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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Jiang, Zifeng Zhang, Xiuyun Xu, Runfei Wang, Zhiqiang Geng, Liqing Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China School of Automobile and Transportation Tianjin University of Technology and Education Tianjin300222 China

To improve the tracking and synchronous performance of the dual PMSM system, this paper proposes a position synchronous control of dual PMSM system based on PDFF+composite control with parameter adaptive adjustment. Firstly, a mathematical model of the dual PMSM system is established. Then, the advantages, disadvantages, and limitations of the traditional cross coupling+single axis three loop control structure, PDFF based control structure, and PDFF based feedforward proportional composite control structure are analyzed. Then, a PDFF+composite control structure based on parameter adaptive adjustment is proposed, and it can improve the system's motion control and synchronous control effect by adaptively adjusting the velocity loop PDFF feedforward coefficient and cross coupling coefficient. Finally, the effectiveness of the proposed position synchronous control strategy is verified through Matlab/Simulink simulation. © The Institution of Engineering & Technology 2024.

关键词： Permanent magnets

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Unified convergence analysis of a second-order method of multipliers for nonlinear conic programming

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Science China Mathematics 2022年第11期65卷 2397-2422页

作者： Liang Chen Junyuan Zhu Xinyuan Zhao School of Mathematics Hunan UniversityChangsha 410082China Hunan Provincial Key Laboratory of Intelligent Information Processing and Applied Mathematics Changsha 410082China School of Mathematics Beijing University of TechnologyBeijing 100124China

In this paper,we accomplish the unified convergence analysis of a second-order method of multipliers(i.e.,a second-order augmented Lagrangian method)for solving the conventional nonlinear conic optimization ***,the algorithm that we investigate incorporates a specially designed nonsmooth(generalized)Newton step to furnish a second-order update rule for the *** first show in a unified fashion that under a few abstract assumptions,the proposed method is locally convergent and possesses a(nonasymptotic)superlinear convergence rate,even though the penalty parameter is fixed and/or the strict complementarity ***,we demonstrate that for the three typical scenarios,i.e.,the classic nonlinear programming,the nonlinear second-order cone programming and the nonlinear semidefinite programming,these abstract assumptions are nothing but exactly the implications of the iconic sufficient conditions that are assumed for establishing the Q-linear convergence rates of the method of multipliers without assuming the strict complementarity.

关键词： second-order method of multipliers augmented Lagrangian method convergence rate generalized Newton method second-order cone programming semidefinite programming

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Competition on robust deep learning

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National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Three-Way Decision-Making Based on Incomplete information System and Its Application 9

Three-Way Decision-Making Based on Incomplete Information Sy...

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Xu, Changlin Mao, Songqian School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

In order to avoid decision makers from making wrong decisions as much as possible and reduce the losses caused by wrong decisions, a new aggregation method of interval loss function is proposed based on incomplete information system. Firstly, the missing values are filled according to their characteristics and combined with probability, and on this basis, the measurement method of similarity is given, and the horizontal similarity class of each object is obtained according to the similarity between objects. Secondly, according to the number of times each object in the similar class appears in its corresponding horizontal similar class, the corresponding weight is obtained;Then the interval loss function of similar class is defined as the aggregation of interval loss functions of all objects in similar class, and each interval is weighted according to the weight, so that multiple intervals in similar class are aggregated into an interval, and then the aggregated interval is converted into a single value by using the conversion function. Finally, according to the aggregation method, three decision rules are given to make decisions, and the effectiveness of the method is verified by a case. © 2023 The authors and IOS Press.

关键词： Decision making

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Heterogeneous Domain Adaptation via Correlative and Discriminative Feature Learning

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IEEE Transactions on Multimedia 2025年 27卷 3447-3461页

作者： Lu, Yuwu Lin, Dewei Shen, Linlin Zhou, Yicong Pan, Jiahui South China Normal University School of Artificial Intelligence Foshan528225 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China University of Nottingham Ningbo China Department of Computer Science Ningbo315100 China University of Macau Department of Computer and Information Science 999078 China

