检索结果-内蒙古大学图书馆

IEEE conference on Vehicular Technology (VTC)

作者： Shaohua Xia Jiangtao Luo Yongyi Ran School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Earth observation satellites in low earth orbit (LEO) collect a large amount of image data daily, while space-to-ground links have become the major bottleneck for data transmission due to the limited bandwidth. Existing approaches focus on exploring more efficient routing strategies to achieve better data transmission but still struggle to keep pace with the surging volume of observed data. However, the advancement of onboard computing power has opened the possibility of processing data on satellites to reduce the transmitted data volume. This paper proposes a distributed deep reinforcement learning (DRL) algorithm to improve transmission efficiency by jointly optimizing computing and routing. Aiming to minimize task latency while considering the limitations of satellite storage resources, the problem is modeled as a partially observable Markov process (POMDP). An algorithm based on dueling double deep Q-Network (Dueling-DDQN) is proposed to achieve dynamic decision-making utilizing local and neighboring resource states. Furthermore, a method for dynamic optimization of backhaul destinations is proposed, using the pre-trained Q-network to estimate action values across multiple candidate destination satellites, thus enabling further optimization of data transmission without additional training. Simulation results indicate that the proposed algorithm achieves the lowest latency across various task loads compared to baseline methods.

关键词： Satellite constellations Satellites Heuristic algorithms Simulation Low earth orbit satellites Routing Delays Data communication Vehicle dynamics Optimization

来源：评论

学校读者我要写书评

暂无评论

Evaluation and optimization of neutral grounding methods for high-proportion cable distribution networks considering wind power access 2

Evaluation and optimization of neutral grounding methods for...

引用

2nd IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Liu, Pengbo Lu, Xiaodong Li, Jiaxin Shandong University School of Electrical Engineering Jinan China Northeast Electric Power University School of Electrical Engineering Jilin China Dezhou Power Supply Company State Grid Dezhou China

ISBN: (纸本)9781728190181

Considering large capacitance current of the high proportion cable distribution network, coupled with the influence of wind turbine access on the selection of the distribution network grounding, single-phase is only generated in the traditional wind-powered high-proportion cable distribution network through ASC grounding Ground fault grounding method is evaluated. Faced with the problems of traditional grounding methods, an optimized grounding scheme was proposed in which the neutral point of the distribution network was connected in parallel with a small resistance via an ASC and wind turbine variable grounding method was used. Theoretical analysis and simulation results show that the improved neutral grounding method is superior to the traditional grounding method in limiting the transient overvoltage of single-phase grounding faults. It can improve the power supply reliability of the distribution network and the equipment safety. The fault line selection is easier without affecting the operation of the wind turbine. It can be applied in the distribution network with a high proportion of the cable line connected by the wind turbine. © 2021 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

A Smart System for Binary image Denoising using DCNN on FPGA

A Smart System for Binary Image Denoising using DCNN on FPGA

引用

International conference on Inventive Computation Technologies (ICICT)

作者： O Vignesh P Sivarajakrishnan V S. Sneha Department of ECE Easwari Engineering College Chennai India

Integrated circuits based on digital image processing have been increasingly important in recent years due to their usefulness in many different fields, including manufacturing, agriculture, remote sensing, and medicine. The development of low-area, low-power, and low-delay processors is a significant challenge. The majority of these efforts have targeted efficient noise removal filtering by minimizing the resources required to accomplish it. Existing filtering approaches require a lot of space, a lot of power, and a lot of time since noise is dynamic and distributed randomly throughout images. Finally, this research work is extended by incorporating QCA technology to reduce the space, power, and time needed to build an efficient filtering method with altered noise deviation features. This research study proposes a majority logic gates technology to create DCNN-based FBF on a field-programmable gate array (FPGA). By using the vast majority of logic gates to design the MCSA, considerable gains can be achieved in terms of speed, execution time, area, power, and latency over the RCA used in traditional CNN. The proposed DCNN-FBF is also used in the implementation of an image denoising application. The proposed methodology has been shown to improve hardware performance relative to state-of-the-art methodologies, as evidenced by both hardware and extensive simulation findings. In addition, as compared to traditional filtering methods, the proposed image denoising solution has achieved higher values of structural similarity (SSIM) and PSNR.

关键词：

来源：评论

学校读者我要写书评

暂无评论

KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matching

KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by...

引用

IEEE International conference on Automation Science and Engineering (CASE)

作者： Frederik Hagelskjær Rasmus Laurvig Haugaard SDU Robotics Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense M Denmark

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we present KeyMatchNet, a novel network for zero-shot pose estimation in 3D point clouds. Our method uses only depth information, making it more applicable for many industrial use cases, as color information is seldom available. The network is composed of two parallel components for computing object and scene features. The features are then combined to create matches used for pose estimation. The parallel structure allows for pre-processing of the individual parts, which decreases the *** a zero-shot network allows for a very short set-up time, as it is not necessary to train models for new objects. However, as the network is not trained for the specific object, zero-shot pose estimation methods generally have lower accuracy compared with conventional methods. To address this, we reduce the complexity of the task by including the scenario information during training. This is typically not feasible as collecting real data for new tasks drastically increases the cost. However, for zero-shot pose estimation, training for new objects is not necessary and the expensive data collection can thus be performed only *** method is trained on 1,500 objects and is only tested on unseen objects. We demonstrate that the trained network can not only accurately estimate poses for novel objects, but also demonstrate the ability of the network on objects outside of the trained class. Test results are also shown on real data. We believe that the presented method is valuable for many real-world scenarios. Project page available at ***

关键词： Training Point cloud compression Three-dimensional displays Costs Computer aided software engineering image color analysis Computational modeling Pose estimation Training data Data collection

来源：评论

学校读者我要写书评

暂无评论

DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis

DCT-HistoTransformer: Efficient Lightweight Vision Transform...

