基于LMDI和STIRPAT模型的区域用水影响因素定量分析研究

区域用水影响因素分析对落实最严格水资源管理制度、实现节水型社会建设具有重要意义。为定量表征区域用水变化和影响因素之间的关系,通过LMDI因素分解识别人口规模、经济发展水平、节水技术水平对用水变化的影响程度,采用STIRPAT模型解析用水总量对人口规模、经济发展水平、节水技术水平变化的变动响应关系,以榆林市2000—2017年用水及影响因素变化为例开展了实证研究。结果显示:人口规模和经济发展水平对用水增长起到正向促进作用,节水技术水平起到负向抑制作用。人口规模、经济发展水平、节水技术水平效应依次为8 163.124万m~3、144 238.924万m~3、-126 161.049万m~3;人口规模、经济发展水平、节水技术水平每变化1%,用水将分别变化1.208%、0.061%和0.037%;经济发展水平和节水技术水平是用水变化的主要影响因素,但用水对人口规模变化敏感程度最高。最后,结合榆林市水资源特点提出针对性措施和建议,以期为榆林市水资源规划与管理提供参考。     

阿姆河流域耕地变化及水土匹配特征分析

基于中亚阿姆河流域4个分区1990—2015年间土地利用变化和水资源等数据,运用耕地变化速率、洛伦兹曲线和基尼系数法以及水土资源匹配系数对阿姆河流域耕地变化和水土匹配状况进行了综合分析。结果表明:1990—2015年阿姆河流域耕地以146.74 km²/a的速度在扩张,且各时期、各分区扩张的速度不同;在不考虑咸海来水不断减少、生态持续恶化的前提下,绿洲区匹配状况属比较公平和相对合理状况;阿姆河流域水土资源空间分布存在明显的错位现象;全流域生态用水被农业用水严重挤占,阿姆河流域灌溉面积、灌溉用水量成为影响咸海水量及面积变化的重要因素,是一种不可持续的用水方式。     

基于IFMOP的玉环市区域水资源配置

引入无概率分布的区间数表示不确定性影响范围,选取经济效益、供需水效率和COD排放量分别构建经济目标、社会目标和环境目标优化函数,构建了玉环市不确定性模糊多目标规划(IFMOP)水资源配置模型,并计算了不确定性解集。结果表明:IFMOP配置计算成果可实现玉环市区域水量水质协调高效利用和社会-经济-生态系统均衡发展;在75%来水保证率下各行业配置总水量不确定性范围为14 375万~15 154万m³。IFMOP水资源配置模型在信息不充分的条件下能够全面使用各类数据信息进行水资源配置,更好地满足水资源在各行业配置过程中的动态管理需求。     

基于多维产出ZSG-DEA模型的中国水资源污染综合分配效率测算

利用考虑多维产出的零和收益-数据包络分析(ZSG-DEA)模型测算2000—2017年中国大陆31个省级行政区(以下简称省)在水资源污染排放总量固定下的污染综合分配效率及生产、生活与生态3个维度的产出效率,并基于效率最大化原则对2017年各省污染排放额度进行调整。结果表明:31个省污染综合分配效率的平均值由2000年的0.322上升至2017年的0.364,生产维度产出效率是拉动污染综合分配效率提升的主要动力;2000—2017年31个省污染综合分配效率的平均值为0.341,生态产出过量与生活维度产出不足导致污染综合分配效率处于较低水平;污染综合分配效率、生态维度产出效率与生活维度产出效率受经济发展水平影响较小,而生产维度产出效率受经济发展水平影响较大;污染综合分配效率分布由大到小为东部、西部、中部,生产维度产出效率分布由大到小为东部、中部、西部,生态维度产出效率分布由大到小为西部、中部、东部,生活维度产出效率分布由大到小为西部、东部、中部;各省污染排放调整额度与污染综合分配效率呈正相关关系,北京、天津、上海、浙江、青海与西藏等20个效率较高的省可增加污染排放额度,河北、安徽、河南、江西与湖北等11个效率较低的省需降低污染排放额度。     

沙颍河流域水资源利用量与经济发展匹配特征

根据沙颍河流域河南段情况确定各地市水资源可利用量,基于数列的匹配度计算方法,对流域内10个地市2014—2018年水资源利用量与经济发展关系进行研究,分析各地市用水量、水资源可利用量与GDP关联程度。结果表明：由于气候条件等因素差异,水资源可利用量差距较大,其中漯河市和周口市单位面积水资源可利用量较多;在研究年限内,各地市水资源可利用量与用水量匹配情况差异较大;在时间维度上,各地市用水量与GDP匹配程度较好,而在空间维度上,开封市匹配情况最差,漯河等城市较好;各地市水资源可利用量与GDP匹配情况存在明显的时空差异。     

