检索结果-内蒙古大学图书馆

2023 International Conference on Intelligent Systems, Communications, and Computer networks, ISCCN 2023

作者： Li, Lihao Lin, Yun Liang, Chenxi Dou, Zheng School of Information and Communication Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9781510666238

With the Internet of Things and big data advancing so quickly, spectrum resources are increasingly in short supply. For unauthorized users, to achieve reliable and stable wireless communication, they must rely on spectrum sensing technology. Traditional spectrum sensing methods have poor performance in situations with a low signal-To-noise ratio, but deep learning's evolution can effectively solve this problem. However, with the improvement of perception accuracy in practical application, the neural network now has many layers and has many large model parameters., which makes the actual deployment of the algorithm require high computing power equipment. To address the issues with conventional spectrum sensing models' excessive resource consumption, poor real time performance, and limited detection accuracy, a deep learning and cyclic spectrum-based approach for blind spectrum sensing is suggested. In this method, a lightweight sensing network based on MobileNet V2 is built, and the fast Fourier transformation accumulation method is used to extract the features of the signals, which are normalized and sent into the network for training. Experimental results show that this algorithm can sense many kinds of digital modulation signals, and the perceived performance is better than other algorithms. © 2023 SPIE.

关键词： computing power

来源：评论

学校读者我要写书评

暂无评论

Deep learning serves traffic safety analysis: A forward-looking review

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2023年第1期17卷 22-71页

作者： Razi, Abolfazl Chen, Xiwen Li, Huayu Wang, Hao Russo, Brendan Chen, Yan Yu, Hongbin Clemson Univ Sch Comp Clemson SC 29634 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA No Arizona Univ Dept Civil Engn Flagstaff AZ 86011 USA Arizona State Univ Polytech Sch Mesa AZ USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ USA

This paper explores deep learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasising driving safety for both autonomous vehicles and human-operated vehicles. A typical processing pipeline is presented, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilisation, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modelling, and anomaly detection. The main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. Existing open-source tools and public datasets that can help train DL models are also reviewed. To be more specific, exemplary traffic problems are reviewed and required steps are mentioned for each problem. Besides, connections to the closely related research areas of drivers' cognition evaluation, crowd-sourcing-based monitoring systems, edge computing in roadside infrastructures, automated driving systems-equipped vehicles are investigated, and the missing gaps are highlighted. Finally, commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems are reviewed.

关键词： traffic engineering computing speed estimation traffic analysts operational safety metrics video signal processing remaining challenges learning (artificial intelligence) road safety trajectory extraction providing general hints open problems neural nets video enhancement video stabilisation traffic videos anomaly detection optical, image and video signal processing Computer vision and image processing techniques traffic safety analysis exemplary traffic problems autonomous vehicles Traffic engineering computing object detection deep learning (artificial intelligence) required steps open-source tools crowd-sourcing-based monitoring systems custom-built processing frameworks driving safety deep learning methods traffic monitoring systems event analysis intelligent transportation systems Video signal processing road traffic successful conventional DL-based algorithms traffic video analysis edge computing road vehicles typical processing pipeline semantic incident segmentation processing framework comparative analysis automated driving systems-equipped vehicles

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of DNN accelerators on FPGA 2

Comparative Study of DNN accelerators on FPGA

引用

2nd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2022

作者： Zhao, Yixuan Liu, Feiyang Yan, Tanbao Gao, Han Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (纸本)9781510663121

As an important technology and research direction to achieve AI, deep learning has been widely applied in the fields of computer vision, speech recognition, and natural language processing. How to effectively accelerate the computing power of deep learning has always been the focus of scientific research. Among various acceleration technologies, FPGA has the advantages of reconfiguration, high performance, small size, and low latency. As more and more FPGA-based neural network accelerators are developed, we notice there is a lack of a complete and detailed overview. In this paper, we give a comparative study of DNN accelerators on FPGA from the aspects of hardware structures, design ideas and optimization strategies. We further compare the performance of different acceleration technologies in different models and present the prospects of the FPGA accelerators for deep learning. © 2023 SPIE.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

A novel blind action quality assessment based on Multi-headed GRU network and attention mechanism 3

A novel blind action quality assessment based on Multi-heade...

