检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Cai, Shilv Liang, Xiaoguo Cao, Shuning Yan, Luxin Zhong, Sheng Chen, Liqun Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China

Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method;and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio (SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio (SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Parallel Gaussian Homotopy Approach for Zeroth-Order Optimization

An Adaptive Parallel Gaussian Homotopy Approach for Zeroth-O...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Bingyi Zhou Guoyou Wang National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varying smoothing factors to generate a series of proxy problems that are easier to solve, subsequently addressing them gradually from simpler to more complex ones. Traditional Gaussian homotopy methods typically utilize a predefined sequence or parameter to update a single smoothing factor serially. However, this sequential updating process is inefficient and unsuitable for parallel homotopy pipelines. Moreover, the utilization of predefined sequences or parameters leads to a lack of adaptability. To address the abovementioned challenges, we propose an adaptive parallel Gaussian homotopy optimization method. Initially, we introduce an adaptive model called Scaling. This model is formulated as a scaler that concurrently adjusts multiple smoothing factors, with each factor aligning to a homotopy level. It can be integrated easily into serial and parallel homotopy pipelines. Furthermore, we establish a collaborative training regimen to jointly train the Scaling model and a parallel homotopy model, named continuation path learning (CPL) [1] model. Throughout the training process, the Scaling model furnishes CPL with multiple scaled smoothing factors and updates CPL implicitly. Extensive experiments demonstrate that the proposed Gaussian homotopy approach performs competitively.

关键词： Training Adaptation models Smoothing methods Pipelines Optimization methods Collaboration Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Offloading Algorithm for Edge Computing Tasks Based on Energy Optimization 8

Offloading Algorithm for Edge Computing Tasks Based on Energ...

引用

8th IEEE International Conference on Smart Cloud, SmartCloud 2023

作者： Gao, Xing Gu, Haonan Liu, Fang Hu, Wei Wuhan City College Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350313505

Current research in computation offloading encompasses various aspects, including task partitioning and allocation, resource management, communication, and data management. This paper focuses on developing new algorithms and techniques to enhance the efficiency and effectiveness of the offloading process while addressing challenges arising from dynamic and heterogeneous mobile environments. Researchers are also examining the trade-off between offloading benefits like improved performance and energy efficiency, and the associated overhead costs such as communication and data management. This paper proposes an optimization strategy for computational offloading to enhance the Grey Wolf algorithm. The solution aims to quickly optimize system energy consumption while considering the arithmetic power constraint of the mobile edge computing server and time delay constraints. Simulation experiments demonstrate that the algorithm in this paper significantly outperforms the traditional Grey Wolf and PSO algorithms in terms of both performance and computational speed. ©2023 IEEE.

关键词： information management

来源：评论

学校读者我要写书评

暂无评论

Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS 1

引用

7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Xun Zhao, Hui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Henan University Kaifeng475000 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, which were also key issues in how to judge parallel calls for some tasks in high-performance computer systems. Modern processors were basically equipped with software control functions such as DVFS (Dynamic Voltage Frequency Scaling), in the actual system operation to ensure that the system could ensure the reasonable operation of the system while reducing energy consumption indicators. This paper considered how the designed scheduling algorithm first divides tasks reasonably to ensure that the maximum completion time and energy consumption of the processor were sufficiently reduced when the directed acyclic graph was executed. Then considered making reasonable adjustments to the processor frequency using DVFS technology to adapt to the task while ensuring the critical path of the task. At the end of the article, make sure that the experimental verification algorithm could ensure that the task was completed and could reduce the energy consumption during task execution as much as possible. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Implicit Learning of Rotational Decoupling Detection Heads Applied to Remote Sensing Object Detection

Implicit Learning of Rotational Decoupling Detection Heads A...