多机器人网络中资源公平分配的研究

就如何实现多机器人网络中各独立任务资源的公平分配及其服务质量最优化提出了一种新方法。该方法通过对增益控制参数的模糊控制,增强了系统的鲁棒性,并尽可能快速平稳地实现各项独立任务所需资源的公平分配。     

基于REST架构的矿山数字移动办公平台

基于面向资源思想以及REST架构,首次提出了矿山资源抽象层的概念,将矿山中各个异构系统的功能、文档、界面、工作流等抽象为能够通过标准URI来唯一标识的抽象资源元数据,在决策或智能分析层通过对元数据的分析和处理来实现相应操作或得出相关的分析结果;提出了基于REST框架的矿山数字移动办公平台设计方案,并以神木张家峁矿业有限公司移动办公平台的建设为例,介绍了将多个异构系统快速集成在移动终端的实现方法。应用结果表明,该移动办公平台运行稳定,操作流畅,使得工作人员能够随时、随地、随手办公,大大提高了办公灵活性及效率。     

基于云自适应遗传算法的NoC路径分配研究

路径分配是NoC设计流程中的两个关键步骤之一;路径分配的结果对NoC系统的性能尤其是通讯延时有着很重要的影响;多约束条件下的NoC路径分配问题是NP完全问题,要求出其最优解比较困难,目前常用的方法是利用启发式算法求得其较优解;文中提出一种基于云自适应遗传算法的NoC路径分配解决方案,该算法利用云模型对传统遗传算法加以改进,采取新的方法自动调整遗传算法过程中的交叉概率pc和变异概率pm,将适应度与云模型的3个参数Ex、En、He相互结合,从而达到优化遗传算法的目的;将此算法应用于2D-Mesh拓扑结构的NoC中,以平衡链路负载和联合优化为实验目标,以优化静态通讯分配结果;实验证明,文章所采取的算法在平衡链路负载和联合优化方面均取得了良好的效果。     

Register allocation for write activity minimization on non-volatile main memory for embedded systems

Non-volatile memories are good candidates for DRAM replacement as main memory in embedded systems and they have many desirable characteristics. Nevertheless, the disadvantages of non-volatile memory co-exist with its advantages. First, the lifetime of some of the non-volatile memories is limited by the number of erase operations. Second, read and write operations have asymmetric speed or power consumption in non-volatile memory. This paper focuses on the embedded systems using non-volatile memory as main memory. We propose register allocation technique with re-computation to reduce the number of store instructions. When non-volatile memory is applied as the main memory, reducing store instructions will reduce write activities on non-volatile memory. To re-compute the spills effectively during register allocation, a novel potential spill selection strategy is proposed. During this process, live range splitting is utilized to split certain long live ranges such that they are more likely to be assigned into registers. In addition, techniques for re-computation overhead reduction is proposed on systems with multiple functional units. With the proposed approach, the lifetime of non-volatile memory is extended accordingly. The experimental results demonstrate that the proposed technique can efficiently reduce the number of store instructions on systems with non-volatile memory by 33% on average.     

Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design

Design of optimal plans for environmental planning and management applications should ideally consider the multiple quantitative and qualitative criteria relevant to the problem. For example, in ground water monitoring design problems, qualitative criteria such as acceptable spatial extent and shape of the contaminant plume predicted from the monitored locations can be equally important as the typical quantitative criteria such as economic costs and contaminant prediction accuracy. Incorporation of qualitative criteria in the problem-solving process is typically done in one of two ways: (a) quantifying approximate representations of the qualitative criteria, which are then used as additional criteria during the optimization process, or (b) post-optimization analysis of designs by experts to evaluate the overall performance of the optimized designs with respect to the qualitative criteria. These approaches, however, may not adequately represent all of the relevant qualitative information that affect a human expert involved in design (e.g. engineers, stakeholders, regulators, etc.), and do not necessarily incorporate the effect of the expert's own learning process on the suitability of the final design. The Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) is a novel approach that addresses these limitations by using a collaborative human-computer search strategy to assist users in designing optimized solutions to their applications, while also learning about their problem. The algorithm adaptively learns from the expert's feedback, and explores multiple designs that meet her/his criteria using both the human expert and a simulated model of the expert's responses in a collaborative fashion. The algorithm provides an introspection-based learning framework for the human expert and uses the human's subjective confidence measures to adjust the optimization search process to the transient learning process of the user. This paper presents the design and testing of this computational framework, and the benefits of using this approach for solving groundwater monitoring design problems.