引用

Iranian conference of Biomedical Engineering (ICBME)

作者： Mahdi Cherakhloo Mahtab Ranjbar Mehdi Mohebbi Bijan Vosoughi. Vahdat Department of Medical Engineering Electrical Engineering Department Sharif University of Technology Department of Mathematical and Computer Sciences Kharazmi University

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, which have shown great promise in computer vision, are now being applied to medical image analysis. However, their application to histopathological images presents challenges due to the need for extensive manual annotations of whole-slide images (WSIs), as these models require large amounts of data to work effectively, which is costly and time-consuming. Furthermore, the quadratic computational cost of Vision Transformers (ViTs) is particularly prohibitive for large, high-resolution histopathological images, especially on edge devices with limited computational resources. In this study, we introduce a novel lightweight breast cancer classification approach using transformers that operates effectively without large datasets. By incorporating parallel processing pathways for Discrete Cosine Transform (DCT) Attention and MobileConv, we convert image data from the spatial domain to the frequency domain to utilize the benefits such as filtering out high frequencies in the image, which reduces computational cost. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. Our proposed model achieves an accuracy of $\mathbf{9 6 0 0 \%} \pm \mathbf{0 4 8 \%}$ for binary classification and $\mathbf{8 7 8 5 \%} \pm 0.93 \%$ for multiclass classification, which is comparable to state-of-the-art models while significantly reducing computational costs. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets.

关键词： Computer vision Solid modeling image analysis Accuracy Computational modeling Frequency-domain analysis Transformers Breast cancer Computational efficiency Discrete cosine transforms

来源：评论

学校读者我要写书评

暂无评论

Collaborative Segmentation Model for Colonoscopy Ulcers based on Fuzzy Labeling*

Collaborative Segmentation Model for Colonoscopy Ulcers base...

引用

International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yanning Lin Jie Chen Ziqian Ding Linxu Chen Le Li Zhongsheng Zhu Zhaoxia Wang Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Children’s Hospital Shenzhen China Gastroenterology Shenzhen Children’s Hospital China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Inflammatory Bowel Disease (IBD) is a global chronic intestinal inflammatory disease, and its incidence rate increases year by year with the progress of economic globalization. Currently, the diagnosis of IBD in children mainly relies on endoscopic examination, but scoring endoscopic images is a challenging issue, especially in distinguishing different types of ulcers. To address this issue, this article designs a mobile application to accelerate data annotation processing and may provide reference for other unlabeled datasets. In the context of image segmentation, blurring labels has become an important issue. Deep learning methods are widely used in medical image segmentation, but their accuracy depends on high-quality annotated data. However, there are low-quality noise areas in the annotated data, and obtaining accurate and high-quality annotations becomes more time-consuming with limited annotation budgets. This article proposes a collaborative training framework to improve learning of noisy pixels. This framework determines the label confidence of an image by calculating the similarity between image pixels and surrounding pixels. Then, two parallel deep networks were constructed for semantic prediction, which aimed to guide each other on pixels that may have noise. By applying consistency in dual network prediction, the semantic information of uncertain pixels is corrected as much as possible. Experimental results have shown that this framework is slightly superior to models trained with pixel level precise labels, thus more effectively utilizing existing annotated data in the case of fuzzy labels.

关键词： Training image segmentation Accuracy Annotations Biological system modeling Semantics Noise Collaboration Colonoscopy Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

parallel High Utility Itemset Mining Algorithm on the Spark 18th

Parallel High Utility Itemset Mining Algorithm on the Spark

引用

18th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Li, Chengyan Zhang, Lei Sun, Anqi Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9789819996360

In the field of efficient utility itemset mining, considering both internal and external utility values provides a more comprehensive approach compared to traditional frequency-based methods. However, the increased complexity of computations and the generation of numerous candidate itemsets pose challenges for efficient mining on large-scale datasets. To address these challenges, this paper proposes a parallel mining algorithm based on the Spark framework. The algorithm leverages a vertical dataset structure to efficiently store and process the data. A utility table is utilized to store the data items along with their corresponding transaction utility values. By utilizing the utility table, the algorithm can directly access transaction utility values, simplifying the computation process and reducing overhead. To further enhance efficiency, the algorithm combines a prefix partitioning strategy with a minimum utility threshold. By employing this strategy, the generation of candidate itemsets is effectively reduced, resulting in a smaller search space and enhancing the efficiency of the mining process. The algorithm is implemented on the Spark framework, leveraging its capabilities in parallel processing and scalability. By leveraging the distributed computing capabilities of Spark, the algorithm can efficiently mine efficient utility frequent item-sets from large-scale datasets. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm in performing efficient utility itemset mining. It surpasses traditional approaches and showcases its ability to handle large-scale datasets while maintaining high performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Extracting the Brain-Like Representation by an Improved Self-Organizing Map for image Classification