引用

2023 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2023

作者： Song, Chengkun Ding, Sheng Chen, Yingying School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Kagoshima University Kagoshima Japan

ISBN: (纸本)9781665457033

Remote sensing image features complicated background, big image size, small object pixels, and variable object direction due to the arbitrary shooting angle. Conventional object detector has poor detection effect on remote sensing image, which is prone to missed detection and false detection. In view of this, an improved YOLOv5 object detection algorithm was proposed for remote sensing image. First, the C3 module was improved. The output feature information of each convolutional module was transmitted to the deep network, so as to preserve more fine-grained feature information and enhance the small object detection capability. The improved downsampling mechanism integrates convolution downsampling with maximum pooling downsampling mechanism in downs amp ling to separate foreground and background information while retaining feature information. A lightweight rotatory decoupling detection head with implicit learning was proposed to address the conflict between regression and classification, thus improving the network detection capability for objects in any direction. In the meantime, an implicit learning matrix was added to guarantee the lightweight detection head and improve the network detection accuracy for multiple tasks. According to experimental results, under the same model size, compared with the original YOLOv5 detection method, the detection accuracy and detection speed of the proposed method were improved by 8% and 15 frames, or 14.7%, respectively on DOTA-v1.5 data set, effectively improving the detection accuracy and speed in remote sensing image detection. The detection accuracy was increased by 3.5% on coco data set in natural scenario. The method herein can still effectively improve the detection accuracy in natural scenario, proving effectiveness of the modified YOLOv5 method. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Daily behavior recognition of cattle based on dynamic region image features in open environment 3

Daily behavior recognition of cattle based on dynamic region...

引用

3rd International Conference on information Science, Parallel and Distributed Systems, ISPDS 2022

作者： Fu, Rao Fang, Jiandong Zhao, Yudong School of Information Engineering Inner Mongolia University of Technology Hohhot China Key Laboratory of Perception Technology and Intelligent System of Inner Mongolia Autonomous Region Hohhot China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

In order to recognize the daily behaviors of cattle in an open environment, the daily behaviors of cattle were classified based on the image features in the dynamic region. Firstly, the target detection model was used to locate the cattle feature parts in the dynamic region of the image, and the image features in the dynamic region were extracted according to the label information of the feature parts, then, the deep neural network was used to classify the image features. Finally, the results show that in the open environment, the accuracy of the model in predicting the feeding, lying and standing behaviors of cattle was 84%. © 2022 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

引用

Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

来源：评论

学校读者我要写书评

暂无评论

Gait Recognition Algorithm GCGait based on Skeleton 3

Gait Recognition Algorithm GCGait based on Skeleton

引用

3rd IEEE International Conference on information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Ma, Ru Zhang, Jikai Jia, Mengyu Lv, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China Inner Mongolia University of Technology School of Information Engineering Hohhot China

ISBN: (纸本)9781665490788

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method. © 2023 IEEE.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

Extended NNs-Based Adaptive Event-Triggered Asymptotic Control for Nonlinear Systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

引用

7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Non-Parametric Target Detection Based on Sparse Representation for Hyperspectral Images

Non-Parametric Target Detection Based on Sparse Representati...

引用

2024 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2024

作者： Wang, Wenhao Liu, Jun Orlando, Danilo Xiao, Li University of Science and Technology of China Department of Electronic Engineering and Information Science Hefei China Università di Pisa Dipartimento di Ingegneria dell' Informazione Pisa Italy University of Science and Technology of China MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition Hefei China

ISBN: (纸本)9798331505585

Target detection is a critical aspect in the practical application of hyperspectral images. Recently, sparse representation has garnered significant attention in this field. The underlying assumption of the sparse representation model is that each pixel can be linearly and sparsely represented by a combination of the prior target and background spectra. However, detection algorithms based on sparse representation heavily rely on preset hyperparameters, which may lead to noticeable performance degradation if not chosen appropriately. To avoid this issue, we propose a non-parametric iterative target detection algorithm that considers uninterested spectra as interference and iteratively estimates target energy while minimizing interference energy. Furthermore, our proposed method enhances the detection performance for multi-pixel targets through appropriate selection of multiple test pixels. Experimental results on real hyperspectral datasets demonstrate the superior performance of our proposed detector over its counterparts. © 2024 IEEE.

关键词： Pixels

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：