Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).

Continuities of Progressive and Mixing Algorithm for Surface Modeling and Editing

The progressive and mixing algorithm(PAMA) is a method for surface modeling and editing,which is developed for effective and flexible applications in many environments,such as computer-aided design(CAD) and computer-aided geometric design(CAGD).In this paper,the construction scheme and continuities of PAMA are discussed,which provide a mathematics analysis of PAMA.The analysis and results show that the PAMA provides a new method of surface modeling and editing with four more degrees of freedom for designers to manipulate a 3D object.

无线传感器网络的ProgressiveSkyline连续查询

随着传感器技术的进步,无线传感器网络支持复杂查询在现实应用中越来越重要。Skyline查询由于可用于多种标准下的决策而受到广泛关注。对无线传感器网络中Skyline连续查询进行了研究,提出了一种增量(progressive)算法来解决静态数据集中的Skyline查询求解问题,这种增量算法能够递增返回Skyline结果,而无需事先扫描整个数据集。设计了一种新颖的算法来解决流数据集中的Skyline查询维护问题。通过实验对算法性能进行了验证,结果表明,该算法能够大大延长网络的生命周期。

基于Progressive多序列比对方法的求解多序列比对的启发式算法

在生物信息学研究中,生物序列比对问题占有重要的地位。多序列比对问题是一个NPC问题,由于时间和空间的限制不能够求出精确解。文中简要介绍了Feng和Doolittle提出的多序列比对算法的基本思想,并改进了该算法使之具有更好的比对精度。实验结果表明,新算法对解决一般的progressive多序列比对方法中遇到的局部最优问题有较好的效果。

Progressive failure processes of reinforced slopes based on general particle dynamic method

In order to resolve grid distortions in finite element method(FEM), the meshless numerical method which is called general particle dynamics(GPD) was presented to simulate the large deformation and failure of geomaterials. The Mohr-Coulomb strength criterion was implemented into the code to describe the elasto-brittle behaviours of geomaterials while the solid-structure(reinforcing pile) interaction was simulated as an elasto-brittle material. The Weibull statistical approach was applied to describing the heterogeneity of geomaterials. As an application of general particle dynamics to slopes, the interaction between the slopes and the reinforcing pile was modelled. The contact between the geomaterials and the reinforcing pile was modelled by using the coupling condition associated with a Lennard-Jones repulsive force. The safety factor, corresponding to the minimum shear strength reduction factor "R", was obtained, and the slip surface of the slope was determined. The numerical results are in good agreement with those obtained from limit equilibrium method and finite element method. It indicates that the proposed geomaterial-structure interaction algorithm works well in the GPD framework.

Bayesian Inference on Type-Ⅰ Progressively Hybrid Competing Risks Model

In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.

面向栅格地图的区域渐进均分算法

单架无人机续航能力限制了区域全覆盖侦察,合理的区域划分是实现多无人机协同全域侦察的关键。栅格法规划侦察区域是无人机区域侦察的常用研究方法。为了解决栅格地图等量划分的问题,提出了一种面向栅格地图的区域渐进均分算法。算法由4个阶段构成。阶段1,建立区域边界确认的跳跃迭代法,根据栅格的特点制定判定条件,进行栅格特征标识。阶段2,提出一种双特征标识方法,对射线法进行改进,确定区域内部栅格。阶段3,模仿水波扩散,提出了一种邻边扩散法,实现区域初步的扩散分割。阶段4,设计补偿规则,通过邻边补偿算法,对各子区域栅格数进行数量补偿。实验证明,区域渐进均分算法相较于其他算法,具有较好的聚集性,连续性和均匀性,为多无人机协同全域侦察提供了理论保证。

算法推荐技术赋能青少年民族团结进步教育的逻辑理路、价值意蕴与实践路径

算法推荐技术是引领新时代青少年民族团结进步教育高质量开展的新兴力量。算法推荐技术与青少年民族团结进步教育在条件、过程、结果上的关联耦合,为充分发挥算法推荐技术的积极功能,提升青少年民族团结进步教育实效,提供了适切性与可行性。通过精准传递、精准生产、精准过滤等优势赋能,算法推荐技术可以助力青少年民族团结进步教育实现对象大众化、内容智慧化和方式生活化。深化算法推荐理念认识、加强算法推荐素养教育、健全算法推荐技术规范,可以促进算法推荐技术势能充分释放,使之成为青少年民族团结进步教育现代化的强大技术支撑。

