基于ArcEngine的江宁区国土资源业务办公系统的设计与实现

本文针对国土资源管理的业务需求,设计出结构合理、功能全面的国土资源业务办公系统,并利用ArcEngine和C#加以实现,系统的建成使用极大地提高了国土资源管理工作效率。     

考虑疫情扩散传播风险的多目标拼车应急管理优化调度模型

为了解决在疫情背景下传统拼车出行模式面临的交互传染风险,对疫情背景下的拼车管理进行分析,提出了将低风险及潜在风险乘客分开服务的拼车策略。在进一步考虑拼车系统应兼顾的社会效益及服务质量的基础上,构建了在满足基本额定盈利及最小化等待时长的双目标拼车应急管理优化调度模型,并给出了求解该模型的组合模拟退火算法。通过具体算例对模型的有效性进行验证,结果表明模型能够较快收敛到一个稳定值,且计算结果能够反映实际场景,说明了模型及算法的有效性。最后,通过一个仿真实验对所构建模型在病毒传播中的作用进行了分析,结果显示所构建的模型可以有效抑制拼车导致的病毒传播速度。     

Multi-criteria Decision-making Approach for Environmental Impact Assessment to Reduce the Adverse Effects Of Dams

Water Resources Management - Environmental Impact Assessments (EIAs) of development projects are necessary to minimize negative impacts and maximize benefits. The objective of this paper is to...     

黄河淤积粉土的两种复合稳定方案与性能对比

为探索路基工程中黄河淤积粉土的稳定技术和性能保障方案,针对德州齐河县境内的黄河淤积粉土,在乳化沥青复合稳定粉土(AE稳定土)的研究基础上,采用无机/有机复合材料制备出一种复合固化剂(FG固化剂),研究了AE稳定土和FG稳定土2种稳定方案下粉土的抗压强度性质、水稳定性和抗冻融能力;结合XRD、SEM表征技术,探讨了FG固化剂的稳定机理。研究认为,FG固化剂稳定土较AE稳定土有着更高的无侧限抗压强度、回弹模量和承载能力,水稳定性良好,在抗冻融能力上有着比AE稳定土更好的效果。XRD与SEM分析表明,FG固化剂提供了活性矿物质,其复合胶凝效应和填充增强保障了粉土的抗压强度,高分子物质起到粘结颗粒界面、填塞孔隙的作用,降低了内部孔隙率;两者造成土体的最大干密度变大;与AE稳定土的稳定机理不同。FG固化剂的使用为黄河粉土的稳定和工程应用提供了参考方法。     

"源—网—厂—河"一体化智慧排水系统的构建设计

为提升城市排水系统运行的效率和质量,开发设计了基于GIS的"源—网—厂—河"一体化智慧排水系统,其层次结构包括基础层、感知层、网络层、应用层.应用层是整个智慧排水系统的核心组成,主要包含六大模块——排水许可信息化管理、拓扑分析、健康评估、终端监测、模型计算及动态巡检.应用该系统构建排水系统模型计算,集成终端在线监测校核分析,分析诊断排水系统健康问题和拓扑问题,定位病害区域,自动制定最优巡检方案,分配派单于外业,外业巡检结果反馈回智慧排水系统,最终实现城市排水一体化智慧管理.     

Defects restoration during cooling and annealing in PTC type barium titanate ceramics

The PTC-type BaTiO₃ ceramics of similar microstructure are obtained by careful control of a sintering scheme. The defect chemistry of them is modified by varying the cooling rate and annealing conditons. In addition to applying Heywang and Jonker models for explaining the resistivity anomaly of these samples, the outward diffusion of oxygen vacancies (V_O), which left excess barium vacancies (V_Ba) behind, is proposed to be the formation mechanism of surface states. The formation of defect complex consisting of a V _O ^.. - _Ba pair is assumed to be the cause of small diffusivity of V_O in these materials. The phenomena, in which the slower cooling rate raises the resistivity of samples, is ascribed to the higher concentration of excess barium vacancies (V_Ba) contained in these samples. However, the maximum potential barrier height (_max) of the samples is the same, irrespective of the amount of surface state concentration (N _s) and is estimated to be _max = 0.66eV, from the In (_max)-1/T _max plot.     

Improvement on dynamic elastic interpolation technique for reconstructing 3-D objects from serial cross sections [biomedical application

An improved method for automatically reconstructing a three-dimensional object from serial cross sections is presented. The method improves the dynamic elastic contour interpolation technique. There are two major improvements: (1) in the case of branching that involves concave contours, instead of pairwise interpolation between the start contour and each goal contour, the goal image is considered globally and local constraints are imposed on the forces exerting upon the vertices; and (2) it takes the continuity of high-order derivatives into consideration and incorporates the schemes of spline theory, quadratic-variation-based surface interpolation, and finite-element-based multilevel surface interpolation to create smoother surfaces of the reconstructed object. Based on the output from the preliminary processing, two alternatives, a quadratic-variation-based surface interpolation algorithm and a finite-element-based multilevel surface interpolation algorithm, can be adopted to obtain the final surface representation     

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.     

A Network Security Risk Assessment Method Based on a B_NAG Model

Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph in the conversion process, a related algorithm is proposed to generate the B_NAG model. Finally, to analyze the reachability of paths in B_NAG, the measuring indexs such as node attack complexity and node state transition are defined, and an iterative algorithm for obtaining the probability of reaching the target node is presented. On this basis, the posterior probability of related nodes can be calculated. A simulation environment is set up to evaluate the effectiveness of the B_NAG model. The experimental results indicate that the B_NAG model is realistic and effective in evaluating vulnerabilities of computer networks and can accurately highlight the degree of vulnerability in a chaotic relationship.