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。     

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。     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

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。     

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.     

电子政务环境下福建省政府信息能力的提升

分析了政府信息能力的内涵,提出电子政务对于政府信息能力的提升作用是从改善政府信息资源的构成要素和提高政府信息资源的应用能力两方面来体现,总结了当前福建省政府信息能力的现状,并提出了如何通过电子政务进一步提升福建省政府的信息能力。     

基于多模型变权重组合的软件可靠性仿真研究

研究提高软件可靠性预测精度问题,对软件可靠性研究已成为当前软件工程的一个研究热点,传统的单一软件可靠性模型由于使用的技术及提取的信息有限,软件可靠性预测精度不高。为提高软件可靠性预测精度,在建立多种单一软件可靠性预测模型的基础上,提出一种样本点的多模型变权重组合模型。将多种预测技术有效地聚合在一起,取长补短,在样本数据有限的情况下,不仅改善了样本内学习能力也增强了样本外的泛化能力,提高了综合预测精度。仿真验证模型无论在样本内还是样本外都较优于经过模拟退火算法优化的BP神经网络(SA-BP)及经过遗传算法优化的最小二乘支持向量机(GA-LSVM),说明变权重组合模型是一种精度更高的软件可靠性失效数据预测模型,具有较好的应用推广价值。     

人工神经网络技术在电工产品可靠性实验中的应用(理论部分)

本文简要介绍了人工神经网络，BP神经网络的结构、训练和学习规则以及人工神经网络非线性时间序列预测的应用概况。     

车联网基于信任度的RSU识别及资源分配算法

在车联网中,由于部分路侧单元（RSU）的位置比较偏远,无法得到网络管理员的及时维护,可能遭到攻击,变为不可信任的路侧单元,降低了车辆的通信质量和网络能效.为了有效地识别RSU的可靠性,提出了一种基于信任度的路侧单元识别及资源分配算法,将路侧单元识别、传输模式的选择和功率分配联合建模为网络能效的优化问题,并分成3个子优化问题,分别求解.首先,采用基于信任度的RSU识别算法,识别出可信任和不可信任的路侧单元;其次,优化车辆资源块配置和链路选择;最后,通过次梯度算法优化传输功率,最大化网络能效.理论分析和仿真结果表明,所提算法具备较低的复杂度,可以达到较高的路侧单元识别准确度,并有效地提升网络总能效.