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Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods： A case of northwestern China

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Research in Cold and Arid Regions 2016年第6期8卷 485-494页

作者： JinKui Wu ShiWei Liu LePing Ma Jia Qin JiaXin Zhou Hong Wei Laboratory of Watershed Hydrology and Ecology Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou Gansu 730000 China State Key Laboratory of Cryospheric Sciences Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou Gansu 730000 China Shule River Basin Water Resources Administration Bureau of Gansu Province Yumen Gansu 735200 China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual pre-cipitation interpolation based on spatial sandwich sampling, including ME （0.1513）, RMSE （95.91）, ASE （101.84）, MSE （？0.0036）, and RMSSE （1.0397）, were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.

关键词： Kriging interpolation method sampling methods spatial sandwich sampling precipitation northwestern China

A Comparison of Terrestrial Arthropod sampling methods

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Journal of Resources and Ecology 2012年第2期3卷 174-182页

作者：邹怡冯金朝薛达元桑卫国 Jan C.AXMACHER 伦敦大学学院地理学系英国伦敦WCIE 6BT 中央民族大学生命与环境科学学院北京100082 植被与环境变化国家重点实验室中国科学院植物研究所北京100093

Terrestrial arthropods are extremely important ecosystem components. The choice of best approaches to collect the wide range of terrestrial arthropods has been a topic of long-lasting debates. This article provides a brief overview of common sampling methods for terrestrial arthropod assemblages. We divide sampling methods into three main categories： passive sampling methods without any ＂activity density＂ bias, passive sampling methods with an ＂activity density＂ bias, and active sampling methods with inherent ＂activity density＂ and often further species-dependent biases, discussing their individual advantages and shortcomings as basis for biodiversity studies and pest control management. The selection of the optimal sampling methods depends strongly on the purpose of individual studies and the ecology and behavior of the arthropod groups targeted. A combination of different suitable methods is highly recommended in many cases.

关键词： terrestrial arthropods sampling methods comparison activity density

IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

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Chinese Journal of Electronics 2025年第1期34卷 322-337页

作者： Jingya Wang Yu Zhang Bin Zhang Jinxiang Xia Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University

This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling-important points sampling method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating method is proposed for feature extraction, improving the network's ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net(IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2 datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

关键词： Point cloud compression Representation learning Geometry Fuses Semantics Artificial neural networks Feature extraction sampling methods Robustness Data mining

Efficient sampling for decision making in materials discovery

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Chinese Physics B 2021年第5期30卷 1-11页

作者： Yuan Tian Turab Lookman Dezhen Xue State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong UniversityXi'an 710049China Los Alamos National Laboratory Los AlamosNM 87545USA

Accelerating materials discovery crucially relies on strategies that efficiently sample the search space to label a pool of unlabeled *** is important if the available labeled data sets are relatively small compared to the unlabeled data *** learning with efficient sampling methods provides the means to guide the decision making to minimize the number of experiments or iterations required to find targeted *** review here different sampling strategies and show how they are utilized within an active learning loop in materials science.

关键词： sampling methods active learning decision making material design Bayesian optimization

A Novel Neighborhood-Weighted sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

Maximum Correntropy High-Order Extended Kalman Filter

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Chinese Journal of Electronics 2022年第1期31卷 190-198页

作者： SUN Xiaohui WEN Chenglin WEN Tao School of Automation Hangzhou Dianzi University School of Automation Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Beijing Jiaotong University

In this paper, a novel maximum correntropy high-order extended Kalman filter(H-MCEKF) is proposed for a class of nonlinear non-Gaussian systems presented by polynomial form. All high-order polynomial terms in the state model are defined as implicit variables and regarded as parameter variables; the original state model is equivalently formulated into a pseudo-linear form with original variables and parameter variables; the dynamic relationship between each implicit variable and all variables is modeled, then an augmented linear state model appears by combing with pseudo-linear state model; similarly, the nonlinear measurement model can be equivalently rewritten into linear form; once again, the statistical characteristics of non-Gaussian modeling error are described by mean value and variance based on their finite samples; combing original measurement model with predicted value regarded as added state measurement, a cost function to solve the state estimation based on maximum correntropy criterion(MCC) is constructed; on the basis of this cost function, the state estimation problem can be equivalently converted into a recursive solution problem in the form of Kalman filter, in which the filter gain matrix is solved by numerical iteration though its fixed-point equation; illustration examples are presented to demonstrate the effectiveness of the new algorithm.

关键词： cost function filter gain matrix numerical iteration matrix algebra state estimation polynomial form nonlinear measurement nonGaussian modeling error Kalman filters nonGaussian systems sampling methods pseudolinear state model recursive solution problem statistical characteristics state measurement polynomials maximum entropy methods implicit variables high-order polynomial terms H-MCEKF nonlinear filters augmented linear state model MCC pseudolinear form parameter variables maximum correntropy high-order extended Kalman filter fixed-point equation iterative methods maximum correntropy criterion finite samples

Knowledge Graph Completion Based on GCN of Multi-Information Fusion and High-Dimensional Structure Analysis Weight

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Chinese Journal of Electronics 2022年第2期31卷 387-396页

作者： NIU Haoran HE Haitao FENG Jianzhou NIE Junlan ZHANG Yangsen REN Jiadong Yanshan University Beijing Information Science and Technology University

Knowledge graph completion(KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node ***, the graph convolutional network(GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.

关键词： data mining encoding information high-dimensional structure analysis weight matrix algebra multiinformation fusion GCN graph convolutional network scoring function structure information sampling methods encoded matrix embeddings convolutional neural nets subgraph sampling method feature extraction node structure extraction KGC knowledge graph completion channel attention convolution encode node structure features data sparsity node feature information graph neural networks data fusion semantic networks

CMOS image sensor with optimal video sampling scheme

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Science China(Information Sciences) 2012年第6期55卷 1429-1435页

作者： GUAN Ning,ZHANG Xu,LIU Bo,DONG Zan,HUANG BeiJu,GUI Yun,HAN JianQiang,WANG Yuan,ZHANG ZanYun & CHEN HongDa State Key Laboratory on Integrated Optoelectronics,Institute of Semiconductors,Chinese Academy of Science,Beijing 100083,China State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Science Beijing China

Time-varying illumination on the focal plane is a three-dimensional *** sam-pling theory proves that the temporal resolution can be optimally improved by a factor of 2～(1/2) while the spatial resolution is reserved by changing the sampling *** on the theory,a prototype multi-field CMOS image sensor (CIS) is designed for a 0.35-μm 2P4M CMOS *** pixels in 4×4-pixel clusters are assembled into 16 fields over the whole *** pins (resets and shutters) of pixels are separated which provides the ability of sampling the illumination with the optimal sampling scheme.

关键词： CMOS image sensors sampling methods deinterlacing high speed photography selective readout

A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

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Journal of Data and Information Science 2021年第1期6卷 178-192页

作者： Ulagapriya Krishnan Pushpa Sangar Department of Computer Science and Engineering St.Peter’s Institute of Higher Education and ResearchChennaiIndia

Purpose: This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine ***/methodology/approach: The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over sampling(ROS), Random Under sampling(RUS), Synthetic Minority Oversampling TEchnique(SMOTE), ADAptive SYNthetic sampling(ADASYN), Edited Nearest Neighbor(ENN), and Condensed Nearest Neighbor(CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is ***: This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced *** limitations: The testing was carried out with limited dataset and needs to be tested with a larger *** implications: This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the ***/value: This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.

关键词： Imbalanced data sampling methods Machine learning Classification