检索结果-内蒙古大学图书馆

International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Liang, Ling Ji, Yuan Ma, Jianwei Zhou, Zhongqiang Zhuo, Chao Zhang, Yunju Guo, Ming Han, Ji’an Power Dispatching and Control Center Guizhou Power Grid Company Guizhou Guiyang550002 China Changsha University of Science and Technology Hunan Changsha410114 China Anshun Power Supply Bureau of Guizhou Power Grid Co. Ltd. Guizhou Anshun561000 China CSG Digital Grid Research Institute Co. Ltd Guangdong Guangzhou510663 China

ISBN: (纸本)9789819620418

The integration of new energy sources presents challenges for dynamic sensing and management of traditional distribution networks. This paper proposes a dynamic cluster sensing modeling method using a pruning-optimized YOLOv7-Tiny model and new energy intelligent agents. YOLOv7-Tiny enables real-time detection and monitoring, integrating multi-source data for comprehensive sensing and dynamic prediction. pruning optimization improves detection accuracy and processing speed for edge devices and real-time monitoring. Experimental results show significant improvement in real-time performance and reliability, supporting optimized operation and proactive maintenance of smart distribution networks. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Distribution Grid New Energy Intelligent Individual pruning algorithm Real-time Monitoring YOLOv7-Tiny

来源：评论

学校读者我要写书评

暂无评论

A neural network algorithm for fast pruning based on remarkable analysis

A neural network algorithm for fast pruning based on remarka...

引用

26th Chinese Control and Decision Conference (CCDC)

作者： Li Fujin Huo Meijie Ren Hongge Zhao Wenbin Hebei United Univ Coll Elect Engn Tangshang 063000 Peoples R China

ISBN: (纸本)9781479937066

Neural network architecture designed for large-scale and the generalization is poor, presents a neural network algorithm for fast pruning based on significance analysis. The essence of the method is based on large-scale neural network perceptron as the research object, the constructor error curved surface model to analyze the network connection weights of disturbance on the network output error caused by the impact of hidden layer neurons carry remarkable analysis, direct remove redundant hidden layer neurons, reach pruning the neural network structure while improving its generalization ability pruning purposes. Experimental results show that the conventional algorithm, the optimal pruning neurosurgery in quick pruning network structure has a simpler and faster learning speed.

关键词： Significance analysis Geural network pruning algorithm Generalization Robot

来源：评论

学校读者我要写书评

暂无评论

Real-Time Detection of Drones Using Channel and Layer pruning, Based on the YOLOv3-SPP3 Deep Learning algorithm

引用

MICROMACHINES 2022年第12期13卷 2199-2199页

作者： Zhang, Xuetao Fan, Kuangang Hou, Haonan Liu, Chuankai Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Ganzhou 341000 Peoples R China Key Lab Magnet Levitat Technol Jiangxi Prov Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Peoples R China Chinese Acad Sci Ganjiang Innovat Acad Ganzhou 341000 Peoples R China Beijing Aerosp Control Ctr Beijing 100000 Peoples R China Natl Key Lab Sci & Technol Space Flight Dynam Beijing 100000 Peoples R China

Achieving a real-time and accurate detection of drones in natural environments is essential for the interception of drones intruding into high-security areas. However, a rapid and accurate detection of drones is difficult because of their small size and fast speed. In this paper a drone detection method as proposed by pruning the convolutional channel and residual structures of YOLOv3-SPP3. First, the k-means algorithm was used to cluster label the boxes. Second, the channel and shortcut layer pruning algorithm was used to prune the model. Third, the model was fine tuned to achieve a real-time detection of drones. The experimental results obtained by using the Ubuntu server under the Python 3.6 environment show that the YOLOv3-SPP3 algorithm is better than YOLOV3, Tiny-YOLOv3, CenterNet, SSD300, and faster R-CNN. There is significant compression in the size, the maximum compression factor is 20.1 times, the maximum detection speed is increased by 10.2 times, the maximum map value is increased by 15.2%, and the maximum precision is increased by 16.54%. The proposed algorithm achieves the mAP score of 95.15% and the detection speed of 112 f/s, which can meet the requirements of the real-time detection of UAVs.

关键词： anti-drone feature extraction real-time detection YOLOv3-SPP3 pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

A neural network algorithm for fast pruning based on remarkable analysis

A neural network algorithm for fast pruning based on remarka...

