Hierarchical pattern recognition of landform elements considering scale adaptation

Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has been extensively applied in prior landform element research,while its efficacy in differentiating similar morphological characteristics remains inadequate to date.To reduce reliance on geomorphometric variables and increase awareness of landform patterns,geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table.Besides,to address the problem of feature similarity,hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features.Thus,combining the advantages of these two aspects,a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors.First,the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold,and subsequently adopted for the primary landform element recognition.Then,as geomorphic units with the same morphology possess different spatial analytical scales,the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information.To ensure the effectiveness of this proposed method,the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model.Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77%average accuracy.Additionally,it delineates local details more effectively than geomorphons in visual assessment,resulting in a 7%accuracy improvement in overall scale.

Multivariable Adaptive Decoupling Controller Using Hierarchical Multiple Models

To solve the problem such as too many models, long computing time and so on, a hierarchical multiple models direct adaptive decoupling controller is designed. It consists of multiple levels. In the upper level, the best model is chosen according to the switching index. Then multiple fixed models are constructed on line to cover the region which the above chosen fixed model lies in.In the last level, one free-running and one re-initialized adaptive model are added to guarantee the stability and improve the transient response. By selection of the weighting polynomial matrix, it not only eliminates the steady output error and places the poles of the closed loop system arbitrarily, but also decouples the system dynamically. At last, for this multiple models switching system, global convergence is obtained under common assumptions. Compared with the conventional multiple models adaptive controller, it reduces the number of the fixed models greatly. If the same number of the fixed models is used, the system transient response and decoupling result are improved. The simulation example illustrates the power of the derived controller.

ADC-DL:Communication-Efficient Distributed Learning with Hierarchical Clustering and Adaptive Dataset Condensation

The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.

Screen image sequence compression method utilizing adaptive block size coding and hierarchical GOP structure

To compress screen image sequence in real-time remote and interactive applications,a novel compression method is proposed.The proposed method is named as CABHG.CABHG employs hybrid coding schemes that consist of intra-frame and inter-frame coding modes.The intra-frame coding is a rate-distortion optimized adaptive block size that can be also used for the compression of a single screen image.The inter-frame coding utilizes hierarchical group of pictures(GOP) structure to improve system performance during random accesses and fast-backward scans.Experimental results demonstrate that the proposed CABHG method has approximately 47%-48% higher compression ratio and 46%-53% lower CPU utilization than professional screen image sequence codecs such as TechSmith Ensharpen codec and Sorenson 3 codec.Compared with general video codecs such as H.264 codec,XviD MPEG-4 codec and Apple's Animation codec,CABHG also shows 87%-88% higher compression ratio and 64%-81% lower CPU utilization than these general video codecs.

Ideal Case Study of Adaptive Localization in Storm-scale Ensemble Kalman Filter Assimilation

This study explores the use of the hierarchical ensemble filter to determine the localized influence of observations in the Weather Research and Forecasting ensemble square root filtering(WRF-EnSRF)assimilation system.With error correlations between observations and background field state variables considered,the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data.Comparisons between adaptive and empirical localization methods are made,and the feasibility of adaptive localization for storm-scale ensemble Kalman filter assimilation is demonstrated.Unlike empirical localization,which relies on prior knowledge of distance between observations and background field,the hierarchical ensemble filter provides continuously updating localization influence weights adaptively.The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations.The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method.Ultimately,combining empirical and adaptive methods can optimize assimilation quality.

噪声和纹理图象的自适应(Adaptive)分割

本文提出了一种自适应的噪声和纹理图象分割算法.观察图象被模拟为由区域过程、映射过程和噪声过程三个层次综合作用构成的.整个算法包括两个独立的步骤:第一步是层次图象模型的参数估计算法,可以处理高斯噪声和出格点(Outlier)的混合噪声情况,因此具有鲁棒性.第二步是基于模型参数的图象分割算法,其核心是一个改进的多值模拟退火技术.计算机模拟实验证明了算法的有效性和鲁棒性.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

