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A Parameters Identification Method of the Equivalent Circuit Model of the Supercapacitor Cell Module Based on segmentation optimization

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IEEE ACCESS 2020年 8卷 92895-92906页

作者： Zhao, Yanming Xie, Wenchao Fang, Ziwei Liu, Shuli Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Engn Res Ctr Hunan Prov Min & Utilizat Wind Turbi Xiangtan 411201 Peoples R China

In practical settings, the supercapacitor is often used as the storage battery, which is composed of several supercapacitor cells in series. In order to accurately estimate the State of Charge (SoC) in the supercapacitor cell module, an equivalent model of supercapacitor cell module is invoked, which is expected to reflect the characteristics of supercapacitor cell module, especially the self-discharge characteristics during standing. The results of parameter identification directly affect the model accuracy. Hitherto, most supercapacitor equivalent models have been proposed for supercapacitor cells, but if the module equivalent model is characterized by connecting many equivalent models of supercapacitor cells in series, it would lead to the cumulative errors and the additional errors, which would incur errors in the parameter identification, and directly affect the model accuracy. The paper aims to obtain the accurate equivalent model parameters, the supercapacitor cell module is regarded as the object, the three-branch equivalent circuit model is established for the supercapacitor cell module, a discussion is given on the parameter identification methods about Circuit Analysis Method (CA) and Recursive Least Squares Method (RLS). This paper establishes the Simulink simulation model for the multi-method parameter identification of supercapacitor cell module, the simulation and analysis are performed to illustrate the advantages and disadvantages of CA and Circuit Analysis-Recursive Least Squares Method (CA-RLS). Then, it proposes a parameters identification method of the equivalent circuit model of supercapacitor cell module based on segmentation optimization (SO). The effectiveness of SO is verified by simulation and error analysis, the results indicate that SO can more effectively reflect the charging characteristics and self-discharge characteristics of the supercapacitor cell module. In particular, the comprehensive error in the static self-discharge phase is

关键词： Supercapacitors Integrated circuit modeling Equivalent circuits Parameter estimation Analytical models Wind turbines Supercapacitor cell module equivalent circuit model parameter identification segmentation optimization

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Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes

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IEEE ACCESS 2021年 9卷 116702-116719页

作者： Tetteh, Gideon Okpoti Gocht, Alexander Erasmi, Stefan Schwieder, Marcel Conrad, Christopher Thunen Inst Farm Econ D-38116 Braunschweig Germany Humboldt Univ Geog Dept D-10099 Berlin Germany Martin Luther Univ Halle Wittenberg Inst Geosci & Geog D-06099 Halle Germany

The Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) considers agricultural fields as one of the essential variables that can be derived from satellite data. We evaluated the accuracy at which agricultural fields can be delineated from Sentinel-1 (S1) and Sentinel-2 (S2) images in different agricultural landscapes throughout the growing season. We used supervised segmentation based on the multiresolution segmentation (MRS) algorithm to first identify the optimal feature set from S1 and S2 images for field delineation. Based on this optimal feature set, we analyzed the segmentation accuracy of the fields delineated with increasing data availability between March and October of 2018. From the S1 feature sets, the combination of the two polarizations and two radar indices attained the best segmentation results. For S2, the best results were achieved using a combination of all bands (coastal aerosol, water vapor, and cirrus bands were excluded) and six spectral indices. Combining the radar and spectral indices further improved the results. Compared to the single-period dataset in March, using the dataset covering the whole season led to a significant increase in the segmentation accuracy. For very small fields (< 0.5 ha), the segmentation accuracy obtained was 27.02%, for small fields (0.5 - 1.5 ha), the accuracy was 57.65%, for medium fields (1.5 ha - 15 ha), the accuracy was 75.71%, and for large fields (>15 ha), the accuracy stood at 68.31%. As a use case, the segmentation result was used to aggregate and improve a pixel-based crop type map in Lower Saxony, Germany.

关键词： Image segmentation Spatial resolution Earth Remote sensing Artificial satellites Agriculture Force Agricultural field delineation band indices essential agricultural variables feature combination image segmentation intersection over union remote sensing segmentation optimization

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OBJECT-BASED RANDOM FOREST CLASSIFICATION FOR MAPPING FLOODPLAIN VEGETATION STRUCTURE FROM NATION-WIDE CIR AND LIDAR DATASETS 6

OBJECT-BASED RANDOM FOREST CLASSIFICATION FOR MAPPING FLOODP...

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6th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS)

作者： Kooistra, L. Kuilder, E. T. Mucher, C. A. Wageningen Univ Lab Geoinformat Sci & Remote Sensing POB 47 NL-6700 AA Wageningen Netherlands Alterra POB 47 NL-6700 AA Wageningen Netherlands

ISBN: (纸本)9781467390125

Very high resolution aerial images and LiDAR (AHN2) datasets with a national coverage provide opportunities to produce vegetation maps automatically. As such the entire area of the river floodplains in the Netherlands may be mapped with high accuracy and regular updates, capturing the dynamic state of the vegetation. In this study, these fused datasets are used to map the vegetation of 936 ha of the floodplain on the north-side of the river Nederrijn near Wageningen into ten vegetation structure classes. The method follows object-based image analysis principles. Objects are defined in segmentation and subsequently labeled using the ensemble-tree classifier random forest. The mapping scale is controlled by selecting segmentation parameters from quantified discrepancies between reference polygons and segmented objects. Effects on the mapping scale of different reference polygons and different segmentation data is investigated. The results show that it is important to be able to select the right segmentation parameters to control the mapping scale. A discrepancy measure with reference polygons is a suitable method to do this objectively. The use of random forest classification on the objects resulted in an estimated classification accuracy of 86% on the basis of the built-in cross-validation estimate of random forest. Variable importance measures of random forest showed that the AHN2 lidar dataset is a valuable addition to the spectral information contained in the aerial images in the classification.

