检索结果-内蒙古大学图书馆

semantic segmentation algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

ENERGIES 2022年第23期15卷 8925页

作者： Wang, Shitao Sun, Mingjian Shen, Yi Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.

关键词： solar energy semantic segmentation algorithm cloud moving prediction cloud shadow cloud speed

来源：评论

学校读者我要写书评

暂无评论

Visual comfort impact assessment for walking spaces of urban historic district in China based on semantic segmentation algorithm

引用

ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 2025年 114卷

作者： Gao, Xiaoming Wang, Hang Zhao, Jilong Wang, Yuetao Li, Chaoxian Gong, Cong Shandong Jianzhu Univ Sch Architecture & Urban Planning Jinan 250101 Peoples R China Shandong Jianzhu Univ Kinet Architecture Modular Res Lab Jinan 250101 Peoples R China Chongqing Univ Sch Architecture & Urban Planning Chongqing 400044 Peoples R China

Urban historic districts (UHDs) are primarily walkable areas in built environments that preserve the traditional and local characteristics of a specific historical period. However, the impacts of the visual perception elements (VPEs) in UHD walking spaces on visual comfort related to the health-benefits have not been studied in depth. In contrast to previous studies, this study develops a comprehensive methodology framework for assessing visual comfort of UHD walking spaces to provide evidence for health-benefits based on semantic segmentation (Semseg) algorithm, integrating street view image data mining, Semseg computing, K-means cluster analysis, visual perception analysis and multiple linear regression analysis. A total of 11,633 images from Baidu MapsPanoramic Static Imagery (BM-PSI) API were collected from 17 typical UHD walking spaces in China, forming 17 datasets of visual sequence images (VSIs). Three types of VPEs (Natural-VPE, Artificial-VPE and Cultural-VPE) were identified and quantified by Semseg Computing to calculate Visual Pixel Ratio (VPR) indicators. 15 representative VSIs were filtered by K-means cluster analysis, which were subsequently evaluated through webbased questionnaires involving 308 respondents with diverse demographic characteristics to derive General Visual Comfort Ratings (G-VCR) and Sub-Visual Comfort Ratings (S-VCR) indicators. Finally, multiple linear regression analysis was conducted to reveal the relationships between VPR and both G-VCR and S-VCR indicators respectively. Overall, the results reveal that Natural-VPE and Cultural-VPE have a significant positive impact on visual comfort, while Artificial-VPE exerts a opposite effect. Furthermore, demographic characteristics affect visual comfort differently: female respondents showed stronger preference for Natural-VPE, while male respondents were more sensitive to Artificial-VPE;middle-aged and elderly respondents favored environments with more Natural-VPE and Cultural-VPE, whereas

关键词： Urban historic district semantic segmentation algorithm Visual comfort impact assessment Visual perception element Demographic characteristics

来源：评论

学校读者我要写书评

暂无评论

Kans-Unet Model and Its Application in Image Patch-Shaped Detection

引用

IEEE ACCESS 2025年 13卷 80508-80519页

作者： Li, Xingsu Li, Zhong Huang, Jianping Han, Ying Zhu, Kexin Hao, Bo Song, Junjie Huo, Yumeng Inst Disaster Prevent Inst Intelligent Emergency Informat Proc Langfang 065201 Peoples R China Inst Disaster Prevent Sch Informat Engn Langfang 065201 Peoples R China Inst Disaster Prevent Sch Emergency Management Langfang 065201 Peoples R China Minist Emergency Management China Natl Inst Nat Hazards Beijing 100085 Peoples R China

This study proposes a new detection method Kans-Unet, which combines Kans and U-net architecture. Specifically, the method embeds a convolution module based on Kans (Kan-Conv) in the encoder. The module applies a learnable activation function at the edge of the network, which not only reduces the number of model parameters but also significantly improves the generalization performance of the network. Secondly, in the transition region between encoder and decoder, the Feature Pyramid Attention (FPA) module is introduced to enhance the ability of the model to capture and analyze multiple scale features. In the decoder part, the CBAM attention mechanism module is integrated to effectively enhance the network's attention to key information, and to improve the expression ability of image features. It is found that a wide range of patch-shaped anomaly regions are distributed in the VLF band power spectrum image of the space ionospheric electric field, and these patch-shaped anomaly regions are effectively detected by using the Kans-Unet model. The experimental results show that the KANs-Unet algorithm exhibits better detection performance compared to current mainstream semantic segmentation algorithms in the task of detecting abnormal speckle areas. It solves the problem of long model training time caused by insufficient computing power and provides a new method for the detection and analysis of abnormal features in power spectrum images.

