Real-time Control Oriented HCCI Engine Cycle-to-cycle Dynamic Modelling

For homogeneous charge compression ignition （HCCI） combustion, the auto-ignition process is very sensitive to in-cylinder conditions, including in-cylinder temperature, in-cylinder components and concentrations. Therefore, accurate control is required for reliable and efficient HCCI combustion. This paper outlines a simplified gasoline-fueled HCCI engine model implemented in Simulink environment. The model is able to run in real-time and with fixed simulation steps with the aim of cycle-to-cycle control and hardware- in-the-loop simulation. With the aim of controlling the desired amount of the trapped exhaust gas recirculation （EGR） from the previous cycle, the phase of the intake and exhaust valves and the respective profiles are designed to vary in this model. The model is able to anticipate the auto-ignition timing and the in-cylinder pressure and temperature. The validation has been conducted using a comparison of the experimental results on Ricardo Hydro engine published in a research by Tianjin University and a JAGUAR V6 HCCI test engine at the University of Birmingham. The comparison shows the typical HCCI combustion and a fair agreement between the simulation and experimental results.

Survey on <i>Essential and Accidental Real-Time Issues</i>in Software Engineering

Software product lines have recently been presented as one of the best promis-ing improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the prob-lems of producing a perfect software scheme. Traditional approaches or recy-cling software are not effective to solve the problems concerning software competence. Since fast developments with software engineering in the past few years, studies show that some approaches are getting extensive attention in both industries and universities. This method is categorized as the software product line improvement;that supports reusing of software in big organizations. Different industries are adopting product lines to enhance efficiency and reduce operational expenses by way of emerging product developments. This research paper is formed to offer in-depth study regarding the software engineering issues such as complexity, conformity, changeability, invisibility, time constraints, budget constraints, and security. We have conducted various research surveys by visiting different professional software development organizations and took feedback from the professional software engineers to analyze the real-time problems that they are facing during the development process of software systems. Survey results proved that complexity is a most occurring issue that most software developers face while developing software applications. Moreover, invisibility is the problem that rarely happens according to the survey.

Transforming Model Oriented Program into Android Source Code Based on Drools Rule Engine

Model transformation is one of the prominent features and the rising research area of Model Driven Engineering (MDE). MDE promotes models to primary artifacts that drive the whole development process. This paper presents the model transformation approach for textual model oriented programs Umple (UML Programming Language) to generate android applications (apps). The proposed approach improved the generation of android source code by using Drools transformation rules and introducing new concern in model driven mobile engineering. The major objective of proposed transformation approach intends to address consistency between source and target model and also intends to handle productivity issues in model driven software development. The main results of model transformation approach are Java class for model layer, XML file for view layer and android activity class for controller layer. Results show that proposed approach achieves high consistency between source and target model and also improves model transformation productivity.

基于ArcGIS Engine的村庄规划数据库质检方法研究

质量检查是数据库建设过程中至关重要的环节,为确保村庄规划数据库成果符合标准要求,该文基于ArcGIS Engine对数据库质量自动化检查方法进行了探索与研究,并采用规则定制化模式开发了质检软件。实际应用结果表明,使用该软件可以大幅度提高数据库质检工作的效率和准确性,为后续村庄规划成果数据上图、统计分析、规划管理和实施等工作的开展提供了可靠的基础数据保障。

Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.

基于CityEngine的三维场景快速建模研究

依据GIS数据作为建模基础,在CityEngine中运用CGA规则,以重庆交通大学为研究区域,对该区域进行了快速模型生成,然后对具体模型进行动态参数调整,对特定建筑进行精细化编辑,最后生成了三维场景。

A Novel Search Engine for Internet of Everything Based on Dynamic Prediction

In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.

Hardware-in-the-Loop Research on the Electronic Control Unit for Diesel Engines

A hardware-in-the-loop simulating platform is developed to avoid designing defects caused by the complicated logical structure and multiple-functional buildup of the dectronic control unit（ECU）in modem diesel engines, and to diminish potential damages on components or human exposure to dangers in R＆D en- deavor. This plat-form consists of a computer installed with software Matlab/Simulink/RTW and dSPACE/ ControlDesk; a diesel engine ECU, and a dSPACE autobox which runs a real-time diesel engine model. A typical model of diesel engine with turbocharger and intercooler is presented. Based on this model our research is carried out with a real ECU to test its software control strategies. Results show that by using the diesel engine model downloaded inside, the hardware-in-the-loop platform can simulate diesel engine＇s working conditions and generate all kinds of sensor signals which ECU needs on a real-time basis. So the ECU control strategies can be validated and relevant parameters roughly calibrated.

