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2nd International ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Chen, Liangyu Zhang, Dongping Yan, Tianxu Li, Zheng Wei, Yutong Qian, Luying College of Information Engineering China Jiliang University Hangzhou China College of Computer and Control Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783031889769

Tooth instance segmentation is a key technology in the field of medical image segmentation, with applications ranging from orthodontic treatment to dental pathology assessment. Although researchers have developed many tooth instance segmentation models, a common drawback is that traditional techniques are not effective in combining global and local background information and fail to make full use of labeled data, resulting in limited performance in different clinical scenarios. This paper proposes a new deep learning method dedicated to the instance segmentation task of dental panoramic images. By introducing the RFEM attention module and the CBAM attention module into the ResNet architecture, we improve the network’s ability to focus on key features by adaptively assigning weights to different channels and further enhance the fusion effect of global and local features. In order to improve the generalization ability of the data, we use a data augmentation algorithm specifically for labeled datasets. Finally, the network is named the fully supervised tooth segmentation model DAE-Net, which performs well on the MICCAI STS 2024 dataset, with a final performance improvement of 32% compared to the original base model, verifying its effectiveness and stability in the tooth instance segmentation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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IMC Based PID controller for Spherical Tank Liquid Level control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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Social NSTransformers: Low-Quality Pedestrian Trajectory Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5575-5588页

作者： Jiang, Zihan Ma, Yiqun Shi, Bingyu Lu, Xin Xing, Jian Goncalves, Nuno Jin, Bo Suzhou215000 China Northeast Forestry University College of Computer and Control Engineering Harbin150000 China University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction. © 2024 IEEE.

关键词： Stars

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Research on the control Strategy and Stability of a Modular Multilevel Converter in an AC-DC Hybrid Power Grid 4

Research on the Control Strategy and Stability of a Modular ...

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4th Asia-Pacific Conference on Communications Technology and computer Science, ACCTCS 2024

作者： Yang, Ye College of Computer and Control Engineering Northeast Forestry University Heilongjiang Harbin China

ISBN: (数字)9798350359985

ISBN: (纸本)9798350359985

AC and DC hybrid power grids are widely used in the power industry due to the continuous development of energy systems. In this paper, the control strategy and stability analysis of MMC (Modular Multilevel Converter) in AC / DC hybrid power grid are studied. First, the network model of the AC-DC hybrid power grid and the protocol specification of MMC are introduced. Secondly, the fuzzy logic controller is designed and implemented based on the performance evaluation, stability analysis, and dynamic response analysis of the MMC. Further, the experimental results of the MMC are evaluated and analyzed using the experimental platform and experimental scheme. At the end of this paper, a summary of the research results is presented, and conclusions are derived regarding MMC control, which is considered to provide a useful reference for the design of AC and DC hybrid power grid stability and control systems. © 2024 IEEE.

关键词： Fuzzy logic Multilevel converters Protocols Power system dynamics Symbols Power transmission Power system stability

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Path Planning Method for Large-Area Stamp-Based DLP Printing 4

Path Planning Method for Large-Area Stamp-Based DLP Printing

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4th International Conference on Electronic Information engineering and computer Science, EIECS 2024

作者： Gao, Xiaokuo Guo, Yanling Li, Jian Wang, Yangwei College of Computer and Control Engineering Northeast Forestry University Heilongjiang Harbin China

ISBN: (纸本)9798331531409

To address the issues of low efficiency and difficulty in interval printing splicing in large-area stamping-based stereolithography, a print path planning method based on an improved ant colony algorithm is proposed. The approach involves adjusting the distribution of ant parameters, updating the pheromone mechanism, and incorporating a time evaluation function to specifically solve the optimal print path problem. Experimental results show that this method achieves shorter print path lengths compared to the Zigzag algorithm and more effectively reduces the number of interval prints and the difficulty of splicing compared to the original ant colony algorithm, thereby improving print efficiency and quality. © 2024 IEEE.

