检索结果-内蒙古大学图书馆

4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Bai, Haiyang Zhu, Xiaoke Dong, Lingyuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

The evaluation of programming course instruction is a key component of educational management. However, relying solely on supervising teachers for assessment poses challenges in comprehensive evaluation and providing feedback on students' classroom learning status. Additionally, a significant amount of monitoring video data from programming courses in Chinese universities remains underutilized. To address these challenges, this paper combines traditional educational management with artificial intelligence and proposes an intelligent algorithm for detecting students' learning status in programming courses. We first establish a dual YOLO network model to analyze students' postures and screen content in programming classroom monitoring videos, thereby identifying their behaviors. Subsequently, we develop a Programming Classroom Student Behavior Recognition System (PCSBS) that integrates data collection, student behavior detection, and result visualization functionalities. Experimental results demonstrate that the system can accurately provide feedback on students' programming course learning status, offering graded results that assist supervising teachers in their classroom instructional evaluation tasks to enhance educational management efficiency. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Considering the full induction control of intersection for intelligent connected new energy vehicles

Considering the full induction control of intersection for i...

引用

2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Wang, Ruihan Liang, Zijun Li, Yuqi Deng, Ran Zhan, Xuejuan Shan, Ailin Wang, Ruipeng Niu, Yiran School of Urban Construction and Transportation Hefei University Anhui Hefei230601 China Anhui Provincial Key Laboratory of Urban Rail Transit Safety and Emergency Management Anhui Hefei230601 China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory Anhui Hefei230601 China

ISBN: (纸本)9781510675032

In response to the inadequacy of timing control methods in adapting to the random changes in traffic flow at intersections and the neglect of the impact of intelligent connected new energy vehicles (ICNEVs), we investigate the effectiveness of a full induction control logic for intersections considering ICNEVs. Firstly, vehicle headway and queue detectors are deployed at each approach of the intersection to monitor real-time traffic conditions. Subsequently, a full induction control logic and corresponding parameters are established based on the clearance and queue status of vehicles at the intersection. The study then considers the influence of different permeability of ICNEVs on the full induction control logic. Finally, a comparison is made among the current state scheme, timed optimization, and full induction control schemes using VISSIM traffic simulation software. Additionally, the study further analyzes the effects of intersection full induction control under different permeability of ICNEVs, thereby validating the effectiveness of the research. © 2024 SPIE.

关键词： Computer software

来源：评论

学校读者我要写书评

暂无评论

BDLR:lncRNA identification using ensemble learning

引用

BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

引用

Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

引用

3rd International Conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

来源：评论

学校读者我要写书评

暂无评论

Semantic Correspondence with Peripheral Position Coding 13

Semantic Correspondence with Peripheral Position Coding

引用

13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Chen, Jinjian Li, Zuoyong Lai, Taotao Xie, Huosheng College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350319156

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

引用

2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Shunted Transformer embedding UNet for remote sensing image semantic segmentation

引用

Neural Computing and Applications 2024年第25期36卷 15705-15720页

作者： Zhou, Huacong Xiao, Xiangling Li, Huihui Liu, Xiaoyong Liang, Peng School of Computer Science Guangdong Polytechnic Normal University Guangdong Guangzhou510630 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangdong Guangzhou510665 China School of Data Science and Engineering Guangdong Polytechnic Normal University Guangdong Guangzhou510630 China Academy of Heyuan Guangdong Polytechnic Normal University Guangdong Heyuan517099 China The Cyberspace Institute of Advanced Technology Guangzhou University Guangdong Guangzhou510006 China

With the development of deep learning, Remote Sensing Image (RSI) semantic segmentation has produced significant advances. However, due to the sparse distribution of the objects and the high similarity between classes, the task of semantic segmentation in RSI is still extremely challenging. In this paper, we propose a novel semantic segmentation framework for RSI called HST-UNet that can overcome the shortcomings of the existing models and extract and recover the global and local features of RSI, which is a hybrid semantic segmentation model with Shunted Transformer as encoder and Multi-Scale Convolutional Attention Network (MSCAN) as decoder. Then, to better fuse the information from the Encoder and the Decoder and alleviate the ambiguity, we design a Learnable Weighted Fusion (LWF) module to effectively connect to the decoder features. Extensive experiments demonstrate that the proposed HST-UNet outperforms the state-of-the-art methods, achieving F1 score/MIoU accuracy of 71.44%/83.00% on the ISPRS Vaihingen dataset and 77.36%/87.09% on ISPRS Potsdam dataset. The code will be available at https://***/HC-Zhou/HST-UNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints 20

Reinforcement Learning Framework for Delivery Capacity Sched...

引用

20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Ma, Zhongneng Qin, Zhuorui Shi, Shuxia Wu, Qingyao South China University of Technology Guangzhou China School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory China

ISBN: (纸本)9798350365856

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage © 2024 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Dual decoder UNet with Contrastive learning for Brain Image Registration

Dual decoder UNet with Contrastive learning for Brain Image ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Ying Guo, Shuai Shi, Dantong Xiang, Jinhai Huazhong Agricultural University College of Informatics Wuhan430070 China Agricultural Bioinformatics Key Laboratory of Hubei Province China Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350386226

The core of the image registration task is to accurately extract and compare the spatial feature information between moving and fixed images. The model must not only be able to capture features inside an image, but also be able to distinguish similarities and differences between moving image and fixed image. Although UNet and its variant networks are widely used in the field of registration, they still present challenges in terms of fine-grained and multi-level feature extraction. In order to address these issues, we propose a dual-decoder UNet network based on contrastive learning. For enhancing the discriminant ability of the network at the feature level, the proposed model applies contrastive learning to the encoder stage of UNet, which strengthens the differential learning in the process of feature extraction. In addition, a multi-level feature fusion method is introduced in the encoder to consider the feature differences at each level more comprehensively. Experimental results show that the proposed method has achieved a significant improvement in the registration accuracy. On the LPBA40 dataset, compared with Affine, VoxelMorph and CLMorph, the Dice scores were increased by 15.2%, 2.5% and 1.9%, respectively, which verified the effectiveness of the proposed method. Our code implementation is publicly available at https://***/LaneAzur/DD-CLUNet. © 2024 IEEE.

关键词： Contrastive 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：