基于ArcEngine的土地利用数据研究

作为土地资源调查工作主要成果的土地利用现状图,主要为反映土地利用的特点和利用的合理程度,改良土地利用方式并进一步挖掘土地潜力提供科学依据。目前土地利用数据由大比例尺向中小比例尺转换工作主要由手工操作或是基于商用软件包进行;由于手工操作环节多,工作量大、所耗周期长、技术性强、作业人员的干扰因素多,较易产生误差和错误;而商用软件包在进行土地利用数据制图综合的过程中则缺乏相关的知识规则。本文以此问题为出发点进行研究,结合土地利用数据制图综合理论体系,在ArcEngine环境下对土地利用制图综合规则及操作进行开发,生成土地利用数据制图综合模块;利用实例数据对土地利用数据制图综合模块的功能进行检验,实验结果显著地克服了手工操作中的缺点,并使综合制图的自动化体系得到完善。

基于校园行为特征的学生就业画像

基于校园行为数据分析学生就业与校园行为之间的关联性是大数据背景下高校就业管理与指导工作的重点。文章以高校本科学生为研究对象,对学生基本信息和校园行为数据进行特征提取和学生标签体系的构建,通过关联规则挖掘学生就业与校园行为之间的关联,探析不同毕业去向学生的行为特征。研究发现:国内读研类学生有4种典型学生群体画像,出国出境类大学生有3种典型学生群体画像,直接就业类大学生有4种典型学生群体画像,不同的学生群体在行为规律性、消费水平和学业水平等方面存在显著区别。文章建议:提供大学生职业生涯规划指导,加强对学生心理状况的关注,开展同学结对帮扶活动。

完善危大工程安全管理实施细则的几点建议——以《安徽省危险性较大的分部分项工程安全管理规定实施细则》为例

住房和城乡建设部37号令颁布的《危险性较大的分部分项工程安全管理规定》作为建筑领域的行业规定,内容偏于笼统,各地根据本省实际出台的实施细则各有特色。《安徽省危险性较大的分部分项工程安全管理规定实施细则》成文于5年前,实施以来工程建设中又出现了一些新情况、新问题,应适时启动对实施细则的评审程序。从增加危大工程清单、扩大危大工程范围、加强危大工程施工和监督的过程管理和验收、加大专家参与度等方面进行必要的补充和完善,以适应危大工程施工安全管理的需要。

油气场站火灾爆炸风险的神经支持决策树识别与预测

为了有效防控油气场站火灾爆炸事故,从影响因素之间因果关系的角度出发,提出利用神经支持决策树(Neural-Backed Decision Tree,NBDT)算法构建油气火灾爆炸可解释预测模型。该方法利用词频逆向文件频率(Term Frequency-Inverse Document Frequency,TF-IDF)算法从风险描述信息中提取出关键词并计算权重,整合得到64个风险二级因素,构建了油气场站的火灾爆炸数据集;采用神经支持决策树算法构建分类模型,对油气场站火灾爆炸事故进行预测和可解释分析,可以基于数据可视化地分析油气火灾爆炸事故的风险与诱因。结果表明,NBDT模型预测准确率为0.976,AUC为0.913,明显优于其他模型;模型可视化结果分别从单因素和多因素角度分析,确立7种二级风险主控因素和6种二级风险组合主控因素。13种风险主控因素的确立,可以为既有油气场站火灾爆炸预测和防控机制提供理论支撑。

基于层次分析和关联规则的陆路交通工程间接勘察技术方案决策方法

勘察技术方案的决策是开展陆路交通工程勘察的首要工作,然而在不同的勘察阶段,由于成本、效率、效果等需求不同,对勘察技术及手段的要求也不同。现阶段勘察技术的选取长期单纯地依赖从业者经验,导致其无法满足信息化、智能化勘察的需求;同时,实用的新型勘察技术由于缺乏科学有效的决策机制,导致工程实践中无法及时融入。因此,需要建立科学的决策机制,结合新技术,制定更加精准、高效的勘察方案。将层次分析法和关联规则进行融合,提出了一种基于两者的间接勘察技术方案决策方法。首先,根据勘察技术本身具备的特性和目标地区地层参数进行定量分析,确定主要的勘察类别;然后,利用层次分析法选取该类别中满足勘察准确性、技术可靠性、经济合理性的最佳勘察技术;最后,运用关联规则围绕最佳勘察技术补齐在应用中存在的短板,综合新型勘察技术的应用实践结果,构建间接勘察技术组合方案。通过本决策方法的应用,极大程度地减小了勘察技术选择的随意性,据此建立的间接勘察技术组合方案符合现场需求和实践经验,证明了本方法的可行性。