关键词： Ant colony optimization

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Diffusion based Counterfactual Augmentation for Dual Sentiment Classification 30

Diffusion based Counterfactual Augmentation for Dual Sentime...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Xin, Dancheng Yuan, Jiawei Li, Yang College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9782493814104

State-of-the-art NLP models have demonstrated exceptional performance across various tasks, including sentiment analysis. However, concerns have been raised about their robustness and susceptibility to systematic biases in both training and test data, which may lead to performance challenges when these models encounter out-of-distribution data in real-world applications. Although various data augmentation and adversarial perturbation techniques have shown promise in tackling these issues, prior methods such as word embedding perturbation or synonymous sentence expansion have failed to mitigate the spurious association problem inherent in the original data. Recent counterfactual augmentation methods have attempted to tackle this issue, but they have been limited by rigid rules, resulting in inconsistent context and disrupted semantics. In response to these challenges, we introduce a diffusion-based counterfactual data augmentation (DCA) framework. It utilizes an antonymous paradigm to guide the continuous diffusion model and employs reinforcement learning in combination with contrastive learning to optimize algorithms for generating counterfactual samples with high diversity and quality. Furthermore, we use a dual sentiment classifier to validate the generated antonymous samples and subsequently perform sentiment classification. Our experiments on four benchmark datasets demonstrate that DCA achieves state-of-the-art performance in sentiment classification tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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Research on Repairing Mobile Cache System of Satellite Internet of Things Based on Fault-tolerant Coding 2

Research on Repairing Mobile Cache System of Satellite Inter...

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2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2022

作者： Zhang, Hua Wang, Jiong College of Computer and Control Engineering Minjiang University China

ISBN: (纸本)9781510663107

Different from source coding, which only emphasizes coding efficiency, fault-tolerant coding adds some redundant information during coding to strengthen the ability of error resistance, so as to obtain the best gain with the least redundancy. Therefore, the application of fault-tolerant coding in the repair of mobile cache system of satellite Internet of Things can effectively improve the security of information storage of satellite Internet of Things. Based on this, this paper introduces the repair method of mobile cache system of satellite Internet of Things based on fault-tolerant coding from the aspects of data deployment and data collection, so as to repair the invalid data in time and ensure the integrity and availability of the saved data. At the same time, a specific method to optimize its system parameters is proposed. The repair of mobile cache system of satellite Internet of Things based on fault-tolerant coding can effectively solve the disadvantages of low price and short service time of sensor node terminal equipment in Internet of Things, and avoid problems such as data storage failure or performance weakening of cache communication system caused by sensor leaving satellite coverage and power exhaustion, so as to provide corresponding reference for the development of satellite Internet of Things. © 2023 SPIE.

关键词： Internet of things

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Learnable three-dimensional Gabor convolutional network with global affinity attention for hyperspectral image classification

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Chinese Physics B 2022年第12期31卷 118-135页

作者： Hai-Zhu Pan Mo-Qi Liu Hai-Miao Ge Qi Yuan College of Computer and Control Engineering Qiqihar UniversityQiqihar 161000China College of Telecommunication and Electronic Engineering Qiqihar UniversityQiqihar 161000China

Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image ***,researchers have found a connection between convolutional neural networks(CNNs)and Gabor ***,some Gabor-based CNN methods have been proposed for HSI ***,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning ***,these methods require patch cubes as network *** patch cubes may contain interference pixels,which will negatively affect the classification *** address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI *** precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training ***,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels *** results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.

关键词： image processing remote sensing 3D Gabor filter neural networks global affinity attention

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Contrastive Consensus Graph Learning for Multi-View Clustering

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2027-2030页

作者： Shiping Wang Xincan Lin Zihan Fang Shide Du Guobao Xiao the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China the College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China

Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clusteri... 详细信息

关键词： letter integrate property

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Conductivity Profile Reconstruction of Stroke Head Models Based on Divide-and-Conquer Method and the Genetic Algorithm

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IEEE Transactions on Magnetics 2024年第3期60卷 1-4页

作者： Choi, Charles T. M. Yu, Chieh-Cheng Lai, Yan-Hung National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Electronics and Electrical Engineering Hsinchu30010 Taiwan Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

This article used finite element (FE) analysis to study and analyze the electrical conductivity profile of simulated stroke patients based on a 45 dB signal-to-noise ratio synthesized measurement. Clinical measurement data obtained from stroke patients with electrical impedance tomography (EIT) was reported to be approximately 45 dB SNR. Weighting factors in conjunction with the divide-and-conquer method and adaptive genetic algorithm (AGA) were used. The AGA was applied with the weighting factors to identify the conductivity distribution inside the brain by minimizing the difference between the simulation and synthesized measured voltages under stroke conditions. The result shows that it might be possible to reconstruct an approximate electrical conductivity distribution based on 45 dB SNR simulated measurement of stroke data. © 2023 IEEE.

关键词： Finite element method

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