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Deep learning algorithm-oriented blended teaching in secondary school mathematics courses in ethnic areas

Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Xu, Shifang Pan, Chunyan School of Mathematics and Statistics Qiannan Normal University for Nationalities Guizhou558000 China Key Laboratory of Complex Systems and Intelligent Computing School of Mathematics and Statistics Qiannan Normal University for Nationalities Guizhou558000 China

To provide teaching assistance to secondary school mathematics teachers in ethnic areas and improve the teaching effectiveness of mathematics courses, a hybrid teaching model based on deep learning algorithms is proposed. Convolutional neural networks and joint probability matrix decomposition are fused to design teaching resource recommendation methods, and hybrid teaching is carried out according to three stages: before, during, and after class. The overall mean of the questionnaire for the experimental class improved from 2.39 to 3.01, and the pass rate, merit rate, and mean score of mathematics scores increased by 13.79%, 9.02%, and 15.17 points, respectively. This method enables educational technology to be effective in mathematics curriculum and improve the quality and information of mathematics education in ethnic areas. © 2023 Shifang Xu and Chunyan Pan, published by Sciendo.

关键词： Learning algorithms

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Learning discriminative foreground-and-background features for few-shot segmentation

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Multimedia Tools and Applications 2024年第18期83卷 55999-56019页

作者： Jiang, Cong Zhou, Yange Liu, Zhaoshuo Feng, Chaolu Li, Wei Yang, Jinzhu School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China College of Science Northeastern University Liaoning Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query’s foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL-5i and COCO-20i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Semantic Segmentation

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Stability Analysis for the Vehicular Platoon System With Time- Varying Delay via A State Decomposition Method

Stability Analysis for the Vehicular Platoon System With Tim...

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Automation, Control and Robotics Engineering (CACRE), International Conference on

作者： Hang Xu Chuan-Ke Zhang Xing-Chen Shangguan Li Jin Yang Li Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

In this paper, the consideration of the stability problem for the vehicular platoon system (VPS) with communication delays under proportional differential (PD) control is studied by the Lyapunov- Krasovskii functional (LKF). Firstly, a VPS considering delay is modeled to analyze the influence of delays for the VPS in operation. Then, an error state-space system of the VPS is constructed and transformed into a singular system. As a result, the stability criterion is proposed based on the state decomposition method via the LKF. Finally, the tolerance for delay is calculated for a case of four vehicles with different controller gains. The result shows that the proportional gain is inversely proportional to the time delay and the differential gain is in direct proportion to the time delay meanwhile. Besides, the high-speed vehicular platoon is more affected by the time delay.

关键词： Automation Delay effects Roads Stability criteria Estimation Delays PD control

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Dual-Layer Collaborative Hunting Algorithm Using Virtual Wolf Pack

Dual-Layer Collaborative Hunting Algorithm Using Virtual Wol...

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第43届中国控制会议

作者： Zhiqiang Miao Jianlei Zhang Chunyan Zhang Chen Wang Guangming Xie State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design LabCollege of EngineeringPeking University

Collaborative hunting,also known as Multi-Agent Pursuit Evasion(MAPE),is a crucial aspect of swarm *** main obstacles in MAPE research involve making real-time decisions in dynamic environments,efficient task allocation,collision avoidance among multiple agents,planning and tracking prey in unknown environments,and information sharing under communication *** paper presents the Dual-Layer Collaborative Hunting Algorithm Using Virtual Wolf Pack(DLCH-VWP),which utilizes biomimetic principles,specifically leveraging the advantages and hunting strategies of wolf packs,to enhance collaborative efficiency and adaptability in complex environments within *** algorithm employs a two-tier strategy,with the strategy planning center using biomimetic wolf pack algorithm to manage environment perception,state assessment,prey movement prediction,and inter-robot *** local response center treats each wolf as an independently operating sub-pack,utilizing a combination of Grey Wolf Optimizer(GWO) and the local A-star algorithm to provide each wolf with individual *** allows for efficient dynamic path planning by determining the path for each wolf based on joint decision-making of the group and *** experiments have confirmed the effectiveness and feasibility of this algorithm for collaborative hunting in multi-agent systems,presenting a novel strategy for addressing MAPE challenges.

