检索结果-内蒙古大学图书馆

Distracted driving behaviour recognition based on transfer learning and model fusion

International Journal of Wireless and Mobile Computing 2023年第2期24卷 159-168页

作者： Luo, Guantai Xiao, Wanghui Chen, Xinwei Tao, Jin Zhang, Chentao -HIT Research Institute of Engineering and Technology Quanzhou Fujian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou Fujian China Department of Instrumental and Electrical Engineering Xiamen University Fujian Xiamen China

In the recognition of distracted driving behaviour, traditional manual feature extraction is subjective and complex;single deep convolutional network also has problems such as insufficient generalisation performance and stability. To solve the above problems, this paper proposes a distracted driving behaviour recognition method based on transfer learning and model fusion. First, based on the transfer learning method, the deep convolutional neural network models ResNet18 and ResNet34 are used to extract the features of some images, respectively. Furthermore, the pre-trained model is fine-tuned to obtain four deep convolutional neural network models. Finally, the four network models are fused by stacking method, using 5-fold cross-validation method to reduce over-fitting. Experimental results show that the recognition accuracy of distracted driving behaviour after model fusion reaches 95.47%. The fusion model has higher model generalisation performance and recognition accuracy, which can provide certain technical support for the research of distracted driving behaviour recognition. © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image 16

Semi-Supervised Learning for Low-Light Image Enhancement by ...

引用

16th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Xie, Shuo Ma, Yingjie Xu, Wei Qiu, Song Sun, Yifei East China Normal University School of Communication and Electronic Engineering China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350330755

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Privacy Protection and Resource Allocation for Vehicular Edge Networks

Joint Optimization of Privacy Protection and Resource Alloca...

引用

2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Gao, Tianqi Tao, Hongfeng Ni, Yuanzhi Shi, Jun Jiang, Fengcheng Jiangnan University School of Internet of Things Engineering Wuxi214122 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350345384

In the last decade, the service of vehicular networks has benefited from the rapid development of advanced communication and computing technologies including edge computing. For vehicular network operators, it is valuable to study how to realize the accurate allocation of edge computing resource on the premise of protecting users' privacy. In this paper, we design a histogram publishing algorithm based on differential privacy. The algorithm first classifies tasks according to the characteristics of computing requests, then interferes categorized tasks and designs a shuffling algorithm based on Blum Blum Shub (BBS). Finally, the frequency histogram of computing resource is obtained by post-processing of the Expectation-Maximum (EM) algorithm. The simulation results verify the accuracy and superiority of the proposed histogram publishing algorithm by comparing it with the existing methods. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Four development stages of collective intelligence

引用

Frontiers of information Technology & Electronic Engineering 2024年第7期25卷 903-916页

作者： Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI *** one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI ***,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral *** 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two ***,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is *** meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI *** is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is *** a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of *** different stages are progressively improved and have good *** to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in *** division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor ***,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

关键词： Collective intelligence Meta-synthesis of wisdom Incompatibility Labor division Cooperative behavior Collective intelligence emergence Large language model

来源：评论

学校读者我要写书评

暂无评论

Efficient Fabric Defect Detection Algorithm Based on Improved YOLOv5

Efficient Fabric Defect Detection Algorithm Based on Improve...

引用

2023 China Automation Congress, CAC 2023

作者： Tian, LiHua Wang, AoZe Sun, ZhiGang Xiao, Li Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350303759

In order to address the issues of real-time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes the ESE-YOLOv5 network based on YOLOv5. Firstly, to address the relative redundancy of the neck detection network feature channels, a relatively lightweight and efficient convolution module is adopted to ensure accuracy while reducing computation and parameter volume. Furthermore, the Efficient Squeeze-Excitation (ESE) module is introduced into the backbone to optimize the dependency of feature channels, which enhances the model's feature extraction capacity and improves detection accuracy. Experimental results show that compared to YOLOv5, the proposed ESE-YOLOv5 model reduces computation and parameter volume while improving accuracy, meeting the needs of fabric defect detection for recognizing fabric defects that have similar characteristics to the background while maintaining real-time performance. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Weed detection based on improved Yolov5 8

Weed detection based on improved Yolov5

引用

8th International Conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Yu, ZiZheng Nie, ZiJun Hu, Yong Qi Yan, HuangBin Lou, LiJun Yin, Zhijian Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350302455

