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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Ren, Mengxi Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important;(2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary;(3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images. © 2024 IEEE.

关键词： Ultrasonic imaging

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Diagnosis of Time-Sensitive Failures in Timed Discrete-Event systems with Metric Interval Temporal Logics

Diagnosis of Time-Sensitive Failures in Timed Discrete-Event...

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IEEE Conference on Decision and control

作者： Weijie Dong Shaoyuan Li Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

In this paper, we address the problem of failure diagnosis in timed discrete-event systems modeled by timed automata. While existing works on this topic typically focus on failures modeled as particular events, many complex applications, especially time-critical systems, require the ability to identify time-sensitive failures associated with real-time information rather than just the occurrence of events at any time. To address this challenge, we propose the use of metric interval temporal logic (MITL) with continuous semantics on Boolean signals to formally describe time-sensitive failures. We introduce a novel concept called time-sensitive diagnosability (TS-diagnosability) to characterize whether or not any violation of the MITL task (i.e., failure) can be determined within a finite time elapsing. Furthermore, we provide a necessary and sufficient condition for verifying TS-diagnosability. Our results offer a more general framework for failure diagnosis of timed discrete-event systems.

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Reconfigurable Nanobook Structure Driven by Polymerase-Triggered DNA Strand Displacement 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Chen, Kuiting Chen, Zhekun Xie, Chun Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to its high degree of customization, DNA origami provides a versatile platform with which to engineer nanoscale structures and devices. Reconfigurable nanodevices driven by DNA strand displacement accomplish the task of transition between different conformations, endowing DNA origami with application values. Herein, we propose a strategy to regulate the conformation of DNA origami using the polymerase-triggered DNA strand displacement (PTSD) reaction. We design a book-shaped DNA origami structure consisting of four pages connected into a cuboid shape. The PTSD reactions initiated by different primer strands selectively remove the connecting strand, transforming the nano book into a two-page or a four-page conformation. We utilize three primer strands to remove thirty-five connecting strands and construct three conformations of the identical DNA origami, illustrating that the PTSD reaction is an effective tool for the reconfiguration of DNA origami. The statistical results of TEM images prove the effectiveness of the proposed method. Our work on the development of PTSD-driven reconfigurable nanostructure will offer a new way to create intelligent materials for advanced nanotechnology applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： DNA

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Operando chemo-mechanical evolution in LiNi0.8Co0.1Mn0.1O2 cathodes

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National Science Review 2024年第9期11卷 253-263页

作者： Yi Zhang Shuaipeng Hao Fei Pei Xiangpeng Xiao Chang Lu Xing Lin Zhe Li Haijin Ji Yue Shen Lixia Yuan Zhen Li Yunhui Huang State Key Laboratory of Material Processing and Die & Mould Technology School of Materials Science and EngineeringHuazhong University of Science and Technology School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access SystemWuhan National Laboratory for OptoelectronicsHuazhong University of Science and Technology Gatan Inc.& EDAX LLC. AMETEK Commercial Enterprise (Shanghai)Co. Ltd.Pilot Free Trade Zone Department of Physics The Chinese University of Hong Kong

Ni-rich LiNixCoyMnzO2（NCMxyz,x+y+z=1,x≥0.8） layered oxide materials are considered the main cathode materials for high-energy-density Li-ion ***,the endless cracking of polycrystalline NCM materials caused by stress accelerates the loss of active materials and electrolyte decomposition,limiting the cycle ***,understanding the chemo-mechanical evolution during（de）lithiation of NCM materials is crucial to performance *** this work,an optical fiber with με resolution is designed to in operando detect the stress evolution of a polycrystalline LiNi0.8Co0.1Mn0.1O2（P-NCM811） cathode during *** integrating the sensor inside the cathode,the stress variation of P-NCM811 is completely transferred to the optical *** find that the anisotropy of primary particles leads to the appearance of structural stress,inducing the formation of microcracks in polycrystalline particles,which is the main reason for capacity *** isotropy of primary particles reduces the structural stress of polycrystalline particles,eliminating the generation of ***,the P-NCM811 with an ordered arrangement structure delivered high electrochemical performance with capacity retention of 82% over 500 *** work provides a brand-new perspective with regard to understanding the operando chemo-mechanical evolution of NCM materials during battery operation,and guides the design of electrode materials for rechargeable batteries.

关键词： Ni-rich layered oxide cathode in operando stress monitoring polycrystalline chemo-mechanical evolution

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Surface Defect Detection of Wooden Boards Based on Multi-Modal Fusion

Surface Defect Detection of Wooden Boards Based on Multi-Mod...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Hongchuan Zheng, Song Zhu, Yuhang Lin, Ye Ai, Zhijie Xu, Zhezhuang Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Automated wood defect detection is of great significance for improving wood utilization. However, the stains on the wood surface resemble defect features and it is difficult to distinguish the appearance of wane from defect-free regions. Relying solely on machine vision for extracting surface features of wood makes it difficult to achieve accurate defect detection. To address these challenges, we propose MMDNet, a multimodal detection network that combines point cloud and image data. By adaptively fusing point cloud and image features at multiple stages, the network effectively highlights wood surface defect regions. Additionally, we introduce an Atrous Spatial Pyramid Pooling (ASPP) module into the network, which expands the network's receptive field and enables a comprehensive perception of the defects. Furthermore, we employ a deep supervision strategy to encourage the network to learn discriminative feature representations, enhancing the model's ability to differentiate defect regions. Experimental results validate the effectiveness of our method in detecting defects that are similar to the background and reducing interference from stains. © 2023 IEEE.

