Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.

Bounded consensus tracking of second-order multi-agent systems with sampling delay under directed networks

The bounded consensus tracking problems of second-order multi-agent systems under directed networks with sam- pling delay are addressed in this paper. When the sampling delay is more than a sampling period, new protocols based on sampled-data control are proposed so that each agent can track the time-varying reference state of the virtual leader. By using the delay decomposition approach, the augmented matrix method, and the frequency domain analysis, necessary and sufficient conditions are obtained, which guarantee that the bounded consensus tracking is realized. Furthermore, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

On the Performance of Blind Chip Rate Estimation in Multi-Rate CDMA Transmissions Using Multi-Rate Sampling in Slow Flat Fading Channels

This paper considers blind chip rate estimation of DS-SS signals in multi-rate and multi-user DS-CDMA systems over channels having slow flat Rayleigh fading plus additive white Gaussian noise. Channel impulse response is estimated by a subspace method, and then the chip rate of each signal is estimated using zero crossing of estimated differential channel impulse response. For chip rate estimation of each user, an algorithm which uses weighted zero-crossing ratio is proposed. Maximum value of the weighted zero crossing ratio takes place in the Nyquist rate sampling frequency, which equals to the twice of the chip rate. Furthermore, bit time of each user is estimated using fluctuations of autocorrelation estimators. Since code length of each user can be obtained using bit time and chip time ratio. Fading channels reduce reliability factor of the proposed algo-rithm. To overcome this problem, a receiver with multiple antennas is proposed, and the reliability factor of the proposed algorithm is analyzed over both spatially correlated and independent fading channels.

Feature Selection for Multi-label Classification Using Neighborhood Preservation

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.

Multi-stage Virtual Over-sample Digital Hystersis Control

并网电流控制是分布式发电系统中的技术难点。在分析传统滞环控制策略的基础上，针对不对称多电平拓扑研究了一种分段式虚拟过采样并网滞环控制策略。通过将母线电压按正弦波形分段的方法，可以有效分配开关管开关频率，降低输出电流的谐波含量。在真实采样点之间插入虚拟采样点的虚拟过采样方法，解决了滞环控制中环宽失效问题。利用仿真软件对多种滞环控制策略进行对比分析，搭建实验平台对提出的算法进行实验验证。仿真与实验结果都验证了所提控制算法的正确性。

A multi-criteria decision-making approach for comparing sample preservation and DNA extraction methods from swine feces

Molecular microbiological methods, such as competetive PCR, real-time PCR, denaturing gradient gel electrophoresis (DGGE) and large-scale parallel-pyrosequencing, require the extraction of sufficient quantity of high quality DNA from microbiologically and chemically complex matrices. Due to difficulties in the field to standardize/select the optimum DNA preservation-extraction methods in view of laboratories differences, this article attempts to present a straight-forward mathematical framework for comparing some of the most commonly used methods. To this end, as a case study, the problem of selecting an optimum sample preservation-DNA extraction strategy for obtaining total bacterial DNA from swine feces was considered. Two sample preservation methods (liquid nitrogen and RNAlater?) and seven extraction techniques were paired and compared under six quantitative DNA analysis criteria: yield of extraction, purity of extracted DNA (A260/280 and A 260/230 ratios), duration of extraction, degradation degree of DNA, and cost. From a practical point of view, it is unlikely that a single sample preservation-DNA extraction strategy can be optimum for all selected criteria. Hence, a systematic multi-criteria decision-making (MCDM) approach was used to compare the methods. As a result, the ZR Fecal DNA MiniPrepTM DNA extraction kit for samples preserved either with liquid nitrogen or RNAlater? were identified as potential optimum solutions for obtaining total bacterial DNA from swine feces. Considering the need for practicality for in situ applications, we would recommend liquid nitrogen as sample preservation method, along with the ZR Fecal DNA MiniPrepTM kit. Total bacterial DNA obtained by this strategy can be suitable for downstream PCR-based DNA analyses of swine feces.

Recent Development in Simultaneous Multi-Element Determination of the Platinum Group Elements and Gold in Geological and Environmental Samples

In recent years, the modern methods of multi-element analysis of precious metals have attracted wide attention in scientific research and industry. The application and development in the decomposition of samples, separation and enrichment, and modern instrumental analysis of the platinum-group elements (PGEs) and gold in geological and environmental samples have been reviewed. Finally, the tendency of analysis of precious metals is also prospected.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Consensus for second-order multi-agent systems with position sampled data

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated.The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example.

基于邻域采样的多任务图推荐算法

近年来,图神经网络(GNN)成为解决协同过滤的主流方法之一。它通过构建用户-物品图,模拟用户与物品的交互关系,并用GNN学习它们的特征表示。尽管现有在模型结构上的研究已取得了较大进展,但如何在图结构上更有效地进行负采样仍未有效解决。为此,提出一种基于邻域采样的多任务图推荐算法。该算法提出了一种基于GNN的邻域采样策略,该策略以每个用户为中心构建子图,将次高阶物品作为用户邻域采样的负样本,可以更有效地挖掘强负样本并提高采样质量。通过GNN对图结点进行信息聚合与特征提取,得到结点的最终嵌入表示。设计一种余弦边际损失来过滤部分冗余负样本,以有效减少采样过程中的噪声数据。同时,该算法引入了多任务策略对模型进行联合优化,以增强模型的泛化能力。在3个公开数据集上进行的大量实验表明,该算法在大多数情况下明显优于其他主流算法。

Diversity Sampling Based Kernel Density Estimation for Background Modeling

A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.