MULTI-HIERARCHICAL STRUCTURE AND JUMP OF TEMPERATUREFOR THE GLOBE, CHINA AND YUNNAN OVER THE PAST 100 YEARS

An analysis has been conducted of the multi-hierarchical structure and jump of temperature variation for the globe, China and Yunnan Province over the past 100 years using an auto-adaptive, multi-resolution data filter set up in You, Lin and Deng (1997). The result is shown below in three aspects. (l1 The variation of global temperature in this period is marked by warming on a large scale and can be divided into three stages of being cold (prior to 1919), warm (between 1920 and 1978) and warmer (since 1 979). Well-defined jumps are with the variation in correspondence with the hierarchical evolution on such scale, occurring in 1920 and 1979 when there is the most substantial jump towards warming. For the evolution on smaller scales, however, the variation has shown more of alternations of cold and warm temperatures. The preceding hierarchical structure and warming jump are added with new ones. (2) The trend in which temperature varies is much the same for China and the Yunnan Province, but it is not consistent with that globally, the largest difference being that a weak period of cold temperature in 1955 - 1978 across the globe was suspended in 1979 when it jumped to a significant warming,while a period of very cold temperature in 1955 - 1986 in China and Yunnan was not followed by warming in similar extent until 1987. (3) Though there are consistent hierarchical structure and jumping features throughout the year in Yunnan, significant changes with season are also present and the most striking difference is that temperature tends to vary consistently with China in winter and spring but with the globe in summer and fall.

Information diffusion on adaptive network

Based on the adaptive network, the feedback mechanism and interplay between the network topology and the diffusive process of information are studied. The results reveal that the adaptation of network topology can drive systems into the scale-free one with the assortative or disassortative degree correlations, and the hierarchical clustering. Meanwhile, the processes of the information diffusion are extremely speeded up by the adaptive changes of network topology.

Energy-Efficient Multi-Mode Clusters Maintenance(M^2CM) for Hierarchical Wireless Sensor Networks

How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.An energy-efficient multi-mode clusters maintenance(M2CM) method is proposed based on localized and event-driven mechanism in this work,which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs.M2 CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field.,the triggers of M2 CM include such events as nodes' residual energy being under the threshold,the load imbalance of cluster head,joining in or exiting from any cluster for new node or disable one,etc.Based on neighboring relationship of the damaged clusters,one can start a single cluster(inner-cluster) maintenance or clusters(inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs.The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network,effectively narrow down the influenced area of clusters maintenance,and increase transmitted data and prolong lifetime of network compared to the traditional schemes.

Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning

Space object observation requirements and the avoidance of specific attitudes produce pointing constraints that increase the complexity of the attitude maneuver path-planning problem.To deal with this issue,a feasible attitude trajectory generation method is proposed that utilizes a multiresolution technique and local attitude node adjustment to obtain sufficient time and quaternion nodes to satisfy the pointing constraints.These nodes are further used to calculate the continuous attitude trajectory based on quaternion polynomial interpolation and the inverse dynamics method.Then,the characteristic parameters of these nodes are extracted to transform the path-planning problem into a parameter optimization problem aimed at minimizing energy consumption.This problem is solved by an improved hierarchical optimization algorithm,in which an adaptive parameter-tuning mechanism is introduced to improve the performance of the original algorithm.A numerical simulation is performed,and the results confirm the feasibility and effectiveness of the proposed method.

基于多目标协调的混合储能功率自适应分配方法

混合储能具有良好的功率可控特性,在孤岛微网中常被用作功率缓冲器,补偿分布式电源和负荷多变的功率潮流.围绕储能的功率响应问题,本文设计了混合储能的功率控制方案,并提出了一种基于多目标协调的子单元功率自适应分配方法.首先基于对母线功率供求平衡关系的分析,设计了主从并联型储能功率控制方案.其次利用积分器的“时空调零”特性,实现了子单元的功率自主分配;基于此,考虑了不同荷电状态(SOC)下储能最大充放电功率的差异,设计了子单元的SOC分层管理和多目标功率协调策略.仿真结果表明,所提策略实现的功率分配效果满足子单元的功率响应特性;随着超级电容SOCscn偏移加深,所提策略对SOCscn的优化能力越强,初始SOCscn为97%时能提高10.10%的优化性能,这保障了超级电容作为电压源的功率输出能力;所提策略降低了超级电容常态下的蓄电池的输出深度,蓄电池的最大功率深度降低了46.70%,这提高了蓄电池的使用寿命.