关键词： Convolution semantic segmentation Electric fields Earthquakes Computer vision Computational modeling Attention mechanisms Kernel Decoding Shape Ionosphere Kans-Unet semantic segmentation algorithm Zhangheng-1 satellite

来源：评论

学校读者我要写书评

暂无评论

Research on the Detection Model of Kernel Anomalies in Ionospheric Space Electric Fields

引用

ATMOSPHERE 2025年第2期16卷 160-160页

作者： Li, Xingsu Li, Zhong Huang, Jianping Han, Ying Huo, Yumeng Song, Junjie Hao, Bo Inst Disaster Prevent Inst Intelligent Emergency Informat Proc Langfang 065201 Peoples R China Inst Disaster Prevent Sch Informat Engn Langfang 065201 Peoples R China Inst Disaster Prevent Sch Emergency Management Langfang 065201 Peoples R China Minist Emergency Management China Natl Inst Nat Hazards Beijing 100085 Peoples R China

Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and U-net networks. The model embeds the KAN-Conv convolutional module based on KANs in the encoder section, introduces the feature pyramid attention module (FPA) at the junction of the encoder and decoder, and introduces the CBAM attention mechanism module in the decoder section. The experimental results show that the improved KANs-Unet model has a mIoU improvement of about 10% compared to the PSPNet algorithm and an improvement of about 7.8% compared to the PAN algorithm. It has better detection performance than the currently popular semantic segmentation algorithms. A higher evaluation index represents that the detected abnormal area is closer to the label value (i.e., the detected abnormal area is more complete), indicating better detection performance. To further investigate the characteristics of kernel anomaly areas and the differences in features during magnetic storms, the author studied the characteristics of kernel anomaly areas during two different intensities of magnetic storms: from November 2021 to October 2022 and from 1 May 2024 to 13 May 2024 (large magnetic storm), and from 11 October 2023 to 23 October 2023 (moderate magnetic storm). During a major geomagnetic storm, the overall distribution of kernel anomaly areas shows a parallel trend with a band-like distribution. The spatial distribution of magnetic latitudes is relatively scattered, especially in the southern hemisphere, where the magnetic latitudes are wider. Additionally, the number of orbits with kernel anomaly areas during ascending increases, especially during peak periods of major geomagnetic storms. The overall spatial distribution of moderate geomagnetic storms does not change significantly, but the g

关键词： China Seismo-Electromagnetic Satellite semantic segmentation algorithm Kolmogorov-Arnold U-shaped network power spectrum great geomagnetic storm

来源：评论

学校读者我要写书评

暂无评论

Lightweight segmentation algorithm of feasible area and targets of unmanned surface cleaning vessels

引用

MACHINE VISION AND APPLICATIONS 2024年第4期35卷 63-63页

作者： Shen, Jingfu Zhang, Yuanliang Liu, Feiyue Liu, Chun Jiangsu Ocean Univ Coll Marine Engn Lianyungang 222005 Peoples R China Jiangsu Ocean Univ Coll Mech Engn Lianyungang 222005 Peoples R China

To achieve real-time segmentation with accurate delineation for feasible areas and target recognition in Unmanned Surface Cleaning Vessel (USCV) image processing, a segmentation approach leveraging visual sensors on USCVs was developed. Initial data collection was executed with remote-controlled cleaning vessels, followed by data cleansing, image deduplication, and manual selection. This led to the creation of WaterSeg dataset, tailored for segmentation tasks in USCV contexts. Upon comparing various deep learning-driven semantic segmentation techniques, a novel, efficient Muti-Cascade semantic segmentation Network (MCSSNet) emerged. Comprehensive tests demonstrated that, relative to the state of the art, MCSSNet achieved an average accuracy of 90.64%, a segmentation speed of 44.55fps, and a 45% reduction in model parameters.

