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2024 China Automation Congress, CAC 2024

作者： Niu, Fei Wang, Xiaofeng Department of Automation School of Information Science & Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Donghua University Shanghai201620 China

ISBN: (纸本)9798350368604

In this paper, we study the susceptible-infected-susceptible (SIS) epidemic spreading model under population migration on multilayer networks. Specifically, we consider two-layer contact-migration networks, in which one layer of the multilayer networks describes the contact patterns between different populations and the other one captures the possible migration paths between any two sites. By using Lyapunov methods as well as Markov chain theory, we study convergence properties of the SIS epidemic dynamics on the two-layer contact-migration networks either by establishing the structural conditions under which the multilayer networked system always evolves into the disease-free equilibrium or by deriving the algebraic conditions below which the system converges to the disease-free equilibrium. Finally, two simulation examples are given to verify our theoretical results. © 2024 IEEE.

关键词： Markov chains

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Improved Object Detectors Based on Semantic Feature Fusion

Improved Object Detectors Based on Semantic Feature Fusion

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2023 International Seminar on Computer Science and engineering technology, SCSET 2023

作者： Wang, Chuyi Wang, Tong Donghua University Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai China College of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798350301472

Object detection methods generally use fused features for box regression and category classification, but shallow fused features lack long-distance contextual information. To address the above issues, we propose an embeddable semantic feature fusion network called UN eck that extracts global information from deep features and iteratively fuses shallow features to improve the performance of the object detectors. © 2023 IEEE.

关键词： Object detection

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Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

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Journal of Donghua University(English Edition) 2023年第1期40卷 27-33页

作者：王恒骞耿君先陈磊 Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationDonghua UniversityShanghai 201620China College of Information Science and Technology Donghua UniversityShanghai 201620China

In polyester fiber industrial processes,the prediction of key performance indicators is vital for product *** esterification process is an indispensable step in the polyester polymerization *** has the characteristics of strong coupling,nonlinearity and complex *** solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification *** the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the *** effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.

关键词： seasonal and trend decomposition using loess(STL) multi-output Gaussian process regression combined kernel function polyester esterification process

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M2S2: A Policy Transfer Framework for Deep Reinforcement Learning

M2S2: A Policy Transfer Framework for Deep Reinforcement Lea...

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2023 China Automation Congress, CAC 2023

作者： Liu, Huashan Yin, Dezhen Li, Xiangjian College of Information Science and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai201620 China

ISBN: (纸本)9798350303759

Deep reinforcement learning (DRL) has greatly promoted the progress of investigations in robotic motion planning. However, when faced with different scenarios, the path obtained from each training for a specific scenario is unique, which means that the DRL needs additional resources to relearn and adapt the policy to the new scenario. Inspired by the mentor-student relationship, a multi-mentor-single-student (M2S2) framework is proposed in this paper to enhance the policy generalization performance among similar task scenarios. It promotes the learning efficiency of the student network by adding networks that have been trained in similar scenarios as the mentor networks in the training, where weight parameters are assigned to balance the relationship among all the networks. The experimental results show that M2S2 has good feasibility and superior performance for policy transfer of DRL among similar scenarios. © 2023 IEEE.

关键词： Motion planning

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Compression Mechanical Performance Simulation and Parameter Optimization of Degradable Ureteral Stent Based on ABAQUS

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Journal of Donghua University(English Edition) 2023年第1期40卷 1-7页

作者：刘璐刘肖燕郝矿荣 College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationDonghua UniversityShanghai 201620China

The compression performance of a degradable ureteral stent is analyzed and the parameters are optimized by a finite element modeling *** degradable ureteral stent explored in this paper is developed from poly(glycolic acid)(PGA)and poly(lactic-co-glycolic acid)(PLGA)degradable *** on the actual measurement of fabric structure parameters,the three-dimensional model of the stent is established with the help of the modeling *** finite element analysis software is used to simulate the compression process of the degradable ureteral *** parameters of materials,interactions and boundary conditions are set according to the compression environment of the stent for modeling and *** this basis,the friction coefficient of yarns,the yarn radius,and the braided angle of the stent are further *** comparison test is carried out by a single *** experimental results show that the change of yarn friction coefficient has little influence on the compressive stress,while the yarn radius and the braided angle of the stent have a great influence on the compressive stress.

关键词： degradable ureteral stent geometric model compression performance finite element modeling method

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Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism

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Journal of Donghua University(English Edition) 2022年第2期39卷 146-155页

作者： YUAN Hui HAO Kuangrong WEI Bing Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationCollege of Information Science and TechnologyDonghua UniversityShanghai 201620China

In the actual complex environment,the recognition accuracy of crop leaf disease is often not *** by the brain parallel interaction mechanism,a two-stream parallel interactive convolutional neural network(TSPI-CNN)is proposed to improve the recognition ***-CNN includes a two-stream parallel network(TSP-Net)and a parallel interactive network(PI-Net).TSP-Net simulates the ventral and dorsal ***-Net simulates the interaction between two pathways in the process of human brain visual information transmission.A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2,PlantVillage,Apple-3 leaf,and Cassava leaf ***,the effect of numbers of interactions on the recognition performance of TSPI-CNN is *** experimental results show that as the number of interactions increases,the recognition accuracy of the network also ***,the network is visualized to show the working mechanism of the network and provide enlightenment for future research.