业务规则引擎Ilog JRules工作引擎的工作机制分析

本文在业务规则管理技术的概念基础上,用ILOGJRules引擎与目前流行的开源Drools引擎的比较,并且对照JSR94标准分析ILOGJRules工作引擎的工作过程,阐述了ILOGJRules作为目前国际领先的商用BRMS产品之一的优点所在。

基于自智网络的5G业务质量端到端分析方案研究与实践

开展5G业务质量端到端分析是运营商提升5G用户感知的重要抓手。针对传统业务质量端到端分析高度依赖固化专家规则的不足,采用规则引擎、AI中台、智能化调度等技术方案,构建5G业务质差识别、质差定界定位、闭环管理、效果分析、模型迭代的“五阶闭环自智流程”,对自智网络的5G业务质量端到端分析方案进行研究与实践,实现高效的自智化协同,保障5G业务用户感知。

无资料地区闸站调度下江湖水量交互规律研究

为解决无资料地区通江湖泊水量交互规律,以涨渡湖为例,将江湖水量交互原理同遥感解译理论相耦合,提出一种无资料地区闸站调度下江湖水量交互规律研究方法,基于Google Earth Engine(GEE)平台分析湖泊面积变化,结合同期降水、蒸发和湖泊特性,计算江湖交互水量。结果表明,湖泊多年月平均面积总体变化趋势一致,呈规律性变化,2~6月呈下降趋势,7~11月呈上升趋势,变化范围在40~47 km^(2)之间;每年2~6、11~12月湖水入江,7~10月、12月~次年1月江水倒灌入湖。结果可为其他无资料地区闸站调度下江湖水量交互规律研究提供参考。

基于可变元胞与跟驰理论的元胞自动机模型

为准确模拟桥址随机车流荷载,提出基于可变元胞与跟驰理论的元胞自动机(cellular automata,CA)模型。首先,重新定义元胞构成,提出以车辆为核心的动态可变元胞,并将精确的轴间距和轴重信息融入车辆元胞,实现车辆荷载的精确模拟;然后,引入跟驰理论,提出基于跟驰理论的状态演化规则,推导每辆车的专有加速度,实现车辆微观交互的模拟;最后,提出基于实测动态称重系统(weigh in motion,WIM)数据的发车规则,依据WIM数据,重构任意时段的实际车队,并建立基于车头时距的发车规则,重现车辆通过WIM时的运动状态。基于所提出的发车规则和动态演化规则,实现车辆从进入道路到驶离道路全过程时空位置的准确模拟,结合融入精确轴载的车辆元胞,实现随机车流荷载的模拟。基于实测WIM数据验证所提模型的可行性和先进性。结果表明:可变元胞可以精确模拟车辆荷载;提出的状态演化规则可以根据不同车辆的运动状态计算得到每辆车的专属加速度,准确模拟每辆车在自由行驶和跟驰行驶时的不同运动状态;新发车规则可以重构任意时段的实测车队,结合新状态演化规则,可以实现桥址任意时段随机车流的模拟。结合精细化车辆荷载模拟和合理的车流模拟,可实现桥址随机车流荷载模拟。

JBoss Rules规则引擎应用程序设计

论述了Java规则引擎的原理,提出了基于规则引擎的系统结构,研究了使用JBoss Rules进行应用程序设计的技术。

大数据环境下基于BIM与CNN的电力工程造价优化算法

针对大数据环境下电力工程造价在精准化、动态化等方面存在的不足,提出了一种基于BIM与CNN的电力工程造价优化算法。利用BIM技术的特点进行电力工程全生命周期的造价管理,实现了造价的动态化管控。并且采用Levenberg-Marquardt规则算法改进卷积神经网络,通过改进后的CNN网络对每个工程环节的造价完成预测,从而优化整个工程的施工方案。结合相关的电力工程造价数据,基于Matlab对所提算法进行实验测试。结果表明,当学习率为0.010时CNN网络的性能最佳,所提算法的预测准确率为94%,并且与造价的真实值最为接近。