关键词： Virtual wolf pack Swarm intelligence Collaborative hunting

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Data Exchange and Sharing Framework for Intermodal Transport Based on Blockchain and Edge computing 1

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2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Wang, Li Zhang, Xue Xu, Lianzheng Fu, Deqian Qiu, Jianlong School of Information Science and Engineering Linyi University Shandong Linyi China Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong Linyi University Shandong Linyi China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

As an intensive and efficient way of cargo transportation, intermodal transport can take full advantage of various modes of transport through effective combinations. However, there is a problem of lacking effective data connection between collaborators leading to broken business processes, reduced collaborative efficiency, and hindering the development of the intermodal transport industry. In this paper, we propose a data exchange and sharing framework by integrating blockchain technology and edge computing technology to realize efficient, convenient, safe, and credible data exchange, which also conveniently connects different partners of various modes of transport and then promotes cross-sector and cross-system data exchange and sharing in the intermodal transport environment. Furthermore, the ecosystem of intermodal transport data exchange and sharing based on the proposed framework can effectively meet the requirements of scalability, security, believability, and privacy protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution 19th

Collaborative Decision-Making Processes Analysis of Service ...

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19th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Yan, Xiangpei Xue, Xiao Peng, Chao Liu, Donghua Feng, Zhiyong Xiao, Wang School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China China Waterborne Transport Research Institute Tianjin China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031545306

With the collaboration of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. As a novel service organization model, the Service Ecosystem (SE) can provide more sophisticated, precise, and thorough services and has attracted widespread attention. However, it also brings negative effects such as involution, and information cocoon room. Thus, how to analyze the collaborative decision-making mechanism between the SE regulation algorithm and the crowd intelligence group, exploring the reasons behind the negative effects, and finding effective intervention strategies have become problems in this field. To solve the challenges, we propose a Computational Experiments-based method Decision-making processes Analysis model in SE, namely CEDA. The proposed CEDA model consists of three modules: the autonomous evolution mechanism module, the learning evolution mechanism module, and the collaborative decision-making analysis module. Among them, the computational experiments can provide a customized test environment for the analysis of collaborative decision-making processes and find out the appropriate intervention strategy. Finally, the validity of the CEDA model is verified through the case of academic ecosystem involution. The results show that computational experiments can provide new ideas and paths for collaborative decision-making processes analysis. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Ecosystems

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A Generative Prediction Model Based on TimeVAE and Crossformer for Predicting Multichannel Data of Transformer Bushing

A Generative Prediction Model Based on TimeVAE and Crossform...

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2024 China Automation Congress, CAC 2024

作者： Huang, Xiangjie Lang, Xun Li, Peng Guo, Tao Yu, Li Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Honghe Power Supply Bureau Yunnan Power Grid Corporation Honghe Yunnan China

ISBN: (纸本)9798350368604

Transformer bushings play a crucial role in power systems. Despite their importance, there has been limited research specifically on the state prediction of transformer bushings. Moreover, currently available models only predict univariate data of bushings without considering the physical connection to other relevant data, which would give rise to a compromised prediction. To achieve a more accurate joint prediction of three critical transformer states: dielectric loss, end shield current, and equivalent capacitance, this work builds upon the state-of-the-art predictive network Crossformer. In addition to Crossformer, a data generation model named TimeVAE is introduced to enlarge the scale of the training dataset. This combination enabled us to achieve accurate multivariate predictions for transformer bushings. To evaluate the effectiveness of the proposed predictor, we conducted comparative experiments with attention-based frameworks (Autoformer, Pyraformer, Informer, Reformer, Transformer), which are considered efficient for multivariable prediction tasks. The results show that our proposed model outperforms these models in four key metrics: mean square error, mean absolute error, root mean squared error, and coefficient of determination. © 2024 IEEE.

关键词： Prediction models

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Directed Brain Network Transformer for Psychiatric Diagnosis 27th

Directed Brain Network Transformer for Psychiatric Diagnosis

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Xu Qi, Zhiwei Yue, Kun Su, Yunshan Duan, Liang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Education Yunnan University Kunming China Department of Radiology Affiliated Hospital of Yunnan University Kunming China

ISBN: (纸本)9783031781940

Human brain is a complex organ that consists of billions of neurons and trillions of connections among the neurons. To describe the correlations among the time series of the brain regions, we model the brain as a functional brain network to diagnose psychosis. However, traditional methods of functional brain network construction are usually noisy and do not consider the causal relationships among brain regions. To obtain the causal relationships and improve diagnosis interpretability, we propose a directed brain network Transformer (DBNT) for psychiatric diagnosis. First, the causal relationships in the blood-oxygen-level-dependent time series of brain regions are extracted to generate a directed brain network. Then, the feature encoding method is proposed to obtain local and global features of the brain networks by using the DBNT. Experimental results demonstrate that the accuracy of DBNT increases by 8.1% and 6.4% compared to state-of-the-art methods on two large-scale brain network datasets. DBNT also highlights the brain regions associated with psychosis and provides interpretation for diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network coding

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