引用

第26届中国控制与决策会议

作者： Li Fujin Huo Meijie Ren Hongge Zhao wenbin College of Electrical Engineering He bei United University

关键词： Significance analysis Geural network pruning algorithm Generalization,Robot

来源：评论

学校读者我要写书评

暂无评论

pruning Elman neural network and its application in bolt defects classification

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第7期10卷 1847-1862页

作者： Sun, Xiaoyun Gong, Shijie Han, Guang Wang, Mingming Jin, An Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang 050043 Hebei Peoples R China

The Elman model is developed based on BP neural network. Because Elman neural network has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the area of classification. The application of Elman has a bottleneck problem, that is, network structure is difficult to determine. This paper proposed an improved Elman neural network model using pruning algorithm for the network structure optimization. Firstly, we choose the bolt defect parameters which are relatively easy to detect as the classification sample, select the initial large-scale network structure, analysis the contribution of hidden nodes in the network, and reduce the dynamic threshold of hidden nodes in combination with the network to obtain the optimal network structure. Simulation results show that pruning Elman neural network (P-Elman) can adaptively obtain the optimal network structure, and achieve a high classification accuracy, which is much higher than other methods.

关键词： Bolt defect classification Elman neural network pruning algorithm Contribution of neuron

来源：评论

学校读者我要写书评

暂无评论

Identification of pruning branches in tall spindle apple trees for automated pruning

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2014年 103卷 127-135页

作者： Karkee, Manoj Adhikari, Bikram Amatya, Suraj Zhang, Qin Washington State Univ Dept Biol Syst Engn Pullman WA 99164 USA Washington State Univ Ctr Precis & Automated Agr Syst Pullman WA 99164 USA

pruning is a labor intensive operation that constitutes a significant component of total apple production cost. As growers are adapting simpler, narrower, more accessible and productive (SNAP) tree architectures such as the tall spindle fruiting wall system, new opportunities have emerged to reduce pruning cost and labor through automated pruning. This work focused on identification of pruning branches on apple trees in a tall spindle architecture. A time-of-flight-of-light-based three dimensional (ToF 3D) camera was used to construct 3D skeletons of apple trees. pruning branches were identified in the reconstructed trees using a simplified two-step pruning rule;(i) maintain specified branch spacing and (ii) maintain specified branch length. Performance of the algorithm was optimized using a training sample of 10 trees to achieve human worker's pruning level. With a selected branch spacing (28 cm) and branch length (20 cm), the algorithm achieved 19.5% branch removal with the training dataset and 19.8% of branch removal with the validation dataset (10 trees) compared to 22% average branch removal by workers. Root Mean Square Deviation (RMSD) between human and algorithm in number of branches identified for pruning was 10% for the training dataset and 13% for the validation dataset. The algorithm and the human pruning resulted in similar average branch spacing. The algorithm maintained an average spacing of 35.7 cm for validation set whereas the average spacing for three workers was 33.7 cm. RMSD in branch spacing between the algorithm and the workers was found to be 13%. The algorithm removed 85% of long branches whereas the overlapping branch removal was only 69%. With some additional work to improve the performance in terms of overlapping branch removal, it is expected that this work will provide a good foundation for automated pruning of tall spindle apple trees in the future. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Apple tree pruning Selective pruning pruning algorithm pruning branch identification Machine vision 3D camera

来源：评论

学校读者我要写书评

暂无评论

A novel valuation pruning optimization fuzzing test model based on mutation tree for industrial control systems

引用

APPLIED SOFT COMPUTING 2018年 70卷 896-902页

作者： Dong, Guofang Sun, Pu Shi, Wenbo Choi, Chang Yunnan Minzu Univ Sch Elect & Informat Technol Kunming 650031 Yunnan Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Peoples R China Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Chosun Univ IT Res Inst Gwangju 61452 South Korea

In order to solve the problem of the low efficiency of the mutation operator test and the insufficient test in the old fuzzing technology for industrial control systems (ICS), this paper presents an improved pruning optimization model based on valuation of fuzzy mutation tree. And then, the initial valuation criterion of each node is given, the intermediate node criterion and the leaf node criterion are proposed accordingly. Finally, the vulnerability of the Serv-u 4.0 version is tested by the proposed scheme and fuzzing test suite generation (FTSG) model. Comparing with the FTSG mathematical model, experimental results indicate that the proposed scheme could successfully detect more vulnerabilities than the FTSG method and a larger amount of data can be measured by the new method than the FTSG method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Fuzzing test Mutation tree pruning algorithm Valuation criterion

来源：评论

学校读者我要写书评

暂无评论

APPQ-CNN: An Adaptive CNNs Inference Accelerator for Synergistically Exploiting pruning and Quantization Based on FPGA