Extracting the Brain-Like Representation by an Improved Self...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiahong Zhang Lihong Cao Moning Zhang Wenlong Fu State Key Laboratory of Media Convergence and Communication Communication University of China Neuroscience and Intelligent Media Institute Communication University of China Beijing China

Backpropagation-based supervised learning has achieved great success in computer vision tasks. However, its biological plausibility is always controversial. Recently, the bioinspired Hebbian learning rule (HLR) has received extensive attention. Self-Organizing Map (SOM) uses the competitive HLR to establish connections between neurons, obtaining visual features in an unsupervised way. Although the representation of SOM neurons shows some brain-like characteristics, it is still quite different from the neuron representation in the human visual cortex. This paper proposes an improved SOM with multi-winner, multi-code, and local receptive field, named mlSOM. We observe that the neuron representation of mlSOM is similar to the human visual cortex. Furthermore, mlSOM shows a sparse distributed representation of objects, which has also been found in the human inferior temporal area. In addition, experiments show that mlSOM achieves better classification accuracy than the original SOM and other state-of-the-art HLR-based methods. The code is accessible at https://***/JiaHongZ/mlSOM.

关键词： Self-organizing feature maps Visualization Neurons Supervised learning Signal processing Encoding Task analysis

来源：评论

学校读者我要写书评

暂无评论

Prior ensemble learning Theory and application to MR image priors

引用

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2021年第11期16卷 1937-1945页

作者： Kubota, Nanako Kasahara, Yufu Harada, Ken Fujimoto, Koji Okada, Tomohisa Inoue, Masato Waseda Univ Grad Sch Adv Sci & Engn Dept Elect Engn & Biosci Tokyo 1698555 Japan Waseda Univ Waseda Res Inst Sci & Engn Tokyo 1698555 Japan Kyoto Univ Grad Sch Med Human Brain Res Ctr Kyoto 6068507 Japan Waseda Univ Fac Sci & Engn Tokyo 1698555 Japan

Purpose Compressed sensing (CS) reduces the measurement time of magnetic resonance (MR) imaging, where the use of regularizers or image priors are key techniques to boost reconstruction precision. The optimal prior generally depends on the subject and the hand-building of priors is hard. A methodology of combining priors to create a better one would be useful for various forms of image processing that use image priors. methods We propose a theory, called prior ensemble learning (PEL), which combines many weak priors (not limited to images) efficiently and approximates the posterior mean (PM) estimate, which is Bayes optimal for minimizing the mean squared error (MSE). The way of combining priors is changed from that of an exponential family to a mixture family. We applied PEL to an undersampled (10%) multicoil MR image reconstruction task. Results We demonstrated that PEL could combine 136 image priors (norm-based priors such as total variation (TV) and wavelets with various regularization coefficient (RC) values) from only two training samples and that it was superior to the CS-SENSE-based method in terms of the MSE of the reconstructed image. The resulting combining weights were sparse (18% of the weak priors remained), as expected. Conclusion By the theory, the PM estimator was decomposed into the sparse weighted sum of each weak prior's PM estimator, and the exponential computational complexity for RCs was reduced to polynomial order w.r.t. the number of weak priors. PEL is feasible and effective for a practical MR image reconstruction task.

关键词： Machine learning Compressed sensing image prior distribution parallel MR imaging

来源：评论

学校读者我要写书评

暂无评论

Contextual Information based Network with High-Frequency Feature Fusion for High Frame Rate and Ultra-Low Delay Small-Scale Object Detection 17

Contextual Information based Network with High-Frequency Fea...

引用

17th International conference on Machine Vision Applications (MVA)

作者： Huang, Dongmei Zhang, Jihan Hu, Tingting Fuchikami, Ryuji Ikenaga, Takashi Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Panasonic Corp Fukuoka 8128531 Japan

ISBN: (纸本)9784901122207

High frame rate and ultra-low delay small-scale object detection plays an important role in factory automation for its timely and accurate reaction. Although many CNN based detection methods have been proposed to improve the accuracy of small object detection for the low resolution and large gap between the object and the background, it is difficult to achieve a trade-off between accuracy and speed. For the pursuit of ultra-low delay processing by utilizing FPGA, this paper proposes: (A) IoU and distance based loss function, (B) Contextual information with high temporal correlation based parallel detection, (C) High frequency feature fusion for enhancing low-bit networks. The proposed methods achieve 45.3 % mAP for test sequences, which is only 0.7 % mAP lower compared with the general method. Meanwhile, the size of the model has been compressed to 1.94 % of the original size and reaches a speed of 278 fps on FPGA and 15 fps on GPU.

关键词： Correlation Machine vision Graphics processing units Object detection Feature extraction Delays High frequency

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：