关键词： brain parallel interaction mechanism recognition accuracy convolutional neural network crop leaf disease recognition

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Leverage Learning Behaviour Data for Students' Learning Performance Prediction and Influence Factor Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2422-2433页

作者： Ni, Qin Zhu, Yanjin Zhang, Lingying Lu, Xiaochen Zhang, Lei Shanghai International Studies University Key Laboratory of Multilingual Education with AI Shanghai201620 China Donghua University College of Information Science and Technology Shanghai201620 China Donghua University Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai201620 China Shanghai Normal University Shanghai Engineering Research Center of Intelligent Education and Bigdata and Lab for Educational Big Data and Policymaking Shanghai200234 China

Online education has become increasingly significant for university students and faculty, especially in the context of the modern remote education landscape. However, the inherent space-time separation in online education can create communication delays between teachers and students, making it challenging to monitor students' behaviors effectively. In an effort to understand the connection between students' online engagement and their learning performance, data annotation has been implemented to address the issue of accurately representing students' learning behaviors. Taking the online teaching data of Shanghai Normal University platform as the research object, the primary online education problem is explored through data mining, which includes correlation analysis, Gini importance ranking, and principal component analysis (PCA). Then, constructing the learning performance prediction model using random forest (RF) based on PCA by comparing various machine learning algorithms. As a consequence, the most influential online learning behaviors are course duration time, document learning time, test average score, and video completion rate. The overall classification accuracy of the learning performance prediction model is 87.45%, and the highest prediction accuracy for a single category is 96.52%. © 2020 IEEE.

关键词： Students

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Time and Energy Optimal Path Planning of USV Under Time-Varying Ocean Currents 1

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Li, Yuan Fan, Yuxin Du, Wenqing Hua, Chenhua Wu, Nailong Jiangsu Automation Research Institute of China Shipbuilding Industry Corporation Jiangsu222006 China College of Information Science and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Donghua University Shanghai201620 China

ISBN: (数字)9789819710959

ISBN: (纸本)9789819710942

An unmanned surface vessel (USV) is often deployed to operate for a long time, but considering that the energy of an unmanned ship should meet the index of long-term endurance, the path-planning technology of the USV needs to be further developed when the USV navigates in the complex time-varying ocean current environment. In this paper, path planning methods based on the Dijkstra algorithm which considers time or energy optimal search are employed. Firstly, the mathematical model that consists of the time-varying ocean current description, the motion model of USV, and the cost function model are introduced. The cost function model established includes the time cost function and the energy cost function. Secondly, the Dijkstra algorithm is utilized to solve the time or energy-optimal search problem. The effectiveness of the proposed path planning methods of USV is verified by simulation in Matlab, in which the electronic chart of the ocean is given and the time-varying ocean current environment is established. In addition, GUI is designed to facilitate operation and display search results. The simulation results show that the path planning method can effectively generate the global path according to the optimal time or energy object. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Cost functions

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Dual-Strategy Pose Estimation Network Based on Mask-RCNN 42

Dual-Strategy Pose Estimation Network Based on Mask-RCNN

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42nd Chinese Control Conference, CCC 2023

作者： Han, Haoyu Ying, Fengkang Pang, Zuowei Liu, Huashan College of Information Science and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai201620 China Huzhou Research Institute Zhejiang University Huzhou313002 China

ISBN: (纸本)9789887581543

6D pose estimation from a given input image is a key technology in computer vision and robotics, which has been broadly leveraged in robot operations, autonomous driving, and augmented reality. The current mainstream methods fall into two main categories, either regressing the rotation and translation directly from the image or constructing a 2D-3D correspondence and then solving the poses indirectly via PnP. In this work, we propose a 6D pose estimation method: a Mask-RCNN-based dual-strategy pose estimation network. First, it divides the pose into two parts, rotation and translation, and uses two different strategies of pose estimation networks to solve the translation and rotation of the object separately, so as to improve the estimation's accuracy and robustness. Secondly, a Mask-based loss function is proposed to update the network and make it increasingly accurate in calculating the coordinates, where the coordinate points of the predicted object region are extracted from the local image by the Mask-RCNN prediction on the object mask. Finally, experiments are conducted on the LINEMOD dataset to demonstrate the superiority of our method. The method is highly applicable and accurate to handle pose estimation problems for untextured and occluded objects. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Augmented reality

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Deep Reinforcement Learning Integrated RRT Algorithm for Path Planning 5

Deep Reinforcement Learning Integrated RRT Algorithm for Pat...

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

作者： Liu, Huashan Gu, Yuqing Li, Xiangjian Xiao, Xinjie Donghua University College of Information Science and Technology China Ministry of Education Shanghai Engineering Research Center of Digitized Textile & Fashion Technology China

ISBN: (纸本)9798350307320

Rapidly-exploring random tree (RRT) algorithm, featured with strong exploration capability, is widely used in path planning tasks. However, it is difficult for RRT to find the optimal path due to its inherent characteristics of random sampling. In this paper, we propose an improved RRT algorithm integrated with deep reinforcement learning (IRRT-DRL), which can effectively search for feasible paths and exploit previous experience for optimization. It includes a unique reward function and a dynamic waypoint selection mechanism that automatically adjusts the interval between adjacent waypoints to help the agent bypass obstacles. Experimental results have verified the feasibility and superiority of the proposed approach. © 2023 IEEE.

关键词： Motion planning

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