The scale of grain production affects human life and development. With the continuous expansion of cultivated land, the reproductive ability of weeds to mutiply gradually increases, which affects the growth of crops. If weeds are not treated properly managed, they can also allow spread indiscriminately in the field and reduce crop yields. The growth ability of weeds is stronger than that of crops, and there are many types of weeds with a wide of species diversity. To solve the problem that the existing weed detection methods cannot detect and classify weeds accurately and quickly, a deep learning method based on improved Yolov5 was designed for weed detection. By replacing the 3 × 3 convolution with multi-head self-attention (MHSA) in the Yolov5's backbone, the accuracy of weed detection is improved. The experimental results show that the improved Yolov5 weed detection algorithm achieves 51.4% accuracy. Compared with the original Yolov5 model, the calculation amount is 3 points less than the original, and total time required is slightly shorter, which improves the accuracy of weed classification and positioning. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

TMAS:A transaction misbehavior analysis scheme for blockchain

引用

Blockchain(Research and Applications) 2024年第3期5卷 42-52页

作者： Shiyong Huang Xiaohan Hao Yani Sun Chenhuang Wu Huimin Li Wei Ren Kim-Kwang Raymond Choo School of Computer Science China University of GeosciencesWuhan430074China Artificial Intelligence Thrust Information HubHong Kong University of Science and TechnologyGuangzhou511400China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang UniversityKunming650233China Fujian Key Laboratory of Financial Information Processing Putian UniversityPutian351100China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly ***,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction *** this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based ***,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical *** evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious ***,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).

关键词： Blockchain Anti-money laundering Bitcoin Cyptocurrency

来源：评论

学校读者我要写书评

暂无评论

Augmented Reality Enabled Telepresence Framework for Intuitive Robotic Teleoperation

Augmented Reality Enabled Telepresence Framework for Intuiti...

引用

2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xu, Zheng Lin, Zecai Liu, Huanghua Ren, Qingsong Gao, Anzhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China

ISBN: (纸本)9798350325706

In terms of telepresence for robotic teleoperation, the existing remote surgery provides the patient's side real-time videos to remote operators. Its effectiveness is limited due to the lack of depth information, occlusion, and restricted viewing angles. To address it, this paper develops a robotic telepresence framework integrating augmented reality (AR) and 3D reconstruction technology, providing a 3D virtual patient's side environment for remote operators. The real scene of the patient's side is reconstructed by a monocular camera with a manipulator before the operation, and the manipulator is teleoperated by the operator at the remote's side. The manipulator's pose is fused into the classic SVO+REMODE algorithm to improve the reconstructed accuracy. On the remote's side, an AR head-mounted display (HMD) is deployed to provide telepresence for operators under a similar environment, and the reconstructed scene of the patient's side is registered to the remote room, from which the operator can see the whole scene of the patient body and surrounding objects. An ArUco cube is used to evaluate the superimposed error between virtual and real displays, and the effectiveness is validated in an experimental platform for remote surgery. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Smoke Detection In Complex Scenery using Improved YOLOV5 8

Smoke Detection In Complex Scenery using Improved YOLOV5

引用

8th International Conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Nie, ZiJun Hu, YongQi Yu, ZiZheng Li, RuJia Yin, ZhiJian Zhan, HuaLin Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9798350302455

Considering smoke detection in a complex scene, high smoke object error rate and low detection efficiency, an improved YOLOV5 for smoke detection is proposed in this paper. To improve the effectiveness of smoke detection algorithm for smoke targets in complex scenes. First, a new lightweight convolution technique GSConv is introduced to replace the Conv layer in the neck layer for feature extraction, and the original C3 module is replaced by Cross Stage Partial Network (GSCSP) module VoVGSCSP. This improvement reduces the computation complexity of the algorithm while maintaining the detection accuracy. Second, CIoU is replaced by SIoU as the regression function of the prediction box to improve the prediction accuracy of the prediction box and reduce the rate of missed detection of the target. Finally, to improve the performance of the deep network, Dynamic ReLU is introduced as an activation function (dynamically adjusting the ReLU parameters depending on the input data). Experiments on the SM-dataset (smoke detection dataset) show that the improved model improves the accuracy and precision of target smoke detection compared to the YOLOV5 model. Its precision is increased by 1.8%, mAP@0.5 is increased by 1.0%, the complexity of the model is reduced by 23.8%. The improved algorithm proposed in this paper can effectively extract smoke features and more suitable for smoke detection tasks in complex scenes. © 2023 IEEE.

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

引用

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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：