Heterogeneous domain adaptation seeks to learn an effective classifier or regression model for unlabeled target samples by using the well-labeled source samples but residing in different feature spaces and lying different distributions. Most recent works have concentrated on learning domain-invariant feature representations to minimize the distribution divergence via target pseudo-labels. However, two critical issues need to be further explored: 1) new feature representations should be not only domain-invariant but also category-correlative and discriminative and 2) alleviating the negative transfer caused by the incorrect pseudo-labeling target samples could boost the adaptation performance during the iterative learning process. To address these issues, in this paper, we put forward a novel heterogeneous domain adaptation method to learn category-correlative and discriminative representations, referred to as correlative and discriminative feature learning (CDFL). Specifically, CDFL aims to learn a feature space where class-specific feature correlations between the source and target domains are maximized, the divergences of marginal and conditional distribution between the source and target domains are minimized, and the distances of inter-class distribution are forced to be maximized to ensure the discriminative ability. Meanwhile, a selective pseudo-labeling procedure based on the correlation coefficient and classifier prediction is introduced to boost class-specific feature correlation and discriminative distribution alignment in an iteration way. Extensive experiments certify that CDFL outperforms the State-of-the-Art algorithms on five standard benchmarks. © 1999-2012 IEEE.

关键词： Contrastive Learning

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Efficient DOA Estimation Based on Cauchy Loss Variable Step Size Algorithm 6

Efficient DOA Estimation Based on Cauchy Loss Variable Step ...

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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Xu, Xingchao Li, Yingsong College of Information and Communications Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9798350399059

The Cauchy loss function is robust to large outliers and has successful applications in signal processing. In this paper, a DOA estimation algorithm based on adaptive zero technology is proposed, which updates the weight of the filter by applying the Cauchy loss function, and further improves the algorithm performance by using the variable step size method. The algorithm step size is adjusted by accumulating errors and establishing a nonlinear function with instantaneous errors. The DOA estimation ability, estimation error and accuracy of the improved Cauchy algorithm in the impulse noise environment are analyzed. The algorithm is verified by simulation experiments. © 2023 IEEE.

关键词： Impulse noise

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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Factory Worker Safety Mask Monitoring System Based on Deep Learning and AI Edge Computing 5

Factory Worker Safety Mask Monitoring System Based on Deep L...

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5th International Conference on Mechatronics, Robotics and Automation, ICMRA 2022

作者： Fang, Donghui Tian, Liguo Li, Meng Wang, Yuesong Liu, Jinqi Chen, Xiang Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9781665472654

When factory workers work on a daily basis, safety accidents may occur due to goggles and protective masks not being worn or not being worn in a standardized manner. Such accidents put workers' lives and health at risk, damage factory equipment, and can cause serious fires and injuries. The system uses Artificial Intelligence (AI) and AI edge computing to monitor workers' protective equipment, using deep learning to train a suitable model, which is remotely transmitted to the AI edge computing module Jetson Nano via Secure Shell (SSH) protocol, and finally the AI edge computing module determines the workers' protective equipment wearing situation. The AI edge computing module provides some practical value for the prevention of factory safety accidents, and also provides a reliable basis for the establishment of smart factories. © 2022 IEEE.

关键词： Edge computing

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Xu, Changlin Wu, Wenyuan School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

The similarity of triangular fuzzy numbers could be served as a measurement of similarity between two triangular cloud models. However, it has been found that existing methods for describing the similarity of triangular cloud models has the drawbacks that can't make full use of utilizing graphic information and assigning inappropriate weights to distances through research and analysis. The method proposed in this literature has adjusted the weight of distance similarity aim to ensure that the characteristic information of the model will not be covered. In addition, the proposed method provides a measurement to describe geometric shape bases on the expected curve included angle of the triangular cloud model. On the foundation of taking abundantly the similarity of distance and shape into account, we provide the measurement to achieve better accuracy. The feasibility and effectiveness of the proposed method have been demonstrated through the time series of KNN algorithm. © 2023 The authors and IOS Press.

关键词： Cloud computing

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FUZZY ALIGNMENTS IN DIRECTED ACYCLIC GRAPH FOR NON-AUTOREGRESSIVE MACHINE TRANSLATION 11

FUZZY ALIGNMENTS IN DIRECTED ACYCLIC GRAPH FOR NON-AUTOREGRE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Ma, Zhengrui Shao, Chenze Gui, Shangtong Zhang, Min Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Harbin Institute of Technology Shenzhen China

Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem. Recently, the structure of directed acyclic graph has achieved great success in NAT, which tackles the multi-modality problem by introducing dependency between vertices. However, training it with negative log-likelihood loss implicitly requires a strict alignment between reference tokens and vertices, weakening its ability to handle multiple translation modalities. In this paper, we hold the view that all paths in the graph are fuzzily aligned with the reference sentence. We do not require the exact alignment but train the model to maximize a fuzzy alignment score between the graph and reference, which takes captured translations in all modalities into account. Extensive experiments on major WMT benchmarks show that our method substantially improves translation performance and increases prediction confidence, setting a new state of the art for NAT on the raw training data. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Benchmarking

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