基于退役电池分级互备自适应控制的一次调频

目的“双碳”目标下新能源大量并网导致电力系统等效惯量降低、电能供应不确定性增加,给电网频率安全带来挑战。电池储能响应快速、调节灵活,近年来在电网调频中得到广泛关注。考虑到目前新能源汽车发展迅猛,随之而来的是汽车电池大量退役,计及电池配置成本问题,本文开展如何在安全使用的前提下,充分利用退役电池参与到电网调频应用的研究。方法首先,构建退役电池参与电网一次调频的AGC模型,并分析退役电池充放电深度对其剩余寿命的影响,确定其运行的SOC阈值范围;其次,通过设计出力比例系数,将虚拟惯性和虚拟下垂优势最大化,同时以退役电池SOC为切换条件进行分级自适应控制;然后,引入频率偏差分配系数使另一组退役电池参与备用配合,避免一组电池频繁切换充放电状态损耗电池剩余使用寿命;最后,在MATLAB/Simulink中搭建仿真模型,设置多组参数并对比。结果仿真结果证明,合理利用退役电池能够有效替代新电池参与到电网调频控制中,对电网频率的稳定性及安全运行提供有效支撑。结论本文所提方案中,退役电池能够有效降低电网最大频率偏差,避免电池SOC越限及频率二次跌落。同时,该方案显著减少了退役电池在负荷波动时的充放电切换次数,有利于延长退役电池剩余使用寿命。

单脉冲测角的二维分级主旁瓣干扰联合抑制方法

针对矩形平面阵列天线同时存在主、旁瓣干扰的单脉冲测角问题,该文设计了2维分级自适应单脉冲波束形成算法(TDHJ-ADBF)。TDHJ-ADBF算法将矩形平面阵分为方位维和俯仰维两个正交维度,采用2维分级处理架构:第1级处理在测角维进行,采用低运算量的压缩多重信号分类法对测角维主瓣干扰进行快速识别与方向估计,构造阻塞矩阵滤除主瓣干扰,获得仅含旁瓣干扰和噪声的协方差矩阵,进而对和、差波束方向图进行指向与鉴角曲线联合约束,完成测角维旁瓣干扰抑制与波束形成处理;第2级在非测角维对残留的测角维主瓣干扰进行抑制。通过2维分级处理实现主、旁瓣干扰联合对抗,并保持单脉冲测角的鉴角曲线线性度。仿真结果表明,TDHJ-ADBF算法实现了对主、旁瓣干扰联合抑制,具有高精度的单脉冲测角性能。

流域初始水权分配的主从递阶双层优化方法

依托“流域-地区-行业”层次的初始水权分配模式,“流域-地区”层次影响制约“地区-行业”层次的初始水权分配,“流域-地区”和“地区-行业”层次的初始水权分配共同影响流域综合效益。现有研究主要聚焦“流域-地区”或“地区-行业”层次的初始水权分配,但将两个层次集成进行初始水权分配仍有待深化研究。为此,构建主从递阶双层优化方法,开展流域初始水权分配研究。具体可表述为:①针对“流域-地区”层次,建立流域综合效益优化函数与约束条件。构造适应性诊断准则和空间均衡性诊断准则,嵌入流域综合效益优化函数中,提高地区之间初始水权分配与经济社会发展的适应性、流域分水的空间均衡性。②针对“地区-行业”层次,建立地区综合效益优化的非线性目标规划模型,保障行业发展用水需求。并构造匹配性诊断准则嵌入非线性目标规划模型中,提高地区不同行业初始水权分配与行业发展的匹配性。③集成“流域-地区”和“地区-行业”层次,应用主从递阶双层优化方法,开展黄河流域初始水权分配实践探索。研究得出:通过黄河流域初始水权分配方案设计,得到黄河流域涉及省区水权分配比例与其经济社会发展综合指标加权值比例的比值,其中,内蒙古-陕西、陕西-宁夏、宁夏-山西、山西-河南、河南-甘肃、甘肃-青海、青海-山东分别为0.84、1.45、0.84、1.08、1.19、1.77、1.02,满足适应性诊断准则。黄河流域初始水权分配的基尼系数为0.10,属于“空间均衡度很高”,满足空间均衡性诊断准则。黄河流域涉及省区不同行业的水权配置与行业发展的匹配度均超过0.90,属于“高度匹配”。同时,黄河流域经济、社会、生态效益优化函数对应的满意度分别达到0.946、0.943、1.000,黄河流域涉及省区的经济、社会、生态效益优化函数对应的满意度均超过0.90。结果表明,该方法能够有效破解流域初始水权分配难题。为保障流域初始水权分配方案的有效落实,必须强化水资源刚性约束和双控行动,提升流域水利科技创新能力,推进流域产业用水结构合理化与产业结构高级化。

层级式朋辈团体辅导助力解决大学新生入学适应问题的探讨

大学新生入学适应问题一直是教育研究领域关注的焦点。随着教育改革的深入,传统的迎新模式已无法满足大学新生多元化的适应需求。而层级式朋辈团体辅导作为一种新兴的辅导模式,为其提供指导性服务、情感关怀和经验分享,在大学新生入学适应过程中发挥着重要作用和价值。文章旨在通过实践研究,探讨层级式朋辈团体辅导在帮助大学新生适应校园生活、学习环境及社交环境等方面的实际效果和策略,使大学新生更快适应大学生活。

