题,本文设计了一种特征模型构件化方法.该方法引入角色的概念,并以角色为中介设计了特征-角色-构件映射算法,将在领域分析过程中提取和抽象的特征映射到不同的构件模型上.通过角色的中介作用,方法实现了特征模型和需求模型的解耦,各个特征模型的可变点可以自由方便地选择和组合,从而提高了软件的构件化水平.

A Brief Introduction to BNU-HESM1.0 and Its Earth Surface Temperature Simulations

Integrated assessment models and coupled earth system models both have their limitations in understanding the interactions between human activity and the physical earth system. In this paper, a new human--earth system model, BNU- HESM1.0, constructed by combining the economic and climate damage components of the Dynamic Integrated Model of Climate Change and Economy to the BNU-ESM model, is introduced. The ability of BNU-HESM1.0 in simulating the global CO2 concentration and surface temperature is also evaluated. We find that, compared to observation, BNU-HESM1.0 underestimates the global CO2 concentration and its rising trend during 1965-2005, due to the uncertainty in the economic components. However, the surface temperature simulated by BNU-HESM1.0 is much closer to observation, resulting from the overestimates of surface temperature by the original BNU-ESM model. The uncertainty of BNU-ESM falls within the range of present earth system uncertainty, so it is the economic and climate damage component of BNU-HESM 1.0 that needs to be improved through further study. However, the main purpose of this paper is to introduce a new approach to investigate the complex relationship between human activity and the earth system. It is hoped that it will inspire further ideas that prove valuable in guiding human activities appropriate for a sustainable future climate.

基于网络模型的组件化软件开发方法研究

文章将网络技术溶入到组件化技术当中,针对已有组件化技术所存在的隐式指定组件连接关系的问题,提出了一种"基于网络模型"的组件化设计方法,并从组件的模型、组件报文处理机制、软件架构、组件的使用以及相应的应用实例等方面对该设计方法进行阐述;该方法为组件的连接提供了一种灵活的调整机制,并使组件具有更强的独立性和封装性;在设计与实现"网络实验室管理系统"的实际应用中,大大提高了软件质量,缩短了开发时间。

特征功能分析在产品线构件设计中的应用研究

以功能交互的视角解剖特征模型结构,从中分析特征之间存在的功能交互,并根据其中的功能实现特点设计了一套特征—构件模型映射算法来构造面向特征的高内聚构件模型。这一过程实现了特征的内在功能完整性,其之上的功能耦合也随之解除。这不仅提高了可复用软件资产自动化效率,且大大减低了日后的维护成本。

近炸引信虚拟样机技术研究

文章对近炸引信虚拟样机的概念进行了探讨,研究了虚拟样机设计环境、设计方法和引信系统、部件模型设计方法。以无线电引信为例,应用Matlab/Simulink软件平台构建了引信虚拟样机模型;利用Simulink环境的图形化建模能力和功能模块库开发了引信部件模型库。并以标准球为目标信号验证了引信部件模型,最后简述了虚拟样机技术的发展趋势。

Catalytic Hydrothermal Liquefaction of Algae for Production of Bio-oil with Solid Superacid Catalyst SO_(4)^(2−)/ZrO_(2)

Solid superacid SO_(4)^(2−)/ZrO_(2)as heterogeneous catalyst was prepared to upgrade the bio-oil in the progress of hydrother-mal liquefaction(HTL)for the represented algae of Chlorella vulgaris and Enteromorpha prolifera.The solid superacid catalyst could obviously adjust the composition of the bio-oil and improve the higher heating values(HHVs).The catalytic performance could be regulated by adjusting the acid amount and acid strength of SO_(4)^(2−)/ZrO_(2).Furthermore,it was explored the catalytic effects of SO_(4)^(2−)/ZrO_(2)by the HTL for algae major model components,including polysaccharides,proteins,lipids,binary mixture and ternary mixture.The results showed that the introducing of SO_(4)^(2−)/ZrO_(2)catalyst could increase the yields of bio-oil from proteins and lipids,and avoid the Maillard reaction between polysaccharides and proteins.Moreover,a possible reaction pathway and mechanisms has proposed for the formation of bio-oils from HTL of algae catalyzed by SO_(4)^(2−)/ZrO_(2)based on the systematic research of the producing bio-oil from major model components.

基于可扩展仿真平台的战术机动模型设计与实现

针对作战仿真实体战术对抗的现实需要,运用组件化建模技术,初步构建了作战实体战术机动模型,在介绍战术机动概念的基础上,详细介绍了机动模型的建模过程,并对模型的移动行为进行了简单阐述。最后用XSIM可扩展仿真平台对战术机动模型进行了实现和测试,验证了战术机动模型的可用性,并为复杂红蓝对抗作战仿真打下了基础。