检索结果-内蒙古大学图书馆

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Cao, Xianyi Wang, Long Li, Tianyi Cai, Minglu Wu, Guiling Chen, Jianping Wu, Kan Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

A dual-comb FMCW Lidar with coherent comb stitching (CCS) technique is firstly demonstrated. CCS can simultaneously achieve 65 GHz high frequency sweeping range, millimeter-level ranging resolution and 100 kHz high vo... 详细信息

关键词： Pulse amplitude modulation

来源：评论

学校读者我要写书评

暂无评论

Monolithic 50 GHz tunable microwave signal generation based on erbium-doped lithium niobate dual lasers

Monolithic 50 GHz tunable microwave signal generation based ...

引用

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Cai, Minglu Zhang, Xujia Li, Tianyi Shi, Hao Li, Tieying Cao, Xianyi Cui, Zekun Chen, Jianping Wu, Kan Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

The monolithic erbium-doped lithium niobate lasers can achieve a tunable wavelength range of 56.25 GHz and generate a 50 GHz microwave signal with a side-mode suppression ratio of 35 dB by dual lasers. © Optica P... 详细信息

关键词： Monolithic microwave integrated circuits

来源：评论

学校读者我要写书评

暂无评论

BSDIFF Difference Algorithm Based on LZMA2 for In-Vehicle ECUs 7th

BSDIFF Difference Algorithm Based on LZMA2 for In-Vehicle EC...

引用

7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Li, Zhihao Qin, Guihe Liang, Yanhua Danzengouzhu Yang, Lifeng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819927296

With cars becoming the primary choice of people’s daily travel, on-board OTA upgrade technology has arisen a common concern in the automotive industry. Traditional on-board OTA upgrade technology, which adopts a method of updating the whole package, successfully avoids offline upgrade. However, it takes too long to download the whole update package and upgrade in-vehicle ECUs because of the huge data transmission. In addition, packet loss and disconnection are easy to occur in case of network congestion. As a result, it will seriously affect users’ online upgrade experience. Therefore, incremental update technology should be used instead of updating the whole package in on-board OTA upgrade technology. In order to solve above problems, in this paper, we propose an improved BSDIFF difference algorithm based on LZMA2 compression algorithm to realize the incremental update of on-board OTA. Traditional BSDIFF can generate a patch file according to an old app and a new app. And a patch file is far smaller than the corresponding new app, which is conducive to remote transmission and reduces the download time of users. Through our experiments, LZMA2-based BSDIFF has a better compression ratio than traditional BSDIFF, which is more suitable for the incremental update of in-vehicle ECUs with limited memory resources. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

引用

Computer Modeling in engineering & Sciences 2022年第3期130卷 1495-1516页

作者： Wei Wan Gaige Wang Junyu Dong Department of Computer Science of Technology Ocean University of ChinaQingdao266100China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for NationalitiesNanning530006China

Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest *** order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant *** this paper,an enhanced version is proposed,namely *** incorporates a strengthened initialization strategy and optimized parameters in contrast to its *** improvements make the direction of crossgeneration mutation more clearly and the ability of searching more *** experiments show that the new algorithm has better diversity and improves convergence to a certain *** the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.

关键词： Differential Evolution(DE) multi-objective optimization(MO) opposition-based learning parameter adaptation

来源：评论

学校读者我要写书评

暂无评论

Surface Defect Detection of Hot-Rolled Strip Based on Improved YOLOv7

Surface Defect Detection of Hot-Rolled Strip Based on Improv...

引用

2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Jin, Yiting Li, Junqiu Zhang, Qinghui School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Southwest Forestry University Key Laboratory of Forest Ecological Big Data of National Forestry and Grassland Administration Kunming China

ISBN: (纸本)9798350314670

In order to increase the detecting precision of surface defects in industrial hot-rolled strip steel, this paper improves the network model for target detection of strip steel surface defects based on the YOLOv7 algorithm. First, to minimize information loss and enhance global dimensional interaction characteristics, the Global Attention Mechanism (GAM) is applied. Second, the YOLOv7 model incorporates the adaptive spatial feature fusion technique to achieve the fusion of top-level features with bottom-level features. Finally, the regression loss function used in the original model is substituted for SIoU Loss to increase the speed and accuracy of the frame regression. The testing outcomes reveal that the method in this paper has an average accuracy improvement of 3.1% compared to the YOLOv7 algorithm before improvements on the industrial hot-rolled strip data set (NEU-DET). © 2023 IEEE.

关键词： Surface defects

来源：评论

学校读者我要写书评

暂无评论

Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction 31

Unified Grid Tagging Scheme for Aspect Sentiment Quad Predic...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Su, Guixin Zhang, Yongcheng Wang, Tongguan Wu, Mingmin Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798891761964

Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance. © 2025 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Improving Small License Plate Detection with Bidirectional Vehicle-Plate Relation 1

引用

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Dai, Songkang Chen, Song-Lu Liu, Qi Zhu, Chao Liu, Yan Chen, Feng Yin, Xu-Cheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China EEasy Technology Company Ltd. Zhuhai China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

License plate detection is a critical component of license plate recognition systems. A challenge in this domain is detecting small license plates captured at a considerable distance. Previous researchers have proved that pre-detecting the vehicle can enhance small license plate detection. However, this approach only utilizes the one-way relation that the presence of a vehicle can enhance license plate detection, potentially resulting in error accumulation if the vehicle fails to be detected. To address this issue, we propose a unified network that can simultaneously detect the vehicle and the license plate while establishing bidirectional relationships between them. The proposed network can utilize the vehicle to enhance small license plate detection and reduce error accumulation when the vehicle fails to be detected. Extensive experiments on the SSIG-SegPlate, AOLP, and CRPD datasets prove our method achieves state-of-the-art detection performance, achieving an average detection AP0.5 of 99.5% on these three datasets, especially for small license plates. When incorporating a license plate recognizer that relies on character detection, we can achieve an average recognition accuracy of 95.9%, surpassing all comparative methods. Moreover, we have manually annotated the vehicles within the CRPD dataset and have made these annotations publicly available at https://***/kiki00007/CRPDV. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： License plates (automobile)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Drug-Drug Interaction Prediction by knowledge Graph Embedding 23

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

引用

23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Liu, Chao Wang, Xizhao Liu, Qiang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug). © 2024 IEEE.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

AF-YOLO:A Road Scene Object Detection Algorithm Fused with Mixed-domain Attention

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第2期8卷 347-363页

作者： Liao, Lyuchao Chen, Ruixiang Shi, Jinjin Zheng, Qi Xiong, Rong Zeng, Jiemao Fujian Provincial Universities Key Laboratory of Industrial Control and Data Analysis Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology Fujian University of Technology Fuzhou350118 China Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology Fujian University of Technology Fuzhou350118 China

Object identification is a significant computer vision issue with a growing number of applications in the area of autonomous driving. In order to solve the problem of difficult detection of small objects and dense objects in the target detection algorithm in the autonomous driving scenarios,we propose an object detection method with a hybrid domain attention mechanism. In the model, the Neck network is integrated with a parallel mixed-domain attention mechanism to increase the usable features in the feature map from both channel and spatial aspects. Then, we improve the Spatial Pyramid Pooling (SPP) module with dilated convolutions to limit the loss of important information in the feature map after max pooling, which further optimizes the detector performance. Finally, a fusion approach is proposed to improve the detection layer and enhance the capability of detecting tiny objects. Extensive experiments on the BDD100k dataset show that the proposed model outperforms the state-of-the-art YOLOv5s model with an average accuracy increase of 6.22%(mAP @0.5). © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

引用

23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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：