检索结果-内蒙古大学图书馆

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

computer Systems science & engineering 2023年第7期46卷 687-699页

作者： VDinesh SSrinivasan Gyanendra Prasad Joshi Woong Cho Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha University602105ChennaiIndia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time *** the same time,vehicles exhibit high velocity,leading to more vehicle *** of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection *** is considered one of the possible solutions for attaining effectual interaction in *** one such difficulty was reducing the cluster number under increasing transmitting *** article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for *** major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping *** addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given *** DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean *** experimental assessment of the DHOGO-EAC technique was carried out under distinct *** comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

关键词： Vehicular networks clustering evolutionary algorithm fitness function distance metric

来源：评论

学校读者我要写书评

暂无评论

Research on Vehicle Detection Algorithms Based on YOLOv5s

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2023年第4期50卷 1453页

作者： Zhang, Lifeng Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

To tackle the challenges associated with low recognition rates and demanding computational requirements in vehicle detection, we suggest a MAGS-YOLOv5s based vehicle detection model that builds on an upgraded version of YOLOv5s. Initial efforts are focused on applying data augmentation, using mosaic augmentation and integrating mix-up augmentation to enhance the model’s generalisation and robustness, thus increasing sample diversity. Secondly, the loss function for predicting bounding boxes is enhanced by including an α coefficient in addition to CIoU, facilitating adjustment to varying data scales and hastening the model’s convergence speed. Moreover, Ghost Shuffle Convolution (GSC) is employed to substitute the convolutional layers in the neck of the initial network model. This replacement decreases the computational load and network parameters, whilst enhancing the feature extraction capability and robustness of the network model. Our proposed SGC block, which is built on the GC block, has been integrated to the C3 module to specifically improve the feature extraction capability of the model for occluded object detection. Experimental results indicate that the enhanced network delivers a boost of 2.9% and 5.3% in mAP (mean average precision) on the Pascal VOC and MS COCO vehicle datasets, respectively, compared to the original model. The improved network displays enhanced multi-scale representation and generalisation capabilities. © 2023, International Association of Engineers. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

引用

computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

来源：评论

学校读者我要写书评

暂无评论

Prediction of global mean temperature based on SARIMA combined model 4

Prediction of global mean temperature based on SARIMA combin...

引用

4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Li, Dongliang Chu, Chengxu Gao, Huihui Yang, Chenming Liu, Xin Huang, Zhenwu School of Humanities and Law Southwest Forestry University Kunming650224 China School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian116034 China Data Science and Intelligent Media School Communication University of China Beijing100024 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781510681637

With the continuous development of human society, the global warming caused by greenhouse gas emissions is becoming more and more serious. In order to better cope with global warming, this paper uses the SARIMA model to predict global temperature. Then, SARIMA (1,1,1) (1,1,1) model is established to forecast the temperature. It is found that the temperature in 2050 and 2100 will not reach 20℃, and the global average temperature is expected to reach 20℃ in about 2180. Through model evaluation, it is found that the R-square of SARIMA model is 0.87. © 2024 SPIE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

User and Item Sub-contexts Induced Fuzzy Concept Set for Recommendation 11th

User and Item Sub-contexts Induced Fuzzy Concept Set for R...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Liu, Zhonghui Tang, Pu Min, Fan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Institute for Artificial Intelligence Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9783031927430

Formal concept analysis (FCA) has been employed for recommendation systems. However, conditional FCA approaches are confined to binary user-item relationships, failing to incorporate explicit rating information and consequently diminishing recommendation accuracy. In this paper, we develop fuzzy sub-contexts with rating information and generate a fuzzy concept set for recommendation. For fuzzy sub-context, we devise dual (user/item) similarity metrics for sub-context generation from fuzzy formal context. For fuzzy concepts, we develop a construction algorithm using concept size as heuristic information, which generates a fuzzy concept set based on the fuzzy sub-context. For the recommendation, we design user weights to calculate recommendation confidence within the fuzzy concept to formulate personalised recommendations. Experiments have been undertaken on nine real datasets. Results show that our algorithm achieves superior recommendation quality compares to seven algorithms: collaborative filtering algorithms (kNN, IBCF, BMF, GreConD-kNN) and formal concept-based recommendation algorithms (GRHC, CSPR, CSBR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

GCSS:a global collaborative scheduling strategy for wide-area high-performance computing

引用

Frontiers of computer science 2022年第5期16卷 1-15页

作者： Yao SONG Limin XIAO Liang WANG Guangjun QIN Bing WEI Baicheng YAN Chenhao ZHANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Smart City College Beijing Union UniversityBeijing 100101China

Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage ***,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more *** achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing *** collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area *** experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.

关键词： high-performance computing scheduling strategy task scheduling data placement

来源：评论

学校读者我要写书评

暂无评论

Introducing on-chain graph data to consortium blockchain for commercial transactions

引用

Frontiers of computer science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

来源：评论

学校读者我要写书评

暂无评论

A LSTM and GRU-Based Hybrid Model in the Cryptocurrency Price Prediction 5th

A LSTM and GRU-Based Hybrid Model in the Cryptocurrency P...

引用

5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Liu, Yue Xiao, Guijiao Chen, Weili Zheng, Zibin School of Computer Science and Engineering Sun Yat-sen University Guangzhou510000 China South China Business College Guangdong University of Foreign Studies Guangzhou510000 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510000 China School of Software Engineering Sun Yat-sen University Guangzhou510000 China

ISBN: (纸本)9789819981038

Cryptocurrency is a new type of digital currency that utilizes blockchain technology and cryptography to achieve transparency, decentralization, and immutability. Bitcoin became the world’s first decentralized cryptocurrency in 2009. With increasing attention given to cryptocurrency, predicting its price has become a popular research topic. Many machine learning and deep learning algorithms, such as Gated Recurrent Unit (GRU), Neural Network (NN), and Long Short-Term Memory (LSTM), have been studied for cryptocurrency price prediction. In this paper, we propose a hybrid cryptocurrency price prediction model based on LSTM and GRU. The model achieves better results than the LSTM model in cryptocurrency price prediction and performs the best among existing hybrid models based on LSTM and GRU. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

来源：评论

学校读者我要写书评

暂无评论

A Graph Neural Network-based Code Recommendation Method for Smart Contract Development

A Graph Neural Network-based Code Recommendation Method for ...

引用

2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Tang, Xiuwen Gan, Jiazhen Jiang, Zigui Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Zhuhai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Smart contracts can be considered as a service in the blockchain system and have been applied in many fields, covering financial products, online games, real estate, transportation and logistics. However, smart contract technology is still in its infancy. Development task is facing many difficulties and challenges, thus providing a set of new or improved development aids for the smart contract ecosystem is an urgent problem that needs to be solved. This paper proposes a smart contract code recommendation method based on graph neural network, which aims to facilitate the development of smart contracts and help developers realize smart contracts faster and more securely. Experimental results show that this method is better than the existing model of smart contract code recommendation in terms of accuracy. © 2022 IEEE.

关键词： Smart contract

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：