Asymptotic bounded consensus tracking of double-integrator multi-agent systems with bounded-jerk target based on sampled-data without velocity measurements

This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to partial agents based on sampled-data without velocity measurements. A sampled-data consensus tracking protocol（CTP） without velocity measurements is proposed to guarantee that double-integrator MASs track an AUABJT available to only partial agents.The eigenvalue analysis method together with the augmented matrix method is used to obtain the necessary and sufficient conditions for ABCT. A numerical example is provided to illustrate the effectiveness of theoretical results.

多舱共配绿色车辆路径问题的改进变邻域搜索算法

针对社区团购前置仓配送场景中“多中心、高时效、多品类、高排放”难题,本文提出多车场带时间窗的绿色多舱车车辆路径问题(MDMCG-VRPTW),构建混合整数线性规划模型,并设计改进的变邻域搜索算法(IVNS)实现求解.采用两阶段混合算法构造高质量初始解.提出均衡抖动策略以充分探索解空间,引入粒度机制以提升局部搜索阶段的寻优效率.标准算例测试结果验证了两阶段初始解构造算法和IVNS算法的有效性.仿真实验结果表明,模型与算法能够有效求解MDMCGVRPTW,且改进策略提高了算法的求解效率和全局搜索能力.最后,基于对配送策略和时效性的敏感性分析,为相关配送企业降本增效提供更多决策依据.

多尺度融合与动态自适应图的公交客流预测模型

公交客流预测是公共交通规划和管理中的重要问题。虽然时空图卷积在地铁客流预测任务中获得了很好的预测效果,但是面对公交更复杂的线路、大规模的节点数据,现有的基于图卷积的空间建模方法将带来巨大的空间内存消耗。同时,公交客流量短时间范围内更可能受到瞬时交通状况的影响。为了解决这些挑战,提出了一种多尺度融合和动态自适应图的公交客流预测模型(MFDAG)。该模型融合客流、时刻和周信息以增加数据的特征维度,用动态自适应图的方法来学习不同站点之间的关系。进一步提出了一种多尺度融合传播的方法来表示复杂的空间依赖关系,同时设计了一种多尺度卷积传播的方法来学习不同尺度的时间依赖关系。在两个真实的客流数据集上进行了实验,并与其他交通预测方法进行了比较。实验结果表明,所提出的多尺度融合和动态自适应图的公交客流预测方法具有更高的预测准确度。

融合二连通模体结构信息的节点分类算法

节点表示学习将图结构数据信息编码到低维的潜在空间中,在节点分类、聚类、链路预测等机器学习任务中被广泛应用。在复杂网络中,节点与节点之间不仅存在直接相连的低阶结构,也存在以特殊连接模式形成的高阶结构,称为模体。提出一种融合二连通模体结构信息的节点分类算法(FMI),利用节点间高阶二连通模体信息学习节点表示,完成节点分类任务。首先,统计网络中的二连通模体,利用其中信息提出一个节点重要性的度量指标——模体比值。根据模体比值计算采样概率进行邻域采样;构造一个带权辅助图以融合网络节点连接的低阶关系与高阶关系,对节点进行加权邻域聚合以得到节点表示。在5个数据集Cora、Citeseer、Pubmed、Wiki和DBLP上执行节点分类任务,与5种经典基准算法进行对比,所提算法FMI在准确度和F1-分数等指标上表现良好。

基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法

断裂带与地震、滑坡等自然灾害的发生有着密切关系,精准提取断裂带不仅可为地震断层的定量化研究提供指导,还可为地震灾害风险评估及防震减灾决策的制定提供科学依据。针对现有方法中LiDAR点云断裂带提取不完整、连续性差及错误率高等问题,文中提出了一种基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法,以便更好地解决复杂地形区域的断裂带自动提取问题。该方法首先构建不同空间尺度的邻域点集,从而更全面地考察点云的局部几何结构特征。考虑到RS-Conv算子能够很好地表征中心点与邻域点的空间关系,文中以RS-Conv算子作为卷积模块构建了多尺度神经网络模型,以提取出LiDAR点云不同尺度的深层次特征,对其进行堆叠并输入到全连接层,以完成对断裂带点的提取。最后,在ISPRS点云数据集、川滇点云数据集和鲜水河数据集上对文中所述方法与张量分解方法和Deep Neural Networks(DNN)方法进行了对比实验,结果表明,文中方法的分类精度最高,分类总误差最低仅为0.3%,较其他方法降低了0.91%~2.79%,证实了该方法在点云断裂带提取方面的优越性。

基于多阶近邻约束的深度不完整多视图聚类方法

多视图聚类是重要的无监督学习方法之一,然而在实际应用中很难获取完整的多视图数据,导致不完整多视图聚类问题.大多数已有的不完整多视图聚类方法只考虑了视图的属性信息,而忽视了数据结构信息对聚类的影响,使提取的特征不能充分表示原始数据的潜在结构.针对以上问题,提出一种基于多阶近邻约束的深度不完整多视图聚类方法.首先,利用具有自注意力机制的深度自编码器获取带有视图间信息交互的深层次隐含特征,并采用加权融合的方式获取视图的公共语义信息;然后,对于不完整多视图中的缺失数据,利用多视图的公共表示进行补全;最后,提出一种多阶近邻约束机制,该机制考虑不完整多视图数据的深层结构信息,利用多视图的互补性构建近似完整的近邻图,引导编码器学习更紧致、更有判别性的高级语义特征.在公共数据集上的实验结果证明了所提方法的有效性.