引用

3rd International Conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Sun, WenHao Hu, YanXiang Zhang, Bo Chen, XinRan Hao, CaiXia Gao, YaRu College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Objective action quality assessment (AQA) is a complex machine vision task because existing AQA assessment models can’t effectively fit the subjective assessment. To address this issue, we propose a novel blind action quality assessment method. By processing the video data with spatial and temporal features, the performance of the model is effectively improved. In addition, we also proposed a new loss function to better train the model, which combines the information entropy of the data. Finally, the experimental results show that on the existing datasets AQA-7 and JIGSAWS are significantly improved, reaching 0.63 and 0.57, respectively. © 2023 SPIE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on the method of high-resolution remote sensing water change detection combining Transformer and Siamese neural network 3

Research on the method of high-resolution remote sensing wat...

引用

3rd International Conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Yi, Fei Hubei University of Technology School of Computer Science Wuhan China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Aiming at the problems of missed detection, false detection and rough edge recognition in the detection of water body changes in high-resolution remote sensing images of the same area before and after, an improved network model is proposed to better improve the extraction accuracy of water body change areas. Combining deep learning with machine vision, we introduce the transformer block into the network. We design a Siamese change detection network with stronger contextual change semantic feature extraction capability SCTNet(Siam-CNN-transformer-network), transformer block directly connects information at any location through a multi-head intention mechanism, efficiently compute long sequence information, mining long-distance dependencies. As the front-end of the network, the CNN model uses convolution to focus on the correlation between two-bit local data, ensuring the correlation between adjacent pixels to enhance the local modeling ability of the model and reduce the model's dependence on the amount of *** proposed model performs well on the water body change dataset made from the WFV image data of GF-1 satellite. The experimental accuracy rate reaches 97.4%, F1-score and mIoU 97.9% and 90.9% *** with other change detection models, all indicators have been significantly improved, the situation of false detection and missed detection has been improved, and the effect of water edge recognition has been enhanced. © 2023 SPIE.

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

A Framework for Automatic Identification of neural network Structural Redundancy Based on Reinforcement Learning 5

A Framework for Automatic Identification of Neural Network S...

引用

5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Wu, Tingting Song, Chunhe Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510662520

The increasing structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. network pruning has become one of the most successful model compression methods in recent years. Existing works usually compress models by removing unimportant filters based on the importance. However, the importance-based algorithms tend to ignore the parameters that extract edge features with small criterion values. And recent studies have shown that the existing criteria rely on norm and lead to similar model compression structures. Aiming at the problems of ignoring edge features and manually specifying the pruning rate in current importance-based model pruning algorithms, this paper proposes an automatic recognition framework for neural network structure redundancy based on reinforcement learning. First, we perform cluster analysis on the filters of each layer, and map the filters into a multidimensional space to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we use reinforcement learning to automatically optimize the cluster dimension, and then determine the optimal pruning rate for each layer to reduce the performance loss caused by pruning. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework. © 2023 SPIE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Arrival time prediction of transport based on recurrent neural network 2

Arrival time prediction of transport based on recurrent neur...

引用

2nd International Conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2022

作者： Lin, Jiacheng Dong, Peixin Hu, Yunpeng Chen, Yonghong Xing, Jianping School of Microelectronics Shandong University Jinan China Public Passenger Transport Management Service Centre Jinan China

ISBN: (纸本)9781510657717

In this paper, through in-depth study of highway traffic and non-highway arrival time prediction model and algorithm, it is found that the performance of DA-RNN (recurrent neural network based on two-stage attention) is better than other classical prediction models. We choose to introduce an attention mechanism to adaptively select the most relevant factors from heterogeneous information and establish a prediction network based on da-rnn (recurrent neural network based on two-stage attention). In this paper, the real data of Jinan bus driving is used to carry out experiments, and the bus travel prediction is realized. Under the same data condition, a variety of methods are tested. Through the comparative analysis of experimental results, the method proposed in this paper performs better on the data set provided by Jinan bus company. © 2022 SPIE. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