关键词： Unmanned surface vessel MCSSNet Multi cascade semantic segmentation algorithm Deep learning

来源：评论

学校读者我要写书评

暂无评论

Detection Method and Application of Nuclear-Shaped Anomaly Areas in Spatial Electric Field Power Spectrum Images

引用

REMOTE SENSING 2024年第4期16卷 726-726页

作者： Li, Xingsu Li, Zhong Huang, Jianping Yang, Xuming Li, Wenjing Huo, Yumeng Song, Junjie Yang, Ruiqi Inst Disaster Prevent Inst Intelligent Emergency Informat Proc Langfang 065201 Peoples R China Inst Disaster Prevent Sch Emergency Management Langfang 065201 Peoples R China Hebei Key Lab Resource & Environm Disaster Mech & Langfang 065201 Peoples R China Minist Emergency Management China Natl Inst Nat Hazards Beijing 100085 Peoples R China Inst Disaster Prevent Sch Culture & Commun Langfang 065201 Peoples R China Inst Disaster Prevent Sch Informat Engn Langfang 065201 Peoples R China

It is found that there are some anomalous high-energy nuclear-shaped regions in the VLF frequency band of the space electric field. To detect and statistically analyze these nuclear-shaped anomaly areas, this paper proposes a nuclear-shaped anomaly area detection method based on the electric field power spectrum image data of the China Seismo Electromagnetic Satellite (CSES-01). First, the logarithm of VLF frequency band data was calculated and rotated counterclockwise to create power spectrum images and label them to form a sample image dataset;then, images were enhanced (which involved resizing, scaling, rotation, gaussian denoising, etc.) to solve the problems of the model overfitting and sample imbalance. Finally, the U-net network model based on the ResNet50 encoder was trained to obtain the optimal kernel anomaly detection model ResNet50_Unet. Comparative experiments with various semantic segmentation algorithms show that the ResNet50_Unet model has the best performance. Applying this model to detect the electric field power spectrum images from November 2021 to February 2022, a total of 101 nuclear-shaped anomaly areas were found, distributed between 45 degrees and 70 degrees of the north-south latitude. This model can quickly detect nuclear-shaped anomaly regions from massive data, providing reference significance for the detection of other types of ionospheric spatial disturbances. At the same time, it has important scientific significance and practical value for understanding the ionosphere and space communication.

关键词： CSES-01 semantic segmentation algorithm power spectrum image ResNet50_Unet

来源：评论

学校读者我要写书评

暂无评论

Research on wheat broken rate and impurity rate detection method based on DeepLab-EDA model and system construction

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 226卷

作者： Zhang, Qi Wang, Ling Ni, Xindong Wang, Faming Chen, Du Wang, Shumao China Agr Univ Coll Engn Beijing 100083 Peoples R China

The broken rate and impurity rate of wheat are important indicators for assessing the quality of combine harvester operations. In view of the overlapping, occlusion and dense adhesion between the scattered grains during the operation of the combine harvester, it is difficult to obtain the grain crushing characteristics and impurity mass, which leads to low detection accuracy. In this paper, a method for detecting wheat broken rate and impurity rate based on DeepLab-EDA semantic segmentation model was proposed, and a detection system was built. In the detection system, an image acquisition device was designed and developed based on the principle of electromagnetic vibration, and the deep learning model was deployed in the embedded processor. Through the human-computer interaction interface design, the online processing and analysis of wheat image data and the display of the detection results of broken rate and impurity rate were realized. Comparative experiments with traditional semantic segmentation models showed that the MIoU, MP and MR of the DeepLab-EDA model were 89.41 %, 95.97 % and 94.83 %, respectively, representing improvements of 9.94%, 7.41%, and 7.52% over the baseline model, and indicating a significant enhancement in the accurate identification and segmentation of broken grain and impurities. Based on this, indoor group matching experiments were conducted with three groups of broken rate and impurity rate levels set at 0.5%, 1.5%, and 2.5%, showing the average errors of 7.54% and 6.30% for broken rate and impurity rate detection systems, respectively. Furthermore, the detection device was installed under the grain outlet of the GM80 combine harvester for field experiments, which showed average errors of 13.32% and 9.77% for wheat broken rate and impurity rate, respectively. The effectiveness and accuracy of the wheat broken rate and impurity rate detection system meet the requirements, which can provide a data basis for intelligent control of combine ha

关键词： Wheat Broken rate and impurity rate DeepLab-EDA model semantic segmentation algorithm Combine harvester

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：