关键词： Surface defects

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Analyzing Behavior Data of Students in the Classroom with Association Rule Mining

Analyzing Behavior Data of Students in the Classroom with As...

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2023 China Automation Congress, CAC 2023

作者： Zeng, Qingbo Xu, Zhezhuang Zheng, Song Liu, Chi Chai, Qinqin Zhang, Ying College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Students behavior in the classroom has a close relation with the academic performance of students. With the widespread use of mobile phones, the students are easily distracted by mobile phones during the class. Therefore, analyzing the students behavior in the classroom becomes important to improve the quality of teaching. However, the existing methods for analyzing students behavior in the classroom, such as facial recognition, has low efficiency with large amounts of irrelevant data. And it also raises concerns about privacy and ethical issues. To solve this problem, in this paper, a novel mobile software called EasyClass has been developed to collect and quantify students behaviors. Based on the data collected by EasyClass, we propose to utilize user profiling to study students behavior in the classroom. The clustering algorithm is firstly employed to classify the data and generate semantically tags which describe students behavior in the classroom. Then a sequence and association rule analysis method is used to analyze the behavior tags and generated association rules. The results enable educators and non-expert users to gain a deeper understanding of students behavior in the classroom, and further improve the quality of the classroom teaching. © 2023 IEEE.

关键词： K-means clustering

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Adaptive Optimal control of Nonlinear systems with Multiple Time-Scale Eligibility Traces

Adaptive Optimal Control of Nonlinear Systems with Multiple ...

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IEEE Conference on Decision and control

作者： Jun Rao Jingcheng Wang Jiahui Xu Shunyu Wu Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

Adaptive dynamic programming (ADP) is one of the main methods to solve the optimal control problem of nonlinear systems. Eligibility traces are utilized in recent years to reduce the computing burden of the value function, but the existing fixed eligibility trace is difficult to ensure stable convergence especially when facing environmental changes and complex neural network structures. To solve the above issues, a novel off-policy algorithm, $T$ -HDP $(\lambda)$ with Multiple Time-scale Eligibility Traces (MET), is proposed. By utilizing MET, the new algorithm can adaptively accumulate gradients and include more gradient information, which guides the control faster in the optimal direction. $T$ -step Truncated $\lambda$ -returns are utilized to solve the infinite-horizon optimal control problems, and a new importance sampling ratio is designed to correct the value function. Furthermore, the convergence and boundedness of the algorithm are proved. Based on the actor-critic network architecture, the optimal value function and policy are well approximated. Finally, compared with the original algorithm by a simulation example, the proposed algorithm has a faster convergence speed and lower variance.

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SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation

arXiv

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arXiv 2024年

作者： Yan, Weihao Qian, Yeqiang Li, Yueyuan Li, Tao Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Semantic segmentation plays an important role in intelligent vehicles, providing pixel-level semantic information about the environment. However, the labeling budget is expensive and time-consuming when semantic segmentation model is applied to new driving scenarios. To reduce the costs, semi-supervised semantic segmentation methods have been proposed to leverage large quantities of unlabeled images. Despite this, their performance still falls short of the accuracy required for practical applications, which is typically achieved by supervised learning. A significant shortcoming is that they typically select unlabeled images for annotation randomly, neglecting the assessment of sample value for model training. In this paper, we propose a novel semi-supervised active domain adaptation (SS-ADA) framework for semantic segmentation that employs an image-level acquisition strategy. SS-ADA integrates active learning into semi-supervised semantic segmentation to achieve the accuracy of supervised learning with a limited amount of labeled data from the target domain. Additionally, we design an IoU-based class weighting strategy to alleviate the class imbalance problem using annotations from active learning. We conducted extensive experiments on synthetic-to-real and real-to-real domain adaptation settings. The results demonstrate the effectiveness of our method. SS-ADA can achieve or even surpass the accuracy of its supervised learning counterpart with only 25% of the target labeled data when using a real-time segmentation model. The code for SS-ADA is available at https://***/ywher/SS-ADA. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Research on Medical Image Classification for Oral Cancer 3rd

Research on Medical Image Classification for Oral Cancer

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

作者： Wang, Kunkun Shi, Linjun Gu, Chaochen Mo, Fan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Oral Mucosal Diseases Ninth People’s Hospital Shanghai Jiaotong University School of Medicine Shanghai China Shanghai Key Laboratory of Stomatology and Shanghai Research Institution of Stomotology National Clinical Research Center of Stomatology Shanghai China

ISBN: (纸本)9789819624676

In recent years, deep learning has made significant progress in medical imaging, deepening the crossover between the medical and industrial fields. However, not all medical images are suitable for deep learning neural networks;simultaneously, some medical images are difficult to collect and preprocess. In this paper, we reconstruct the image information collected by dental clinicians over many years, normalizing it with diagnostic results as the gold standard. On this basis, we explore the applicability and potential of deep learning on complex medical images. Further, we compare the performance of multiple models on this dataset and visualize the learning outcomes, demonstrating that deep learning networks exhibit good learning capabilities on certain images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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