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

引用

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第6期9卷 874-888页

作者： Zhang, Xian Xiao, Guoqing Duan, Mingxing Chen, Yuedan Li, Kenli Huaihua Univ Sch Comp & Artificial Intelligence Huaihua 418000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Shenzhen Inst Shenzhen 518063 Peoples R China

Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing applications such as computational vision and image processing. However, as the number of layers of the CNN model increases, the number of parameters and computations gets larger, making it increasingly challenging to accelerate in edge computing applications. To effectively adapt to the tradeoff between the speed and accuracy of CNNs inference for smart applications. This paper proposes an FPGA-based adaptive CNNs inference accelerator synergistically utilizing filter pruning, fixed-point parameter quantization, and multi-computing unit parallelism called APPQ-CNN. First, the article devises a hybrid pruning algorithm based on the L1-norm and APoZ to measure the filter impact degree and a configurable parameter quantization fixed-point computing architecture instead of floating-point architecture. Then, design a cascade of the CNN pipelined kernel architecture and configurable multiple computation units. Finally, conduct extensive performance exploration and comparison experiments on various real and synthetic datasets. With negligible accuracy loss, the speed performance of our accelerator APPQ-CNN compares with current state-of-the-art FPGA-based accelerators PipeCNN and OctCNN by 2.15x and 1.91x, respectively. Furthermore, APPQ-CNN provides settable fixed-point quantization bit-width parameters, filter pruning rate, and multiple computation unit counts to cope with practical application performance requirements in edge computing.

关键词： Convolutional neural networks Convolution Computational modeling Field programmable gate arrays Kernel Adaptation models Quantization (signal) fixed-point quantization FPGA inference accelerator pipeline pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

A pruning feedforward small-world neural network based on Katz centrality for nonlinear system modeling

引用

NEURAL NETWORKS 2020年 130卷 269-285页

作者： Li, Wenjing Chu, Minghui Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China Beijing Adv Innovat Ctr Future Internet Technol Beijing 100124 Peoples R China

Approaching to the biological neural network, small-world neural networks have been demonstrated to improve the generalization performance of artificial neural networks. However, the architecture of small-world neural networks is typically large and predefined. This may cause the problems of overfitting and time consuming, and cannot obtain an optimal network structure automatically for a given problem. To solve the above problems, this paper proposes a pruning feedforward small-world neural network (PFSWNN), and applies it to nonlinear system modeling. Firstly, a feedforward small-world neural network (FSWNN) is constructed according to the rewiring rule of Watts-Strogatz. Secondly, the importance of each hidden neuron is evaluated based on its Katz centrality. If the Katz centrality of a hidden neuron is below the predefined threshold, this neuron is considered to be an unimportant node and then merged with its most correlated neuron in the same hidden layer. The connection weights are trained using the gradient-based algorithm, and the convergence of the proposed PFSWNN is theoretically analyzed in this paper. Finally, the PFSWNN model is tested on some problems for nonlinear system modeling, including the approximation for a rapidly changing function, CATS missing time-series prediction, four benchmark problems of UCI public datasets and a practical problem for wastewater treatment process. Experimental results demonstrate that PFSWNN exhibits superior generalization performance by small-world property as well as the pruning algorithm, and the training time of PFSWNN is shortened owning to a compact structure. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Small-world neural network pruning algorithm Katz centrality Nonlinear system modeling

来源：评论

学校读者我要写书评

暂无评论

Real-time Vehicle Detection and Tracking Based on YOLOv3 pruning Model 33

Real-time Vehicle Detection and Tracking Based on YOLOv3 Pru...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Lin, Mingxiu Li, Jiayi Zhang, Jiaxin Li, Xinghui Ji, Shiyao He, Yunli Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781665440899

Real-time detection and tracking of vehicles is very important in the field of automatic driving. In this paper, a YOLOv3 network based on pruning algorithm is proposed to solve the problem of real-time vehicle detection. Through reducing the number of channels and layers in the backbone network, the computation of the model is reduced. And thus, the rate of detection is greatly increased. On the basis of vehicle detection, the real-time tracking of multiple vehicle targets is completed by using Kalman filter algorithm for prediction and Hungarian algorithm for data association. The experimental results show that compared with the original YOLOv3 network, the model size is compressed by 95% to 11.25 MB and the detection rate is doubled to 128.1 frames/s while the average accuracy is basically unchanged. The detection and tracking frame rate of the whole algorithm is 18fps, and the recall is 98.5%. The algorithm also has strong robustness on complex traffic roads, and can basically realize real-time detection and tracking of road vehicles.

关键词： Real-time detection and tracking pruning algorithm Kalman filter algorithm Hungarian algorithm

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：