2023 International Conference on Intelligent computing, Communication, networking and Services, ICCNS 2023

2023 International Conference on Intelligent Computing, Comm...

引用

2023 International Conference on Intelligent computing, Communication, networking and Services, ICCNS 2023

ISBN: (纸本)9798350339291

The proceedings contain 43 papers. The topics discussed include: detection of malicious requests to protect web applications and DNS servers against SQL injection using machine learning;importance of optical metrics for IGP configuration change prediction;denoising task-based functional magnetic resonance imaging data using hybrid deep neural network model;examining journalistic practices in online newspapers in the era of artificial intelligence;federated learning support for cybersecurity: fundamentals, applications, and opportunities;subsurface heat stress detection in plants using machine learning regression models;overlooked aspects of data governance: workflow framework for enterprise data deduplication;a comparison study of deep learning algorithms for metasurface harvester designs;early alert of an outbreak based on event detection and tracking on social networks: the case of Covid-19 and meningitis;DNN feature map gram matrices for mitigating white-box attacks on image classification;and social media narratives of Covid-19 pandemic and misinformation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

24th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2023

24th International Conference on Parallel and Distributed Co...

引用

24th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2023

ISBN: (纸本)9789819982103

The proceedings contain 28 papers. The special focus in this conference is on Parallel and Distributed computing, Applications and Technologies. The topics include: Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics;link Attributes Based Multi-service Routing for Software-Defined Satellite networks;A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets;SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval;fine Time Granularity Allocation Optimization of Multiple networks Industrial Chains in Task Processing Systems;Ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning;effective Density-Based Concept Drift Detection for Evolving Data Streams;an End-to-End Multiple Hyper-parameters Prediction Method for Distributed Constraint Optimization Problem;Formalization and Verification of the Zab Protocol Using CSP;dynamic Priority Coflow Scheduling in optical Circuit Switched networks;Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic;Triple-Path RNN network: A Time-and-Frequency Joint Domain Speech Separation Model;design of Query Based Gallery Selector and Mask-Aware Loss for Person Search;a Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data;R-RPT-A Reliable Routing Protocol for Industrial Wireless Sensor networks;action Segmentation Based on Encoder-Decoder and Global Timing Information;Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher;The Prediction Model of Water Level in Front of the Check Gate of the LSTM neural network Based on AIW-CLPSO;Using MPIs Non-Blocking Allreduce for Health Checks in Dynamic Simulations;parallelizable Loop Detection using Pre-trained Transformer models for Code Understanding;list-Based Workflow Scheduling Utilizing Deep Reinforcement Learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improve Light Weight Convolutional neural networks for Face Recognition with Multi-Style Recalibration Module

Improve Light Weight Convolutional Neural Networks for Face ...

引用

2022 International Conference on Electronic Information Engineering and Computer Science, EIECS 2022

作者： Wu, Juepeng School of Computing University of Leeds Yorkshire United LeedsLS2 9JT United Kingdom

ISBN: (纸本)9781510663312

With the rapid development of deep learning, many areas have achieved impressive progresses. However, deep learning often suffers from complex computation paradigms and huge memory occupancies, lightweight networks attract gradually increasing attention recently. Various lightweight network models have emerged in recent years, and are also widely used in small mobile devices. Then, there is still a lack of expressive power for lightweight networks compared to deep networks. This study leverages multiple statistics as representative of a feature map and proposes a Multi-Style Recalibration Module of a channel attention mechanism to improve the accuracy of lightweight network model recognition without increasing their storage space. This mechanism is embedded on MobileFaceNet as a study case. We test the new model on LFW dataset for demonstration and find that its occupancy only increases 0.2 MB, while the accuracy is increased by 0.18%. © 2023 SPIE.

关键词： Face recognition

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：