行为认知视域下智适应融合式学习对学生的影响探究

随着人工智能教育的发展,智适应学习系统逐步得到应用。为帮助学生提升学习效果,以行为认知视角切入,对不同学习方式的学生进行问卷调查,针对调查结果分别使用层次综合评价模型研究智适应融合式学习与传统学习方式对学生学习成效的差异性,并使用结构方程模型研究智适应融合式学习方式对学生影响的具体路径及影响程度,从而得出相关研究结论,并对未来教育学习方式提出建议与展望。

兼顾通信效率与效用的自适应高斯差分隐私个性化联邦学习

近年来,由于联邦学习中的通信参数(或梯度)会给参与方本地敏感数据带来重大的隐私泄露风险,联邦学习隐私保护引起了广泛的关注.然而,梯度交换频繁、数据分布异构、参与方本地硬件资源受限等一系列不可避免的因素给联邦学习隐私保护增加了挑战难度.为了以一种统一的方式同时有效地解决数据隐私、模型效用、通信效率以及参与方数据非独立同分布等四个方面的问题,本文提出了一种新的兼顾通信效率与效用的自适应高斯差分隐私个性化联邦学习(Communication-efficient and Utility-aware Adaptive Gaussian Differential Privacy for Personalized Federated Learning,CUAG-PFL)方法.具体而言,本文提出一种动态层级压缩模型梯度的方案先为通信模型梯度每一层动态生成特定的压缩率,再根据压缩率构造对应的确定性二进制测量矩阵去除梯度冗余信息.随后,通过同时优化裁剪阈值、敏感度和噪声尺度等隐私相关参数来对压缩的模型梯度执行自适应高斯差分隐私操作.此外,本文对CUAG-PFL进行了严格的隐私分析.为了验证CUAG-PFL在隐私、效用、通信效率以及个性化四个方面的优势,本文在CIFAR-10和CIFAR-100两个真实联邦数据集上进行了大量实验模拟、对比和分析,结果表明CUAG-PFL能够提高参与方本地数据隐私性、通信效率和模型效用,同时解决了数据非独立同分布的问题.特别地,即使在隐私预算仅为0.92且上行通信量减少68.6%时,CUAG-PFL因隐私保护和梯度压缩所引起的模型效用损失仅为1.66%.

层次多叉树映射布尔体廓后验误差的自适应层切

在给定构建方向下,层切厚度影响熔融沉积成型零件表面的阶梯效应,层切轮廓及其层次关系是路径规划的研究基础.为此,提出基于布尔体廓运算的自适应层切方法,在零件模型不同高度层切面上构建局部轮廓拓扑并计算内核域,根据内核点连线与轮廓环的交点,将轮廓环数据映射到一维坐标轴,基于拓扑映射判定轮廓环层次关系.建立层次关系多叉树,以树高映射体廓特征变化的布尔运算,根据层变阈值设计自适应层厚,通过体廓后验误差估计任意层厚的体积误差,验证自适应层切的有效性.实例打印及不同自适应层切方法对比结果表明,采用体廓后验误差计算的层切体积误差精度比牙尖高度法的更高;通过控制体廓特征部位的层厚来减小体积误差,有助于提高模型特征部位的表面成型精度.

混合云环境下面向数据生命周期的自适应访问控制

混合云模式下企业业务应用和数据经常跨云流转迁移,面对多样复杂的云服务环境,当前大多数混合云应用仅以主体为中心制定数据的访问控制策略并通过人工调整策略,无法满足数据在全生命周期不同阶段时的细粒度动态访问控制需求.为此,提出一种混合云环境下面向数据生命周期的自适应访问控制方法AHCAC.该方法首先采用基于关键属性的策略描述思想去统一混合云下数据全生命周期的异构策略,尤其引入“阶段”属性显式标识数据的生命周期状态,为实现面向数据生命周期的细粒度访问控制提供基础;其次针对数据生命周期同阶段策略具有相似性和一致性的特点,定义策略距离,引入基于策略距离的层次聚类算法实现数据生命周期各阶段对应访问控制策略的构建;最后通过关键属性匹配实现当数据所处阶段变化时,触发策略评估引擎上数据对应阶段策略的自适应调整和加载,最终实现面向数据生命周期的自适应访问控制.在OpenStack和开源策略评估引擎Balana上通过实验验证了所提方法的有效性和可行性.