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 supercapaci...
详细信息
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
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...
详细信息
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.
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...
详细信息
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.
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,...
详细信息
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.
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 homogeneou...
详细信息
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
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 expansi...
详细信息
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.
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 ...
详细信息
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.
暂无评论