关键词： Object Based Image Analysis (OBIA) reference polygons segmentation optimization variable importance vegetation structure classes

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Edge-Preserving Stereo Matching Using Minimum Spanning Tree

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IEEE ACCESS 2019年 7卷 177909-177921页

作者： Zhang, Congxuan He, Chao Chen, Zhen Liu, Wen Li, Ming Wu, Junjie Nanchang Hangkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China Nanjing Univ Aeronaut & Astronaut Sch Automat Nanjing 211106 Peoples R China Univ Kansas Dept Phys Therapy & Rehabil Sci Kansas City KS 66160 USA

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.

关键词： Stereo matching minimum spanning tree edge-preserving brightness information disparity range estimation segmentation optimization

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Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期238卷

作者： Zhao, Yan Li, Weimin Liu, Fangfang Wang, Jingchao Luvembe, Alex Munyole Shanghai Univ Dept Comp Engn & Sci Shanghai Peoples R China

Community detection aims to discover hidden communities or groups in complex networks and is essentially unsupervised clustering behavior. However, most of the existing unsupervised methods are designed for homogeneous networks;therefore, they cannot effectively handle heterogeneous structures and rich semantic information. Under such a situation, it is difficult to accurately detect communities in heterogeneous networks that better reflect the real world. Therefore, this work aims to design an unsupervised framework to fuse heterogeneous structure information and interpret the rich semantics of the network in the form of community semantics. Thus, a heterogeneous network community detection method, called HAESF, is introduced. It includes two modules: the Heterogeneous Auto-Encoder (HAE) and the Semantic Factorization (SF) modules. In more detail, the HAE module adopts a hierarchical attention scheme to represent and aggregate the het-erogeneous structure of the network. And it proposes the concept of heterogeneous information combinatorial graphs for structural reconstruction to achieve unsupervised detection. Concerning the SF module, it focuses on learning the semantic information in the network from the community point of view. It uses nonnegative matrix factorization to decompose the network features for obtaining community semantics. Once both modules are implemented, the objective of restricting community segmentation based on these semantics is achieved. The constraint is based on community semantic homogeneity to correct inaccurate node delineation. Furthermore, to improve the algorithm efficiency, a unified framework is designed to optimize the HAE and SF modules jointly. Within this new framework, the SF loss is innovatively used as a judgmental loss for selective segmentation optimizations, helping to obtain more reliable community detection results. As for the results, extensive experiments are performed on three public datasets. The findings show that

关键词： Heterogeneous network Community detection Heterogeneous auto-encoder Community semantic segmentation optimization

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Toward scalable graph-based security analysis for cloud networks

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COMPUTER NETWORKS 2022年第0期206卷 108795-108795页

作者： Sabur, Abdulhakim Chowdhary, Ankur Huang, Dijiang Alshamrani, Adel Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Taibah Univ Coll Comp Sci & Engn Medinah Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Jeddah Saudi Arabia

Cloud-based systems and services are seeing exponential growth in the last few years. Many companies and digital services are actively migrating their storage and computational needs to the cloud. With such an expansion of virtual services, security threats are also significantly increasing. Utilizing the Attack Representation Methods (ARMs) and Attack Graph (AG) enables the security administrator to understand the cloud network's current security situation. However, the AG suffers from scalability challenges. It relies on the connectivity between the services and the vulnerabilities associated with the services to allow the system administrator to realize its security state. This approach caused the AG to be vast and challenging to generate and analyze. To address the scalability challenges, we propose a segmentation-based scalable security state (S3) framework for the network. Our framework utilizes the well-known divide-and-conquer approach to divide the large network region into smaller, manageable segments. We follow a well-known segmentation approach derived from the K-means clustering algorithm to partition the system into segments based on the similarity between the services. A distributed firewall (DFW) separates the segments to ensure the attacker cannot move laterally and compromise them. Our evaluation shows that the separation of segments not only preserves the original reachability and connectivity but also enhances the scalability of the AG. The presented framework (a) provides a scalable attack graph generation algorithm by reducing attack graph generation time and density, which in turn reduces the complexity of security analysis on an extensive cloud network, (b) ensures a loop-free attack graph through the utilization of cycle detection and removal algorithm, and (c) presents an approach to provide the optimal number of segments based on the cost of implementing the segmentation using the distributed firewall rules.

关键词： Attack graphs Distributed firewall (DFW) Microsegmentation Software-defined network (SDN) Security analysis segmentation optimization

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Optimized segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping

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REMOTE SENSING 2020年第5期12卷 793-793页

作者： Ding, Hu Liu, Kai Chen, Xiaozheng Xiong, Liyang Tang, Guoan Qiu, Fang Strobl, Josef Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China South China Normal Univ Sch Geog Guangzhou 510631 Peoples R China Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China Univ Texas Dallas Dept Geospatial Sci Richardson TX 75080 USA Univ Salzburg Dept Geoinformat Z GIS A-5020 Salzburg Austria

The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran's I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.

关键词： object-based image analysis gully mapping segmentation optimization unmanned aerial vehicle (UAV) XGBoost

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