基于粒子群算法的复杂山区铁路土方调配与取弃土场选址协同优化

复杂山区铁路线路土方调配及取弃土场选址对铁路项目的成本、工期、安全性、环境影响程度等产生长久深远影响,二者之间存在着复杂关联耦合关系。然而,既有方法忽略了上述联系,难以实现铁路线路土方调配与取弃土场选址协同优化。对于复杂山区铁路线路,构建了融合调配段落划分点、土方调配量、取弃土场的统一自变量集合,考虑了土方调配、取弃土场选址复杂约束,以土方调配成本、取弃土场建设成本及便道建设成本的综合费用为目标函数,集成综合基础信息子系统、土方调配优化子系统与取弃土场选址优化子系统,建立了土方调配与取弃土场选址的协同优化模型。为求解此协同优化模型,提出一种基于粒子群算法的累进求解策略。首先根据线路划分土方调配段落,进入土方调配优化子系统求得土方调配解,借助移动窗口法构建取弃土场备选池,进入取弃土场选址优化子系统求得取弃土场址解;其次,基于粒子群算法通过更新线路土方调配段落划分来累进迭代寻求最优解。以某复杂山区铁路为例,对铁路线路土方调配及取弃土场选址协同优化模型进行案例验证,与人工方案相比,最终机选最优方案可节省13.77%的综合费用。测试结果表明本方法的有效性,可提高铁路土方调配与取弃土场选址求解效率,为设计人员提供参考。

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

机载LiDAR点云数据的组合滤波算法研究

针对采用渐进式形态学滤波算法进行机载LiDAR点云滤波时存在的滤波效果不佳、地形特征保留不明显的问题,本文提出了一种改进不规则三角网的后处理滤波算法,构建组合式机载LiDAR点云滤波算法。该组合算法有效地结合了渐进式形态学滤波算法与改进TIN滤波算法的优势,首先采用渐进式形态学滤波算法对原始机载LiDAR点云数据进行处理,提取得到初始地面点;其次优化传统TIN滤波算法,以初始地面点及种子点构建TIN,通过连续迭代提取得到精细化地面点。为验证本文提出滤波算法的可靠性与优越性,选取宁波市某地2组机载LiDAR点云数据进行实验,结果表明,与较单一的渐进式形态学滤波算法、TIN滤波算法地面点提取结果相比较,本文改进滤波算法提取地面点的Ⅰ类误差、Ⅱ类误差及总误差均更低,且不受地形条件限制,具有较高的适应性,验证了本文提出改进滤波算法的可靠性与优越性。

基于递进优化算法的城市轨道交通网络末班车换乘优化研究

城市轨道交通在城市公共交通体系中占重要地位,随着其客运分担率的持续上升,优化网络末班车换乘成为提升乘客出行体验与资源配置效率的关键。文章提出一种基于递进优化算法的网络末班车换乘优化方法,该方法通过末班车换乘优化算法、延长换乘站停站时间的扩大优化算法和回场车临时载客的末班车换乘优化算法层层递进,从单向换乘衔接转变为双向换乘衔接,以实现换乘量的最大化。为验证该方法的有效性,以石家庄市轨道交通为例进行实证研究。结果表明,基于递进优化算法的网络末班车换乘优化方法不仅适应性强,而且具有高度的可实践性,对于提升城市轨道交通运营效率和服务质量具有重要意义。

车辆自适应巡航下的MPC方法的研究

提出了一种新颖的车辆自适应巡航控制(ACC)系统,该系统可以在保障跟车距离的同时,提升车辆的燃油经济性。基于模型预测控制(MPC)进行ACC的上层控制器搭建,并在此基础上采用高斯过程回归(GPR),网格搜索(GS)和自适应方法进行三轨参数调整,将控制域和预测域调整到最优状态。此外,为了减少计算量并提高稳定性,系统采用了粒子群优化算法(PSO)对系统进行升级改进。仿真结果表明,基于MPC控制的车辆自适应巡航控制系统可以在保证良好跟踪性能的同时降低燃油消耗率。

基于随钻数据的岩性识别机器学习算法研究进展

机器学习算法是岩性识别领域重点研究内容之一。与传统岩性识别方法相比,通过监测随钻参数变化进行岩性识别,具有高精度、多信息、集成化、智能化的优点。近年来,随着岩性识别技术不断发展,机器学习算法在岩性识别领域的研究和应用日益广泛。利用机器学习算法分析随钻数据,能够提高岩性识别结果的准确性,更高效地识别地层的岩性和构造。为了厘清岩性识别机器学习算法的发展现状,发掘其在岩性识别技术领域中的技术难题,综述了岩性识别机器学习算法的研究进展。首先,简要介绍了机器学习的概念与发展历程;其次,分类阐述能够用于岩性识别领域的机器学习算法;再次,总结了岩性识别领域各类常用机器学习算法的应用现状,比较了各类算法在岩性识别应用中的优缺点;最后,总结了岩性识别算法存在的问题和面临的挑战,并对其下一步发展方向提出了建议,使未来能更加准确高效地利用机器学习算法分析处理随钻数据,实现机器学习算法与岩性识别技术的深度结合。

Application of PI Control Algorithm to Discrete Manufacturing Systems

PI （proportional-integral） control algorithm is applied to control WlP （work-in-progress） in a discrete manufacturing system, where the cascade control of PI controllers is presented. It is in the frequency domain that the PI controller is designed with constraints on sensitivity options to ensure the stability and robustness of its parameters. A case is evaluated on a motorcycle engine crankcase production system, whose simulation results confirm that demand fluctuations can be compensated by PI controllers under a normal demand. PI controllers also possess low sensitivity to the distribution of production times.

基于三支决策的KNN渐进式文本分类方法

在传统的文本分类中,KNN算法以其简单、分类准确率高、非参数得到了广泛的应用。但是传统KNN算法在进行文本分类的过程中,需要计算待分类文本与每一个训练样本的相似度,当面对海量的文本时,分类的效果会明显降低。针对此问题,提出了一种基于三支决策的KNN渐进式文本分类方法用于提高其分类效率,结合三支决策在分类问题中的优势,将三支决策与KNN算法相结合,对标题、摘要、关键词等进行渐进式的分类处理,从而完成待分类文本的分类,提高文本分类的效率和性能。实验表明,该算法能够在确保KNN算法分类准确率的基础上,同时提高分类效率。

基于PIA的非均匀三次B样条曲线Hermite插值

提出基于渐进迭代逼近(Progressive Iteration Approximation,PIA)的非均匀三次B样条曲线Hermite插值算法.首先,以给定数据点作为初始控制顶点,采用累加弦长法得到节点序列,通过构造误差向量更新控制顶点,迭代生成插值数据点的非均匀三次B样条曲线.当需要同时插值数据点和单位切向时,在每个节点区间上插入一个节点;当需要同时插值数据点、单位切向和曲率向量时,在每个节点区间上插入两个节点;更新初始控制顶点,进而迭代得到插值B样条曲线.理论分析表明算法是收敛的.数值算例结果说明,与均匀三次B样条曲线插值算法相比,当相邻数据点间距离变化程度越大时,该算法的收敛速度越快,在相同误差条件下迭代次数更少.

隐式B样条曲线拟合的加权PIA算法

为了使拟合数据点的曲线生成速度更快、误差更小,提出一种隐式B样条曲线拟合数据点的加权PIA算法.首先,用待拟合数据点以及给定法向量生成偏移点集.然后,通过偏移点集构造差分向量,从而得到需要调整的误差控制系数,为了使迭代效率更高,在迭代过程中对误差控制系数做加权处理.最后,用最新的控制系数矩阵得到拟合数据点的曲线.文中5个数值算例采用均匀节点序列,实验结果表明,在相同迭代次数下,相对于I-PIA算法,该算法得到的拟合曲线误差值更小,曲线能更好保特征.

多轴3D打印技术研究进展

3D打印技术通过逐层堆积的方式成型,克服了零件制造的几何限制,受到了学术界和工业界的广泛关注。但这种定向构建方式会导致零件存在阶梯效应,强度不足且需要添加支撑结构等一系列问题。为了解决传统层式打印存在的问题,研究人员开发了多轴3D打印技术。多轴3D打印技术使用多自由度机器人对零件进行动态构建,克服了传统层式打印的限制,显示出了极高的零件制造灵活性。鉴于此,对多轴3D打印技术进行了回顾,首先分析了传统3D打印存在的一些问题,然后综述了多轴3D打印技术的研究进展,包括多轴3D打印技术在路径规划、零件力学性改善、去支撑化、消除阶梯效应以及大尺寸打印上的研究概况。最后探讨了多轴3D打印技术目前存在的问题和未来可能发展的方向。